U.S. patent application number 15/577733 was filed with the patent office on 2018-04-19 for validating biomarker measurement.
The applicant listed for this patent is ImmuneXpress Pty Ltd. Invention is credited to Leo Charles MCHUGH.
Application Number | 20180107783 15/577733 |
Document ID | / |
Family ID | 57392259 |
Filed Date | 2018-04-19 |
United States Patent
Application |
20180107783 |
Kind Code |
A1 |
MCHUGH; Leo Charles |
April 19, 2018 |
VALIDATING BIOMARKER MEASUREMENT
Abstract
A method for validating quantification of biomarkers, the
biomarkers being quantified using a quantification technique of a
selected type, and the method including determining a plurality of
biomarker values, each biomarker value being indicative of a value
measured or derived from a measured value, for at least one
corresponding biomarker of the biological subject and being at
least partially indicative of a concentration of the biomarker in a
sample taken from the subject, determining at least one control
value by determining a combination of biomarker values, comparing
each control value to a respective control reference and
determining if the biomarker values are valid using results of the
comparison.
Inventors: |
MCHUGH; Leo Charles;
(Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ImmuneXpress Pty Ltd |
Boonah |
|
AU |
|
|
Family ID: |
57392259 |
Appl. No.: |
15/577733 |
Filed: |
May 20, 2016 |
PCT Filed: |
May 20, 2016 |
PCT NO: |
PCT/AU2016/050388 |
371 Date: |
November 28, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 35/00 20130101;
G01N 2800/70 20130101; G16B 25/00 20190201; G16B 40/00 20190201;
C12Q 2600/158 20130101; G01N 33/50 20130101 |
International
Class: |
G06F 19/20 20060101
G06F019/20; G01N 33/50 20060101 G01N033/50; G01N 35/00 20060101
G01N035/00; G06F 19/24 20060101 G06F019/24 |
Foreign Application Data
Date |
Code |
Application Number |
May 28, 2015 |
AU |
2015901982 |
Claims
1.-47. (canceled)
48. A method for validating quantification of biomarkers, the
biomarkers being quantified using a quantification technique of a
selected type, and the method including: a) determining a plurality
of biomarker values, each biomarker value being indicative of a
value measured or derived from a measured value, for at least one
corresponding biomarker of the biological subject and being at
least partially indicative of a concentration of the biomarker in a
sample taken from the subject; b) determining control values by
determining combinations of biomarker values; c) comparing each
control value to a respective control reference; and, d)
determining if the biomarker values are valid using results of the
comparison.
49. A method according to claim 48, wherein at least first and
second biomarker values are used to determine an indicator
indicative of a test result, and wherein the method includes
determining control values including: a) a combination of the first
and at least one other biomarker value; and, b) a combination of
the second and at least one other biomarker value.
50. A method according to claim 49, wherein the method includes: a)
determining at least four biomarker values, the indicator being
based on a combination of: i) a first indicator value calculated
using first and second biomarker values; and, ii) a second
indicator value calculated using third and fourth biomarker values;
and, b) determining control values including: i) a first control
value calculated using first and third biomarker values; ii) a
second control value calculated using first and fourth biomarker
values; iii) a third control value calculated using second and
third biomarker values; and, iv) a fourth control value calculated
using second and fourth biomarker values.
51. A method according to claim 50, wherein the method includes
calculating at least one of the indicator values and the control
values by applying a function to the respective biomarker values
wherein the function includes at least one of: a) multiplying two
biomarker values; b) dividing two biomarker values; c) a ratio of
two biomarker values; d) adding two biomarker values; e)
subtracting two biomarker values; f) a weighted sum of at least two
biomarker values; g) a log sum of at least two biomarker values;
and, h) a sigmoidal function of at least two biomarker values.
52. A method according to claim 48, wherein the method includes
determining: a) a first control value using a ratio of first and
third biomarker values; b) a second control value using a ratio of
first and fourth biomarker values; c) a third control value using a
ratio of second and third biomarker values; and, d) a fourth
control value using a ratio of second and fourth biomarker
values.
53. A method according to claim 48, wherein the method includes: a)
determining a validity probability based on the result of the
comparison; and, b) using the validity probability to determine if
the biomarker values are valid.
54. A method according to claim 53, wherein the method includes: a)
determining a control value probability for the comparison of each
control value to the respective control reference; and, b)
combining the control value probabilities to determine the validity
probability.
55. A method according to claim 54, wherein the control reference
is derived from biomarker values collected from a number of
individuals in a sample population and is at least one of: a) a
control value threshold range; b) a control value threshold; and,
c) a control value distribution.
56. A method according to claim 55, wherein the control reference
is a control value threshold range, and wherein the method
includes: a) comparing each control value to a respective control
value threshold range; and, b) determining at least one of the
biomarker values to be invalid if any one of the control values
falls outside the respective control value threshold range.
57. A method according to claim 55, wherein the control reference
is a control value distribution, and wherein the method includes:
a) comparing each control value to a respective control value
distribution; and, b) determining the validity using the results of
the comparisons.
58. A method according to claim 49, wherein the indicator is for
use in determining the likelihood that a biological subject has at
least one medical condition, the control reference is derived from
biomarker values collected from a number of individuals in a sample
population and the sample population includes: a) individuals
presenting with clinical signs of the at least one medical
condition; b) individuals diagnosed with the at least one medical
condition; and, c) healthy individuals.
59. A method according to claim 49, wherein the indicator is
determined by combining the first and second derived indicator
values using a combining function, the combining function being at
least one of: a) an additive model; b) a linear model; c) a support
vector machine; d) a neural network model; e) a random forest
model; f) a regression model; g) a genetic algorithm; h) an
annealing algorithm; i) a weighted sum; and, j) A nearest neighbour
model.
60. A method according to claim 49, wherein the method includes: a)
determining an indicator value indicative of a likelihood of the
subject having at least one medical condition; b) comparing the
indicator value to at least one indicator value range; c)
determining the indicator at least in part using a result of the
comparison; and, d) generating a representation of the
indicator.
61. A method according to claim 48, wherein the biomarker value is
indicative of a level or abundance of a molecule, cell or organism
selected from one or more of: a) proteins; b) nucleic acids; c)
carbohydrates; d) lipids; e) proteoglycans; f) cells; g)
metabolites; h) tissue sections; i) whole organisms; and, j)
molecular complexes.
62. A method according to claim 48, wherein the method is performed
at least in part using one or more electronic processing devices
wherein the method includes, in the one or more electronic
processing devices: a) receiving the biomarker values; b)
determining the at least one control value using at least two of
the biomarker values; c) retrieving the indicator reference from a
database; d) comparing the at least one control value to the
respective control value threshold; and, e) determining if the test
is a valid test using the results of the comparison.
63. A method according to claim 48, wherein the biomarkers are gene
expression products and wherein the method includes: a) obtaining a
sample from a biological subject, the sample including the gene
expression products; b) amplifying at least the gene expression
products in the sample; and, c) for each gene expression product,
determining an amplification amount representing a degree of
amplification required to obtain a defined level of the respective
gene expression.
64. A method according to claim 63, wherein the amplification
amount is at least one of: a) a cycle time; b) a number of cycles;
c) a cycle threshold; and, d) an amplification time.
65. A method according to claim 63, wherein the biomarkers are gene
expression products and wherein the method includes, determining a
combination of biomarker values by subtracting amplification
amounts for the respective gene expression products so that the
combination of biomarker values represents a ratio of the relative
concentration of the respective gene expression products.
66. A method according to claim 48, wherein the biomarker values
are obtained from a biological subject presenting with clinical
signs of at least one medical condition wherein the at least one
condition includes ipSIRS and wherein the biomarker values
correspond to relative concentrations of LAMP1, CEACAM4, PLAC8 and
PLA2G7.
67. A method according to claim 66, wherein the biomarker values
are obtained from a biological subject presenting with clinical
signs common to first and second conditions and wherein the
indicator is for use in distinguishing between the first and second
conditions and wherein the first and second conditions include
inSIRS and ipSIRS.
68. A method according to claim 48, wherein the quantification
technique is at least one of: a) a nucleic acid amplification
technique; b) polymerase chain reaction (PCR); c) a hybridisation
technique; d) microarray analysis; e) low density arrays; f)
hybridisation with allele-specific probes; g) enzymatic mutation
detection; h) ligation chain reaction (LCR); i) oligonucleotide
ligation assay (OLA); j) flow-cytometric heteroduplex analysis; k)
chemical cleavage of mismatches; l) mass spectrometry; m) flow
cytometry; n) liquid chromatography; o) gas chromatography; p)
immunohistochemistry; q) nucleic acid sequencing; r) single strand
conformation polymorphism (SSCP); s) denaturing gradient gel
electrophoresis (DGGE); t) temperature gradient gel electrophoresis
(TGGE); u) restriction fragment polymorphisms; v) serial analysis
of gene expression (SAGE); w) affinity assays; x) radioimmunoassay
(RIA); y) lateral flow immunochromatography; z) flow cytometry; aa)
electron microscopy (EM); and, bb) enzyme-substrate assay.
69. Apparatus for validating measurement of biomarker values used
in generating an indicator, the biomarkers being quantified using a
quantification technique of a selected type, and the apparatus
including at least one processing device that: a) determines a
plurality of biomarker values, each biomarker value being
indicative of a value measured or derived from a measured value,
for at least one corresponding biomarker of the biological subject
and being at least partially indicative of a concentration of the
biomarker in a sample taken from the subject; b) determines control
values by determining different combinations of biomarker values;
c) compares each control value to a respective control reference;
and, d) determines if the biomarker values are valid using results
of the comparison.
70. A method according to claim 48, wherein the biomarker values
are used to determine an indicator indicative of a test result, and
wherein the combinations include ratios of the biomarker values.
Description
BACKGROUND OF THE INVENTION
[0001] The present invention relates to a method and apparatus for
validating measurement of biomarker values used in generating an
indicator, and in one example, to a method and apparatus for
validating an indicator used in determining the likelihood of a
biological subject having at least one medical condition.
DESCRIPTION OF THE PRIOR ART
[0002] The reference in this specification to any prior publication
(or information derived from it), or to any matter which is known,
is not, and should not be taken as an acknowledgment or admission
or any form of suggestion that the prior publication (or
information derived from it) or known matter forms part of the
common general knowledge in the field of endeavour to which this
specification relates.
[0003] Measurement of gene expression (as RNA or protein) in
samples taken from living organisms has practical applications
including, but not limited to, determining a disease state,
determining disease extent or severity, disease prognosis and early
identification, identifying a tissue type (both normal and diseased
including cancers), identifying and enumerating cell types in a
cell mix, and understanding normal metabolic processes and their
response to external factors or insults (including injury, wounds,
burns, stress, viral or bacterial or parasitic or fungal infection,
exercise, diet, therapeutics, toxins, therapies, treatments and
experimental procedures). There are a number of methods available
for measuring gene expression (as RNA or protein) that are well
known in the art, from low-throughput (single genes and gene
products) to high-throughput (exome and arrays), including northern
blots, polymerase chain reaction (qPCR), microarrays, RNA
sequencing (RNA-seq), targeted RNA sequencing, ELISA, EIA, mass
spectrometry, HPLC, SNP analysis, and epigenetic technologies
(ChIP-Seq, Chromatin Conformational Signatures (CCA), DNA
methylation analyses).
[0004] Each of these technologies produces a value, or a set of
values for each of the products measured. In the context of medical
discovery and its applications, these values are termed
`biomarkers`. A measured value for any gene expression product (as
RNA or protein) is defined as a measured biomarker, as measured by
the processing instrument or device. Examples of a measured
biomarker include a protein concentration for a specified protein,
the transcript count for a single transcript in the case of RNA
sequencing, the expression value for an exon or transcript in the
case of microarrays, an m/z value in the case of mass spectrometry
or a fluorescence value in the case of flow cytometry. Measured
biomarkers can be understood as `raw data`, as measured by the
instrument. Multi-biomarker assays will measure a number of
biomarkers in parallel, reporting a collection of measured
biomarkers.
[0005] Indicator values are values that are designed to correlate,
classify, or otherwise be indicative of some condition, stage,
diagnosis or prognosis or absence thereof. For example temperature
reported in degrees is an indicator value for fever. Arbitrarily
complex indicators may be built for any purpose, and in the case of
multi-biomarker medical devices, the indicator will be some
combination of biomarkers that, through an equation, generate an
indicator value that correlates to some state or condition (or the
absence of such) for a patient.
[0006] The development and use of indicator values requires
accurate and valid measurement of gene expression (as RNA or
protein) measured values, and can be achieved through the use of
two key steps: normalisation and controls. Controls provide a check
that the underlying values are valid, and normalisation is any
method by which samples can be made comparable by removing
non-biological sources of variation between samples.
[0007] Controls are used to ensure that relevant potential modes of
failure can be detected. If a failure is detected in a control, the
assay or experiment can be declared failed and the indicator value
(if any) will consequently also be invalid. In the context of
medical devices, controls guard against the results of the test
(indicator values) being reported when the underlying inputs to the
indicator may be invalid thus avoiding the potential of the
operator drawing false conclusions. Controls that can be used
include the following (which ones used depends in part upon the
user, the application and the stage of development of the assay):
[0008] No template control (run in parallel with other reactions to
determine the extent of contamination--for PCR) [0009] No
amplification control for assays relying on nucleic acid
amplification (e.g., contains no polymerase--this checks for
example the integrity of a labelled probe relying on the FRET
principle--for PCR) [0010] Positive control (a sample used during
assay use to confirm that the test is able to produce a result in
the reportable range). This does not imply that the reported value
for this control will be above a given threshold (for example
positive for a disease), merely that the control is positively able
to generate a value in the reportable range of the test. Positive
controls may be biological in origin or synthetic (or a hybrid of
the two, such as recombinant products), and the positive controls
can be internal (coming from within a sample being tested) or
external (run in parallel and independent of the sample being
tested). Positive controls can include positive or negative
biological or synthetic controls. [0011] A control containing no
reverse transcriptase (to determine the extent of contaminating DNA
especially if primers are not designed across an exon/intron
border--for PCR) [0012] Spike-in controls include artificial
nucleic acid added to either the sample to be tested (at any stage)
and are used to determine the extent of PCR inhibition and for
quality control in array and RNA-seq (for quantification,
sensitivity, coverage and linearity).
[0013] Of these measured controls perhaps the most common are
external positive controls containing known concentrations of a
given analyte, and spike-ins. The use of such controls contributes
to the expense and complexity of running an experiment, or assay,
through having to purchase reagents and the controls themselves,
through the use of experimental "real estate" which could otherwise
be used for targets, and in the additional resources and complexity
inherent in having these control targets in addition to the targets
required to produce the indicator value. It is therefore
advantageous to reduce the additional measured controls not
measured in the course of determining the indictor value.
[0014] Normalisation is an important step that ensures that
comparisons between samples, or between a reference and a sample
can be made. The objective of the normalisation step is to remove
differences not attributable to biological variability, such as
batch effect and other sources of technical variability including
those introduced by concentration, time, temperature, instruments,
operators or assay parameters (including those unknown or outside
of the control of the assay users) such as those introduced in a
typical workflow, such as that described below.
[0015] Measurement of gene expression using microarrays or PCR or
RNA-seq by example usually involves some or all of the following
steps depending on the method (similar types of controls are
required in most experiments measuring biomarkers): [0016]
Experimental design including power calculation and number of
replicates to be used [0017] Isolation of RNA or mRNA from
sample(s) of interest [0018] Determination of RNA quality and
quantity [0019] Fragmentation and size selection (for RNA-seq)
[0020] Conversion of RNA to complementary DNA (cDNA) [0021]
Conversion of cDNA to cRNA (for certain microarrays) [0022]
Fragmentation and labelling (for arrays, or use of a labelled probe
for PCR) [0023] Detection [0024] Data capture [0025] Determination
of data quality [0026] Data normalisation [0027] Control for false
discovery.
[0028] Some of the experimental method variables that need to be
controlled for (normalized) are detailed in Table 1 below, adapted
from Roche Applied Science Technical Note No. LC15/2002, under the
appropriate step.
TABLE-US-00001 TABLE 1 Sample DNA/RNA cDNA Sample Nucleic acid
Reverse Product Preparation isolation transcription Amplification
Detection Preparation Isolation Efficiency Efficiency Method used
method method Enzyme Enzyme Linearity of Stability of Purity
variability variability assay nucleic acid Variability Storage of
isolation Storage
[0029] So that datasets can be compared, and that publicly
available data is of high quality, minimum information guidelines
for gene expression analysis experiments have been published in
scientific journals for both PCR and microarrays (Bustin S A, Benes
V, Garson J A, Hellemans J, Huggett J, et al. (2009) The MIQE
Guidelines: Minimum Information for Publication of Quantitative
Real-Time PCR Experiments. Clinical Chemistry 55: 611-622) (Brazma
A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, et al. (2001)
Minimum information about a microarray experiment (MIAME)-toward
standards for microarray data. Nat Genet 29: 365-371) and are
publicly available for RNA-seq
(MINSEQE--www.mged.org/minseqe/).
[0030] Normalisation of data to account for these effects using
measured biomarkers is common. For example, an external positive
control at a known concentration may be run in parallel with a
sample. The value of the measured biomarker value in the sample can
then be inferred (normalized) with reference to the measured
external positive control. This is the concept behind a standard
calibration curve used for normalisation. Another common
normalisation method using measured biomarkers uses internal
positive controls; for example, in an RNA sequencing experiment,
certain genes (or groups of genes) may be assumed to have a
constant biological level of expression (these are the normalizer
biomarkers). Differences in the measured values for these
normalizer biomarkers between samples is then assumed to be
non-biological. The measured values for each sample are then
adjusted up or down such that the normalizer biomarkers in each
sample have the same value and the data is then said to be
normalized. The normalized values of each biomarker may then be
directly compared between samples, for example for the diagnosis of
a medical condition. Extensions of this concept are also known, for
example Robust Microarray Analysis (Irizarry, R A; Hobbs, B;
Collin, F; Beazer-Barclay, Y D; Antonellis, K J; Scherf, U; Speed,
T P (2003). "Exploration, normalisation, and summaries of high
density oligonucleotide array probe level data.". Biostatistics 4
(2): 249-64) where the measured values for each sample are adjusted
such that the normalized values for each sample fit the same
distribution.
[0031] In practice, microarrays and RNA-seq and other platforms are
often used in the early "discovery" or research stage of
experimentation to generate sets of measured biomarkers covering
the exome or genome or regulatory mechanisms thereof. The set of
measured biomarkers generated in such discovery experiments may be
upwards of 6,000 genes or transcripts, or up to 1,000,000 peaks in
the case of tandem mass spectrometry discovery datasets. There are
typically many more measured biomarkers in each dataset than
patient samples. This leads directly to false discovery problems as
will be appreciated by someone skilled in the art of biomarker
discovery. A false discovery is when a measured biomarker with no
genuine biological correlation to the condition under consideration
by chance happens to correlate to said condition. These false
discoveries are indistinguishable from true discoveries until more
patient samples have been tested.
[0032] Once certain biomarkers have been "discovered", or shown to
be significantly correlated to the desired experimental endpoint, a
minimal set of biomarkers is often migrated to an appropriate
clinical device, such as qPCR or Point-Of-Care RNA-sequencing
platforms, along with a minimal set of appropriate controls.
[0033] qPCR currently has significant and commercially attractive
advantages over microarrays and RNA-seq (including targeted
RNA-seq), especially when used in a clinical environment. Such
advantages include fast turnaround time, limited technician
hands-on-time to set up an assay, limited technical skill level
required to run an assay, accessibility and availability of PCR
machines, small footprint of PCR machines, ease of results
interpretation, limited need for supporting information technology
infrastructure (software, algorithms, hardware, networks), limited
license fees, availability and cost of reagents. Such factors lead
to reduced cost of goods sold and a higher likelihood of market
acceptance of an assay.
[0034] The successful migration of relevant biomarkers to qPCR is
currently limited by a number of factors including: [0035] Limited
multiplexing capability [0036] Limited reporter dyes (with
non-overlapping emission spectra or with good spectral resolution)
[0037] The need for positive controls and spike-ins [0038] The need
to run a passive reference dye [0039] Cost of spike-in controls
[0040] Cost and added complexity of controls, especially external
controls [0041] Sample prep limitations.
[0042] Such factors generally limit multiplex qPCR to two to four
targets at the maximum since up to three dyes are used as controls
(passive reference, internal, spike-in).
[0043] Thus, for cost and practical reasons, there is a need for a
better control strategy in gene expression analysis, and in
particular one tailored for use in medical devices.
[0044] Prior art practices in the design and use of controls in
gene expression analyses is limited and is generally based on
variations on the themes of the use of spike-ins (artificial
sequences and naturally occurring sequences) and internal measured
controls. For example, Vandesompele et al., (2002) (Vandesompele J,
De Preter K, Pattyn F, Poppe B, Van Roy N, et al. (2002) Accurate
normalisation of real-time quantitative RT-PCR data by geometric
averaging of multiple internal control genes. Genome Biol 3:
RESEARCH0034) describe the use of multiple internal control genes
(a collection of measured biomarkers), rather than just a single
internal control gene, and a method of identifying stably expressed
genes in different tissues for the use of tissue-specific internal
control genes. The authors suggest that different tissues may
require the use of different internal control genes and that the
use of more than one internal control gene provides more consistent
results with respect to normalisation. Prior to this publication it
was generally accepted that a single gene was sufficient for
normalisation and that the genes GAPDH, beta-2 microglobulin or 18S
ribosomal were stably expressed across all tissues and all
conditions, which has since been proven to be incorrect, especially
in conditions that have a large effect on gene expression, such as
peripheral blood gene expression in sepsis.
[0045] Fardin et al., (2007) (Fardin P, Moretti S, Biasotti B,
Ricciardi A, Bonassi S, et al. (2007) Normalisation of low-density
microarray using external spike-in controls: analysis of macrophage
cell lines expression profile. BMC Genomics 8: 17.
doi:10.1186/1471-2164-8-17) describe the use of artificial spike-in
RNAs as a method of providing more consistent normalisation for low
density array qPCR data, especially when the distribution of up-
and down-regulated genes is asymmetric. Similarly, Jiang et al.,
(2011) (Jiang L, Schlesinger F, Davis C A, Zhang Y, Li R, et al.
(2011) Synthetic spike-in standards for RNA-seq experiments. Genome
Res 21: 1543-1551. doi:10.1101/gr.121095.111) describe synthetic
RNA spike-in controls for use in RNA-seq experiments.
[0046] Various published patents describe the use of internal
control genes (measured biomarkers) specifically for blood
(EP2392668A2, US20100184608) or artificial universal spike-in
(external) controls for use with any tissue type
(US20030148339).
[0047] In the patent entitled "Diagnostic and Prognostic Tests"
(U.S. Pat. No. 7,622,260) the inventors describe an approach using
ratios of gene expression to diagnose biological states or
conditions, in particular cancer, and for distinguishing malignant
pleural mesothelioma from other lung cancers or from normal lung
tissue, and for distinguishing between subclasses of malignant
pleural mesothelioma.
SUMMARY OF THE PRESENT INVENTION
[0048] In one broad form the present invention seeks to provide a
method for validating quantification of biomarkers, the biomarkers
being quantified using a quantification technique of a selected
type, and the method including: [0049] a) determining a plurality
of biomarker values, each biomarker value being indicative of a
value measured or derived from a measured value, for at least one
corresponding biomarker of the biological subject and being at
least partially indicative of a concentration of the biomarker in a
sample taken from the subject; [0050] b) determining at least one
control value by determining a combination of biomarker values;
[0051] c) comparing each control value to a respective control
reference; and, [0052] d) determining if the biomarker values are
valid using results of the comparison.
[0053] Typically at least first and second biomarker values are
used to determine an indicator indicative of a test result, and
wherein the method includes determining control values including:
[0054] a) a combination of the first and at least one other
biomarker value; and, [0055] b) a combination of the second and at
least one other biomarker value.
[0056] Typically the method includes: [0057] a) determining at
least four biomarker values, the indicator being based on a
combination of: [0058] i) a first indicator value calculated using
first and second biomarker values; and, [0059] ii) a second
indicator value calculated using third and fourth biomarker values;
and, [0060] b) determining control values including: [0061] i) a
first control value calculated using first and third biomarker
values; [0062] ii) a second control value calculated using first
and fourth biomarker values; [0063] iii) a third control value
calculated using second and third biomarker values; and, [0064] iv)
a fourth control value calculated using second and fourth biomarker
values.
[0065] Typically the method includes determining control values
including: [0066] a) a fifth control value calculated using first
and second biomarker values; [0067] b) a sixth control value
calculated using third and fourth biomarker values; and, [0068] c)
control values calculated using a combination of measured
biomarkers not used in determining an indicator value.
[0069] Typically the method includes calculating at least one of
the indicator values and the control values by applying a function
to the respective biomarker values.
[0070] Typically the function includes at least one of: [0071] a)
multiplying two biomarker values; [0072] b) dividing two biomarker
values; [0073] c) a ratio of two biomarker values; [0074] d) adding
two biomarker values; [0075] e) subtracting two biomarker values;
[0076] f) a weighted sum of at least two biomarker values; [0077]
g) a log sum of at least two biomarker values; and, [0078] h) a
sigmoidal function of at least two biomarker values.
[0079] Typically the method includes determining: [0080] a) a first
control value using a ratio of first and third biomarker values;
[0081] b) a second control value using a ratio of first and fourth
biomarker values; [0082] c) a third control value using a ratio of
second and third biomarker values; and, [0083] d) a fourth control
value using a ratio of second and fourth biomarker values.
[0084] Typically the method includes determining control values
including: [0085] a) a fifth control value using a ratio of first
and second biomarker values; [0086] b) a sixth control value using
a ratio of third and fourth biomarker values; and, [0087] c)
controls values calculated using a ratio of measured biomarkers not
used in determining an indicator value.
[0088] Typically the method includes: [0089] a) determining a
validity probability based on the result of the comparison; and,
[0090] b) using the validity probability to determine if the
biomarker values are valid.
[0091] Typically the method includes: [0092] a) determining a
control value probability for the comparison of each control value
to the respective control reference; and, [0093] b) combining the
control value probabilities to determine the validity
probability.
[0094] Typically the control reference is at least one of: [0095]
a) a control value threshold range; [0096] b) a control value
threshold; and, [0097] c) a control value distribution.
[0098] Typically the control reference is a control value threshold
range, and wherein the method includes: [0099] a) comparing each
control value to a respective control value threshold range; and,
[0100] b) determining at least one of the biomarker values to be
invalid if any one of the control values falls outside the
respective control value threshold range.
[0101] Typically the control reference is a control value
distribution, and wherein the method includes: [0102] a) comparing
each control value to a respective control value distribution; and,
[0103] b) determining the validity using the results of the
comparisons.
[0104] Typically each respective reference is derived from
biomarker values collected from a number of individuals in a sample
population.
[0105] Typically each respective reference is determined for at
least part of the sample population.
[0106] Typically the sample population includes: [0107] a) a
plurality of individuals of different sexes; [0108] b) a plurality
of individuals of different ethnicities; [0109] c) a plurality of
healthy individuals; [0110] d) a plurality of individuals suffering
from at least one diagnosed medical condition; [0111] e) a
plurality of individuals showing clinical signs of at least one
medical condition; and, [0112] f) first and second groups of
individuals, each group of individuals suffering from a respective
diagnosed medical condition.
[0113] Typically the indicator is for use in determining the
likelihood that a biological subject has at least one medical
condition, and wherein the sample population includes: [0114] a)
individuals presenting with clinical signs of the at least one
medical condition; [0115] b) individuals diagnosed with the at
least one medical condition; and, [0116] c) healthy
individuals.
[0117] Typically the indicator is determined by combining the first
and second derived indicator values using a combining function, the
combining function being at least one of: [0118] a) an additive
model; [0119] b) a linear model; [0120] c) a support vector
machine; [0121] d) a neural network model; [0122] e) a random
forest model; [0123] f) a regression model; [0124] g) a genetic
algorithm; [0125] h) an annealing algorithm; [0126] i) a weighted
sum; and, [0127] j) A nearest neighbour model.
[0128] Typically the method includes: [0129] a) determining an
indicator value; [0130] b) comparing the indicator value to at
least one indicator value range; and, [0131] c) determining the
indicator at least in part using a result of the comparison.
[0132] Typically the indicator is indicative of a likelihood of the
subject having at least one medical condition.
[0133] Typically the method includes generating a representation of
the indicator.
[0134] Typically the representation includes: [0135] a) an
alphanumeric indication of the indicator value; [0136] b) a
graphical indication of a comparison of the indicator value to one
or more thresholds; and, [0137] c) an alphanumeric indication of a
likelihood of the subject having at least one medical
condition.
[0138] Typically the biomarker value is indicative of a level or
abundance of a molecule, cell or organism selected from one or more
of: [0139] a) proteins; [0140] b) nucleic acids; [0141] c)
carbohydrates; [0142] d) lipids; [0143] e) proteoglycans; [0144] f)
cells; [0145] g) metabolites; [0146] h) tissue sections; [0147] i)
whole organisms; and, [0148] j) molecular complexes.
[0149] Typically the method is performed at least in part using one
or more electronic processing devices.
[0150] Typically the indicator reference is retrieved from a
database.
[0151] Typically the method includes, in the one or more electronic
processing devices: [0152] a) receiving the biomarker values;
[0153] b) determining the at least one control value using at least
two of the biomarker values; [0154] c) comparing the at least one
control value to the respective control value threshold; and,
[0155] d) determining if the test is a valid test using the results
of the comparison.
[0156] Typically the method includes, in the one or more electronic
processing devices: [0157] a) determining the indicator by: [0158]
i) calculating a first indicator value using a ratio of first and
second biomarker values; [0159] ii) calculating a second indicator
value using a ratio of third and fourth second biomarker values;
and, [0160] iii) determining a sum of the first and second
indicator values; [0161] b) determining a plurality of control
values by: [0162] i) calculating a first control value using a
ratio of the first and third biomarker values; [0163] ii)
calculating a second control value using a ratio of the first and
fourth biomarker values; [0164] iii) calculating a third control
value using a ratio of the second and third biomarker values; and,
[0165] iv) calculating a fourth control value using a ratio of the
second and fourth biomarker values; [0166] c) comparing each
control value to a respective threshold range; and, [0167] d)
displaying the indicator in response to a successful comparison for
each control value.
[0168] Typically the method includes, in the one or more electronic
processing devices: [0169] a) calculating a fifth control value
using a ratio of the first and second biomarker values; [0170] b)
calculating a sixth control value using a ratio of the third and
fourth biomarker values; and, [0171] c) calculating an additional
or set of control values by using a combination of biomarkers not
used in determining an indicator value.
[0172] Typically the biomarkers are gene expression products and
wherein the method includes: [0173] a) obtaining a sample from a
biological subject, the sample including the gene expression
products; [0174] b) amplifying at least the gene expression
products in the sample; and, [0175] c) for each gene expression
product, determining an amplification amount representing a degree
of amplification required to obtain a defined level of the
respective gene expression.
[0176] Typically the amplification amount is at least one of:
[0177] a) a cycle time; [0178] b) a number of cycles; [0179] c) a
cycle threshold; and, [0180] d) an amplification time.
[0181] Typically the biomarkers are gene expression products and
wherein the method includes, determining a combination of biomarker
values by subtracting amplification amounts for the respective gene
expression products so that the combination of biomarker values
represents a ratio of the relative concentration of the respective
gene expression products.
[0182] Typically the biomarker values are obtained from a
biological subject presenting with clinical signs of at least one
medical condition.
[0183] Typically the at least one condition includes ipSIRS
(infection positive Systemic Inflammatory Response Syndrome) and
wherein the biomarker values correspond to relative concentrations
of LAMP1, CEACAM4, PLAC8 and PLA2G7.
[0184] Typically the biomarker values are obtained from a
biological subject presenting with clinical signs common to first
and second conditions and wherein the indicator is for use in
distinguishing between the first and second conditions.
[0185] Typically the first and second conditions include inSIRS
(infection negative Systemic Inflammatory Response Syndrome) and
ipSIRS.
[0186] Typically the quantification technique is at least one of:
[0187] a) a nucleic acid amplification technique; [0188] b)
polymerase chain reaction (PCR); [0189] c) a hybridisation
technique; [0190] d) microarray analysis; [0191] e) low density
arrays; [0192] f) hybridisation with allele-specific probes; [0193]
g) enzymatic mutation detection; [0194] h) ligation chain reaction
(LCR); [0195] i) oligonucleotide ligation assay (OLA); [0196] j)
flow-cytometric heteroduplex analysis; [0197] k) chemical cleavage
of mismatches; [0198] l) mass spectrometry; [0199] m) flow
cytometry; [0200] n) liquid chromatography; [0201] o) gas
chromatography; [0202] p) immunohistochemistry; [0203] q) nucleic
acid sequencing; [0204] r) single strand conformation polymorphism
(SSCP); [0205] s) denaturing gradient gel electrophoresis (DGGE);
[0206] t) temperature gradient gel electrophoresis (TGGE); [0207]
u) restriction fragment polymorphisms; [0208] v) serial analysis of
gene expression (SAGE); [0209] w) affinity assays; [0210] x)
radioimmunoassay (RIA); [0211] y) lateral flow
immunochromatography; [0212] z) flow cytometry; [0213] aa) electron
microscopy (EM); and, [0214] bb) enzyme-substrate assay.
[0215] In one broad form the present invention seeks to provide
apparatus for validating measurement of biomarker values used in
generating an indicator, the biomarkers being quantified using a
quantification technique of a selected type, and the apparatus
including at least one processing device that: [0216] a) determines
a plurality of biomarker values, each biomarker value being
indicative of a value measured or derived from a measured value,
for at least one corresponding biomarker of the biological subject
and being at least partially indicative of a concentration of the
biomarker in a sample taken from the subject; [0217] b) determines
at least one control value by determining a combination of
biomarker values; [0218] c) compares each control value to a
respective control reference; and, [0219] d) determines if the
biomarker values are valid using results of the comparison.
[0220] In one broad form the present invention seeks to provide a
method for validating an indicator used in determining the
likelihood of a biological subject having at least one medical
condition, the biomarkers being quantified using a quantification
technique of a selected type and the method including: [0221] a)
determining a plurality of biomarker values, each biomarker value
being indicative of a value measured or derived for at least one
corresponding biomarker of the biological subject; [0222] b)
determining an indicator indicative of the likelihood of a
biological subject having at least one medical condition by: [0223]
i) calculating a first indicator value using first and second
biomarker values; [0224] ii) calculating a second indicator value
using third and fourth second biomarker values; and, [0225] iii)
determining the indicator using the first and second indicator
values; [0226] c) calculating at least one of: [0227] i) a first
control value using the first and third biomarker values; [0228]
ii) a second control value using the first and fourth biomarker
values; [0229] iii) a third control value using the second and
third biomarker values; [0230] iv) a fourth control value using the
second and fourth biomarker values; [0231] v) a fifth control value
using the first and fourth second values; [0232] vi) a sixth
control value using the third and fourth biomarker values; and,
[0233] vii) an additional or set of control values by using a
combination of biomarkers not used in determining an indicator
value; [0234] d) comparing the at least one control value to a
respective control value threshold; and, [0235] e) selectively
validating the indicator using the results of the comparison.
[0236] In one broad form the present invention seeks to provide
apparatus for validating an indicator indicative of measured values
of gene expression products, the biomarkers being quantified using
a quantification technique of a selected type, the apparatus
including at least one processing device that: [0237] a) determines
a plurality of biomarker values, each biomarker value being
indicative of a value measured or derived for at least one
corresponding biomarker of the biological subject; [0238] b)
determines the indicator by: [0239] i) calculating a first
indicator value using first and second biomarker values; [0240] ii)
calculating a second indicator value using third and fourth second
biomarker values; and, [0241] iii) determining the indicator using
the first and second indicator values; [0242] c) calculates at
least one of: [0243] i) a first control value using the first and
third biomarker values; [0244] ii) a second control value using the
first and fourth biomarker values; [0245] iii) a third control
value using the second and third biomarker values; [0246] iv) a
fourth control value using the second and fourth biomarker values;
[0247] v) a fifth control value using the first and second
biomarker values; [0248] vi) a sixth control value using the third
and fourth biomarker values; and, [0249] vii) an additional or set
of control values by using a combination of biomarkers not used in
determining an indicator value. [0250] d) compares the at least one
control value to a respective control value threshold; and, [0251]
e) selectively validates the indicator using the results of the
comparison.
[0252] In one broad form the present invention seeks to provide a
method for validating an indicator used in determining the
likelihood of a biological subject having at least one medical
condition, the biomarkers being quantified using a quantification
technique of a selected type and the method including: [0253] a)
obtaining a sample from a biological subject, the sample including
gene expression products; [0254] b) quantifying at least some gene
expression products in the sample to determine a concentration of
the gene expression product in the sample; [0255] c) determining an
indicator indicative of the likelihood of a biological subject
having at least one medical condition by combining: [0256] i) a
first indicator value indicative of a ratio of the concentration of
the first and second gene expression products; and, [0257] ii) a
second indicator value indicative of a ratio of the concentration
of the third and fourth gene expression products; [0258] d)
determining control values by determining at least one of: [0259]
i) a first control value indicative of a ratio of the concentration
of the first and third gene expression products; [0260] ii) a
second control value indicative of a ratio of the concentration of
the first and fourth gene expression products; [0261] iii) a third
control value indicative of a ratio of the concentration of the
second and third gene expression products; [0262] iv) a fourth
control value indicative of a ratio of the concentration of the
second and fourth gene expression products; [0263] v) a fifth
control value indicative of a ratio of the concentration of the
first and second gene expression products; [0264] vi) a sixth
control value indicative of a ratio of the concentration of the
third and fourth gene expression products; and, [0265] vii) an
additional or set of control values by using a combination of gene
expression products not used in determining an indicator value;
[0266] e) comparing each control value to a respective control
value threshold range; and, [0267] f) validating the indicator if
each of the control values is within the respective control value
range.
[0268] Typically the method includes quantifying the concentration
of the gene expression products by: [0269] a) amplifying at least
some gene expression products in the sample; and, [0270] b) for
each of a plurality of gene expression products, determining an
amplification amount representing a degree of amplification
required to obtain a defined level of the respective gene
expression product.
[0271] Typically the method includes: [0272] a) determining the
indicator by: [0273] i) determining a first indicator value
calculated using the first and second amplification times
indicative of the concentration of first and second gene expression
products; and, [0274] ii) a second indicator value calculated using
third and fourth amplification times indicative of the relative
concentration of third and fourth gene expression products; and,
[0275] b) determining control values by determining at least one
of: [0276] i) a first control value calculated using first and
third amplification times indicative of the relative concentration
of first and third gene expression products; [0277] ii) a second
control value calculated using first and fourth amplification times
indicative of the relative concentration of first and fourth gene
expression products; [0278] iii) a third control value calculated
using second and third amplification times indicative of the
relative concentration of second and third gene expression
products; [0279] iv) a fourth control value calculated using second
and fourth amplification times indicative of the relative
concentration of second and fourth gene expression products; [0280]
v) a fifth control value calculated using first and second
amplification times indicative of the relative concentration of
first and second gene expression products; [0281] vi) a sixth
control value calculated using third and fourth amplification times
indicative of the relative concentration of third and fourth gene
expression products; and [0282] vii) an additional or set of
control values by using a combination of amplification times not
used in determining an indicator value.
[0283] In one broad form the present invention seeks to provide
apparatus for validating an indicator used in determining the
likelihood of a biological subject having at least one medical
condition, the apparatus including: [0284] a) a sampling device
that obtains a sample from a biological subject, the sample
including gene expression products; [0285] b) a quantification
device that quantifies at least some gene expression products in
the sample to determine a concentration of the gene expression
product in the sample; and, [0286] c) at least one processing
device that: [0287] i) determines an indicator indicative of the
likelihood of a biological subject having at least one medical
condition by combining: [0288] (1) a first indicator value
indicative of a ratio of the concentration of the first and second
gene expression products; and, [0289] (2) a second indicator value
indicative of a ratio of the concentration of the third and fourth
gene expression products; [0290] ii) determines control values by
determining at least one of: [0291] (1) a first control value
indicative of a ratio of the concentration of the first and third
gene expression products; [0292] (2) a second control value
indicative of a ratio of the concentration of the first and fourth
gene expression products; [0293] (3) a third control value
indicative of a ratio of the concentration of the second and third
gene expression products; [0294] (4) a fourth control value
indicative of a ratio of the concentration of the second and fourth
gene expression products; [0295] (5) a fifth control value
indicative of a ratio of the concentration of the first and second
gene expression products; [0296] (6) a sixth control value
indicative of a ratio of the concentration of the third and fourth
gene expression products; and, [0297] (7) an additional or set of
control values indicative of a ratio of the concentration of
biomarkers not used in determining an indicator value; [0298] iii)
compares each control value to a respective control value threshold
range; and, [0299] iv) validates the indicator if each of the
control values is within the respective control value range.
[0300] In one broad form the present invention seeks to provide a
method for validating quantification of biomarkers, and the method
including: [0301] a) determining a plurality of biomarker values,
each biomarker value being indicative of a value measured or
derived from a measured value, for at least one corresponding
biomarker of the biological subject and being at least partially
indicative of a concentration of the biomarker in a sample taken
from the subject; [0302] b) determining at least one control value
by determining a combination of biomarker values; [0303] c)
comparing each control value to a respective control reference;
and, [0304] d) determining if the biomarker values are valid using
results of the comparison, wherein the biomarker value is
indicative of a level or abundance of a molecule, cell or organism
selected from one or more of: [0305] i) proteins; [0306] ii)
nucleic acids; [0307] iii) carbohydrates; [0308] iv) lipids; [0309]
v) proteoglycans; [0310] vi) cells; and, [0311] vii) pathogenic
organisms.
[0312] In one broad form the present invention seeks to provide a
method for validating quantification of biomarkers, the method
including: [0313] a) determining a plurality of biomarker values,
each biomarker value being indicative of a value measured or
derived from a measured value, for at least one corresponding
biomarker of the biological subject and being at least partially
indicative of a concentration of the biomarker in a sample taken
from the subject; [0314] b) determining at least one control value
by determining a combination of biomarker values; [0315] c)
comparing each control value to a respective control reference;
and, [0316] d) determining if the biomarker values are valid using
results of the comparison, wherein the biomarker value is
indicative of a level or abundance of a molecule, cell or organism
selected from one or more of: [0317] i) proteins; [0318] ii)
nucleic acids; [0319] iii) carbohydrates; [0320] iv) lipids; [0321]
v) proteoglycans; [0322] vi) cells; [0323] vii) metabolites; [0324]
viii) tissue sections; [0325] ix) whole organisms; and, [0326] x)
molecular complexes.
[0327] In one broad form the present invention seeks to provide a
method for validating quantification of biomarkers, the method
including: [0328] a) determining a plurality of biomarker values,
each biomarker value being indicative of a value measured or
derived from a measured value, for at least one corresponding
biomarker of the biological subject and being at least partially
indicative of a concentration of the biomarker in a sample taken
from the subject; [0329] b) determining at least one control value
by determining a combination of biomarker values; [0330] c)
comparing each control value to a respective control reference;
and, [0331] d) determining if the biomarker values are valid using
results of the comparison, wherein the biomarkers are quantified
using at least one of: [0332] i) a nucleic acid amplification
technique; [0333] ii) polymerase chain reaction (PCR); [0334] iii)
a hybridisation technique; [0335] iv) microarray analysis; [0336]
v) low density arrays; [0337] vi) hybridisation with
allele-specific probes; [0338] vii) enzymatic mutation detection;
[0339] viii) ligation chain reaction (LCR); [0340] ix)
oligonucleotide ligation assay (OLA); [0341] x) flow-cytometric
heteroduplex analysis; [0342] xi) chemical cleavage of mismatches;
[0343] xii) mass spectrometry; [0344] xiii) flow cytometry; [0345]
xiv) liquid chromatography; [0346] xv) gas chromatography; [0347]
xvi) immunohistochemistry; [0348] xvii) nucleic acid sequencing;
[0349] xviii) single strand conformation polymorphism (SSCP);
[0350] xix) denaturing gradient gel electrophoresis (DGGE); [0351]
xx) temperature gradient gel electrophoresis (TGGE); [0352] xxi)
restriction fragment polymorphisms; [0353] xxii) serial analysis of
gene expression (SAGE); [0354] xxiii) affinity assays; [0355] xxiv)
radioimmunoassay (RIA); [0356] xxv) lateral flow
immunochromatography; [0357] xxvi) flow cytometry; [0358] xxvii)
electron microscopy (EM); and, [0359] xxviii) enzyme-substrate
assay.
[0360] It will be appreciated that the broad forms of the invention
and their respective features can be used in conjunction,
interchangeably and/or independently, and reference to separate
broad forms is not intended to be limiting.
BRIEF DESCRIPTION OF THE DRAWINGS
[0361] An example of the present invention will now be described
with reference to the accompanying drawings, in which:--
[0362] FIG. 1A is a flow chart of an example of a method for
validating measurement of biomarker values;
[0363] FIGS. 1B and 1C are flow charts of an example of the
comparison of independent and relative control approaches;
[0364] FIG. 2 is a schematic diagram of an example of a distributed
computer architecture;
[0365] FIG. 3 is a schematic diagram of an example of a base
station processing system;
[0366] FIG. 4 is a schematic diagram of an example of a client
device of FIG. 2;
[0367] FIG. 5 is a flowchart of an example of a method for
validating an indicator derived from biomarker measurements and
corresponding reference distributions;
[0368] FIG. 6 is a flowchart of an example of a method for
validating an indicator derived from biomarker measurements;
[0369] FIG. 7A is schematic diagram of an indication of the
relationship of biomarker values in the process of FIG. 5;
[0370] FIG. 7B is schematic diagram of an indication of the
relationship of biomarker values to a control in a standard control
arrangement;
[0371] FIGS. 8A and 8B are a flowchart of an example of a method
for validating an indicator derived from biomarker
measurements;
[0372] FIGS. 9A and 9B are schematic diagrams of examples of
representations of indicator values;
[0373] FIG. 10A is a flow chart of an example of the standard use
of controls in a multi-biomarker medical device;
[0374] FIG. 10B is a flow chart of an example of the use of
relative controls in place of standard controls in a
multi-biomarker medical device;
[0375] FIG. 10C is a flow chart of an example of the us of a hybrid
of standard and relative controls in a multi-biomarker medical
device
[0376] FIG. 11A is plots of cycle times obtained for measured
biomarkers over a range of concentrations;
[0377] FIG. 11B is plots indicator values for biomarker values
derived from the cycle times of FIG. 11A;
[0378] FIG. 12A is a density plot of measured biomarker values for
a sample population;
[0379] FIG. 12B (a)-(f) are plots of each measured biomarker value
for an invalid sample shown against the reference population of
measured biomarkers;
[0380] FIG. 12C (a)-(f) are plots of derived control values for an
invalid sample shown against the reference derived control
values.
[0381] FIG. 12D is a scatterplot showing the invalid sample against
one of the reference derived control biomarkers.
[0382] FIG. 13A (a)-(d) are plots showing an invalid sample against
a reference population of measured biomarkers.
[0383] FIG. 13B (a)-(f) are plots showing the same invalid sample
against a reference population of derived control biomarkers.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0384] An example of a process for validating measurement of
biomarkers for use in determining an indicator, such an as an
indicator indicative of the likelihood of a biological subject
having at least one predominant medical condition will now be
described with reference to FIG. 1.
[0385] For the purpose of explanation, a number of different terms
will be used.
[0386] For example, the term "biomarker" refers to any quantifiable
value, or combination or derivative of parameters, that can be used
as an indicator of a biological state. In the context of the
current application, biomarkers include proteins, nucleic acids,
such as DNA, RNA or the like, carbohydrates, lipids, proteoglycans,
cells, metabolites, tissue sections, whole organisms (e.g.
pathogenic and non-pathogenic microorganisms) and molecular
complexes (e.g. protein/nucleic acid complex), or the like.
[0387] The term "biomarker value" refers to a value determined by
quantifying the amount of, abundance of, level of, concentration
of, quantity of, or activity of, the corresponding biomarker within
a subject or individual. The biomarker value can be based on a
measured biomarker value or a value derived therefrom, and examples
will be described in more detail below.
[0388] The term "reference biomarkers" is used to refer to
biomarkers whose values are known for a sample population of one or
more individuals having one or more conditions, stages of one or
more conditions, subtypes of one or more conditions or different
prognoses. The term "reference data" refers to data measured for
one or more individuals in a sample population, and may include
quantification of the level or activity of the biomarkers measured
for each individual, information regarding any conditions of the
individuals, and optionally any other information of interest
including derived biomarkers which have been derived from measured
markers. Reference biomarkers are named for their primary purpose
of providing a reference against which new or unknown samples can
be compared.
[0389] The term "indicator values" is used to refer to combinations
of biomarker values that are used in deriving an indicator, which
may be indicative of the likelihood of a subject suffering from a
biological condition. The indicator could be in the form of an
absolute or relative numerical or other value, and could be based
on comparison of a value to one or more thresholds.
[0390] The term "test" is used to refer to mechanism that is used
in quantifying a plurality of biomarkers to determine respective
biomarker values, which can then be used subsequently in
determining indicator values. The "test" could include one or more
measurement processes or steps, that could be performed
collectively or independently, but which are performed using a
quantification platform or technique of a selected type. The "test"
may form a part of a broader "medical assessment", which could
include a number of different tests, performed to allow for the
diagnosis of a presence, absence, degree or prognosis associated
with a medical condition.
[0391] The terms "quantification platform of a selected type" and
"quantification technique of a selected type" are used
interchangeably herein to refer to a device and/or method or
combination of devices and/or methods that can determine the amount
of, abundance of, level of, concentration of, quantity of, or
activity of, one or more biomarkers of interest where either
quality control measures are used as part of the overall procedure,
or the use of control(s) is/are used. Representative examples of
such include nucleic acid amplification techniques including
polymerase chain reaction (PCR) (e.g., PCR-based methods such as
real time polymerase chain reaction (RT-PCR), quantitative real
time polymerase chain reaction (Q-PCR/qPCR), use of PCR to analyse
chromatin conformation (CCA), and the like), hybridisation
techniques including microarray analysis, low density arrays,
hybridisation with allele-specific probes, enzymatic mutation
detection, ligation chain reaction (LCR), oligonucleotide ligation
assay (OLA), flow-cytometric heteroduplex analysis, chemical
cleavage of mismatches, mass spectrometry, flow cytometry, liquid
chromatography, gas chromatography, immunohistochemistry, nucleic
acid sequencing (including next generation sequencing, ChIP-seq,
DNA methylation analyses), single strand conformation polymorphism
(SSCP), denaturing gradient gel electrophoresis (DGGE), temperature
gradient gel electrophoresis (TGGE), restriction fragment
polymorphisms, serial analysis of gene expression (SAGE), affinity
assays including immunoassays such as immunoblot,
immunoprecipitation, enzyme-linked immunosorbent assay (ELISA;
EIA), lateral flow immunochromatography, radioimmunoassay (RIA),
electron microscopy (EM), enzyme-substrate assay, or combinations
thereof.
[0392] The term "control" is used to refer to a mechanism utilised
on order to determine a pass or fail state for the validity of a
test, and therefore the validity of the output.
[0393] Controls can include "independent controls", which are added
to a test and are independent of the biomarkers being quantified.
Thus, the independent controls are independent of the measured
biomarkers and can be considered a stand-alone test for the
validity of the test overall. An example is a synthetically
produced in vitro transcript in a gene expression test at a known
concentration. In this case, the sample being tested (ie blood)
does not interact at all with the independent control. The control
serves only to ensure that the reagents used across the whole test
are capable of reproducing a value for this independent control to
the expected value.
[0394] The term "control values" is used to refer to combinations
of biomarker values or indicators that are used in assessing
whether biomarker values, such as the biomarker values used to
derive indicator values and the resulting indicator values, are
valid. In this regard, a biomarker value or indicator value may be
invalid if it has been incorrectly measured, calculated or
quantified and as such is not genuinely indicative of the target
condition, or if it's value is sufficiently rarely represented in
the corresponding reference data that it could be reasonably
presumed as true that the value could not have derived from a
successful test and therefore the assay should be declared invalid
(in the case of a failed control). Examples of p values that may be
considered presumed to be true range from: [0395] 0.50 to 0.20
[0396] 0.20 to 0.10 [0397] 0.10 to 0.05 [0398] 0.05 to 0.01 [0399]
0.01 to 0.001 [0400] 0.001 to zero.
[0401] Control values are "Relative Controls" that define a pass or
fail state of a test that are not independent of the biomarkers
being quantified. For example, if there are two markers measured in
the test marker A and marker B, then one way in which these markers
may be relative to each other is the ratio of marker A to marker B.
This relationship is a control if its value is used to pass or fail
the validity of a test. In this example, if the ratio of marker A
to marker B is a value outside of an acceptable range, the test
will be declared invalid.
[0402] A "positive control" is used to show that the test is able
to produce a positive result. Typically the positive control is
designed so that when exposed to the same treatment as the other
markers being measured it will result in a detection at a certain
level. The assumption is that if the treatment worked acceptably
for the positive control, then it also worked for the other assays
in the test. An example of where this will be useful is in the case
where the test has been exposed to unacceptable temperatures during
transport, which has destroyed some key ingredient in the test.
With a key ingredient destroyed, the positive control will not work
as expected and the test will be declared invalid.
[0403] A "negative control" is used to show that the test is able
to produce a negative result. Typically the negative control is
designed so that when exposed to the same treatment as the other
markers being measured it will result in a detection below a
certain level (usually below the detectable limit of the test). The
assumption is that if the treatment did not result in positive
detection for the negative control, then the other assays in the
test are also capable of a negative detection.
[0404] The terms "biological subject", "subject," "individual" and
"patient" are used interchangeably herein to refer to an animal
subject, particularly a vertebrate subject, and even more
particularly a mammalian subject. Suitable vertebrate animals that
fall within the scope of the invention include, but are not
restricted to, any member of the subphylum Chordata including
primates, rodents (e.g., mice rats, guinea pigs), lagomorphs (e.g.,
rabbits, hares), bovines (e.g., cattle), ovines (e.g., sheep),
caprines (e.g., goats), porcines (e.g., pigs), equines (e.g.,
horses), canines (e.g., dogs), felines (e.g., cats), avians (e.g.,
chickens, turkeys, ducks, geese, companion birds such as canaries,
budgerigars etc.), marine mammals (e.g., dolphins, whales),
reptiles (snakes, frogs, lizards, etc.), and fish. A preferred
subject is a primate (e.g., a human, ape, monkey, chimpanzee).
[0405] As used herein, the term SIRS ("systemic inflammatory
response syndrome") refers to a clinical response arising from a
non-specific insult with two or more of the following measurable
clinical characteristics; a body temperature greater than
38.degree. C. or less than 36.degree. C., a heart rate greater than
90 beats per minute, a respiratory rate greater than 20 per minute,
a white blood cell count (total leukocytes) greater than 12,000 per
mm.sup.3 or less than 4,000 per mm.sup.3, or a band neutrophil
percentage greater than 10%. From an immunological perspective, it
may be seen as representing a systemic response to insult (e.g.,
major surgery) or systemic inflammation. As used herein, "inSIRS"
(which includes within its scope "post-surgical" (PS) inflammation)
includes the clinical response noted above but in the absence of a
systemic infectious process (infection-negative systemic
inflammatory response syndrome). By contrast, "ipSIRS"
(infection-positive systemic inflammatory response syndrome)
includes the clinical response noted above but in the presence of a
presumed or confirmed infection. Presumed infection can be based on
clinician's judgement whereas confirmation of an infection can be
determined using microbiological culture, isolation or detection of
the infectious agent or through the use of other parameters that
provide evidence of infection. From an immunological perspective,
ipSIRS may be seen as a systemic response to microorganisms, be it
a local, peripheral or systemic infection.
[0406] As used herein, the term "likelihood" of a condition refers
to a level of certainty associated with whether or not the subject
may be suffering from a condition. It should be noted that this
does not necessarily correlate with a degree, seriousness,
severity, stage or state of a condition.
[0407] It will be appreciated that the above described terms and
associated definitions are used for the purpose of explanation only
and are not intended to be limiting.
[0408] In this example, the method includes determining a plurality
of biomarker values at step 100, each biomarker value being
indicative of a value measured or derived for at least one
biomarker of the biological subject.
[0409] The biomarker values can be of any appropriate form and in
particular can relate to any attribute of a subject for which a
value can be quantified. This technique is particularly suited to
high-throughput technologies such as mass spectrometry, sequencing
platforms, array and hybridisation platforms, immunoassays, flow
cytometry, and in one preferred example, the biomarker values
relate to a level of activity or abundance of an expression product
or other measurable molecule.
[0410] The biomarker values could be measured biomarker values,
which are values of biomarkers measured for the subject, or
alternatively could be derived biomarker values, which are values
that have been derived from one or more measured biomarker values,
for example by applying a function to the one or more measured
biomarker values. As used herein, biomarkers to which a function
has been applied are referred to as "derived biomarkers".
[0411] The biomarker values may be determined in any one of a
number of ways. In one example, the process of determining the
biomarker values can include measuring the biomarker values, for
example by obtaining a sample from the biological subject and then
quantifying the biomarkers within the sample. More typically
however, the step of determining the biomarker values includes
having an electronic processing device receive or otherwise obtain
biomarker values that have been previously measured or derived.
This could include for example, retrieving the biomarker values
from a data store such as a local or remote instrument or database,
obtaining biomarker values that have been manually input, using an
input device, or the like.
[0412] At step 110 an indicator can optionally be determined with
the indicator being at least partially based on the biomarker
values. The indicator is generally indicative of a test result and
can be determined in any one of a number of ways and may be at
least partially based on a ratio of biomarker values, as will be
described in more detail below. However, this is not essential and
alternatively the biomarker values could be used to validate that
the quantification has been performed correctly, with indicators or
other interpretation of the biomarker values being performed in
subsequent downstream processes.
[0413] At step 120 one or more control values are determined. The
control values are determined based on a combination of the
biomarker values. The biomarker values can be combined in any one
of a number of ways and this can include for example adding,
multiplying, subtracting, or dividing biomarker values to determine
the control value. This step is performed so that multiple
biomarker values can be combined into a single control value, and
typically a self-normalised value, as will be described in more
detail below.
[0414] At step 130 each control value is compared to a respective
control reference. The respective control reference is typically
established based on reference control values determined for a
sample population including a mixture of healthy individuals and
individuals suffering from or demonstrating clinical signs of one
or more conditions. The control reference can be a single threshold
value or a range defined by respective upper and lower values but
more typically is in the form of distribution of control
values.
[0415] At step 140 measurements of the biomarker values are
validated using results of the comparison. Thus, if any control
values are beyond/under the threshold, outside of a defined
threshold range, or beyond a certain point in the threshold, or
beyond a certain point of the distribution, this is used to
indicate that the ascertained biomarker values measured are not
suitable for use in generating an indicator that is reliable enough
for use in determining the likelihood of a condition.
[0416] Accordingly, the above technique uses different combinations
of biomarker values to identify if biomarker values are valid.
[0417] In one example, the control values are based on a
combination of biomarker values, which differs to a combination of
biomarkers used to establish an indicator indicative of a test
result. For example, if values are quantified for three biomarkers
for the subject, namely A, B and C, and the biomarker values A and
B are used to establish the indicator, then combinations of A and C
and B and C can be used to determine the control values.
[0418] In this example, if measurement of biomarker A is spurious,
for example, due to failures in acquiring, storing or processing of
a sample from the subject, or the like, this could result in an
indicator value based on the combination of biomarkers A and B
which is indicative of the subject having or not having a
condition. However, in reality, because the measurement biomarker A
is incorrect, this result is meaningless, and hence could lead to
inaccurate diagnosis if relied upon.
[0419] In this case, by also determining values of control values
using the combinations of A and C and B and C, it will be
identified that the control value corresponding to A and C is
outside an expected range for individuals either having or not
having a condition of interest, meaning that the biomarker values
for A and/or B are not valid, and hence can't be used in
establishing an accurate indicator.
[0420] Thus, the above described process recognises that biomarkers
values are typically within defined ranges for individuals
regardless or not of whether they are suffering from conditions.
Thus by measuring various combinations of different biomarker
values, and comparing these to established ranges for a reference
population of individuals having a range of different conditions,
including healthy individuals, this can be used to establish
whether the biomarker values are within expected ranges.
[0421] It will be further appreciated that whilst this could in
theory be performed using individual biomarkers as opposed to
combinations, this would require the ability to measure absolute
values, such as absolute concentrations of biomarkers within a
sample, which generally cannot be achieved. This is typically
addressed through the use of independent controls, so that the
concentration of biomarkers relative to a control of known
concentration is measured. However, the use of such independent
controls is typically expensive, as the control biomarkers
themselves are difficult to produce, introduce complexity, and also
limit the number of biomarkers that can be measured by the ability
of the measuring procedures, so as more controls are introduced,
this reduces the number of biomarkers that can be measured for the
subject. However, by using combinations of biomarker values, such
as ratios, or the like, this allows the measured biomarker values
to be indicative of relative concentrations, and hence
self-normalising. In particular, if the ultimate output is based
on, for example, ratios of genes, then measurements of validity
using similar ratios of genes is more intuitive, robust, and
appropriate. Thus, by comparing different combinations of biomarker
values to thresholds, this allows checks to be performed of the
validity of the measured value in the native measurement space
(i.e. ratios), essentially leading to a self-validating test
without the need for measurement of independent controls.
[0422] Such an approach provides a better control strategy. Using
the biomarkers being measured as controls specifically addresses
issues associated with normalising results, improves the
statistical power for the detection of failed assays, reduces the
overall number of controls used, reduces the complexity of an assay
and reduces overall assay cost and risk.
[0423] Firstly and by example, by using the described control
strategy, many biomarkers can be used to define derived biomarkers
for use as control ranges against a corresponding reference range
for each derived biomarker. These biomarkers need not be those
involved in the indictor biomarkers used for classification of the
patient for the condition of interest. Using many relative internal
biomarkers for this purpose has a smoothing and stabilizing effect
on normalisation thereby reducing overall variance.
[0424] Secondly, by relying on external or spike-in controls, if
there is a failure of these controls, the assay will be called
invalid, even if the result from the measured genes, and therefore
of the indicator value, is accurate. Thirdly, by measuring multiple
interactions between measured biomarkers by looking at the larger
number of relative biomarkers available, there are more relevant
control checks for each biomarker being measured, resulting in
higher statistical power, confidence and sensitivity. Fourthly, by
avoiding the use of external controls, or use of extraneous
housekeeping controls, the complexity of the assay is reduced which
translates to decreased cost and risk.
[0425] In particular, this technique can avoid the need for
independent controls, by using control values derived from measured
biomarkers of interest to self validate a test. This approach is
exemplified by comparison of the independent and relative control
approaches, shown in FIGS. 1B and 1C.
[0426] As shown in this example, in each case, biomarker values are
measured at steps 151, 161 and used to generate indicator values at
steps 152, 162. In the dependent controls process, separate
controls are measured at step 153 and assessed to determine if
these are in an expected range at step 154. In contrast, in the
relative controls approach, the measured biomarker values are used
to derive control values at step 163, which are then assessed to
determine if they are within the expected range at step 164. In
each case, if the control is in range, the test results are
reported at steps 155, 165, otherwise the test is failed at step
156, 166.
[0427] Thus, it can be seen the relative controls formed from
control values derived from the measured biomarker values can be
used in a manner similar to independent controls, but without
requiring the presence of independent controls. This avoids the
need for additional control markers, meaning the test can be
cheaper. This also avoids the need for added independent controls
failing independently, which can needlessly invalidate a valid
test. Additionally, relationships between measured markers put
tighter and more numerous constraints on expected values, thus
increasing statistical power and therefore confidence in detection
of an invalid test, as will be described in more detail below.
[0428] A number of further features will now be described.
[0429] In one example, at least three biomarker values are used,
with first and second biomarker values being used to determine the
indicator and with the control values being determined using a
combination of the first and at least one other biomarker value and
the second and at least one other biomarker value. However, in
another preferred example, the method includes determining at least
four biomarker values. In this case, the indicator can be based on
a combination of a first indicator value calculated using first and
second biomarker values and a second indicator value calculated
using third and fourth biomarker values. These two indicator values
can then be combined to form the indicator, which combines the
discriminatory power of each of the first two indicator values.
This allows two independent pairs of biomarker values to be
combined and used to establish the indicator, which can
significantly enhance the ability of the indicator to discriminate
the likelihood of the subject having the condition.
[0430] Furthermore, when using four biomarker values, this allows
at least four control values to be determined including a first
control value calculated using first and third biomarker values, a
second control value calculated using first and fourth biomarker
values, a third control value calculated using second and third
biomarker values and a fourth control value calculated using second
and fourth biomarker values. Thus, again, this allows for
additional control values to be utilised, further increasing the
likelihood that invalid measurements can be accurately
discriminated. It will be appreciated that combinations of
biomarkers comprising the indicator value can also be control
values: in this example the first and second biomarkers and the
third and fourth biomarkers make up the indicator value, and they
too, if out of range to a corresponding reference, may indicate
failure of the assay.
[0431] It will also be appreciated that in the above example, each
of the biomarker values used in establishing the indicator are also
used in the validation check. This maximises the use of biomarkers,
so that in effect each measured biomarker value is used in both
generating the indicator and the validation. For platforms and
processes that can only handle limited numbers of biomarker values,
this can therefore maximise the discriminatory power of the
indicator, by allowing all measured biomarker values to be used in
determining the indicator, whilst still ensuring indicator
validity. However, this is not essential, and additionally and/or
alternatively, comparison to a biomarker value measured for the
subject, but not used in generating the indicator could be
performed.
[0432] It should also be noted that the indicator values could also
be used as control values. In this instance, typically an
acceptable range for indicator values would be specified for
assessing the likelihood of a subject having a condition, with this
range representing the maximum and minimum indicator values
observed or expected in the target population. Values outside of
this range may imply a problem with at least one of the underlying
values comprising the indicator value, and the test will be
declared invalid. Accordingly, in this example, the method includes
determining control values including one or more of a fifth control
value using a ratio of first and second biomarker values, a sixth
control value using a ratio of third and fourth biomarker values
and, a single or set of controls values calculated using a ratio of
measured biomarkers not used in determining an indicator value.
[0433] The method typically includes calculating at least one of
the indicator values and the control values by applying a function
to the respective biomarker values. The function used will
therefore vary depending on the preferred implementation. In one
example, the function includes at least one of multiplying two
biomarker values, dividing two biomarker values; adding two
biomarker values, subtracting two biomarker values, a weighted sum
of at least two biomarker values, a log sum of at least two
biomarker values and, a sigmoidal function of at least two
biomarker values.
[0434] More typically the function is division of two biomarker
values, or log subtraction (which is equivalent to division of
absolute values) so that the derived biomarker value corresponds to
a ratio of two measured biomarker values. There are a number of
reasons why the ratio might be preferred. For example, use of a
ratio is self-normalising, meaning variations in measuring
techniques will automatically be accommodated. For example, if the
input concentration of a sample is doubled, the relative
proportions of biomarkers will remain the same. As a result, the
type of function therefore has a stable profile over a range of
input concentrations, which is important because input
concentration is a known variable for expression data.
Additionally, many biomarkers are nodes on biochemical pathways, so
the ratio of biomarkers gives information about the relative
activation of one biological pathway to another, which is a natural
representation of biological change within a system. Finally,
ratios are typically easily interpreted.
[0435] In one example, the control values are ratios, with each
control value being compared to a respective control value
threshold range and determining at least one of the biomarker
values to be invalid if any one of the control values falls outside
the respective control value threshold range. In this instance,
each respective threshold range is typically derived from biomarker
values collected from a number of individuals in a sample
population. This can be performed for example using a statistical
method or computer-implemented classifier algorithm trained on
biomarker values for the sample population. The sample population
typically includes a plurality of healthy individuals, a plurality
of individuals suffering from at least one diagnosed medical
condition, a plurality of individuals showing clinical signs of at
least one medical condition or first and second groups of
individuals, each group of individuals suffering from a respective
diagnosed medical condition. This can be used to provide a suitable
cross section of the population and to ensure that the control
value threshold ranges are not influenced by the presence or
absence of conditions.
[0436] In particular, when an indicator is for use in determining
the likelihood that a biological subject has a specific medical
condition, the sample population includes individuals presenting
with clinical signs of the specific medical condition, individuals
diagnosed or confirmed to have or have had (including
retrospectively) the specific medical condition and/or healthy
individuals. This ensures that the assessment of indicator validity
applies regardless of not or whether the individual has the
specific condition or not.
[0437] It will also be appreciated that the sample population could
also include a plurality of individuals of different sexes,
ethnicities, ages, or the like, allowing the control value ranges
to be common across populations. However, this is not essential,
and alternatively control value thresholds could be established
that are specific to a particular sub-set of the population. In
this case, it would be necessary to ensure that the control value
threshold ranges used are appropriate for the subject under
consideration.
[0438] Typically the indicator is determined by combining the first
and second derived indicator values using a combining function, the
combining function being at least one of an additive model, a
linear model, a support vector machine, a neural network model, a
random forest model, a regression model, a genetic algorithm, an
annealing algorithm, a weighted sum and a nearest neighbour
model.
[0439] In one example, the method further includes determining an
indicator value, comparing the indicator value to at least one
indicator value range and determining the indicator at least in
part using a result of the comparison. Thus, once it has been
established that the biomarker values are suitable for use in
determining the indicator, the indicator can be calculated and
compared to an indicator value range to assess the likelihood of
the subject having at least one medical condition.
[0440] Following this, the method can further include generating a
representation of the indicator. In this regard, the representation
allows the indicator to be viewed, for example by a medical
practitioner, allowing the medical practitioner to perform a
diagnosis and assess what intervention, if any, to perform. The
representation can be of any appropriate form and can include one
or more of an alphanumeric indication of an indicator value, a
graphical indication of a comparison of the indicator value to one
or more thresholds and an alphanumeric indication of a likelihood
of the subject having at least one medical condition. A specific
example representation will be described in more detail below.
[0441] The method is typically performed at least in part using one
or more electronic processing devices, for example forming part of
one or more processing systems, such as computers or servers, which
could in turn connected to one or more other computing devices,
such as mobile phones, portable computers or the like, via a
network architecture, as will be described in more detail
below.
[0442] In one example, the one or more electronic processing
devices receive the biomarker values, determine the indicator using
biomarker values, determine the at least one control value using at
least two of the biomarker values, compare the at least one control
value to the respective control value threshold and determine if
the test is a valid test using the results of the comparison.
[0443] In this regard, the biomarker values can be received from a
database or the like, in which the values have been previously
stored, or could be received directly from a measuring device, such
as a PCR machine or the like, which is used in determining the
biomarker values. The processing devices can then automatically
assess the validity of the measurements and then, if valid
calculate the indicator, generating and displaying a representation
of this as required. Thus, it will be appreciated that this can
provide a substantially automated procedure from the point at which
a sample is loaded into a measuring device.
[0444] In one example, the one or more electronic processing
devices determine the indicator by calculating a first indicator
value using a ratio of first and second biomarker values,
calculating a second indicator value using a ratio of third and
fourth second biomarker values and determining a sum of the first
and second indicator values. The one or more electronic processing
devices similarly determine a plurality of internal relative
control values by calculating a first control value using a ratio
of the first and third biomarker values, calculating a second
control value using a ratio of the first and fourth biomarker
values, calculating a third control value using a ratio of the
second and third biomarker values and calculating a fourth control
value using a ratio of the second and fourth biomarker values,
before comparing each control value to a respective threshold range
and displaying the indicator in response to a successful comparison
for each control value.
[0445] When the biomarkers are gene expression products, the
relative abundance of target biomarkers can be determined thus;
obtain a sample from a biological subject, such that the sample
includes the target gene expression products, then amplify at least
the target gene expression products in the sample, then for each
gene expression product determine an amplification amount required
to obtain a defined level of the respective gene expression
product, the amplification amount being dependent on the
concentration of the gene expression product in the sample being
based on a cycle time, number of amplification cycles, a cycle
threshold, an amplification time, or the like. In this case,
relative biomarkers can be generated using combinations of
amplification times by subtracting amplification times for the
respective gene expression products so that these relative
biomarker values represent a ratio of the relative concentration of
the respective gene expression products.
[0446] It will be appreciated that the above described process is
typically performed on a biological subject presenting with
clinical signs of at least one medical condition. In this case, a
medical practitioner will typically perform an initial assessment
of the clinical signs and establish a specific test to be
performed. For example, if the practitioner identifies that the
subject may have ipSIRS, the above described process is typically
performed with relative biomarker values corresponding to relative
concentrations of LAMP1, CEACAM4, PLAC8 and PLA2G7.
[0447] More typically the clinical signs could be common to first
and second conditions, which case the indicator is for use in
distinguishing between the first and second conditions. Thus, for
example, inSIRS and ipSIRS typically have similar clinical signs,
so practitioners can use the indicator to distinguish between the
conditions.
[0448] Thus, the above could be used for validating an indicator
used in determining the likelihood of a biological subject having
at least one medical condition, the biomarkers being quantified
using a quantification technique of a selected type and the method
including: [0449] a) determining a plurality of biomarker values,
each biomarker value being indicative of a value measured or
derived from at least one corresponding measured biomarker of the
biological subject; [0450] b) determining an indicator indicative
of the likelihood of a biological subject having at least one
medical condition by: [0451] i) calculating a first indicator value
using first and second biomarker values; [0452] ii) calculating a
second indicator value using third and fourth second biomarker
values; [0453] iii) determining the indicator using the first and
second indicator values; and, [0454] c) calculating at least one
of: [0455] i) a first control value using the first and third
biomarker values; [0456] ii) a second control value using the first
and fourth biomarker values; [0457] iii) a third control value
using the second and third biomarker values; [0458] iv) a fourth
control value using the second and fourth biomarker values; [0459]
d) comparing the at least one control value to a respective control
value threshold; and, [0460] e) selectively validating the
indicator using the results of the comparison.
[0461] Thus, the above could also be used for validating an
indicator used in determining the likelihood of a biological
subject having at least one medical condition, the biomarkers being
quantified using a quantification technique of a selected type and
the method including: [0462] a) obtaining a sample from a
biological subject, the sample including gene expression products;
[0463] b) quantifying at least some gene expression products in the
sample to determine a concentration of the gene expression product
in the sample; [0464] c) determining an indicator indicative of the
likelihood of a biological subject having at least one medical
condition by combining: [0465] i) a first indicator value
indicative of a ratio of the concentration of the first and second
gene expression products; and, [0466] ii) a second indicator value
indicative of a ratio of the concentration of the third and fourth
gene expression products; and, [0467] d) determining control values
by determining: [0468] i) a first control value indicative of a
ratio of the concentration of the first and third gene expression
products; [0469] ii) a second control value indicative of a ratio
of the concentration of the first and fourth gene expression
products; [0470] iii) a third control value indicative of a ratio
of the concentration of the second and third gene expression
products; and, [0471] iv) a fourth control value indicative of a
ratio of the concentration of the second and fourth gene expression
products; [0472] e) comparing each control value to a respective
control value threshold; and, [0473] f) selectively validate the
indicator using the results of the comparison.
[0474] In one example, the process is performed by one or more
processing systems operating as part of a distributed architecture,
an example of which will now be described with reference to FIG.
2.
[0475] In this example, a number of base stations 201 are coupled
via communications networks, such as the Internet 202, and/or a
number of local area networks (LANs) 204, to a number of client
devices 203 and one or more measuring devices 205, such as PCR,
sequencing machines, or the like. It will be appreciated that the
configuration of the networks 202, 204 are for the purpose of
example only, and in practice the base stations 201, client devices
203 and measuring devices 205, an communicate via any appropriate
mechanism, such as via wired or wireless connections, including,
but not limited to mobile networks, private networks, such as an
802.11 networks, the Internet, LANs, WANs, or the like, as well as
via direct or point-to-point connections, such as Bluetooth, or the
like.
[0476] In one example, each base station 201 includes one or more
processing systems 210, each of which may be coupled to one or more
databases 211. The base station 201 is adapted to be used in
calculating and validating indicators and generating
representations for these to be displayed via client devices. The
client devices 203 are typically adapted to communicate with the
base station 201, allowing indicator representations to be
displayed.
[0477] Whilst the base station 201 is a shown as a single entity,
it will be appreciated that the base station 201 can be distributed
over a number of geographically separate locations, for example by
using processing systems 210 and/or databases 211 that are provided
as part of a cloud based environment. However, the above described
arrangement is not essential and other suitable configurations
could be used.
[0478] An example of a suitable processing system 210 is shown in
FIG. 3. In this example, the processing system 210 includes at
least one microprocessor 300, a memory 301, an optional
input/output device 302, such as a keyboard and/or display, and an
external interface 303, interconnected via a bus 304 as shown. In
this example the external interface 303 can be utilised for
connecting the processing system 210 to peripheral devices, such as
the communications networks 202, 204, databases 211, other storage
devices, or the like. Although a single external interface 303 is
shown, this is for the purpose of example only, and in practice
multiple interfaces using various methods (e.g., Ethernet, serial,
USB, wireless or the like) may be provided.
[0479] In use, the microprocessor 300 executes instructions in the
form of applications software stored in the memory 301 to allow the
required processes to be performed. The applications software may
include one or more software modules, and may be executed in a
suitable execution environment, such as an operating system
environment, or the like.
[0480] Accordingly, it will be appreciated that the processing
system 210 may be formed from any suitable processing system, such
as a suitably programmed client device, PC, web server, network
server, or the like. In one particular example, the processing
system 210 is a standard processing system, which executes software
applications stored on non-volatile (e.g., hard disk) storage,
although this is not essential. However, it will also be understood
that the processing system could be any electronic processing
device such as a microprocessor, microchip processor, logic gate
configuration, firmware optionally associated with implementing
logic such as an FPGA (Field Programmable Gate Array), or any other
electronic device, system or arrangement.
[0481] As shown in FIG. 4, in one example, the client device 203
includes at least one microprocessor 400, a memory 401, an
input/output device 402, such as a keyboard and/or display, and an
external interface 403, interconnected via a bus 404 as shown. In
this example the external interface 403 can be utilised for
connecting the client device 203 to peripheral devices, such as the
communications networks 202, 204, databases, other storage devices,
or the like. Although a single external interface 403 is shown,
this is for the purpose of example only, and in practice multiple
interfaces using various methods (e.g., Ethernet, serial, USB,
wireless or the like) may be provided.
[0482] In use, the microprocessor 400 executes instructions in the
form of applications software stored in the memory 401 to allow
communication with the base station 201, for example to allow for
selection of parameter values and viewing of representations, or
the like.
[0483] Accordingly, it will be appreciated that the client devices
203 may be formed from any suitable processing system, such as a
suitably programmed PC, Internet terminal, laptop, or hand-held PC,
and in one preferred example is either a tablet, or smart phone, or
the like. Thus, in one example, the processing system 210 is a
standard processing system, which executes software applications
stored on non-volatile (e.g., hard disk) storage, although this is
not essential. However, it will also be understood that the client
devices 203 can be any electronic processing device such as a
microprocessor, microchip processor, logic gate configuration,
firmware optionally associated with implementing logic such as an
FPGA (Field Programmable Gate Array), or any other electronic
device, system or arrangement.
[0484] Examples of the processes for determining and validating
measurements of indicators will now be described in further detail.
For the purpose of these examples it is assumed that one or more
processing systems 210 acts to receive measured biomarker values
from the measuring devices, calculate indicator values and control
values, and use these to calculate and validate an indicator which
can then be displayed as part of a representation via hosted
webpages or an App residing on the client device 203. The
processing system 210 is therefore typically a server which
communicates with the client device 203 and measuring devices 205
via a communications network, or the like, depending on the
particular network infrastructure available.
[0485] To achieve this the processing system 210 of the base
station 201 typically executes applications software for performing
required processes, with actions performed by the processing system
210 being performed by the processor 300 in accordance with
instructions stored as applications software in the memory 301
and/or input commands received from a user via the I/O device 302,
or commands received from the client device 203.
[0486] It will also be assumed that the user interacts with the
processing system 210 via a GUI (Graphical User Interface), or the
like presented on the client device 203, and in one particular
example via a browser application that displays webpages hosted by
the base station 201, or an App that displays data supplied by the
processing system 210. Actions performed by the client device 203
are performed by the processor 400 in accordance with instructions
stored as applications software in the memory 401 and/or input
commands received from a user via the I/O device 402.
[0487] However, it will be appreciated that the above described
configuration assumed for the purpose of the following examples is
not essential, and numerous other configurations may be used. It
will also be appreciated that the partitioning of functionality
between the client devices 203, and the base station 201 may vary,
depending on the particular implementation.
[0488] An example process for establishing control and indicator
references will now be described in more detail with reference to
FIG. 5.
[0489] In this example, at step 500 the processing system 210
determines reference data in the form of biomarker values obtained
for a reference population.
[0490] A reference population is any population of interest for
which information is collected against which reference can be made.
For example the population may be characterized into those with or
without a condition, or with varying degrees of severity,
prognosis, stage, or similar disease or condition stratification
method.
[0491] The reference data may be acquired in any appropriate manner
but typically this involves obtaining gene expression product data
from a plurality of individuals, selected to include individuals
diagnosed with one or more conditions of interest, as well as
healthy individuals. The terms "expression" or "gene expression"
refer to production of RNA only or production of RNA and
translation of RNA into proteins or polypeptides. In specific
embodiments, the terms "expression" or "gene expression" refer to
production of messenger RNA (mRNA), ribosomal RNA (rRNA), microRNA
(miRNA) or other RNA classes such mitochondrial RNA (mtRNA),
non-coding RNA (ncRNA, lncRNA (long)), small interfering RNA
(siRNA), transfer RNA (tRNA) or proteins.
[0492] As used herein, the terms "microRNA" or "miRNA" refer to a
short ribonucleic acid (RNA) approximately 18-30 nucleotides in
length (suitably 18-24 nucleotides, typically 21-23 nucleotides in
length) that regulates a target messenger RNA (mRNA) transcript
post-transcriptionally through binding to the complementary
sequences on the target mRNA and results in the degradation of the
target mRNA. The terms also encompass the precursor (unprocessed)
or mature (processed) RNA transcript from a miRNA gene. The
conversion of precursor miRNA to mature miRNA is aided by RNAse
such as Dicer, Argonaut, or RNAse III.
[0493] The conditions captured in the reference data are typically
medical, veterinary or other health status conditions and may
include any illness, disease, stages of disease, disease subtypes,
severities of disease, diseases of varying prognoses, or the
like.
[0494] Example reference biomarkers could include expression
products such as nucleic acid or proteinaceous molecules, as well
as other molecules relevant in making a clinical assessment.
[0495] The individuals in the reference population also typically
undergo a clinical assessment allowing any conditions to be
clinically identified as part of the characterization process for
the reference population, and with an indication of any assessment
or condition forming part of the reference data. Whilst any
conditions can be assessed, in one example the process is utilized
specifically to identify conditions such as SIRS (Systemic
Inflammatory Response Syndrome) (M S Rangel-Frausto, D Pittet, M
Costigan, T Hwang, C S Davis, and R P Wenzel, "The Natural History
of the Systemic Inflammatory Response Syndrome (SIRS). a
Prospective Study.," JAMA: the Journal of the American Medical
Association 273, no. 2 (Jan. 11, 1995): 117-123.). SIRS is an
overwhelming whole body reaction that may have an infectious or
non-infectious aetiology, whereas sepsis is SIRS that occurs during
infection. Both are defined by a number of non-specific host
response parameters including changes in heart and respiratory
rate, body temperature and white cell counts (Mitchell M Levy et
al., "2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions
Conference," Critical Care Medicine 31, no. 4 (April 2003):
1250-1256.; K Reinhart, M Bauer, N C Riedemann, and C S Hartog,
"New Approaches to Sepsis: Molecular Diagnostics and Biomarkers,"
Clinical Microbiology Reviews 25, no. 4 (Oct. 3, 2012): 609-634).
To differentiate these conditions they are referred herein to as
SIRS (both conditions), infection-negative SIRS (SIRS without
infection, hereafter referred to as "inSIRS") and
infection-positive SIRS (sepsis, SIRS with a known or suspected
infection, hereafter referred to as "ipSIRS"). The causes of SIRS
are multiple and varied and can include, but are not limited to,
trauma, burns, pancreatitis, endotoxaemia, surgery, adverse drug
reactions, and infections (local and systemic). It will be
appreciated from the following, however, that this can be applied
to a range of different conditions, and reference to inSIRS or
ipSIRS is not intended to be limiting.
[0496] Additional reference data may also be collected for the
reference population and may include additional biomarkers such as
one or more phenotypic or clinical parameters of the individuals
and/or their relatives that has not been generated or captured by
instrument measurements or a clinical assessment. Phenotypic
parameters can include information such as the gender, ethnicity,
age, hair colour, eye colour, height, weight, waist and hip
circumference, or the like. Also, in the case of the technology
being applied to individuals other than humans, this can also
include information such as designation of a species, breed or the
like. Clinical traits may include genetic information, white blood
cell count, diastolic blood pressure and systolic blood pressure,
bone density, body-mass index, presence of diabetes or not, resting
heart rate, HOMA (homeostasis model assessment), HOMA-IR
(homeostasis model assessment insulin resistance), IVGT
(intravenous glucose tolerance test), resting heart rate, 3 cell
function, macrovascular function, microvascular function,
atherogenic index, low-density lipoprotein/high-density lipoprotein
ratio, intima-media thickness, body temperature, Sequential Organ
Failure Score (SOFA) and the like.
[0497] The reference population has two functions, the first is to
characterize patients with respect to the condition of interest, in
this example categorize patients into inSIRS and ipSIRS. The second
is to capture the values required to generate values used in the
assay. Thus, for a reference population and for a specific
indicator, application of the indicator to the reference data will
produce a reference indicator distribution of values corresponding
to known categories or degrees such as inSIRS and ipSIRS, against
which indicator values determined from new samples can be
compared.
[0498] Similarly, internal relative controls can be generated using
the reference population data and compared to internal relative
controls similarly generated for each new sample.
[0499] Each individual within the reference population is typically
allocated to a group. The groups may be defined in any appropriate
manner such as any one or more of an indication of a presence,
absence, degree, stage, severity, prognosis or progression of a
condition, other tests or assays, or measured biomarkers associated
with the individuals.
[0500] For example, a first selection of groups may be used to
identify one or more groups of individuals suffering from SIRS, one
or more groups of individuals suffering ipSIRS, and one or more
groups of individuals suffering inSIRS. Further groups may also be
defined for individuals suffering from other conditions. The groups
may include overlapping groups, so for example it may be desirable
to define groups of healthy individuals and individuals having
SIRS, with further being defined to distinguish inSIRS patients
from ipSIRS patients, as well as different degrees of inSIRS or
ipSIRS, with these groups having SIRS in common, but each group of
patients differing in whether a clinician has determined the
presence of an infection or not. Additionally, further subdivision
may be performed based on phenotypic traits, so groups could be
defined based on gender, ethnicity or the like so that a plurality
of groups of individuals suffering from a condition are defined,
with each group relating to a different phenotypic trait.
[0501] It will also be appreciated, however, that identification of
different groups can be performed in other manners, for example on
the basis of particular activities or properties of biomarkers
within the biological samples of the reference individuals and
accordingly, reference to conditions is not intended to be limiting
and other information may be used as required.
[0502] The manner in which classification of patients in the
reference population into groups is performed may vary depending on
the preferred implementation. In one example, this can be performed
automatically by the processing system 201, for example, using
unsupervised methods such as Principal Components Analysis (PCA),
or supervised methods such as k-means or Self Organising Map (SOM).
Alternatively, this may be performed manually by an operator by
allowing the operator to review reference data presented on a
Graphical User Interface (GUI), and define respective groups using
appropriate input commands.
[0503] Accordingly, in one example the reference data can include
for each of the reference individuals information relating to at
least one and desirably to a plurality of reference biomarkers and
a presence, absence, degree or progression of a condition.
[0504] The reference data may be collected from individuals
presenting at a medical centre with clinical signs relating to any
relevant conditions of interest, and may involve follow-on
consultations in order to confirm clinical assessments, as well as
to identify changes in biomarkers, and/or clinical signs, and/or
severity of clinical signs, over a period of time. In this latter
case, the reference data can include time series data indicative of
the progression of a condition, and/or the activity of the
reference biomarkers, so that the reference data for an individual
can be used to determine if the condition of the individual is
improving, worsening or static. It will also be appreciated that
the reference biomarkers are preferably substantially similar for
the individuals within the sample population, so that comparisons
of measured activities between individuals can be made.
[0505] This reference data could also be collected from a single
individual over time, for example as a condition within the
individual progresses, although more typically it would be obtained
from multiple individuals each of which has a different stage of
the one or more conditions of interest.
[0506] It will be appreciated that once collected, the reference
data can be stored in the database 211 allowing this to be
subsequently retrieved by the processing system 210 for subsequent
analysis, or could be provided directly to the processing system
210 for analysis.
[0507] In one example, the measurements are received as raw data,
which then undergoes preliminary processing. Such raw data
corresponds to information that has come from a source without
modification, such as outputs from instruments such as PCR
machines, array (e.g., microarray) scanners, sequencing machines,
clinical notes or any other biochemical, biological, observational
data, or the like. This step can be used to convert the raw data
into a format that is better suited to analysis. In one example
this is performed in order to normalise the raw data and thereby
assist in ensuring the biomarker values demonstrate consistency
even when measured using different techniques, different equipment,
or the like. Thus, the goal of normalisation is to remove the
variation within the samples that is not directly attributable to
the specific analysis under consideration. For example, to remove
variances caused by differences in sample processing at different
sites. Examples of normalisation that are well known in the art
include z-score transformation for generic data, or popular domain
specific normalisations, such as RMA normalisation for
microarrays.
[0508] However, it will also be appreciated that in some
applications, such as a single sample experiment run on a single
data acquisition machine, this step may not strictly be necessary,
in which case the function can be a Null function producing an
output identical to the input.
[0509] In one example, the preferred approach for generating
reference data is a paired function approach over log normalised
data. Log normalisation is a standard data transformation on gene
and protein expression data, because the measured biomarkers follow
a log-normal distribution as directly measured by the instrument.
Applying a log transform turns the data into process-friendly
normally distributed data. The biomarker values measured will
depend on the predominant condition that is being assessed so, for
example, in the case of determining the likelihood of a subject
having ipSIRS as opposed to inSIRS, the RNA biomarkers Bm.sub.1,
Bm.sub.2, Bm.sub.3, Bm.sub.4 used could be LAMP1, CEACAM4, PLAC8
and PLA2G7. A second possible example, in the case of determining
the likelihood of a subject having liver disease, the protein
biomarkers Bm.sub.1, Bm.sub.2, Bm.sub.3, Bm.sub.4 used could be
Alkaline Phosphatase (AP), Aminotransferase (AT), Aspartate
Aminotransferase (AspAT) and Gamma-glutamyl transpeptidase
(GGT).
[0510] As part of the above process, at step 510 the measurements
are validated using traditional prior art techniques, to ensure
that the measurements have been performed successfully, and hence
are valid.
[0511] At step 520 at least four internal relative control values
Ctrl.sub.1, Ctrl.sub.2, Ctrl.sub.3, Ctrl.sub.4, are determined for
the reference population, with two additional control values
Ctrl.sub.5, Ctrl.sub.6, being optionally determined as follows:
Ctrl.sub.1=(Bm.sub.1/Bm.sub.3)
Ctrl.sub.2=(Bm.sub.1/Bm.sub.4)
Ctrl.sub.3=(Bm.sub.2/Bm.sub.3)
Ctrl.sub.4=(Bm.sub.2/Bm.sub.4)
Ctrl.sub.5=(Bm.sub.1/Bm.sub.2)
Ctrl.sub.6=(Bm.sub.3/Bm.sub.4)
[0512] At step 530, the control values used to update or create
respective control reference data. In this regard, in the current
example, each control reference is in the form of a distribution of
control values for the reference population including healthy
individuals and individuals suffering from the conditions of
interest. The distribution itself can be used as a control
reference, or alternatively one or more values could be derived
therefrom, such as to define a threshold range. For example this
could be set to encompass 99% of the distribution.
[0513] Additionally, the control reference could be defined so that
it is specific to characteristics of the individuals, such as the
sex, ethnicity, age, weight, height or other physical
characteristic of the subject, thereby allowing different control
references to be defined for different groups of individuals with
similar characteristics.
[0514] Once created the control references and in particular the
control distributions, are stored in the database 211 for
subsequent use.
[0515] At step 540, first and second indicator values are
determined. The first and second indicator values In.sub.1,
In.sub.2 are determined on a basis of ratios of first and second,
and third and fourth biomarker values respectively:
In.sub.1=(Bm.sub.1/Bm.sub.2)
In.sub.2=(Bm.sub.3/Bm.sub.4)
[0516] The indicator values used to update or create a set of
indicator references at step 550, which is used in analysing
measured indicator values for a subject to establish a likelihood
of the subject having a condition. In particular, indicator values
for each reference group are statistically analysed to establish a
range or distribution of indicator values that is indicative of
each group, thereby allowing the indicator values to be used to
discriminate between the different groups, and hence ascertain the
likelihood a subject is suffering from a particular condition, as
will be described in more detail below.
[0517] An example process of a process for validating measurement
of biomarker values used in generating an indicator will now be
described in more details with reference to FIG. 6.
[0518] In this example, at step 600 values of four biomarkers
Bm.sub.1, Bm.sub.2, Bm.sub.3, Bm.sub.4 are measured by the
measuring device 205. The four biomarker values selected will
depend on the predominant condition that is being assessed. For
example, in the case of determining the likelihood of a patient
having ipSIRS as opposed to inSIRS, the biomarkers Bm.sub.1,
Bm.sub.2, Bm.sub.3, Bm.sub.4 used will be LAMP1, CEACAM4, PLAC8 and
PLA2G7.
[0519] At step 610 the processing system 210 determines first and
second indicator values, either directly from the measuring device
205, or by retrieving the values after storage in a database 211 or
other data store. The first and second indicator values In.sub.1,
In.sub.2 are determined on a basis of ratios of first and second,
and third and fourth biomarker values respectively:
In.sub.1=(Bm.sub.1/Bm.sub.2)
In.sub.2=(Bm.sub.3/Bm.sub.4)
[0520] At step 620 the processing device 210 combines the indicator
values to determine an indicator In which may be achieved utilising
a sum of the first and second indicator values or other similar
measure. So for example:
In=In.sub.1+In.sub.2(Bm.sub.1/Bm.sub.2)+(Bm.sub.3Bm.sub.4)
[0521] At step 630, the processing device 210 determines the four
control values Ctrl.sub.1, Ctrl.sub.2, Ctrl.sub.3, Ctrl.sub.4, and
optionally additional control values Ctrl.sub.5, Ctrl.sub.6, as
follows:
Ctrl.sub.1=(Bm.sub.1/Bm.sub.3)
Ctrl.sub.2=(Bm.sub.1/Bm.sub.4)
Ctrl.sub.3=(Bm.sub.2/Bm.sub.3)
Ctrl.sub.4=(Bm.sub.2/Bm.sub.4)
Ctrl.sub.5=(Bm.sub.1/Bm.sub.2)
Ctrl.sub.6=(Bm.sub.3/Bm.sub.4)
[0522] Thus, as shown in FIG. 6, each possible ratio of the values
of the four biomarkers Bm.sub.1, Bm.sub.2, Bm.sub.3, Bm.sub.4 is
calculated, with two of the ratios being used to form the indicator
values and hence the indicator, and all of the ratios being used
for control values.
[0523] Each of the control values is then compared to a respective
control reference, and in particular control distribution, at step
640. In this regard, it will be appreciated that the processing
system 210 will retrieve respective control distributions for the
particular biomarkers that are used to determine the respective
control values Ctrl.sub.1, Ctrl.sub.2, Ctrl.sub.3, Ctrl.sub.4,
optionally additional control values Ctrl.sub.5, Ctrl.sub.6 with
these control distributions being previously determined and stored
in the database 211, as described above. At step 650, the
processing system 210 determines if each control value is
acceptable based on the results of the comparison. In this regard,
if any one control value is outside the defined control value
threshold range, then this is indicative of a test failure which is
communicated to the user at step 660, for example by providing an
indication on a client device 203 of a medical practitioner
requesting the test. Otherwise a representation of the indicator is
displayed at step 670 on the client device 203, as will be
described in more detail below.
[0524] In the above described process, the values of the four
biomarkers Bm.sub.1, Bm.sub.2, Bm.sub.3, Bm.sub.4 are used to
determine four (or optionally six) control values. Because each
biomarker (Bm.sub.1, Bm.sub.2, Bm.sub.3, Bm.sub.4) is involved in
multiple comparisons with other biomarkers as controls (Ctrl.sub.1,
Ctrl.sub.2, Ctrl.sub.3, Ctrl.sub.4), there are more opportunities
for detection of an invalid underlying biomarker than if each
biomarker was measured against only a single expected range or
against a single control biomarker. This multiple testing of each
biomarker results in far greater sensitivity than would be achieved
with individual comparison to an independent control, as shown in
the arrangement of FIG. 7B.
[0525] For example, in the case of FIG. 7B, representing a standard
control strategy based on measured biomarkers, Bm.sub.1 is measured
relative to the control, or equivalently to an absolute value using
the control to derive the value. The measured value being outside
an expected range is only seen once in every thousand samples, so
if the measured value falls outside of this range, the probability
that this sample measurement belongs to the distribution (that is,
it is valid) is therefore 1/1000 and p value for failure detection:
0.001.
[0526] In contrast in the case of the current system of FIG. 7A,
the measured value of biomarker Bm.sub.1 is compared to each of
biomarkers Bm.sub.2, Bm.sub.3, Bm.sub.4. Each represents an
independent check on the measured value of Bm.sub.1, so values
outside the range are only once every 1000 samples for each
comparison. The probability that this sample is not in the
distribution of valid samples (a failure) is 1/1000.times.
1/1000.times. 1/1000, so that the p value for failure detection:
1e-9, meaning this relative control combination shown in FIG. 7A is
a million times more sensitive than a standard measured control
shown in FIG. 7B.
[0527] A further example will now be described with reference to
FIGS. 8A and 8B.
[0528] In this example, at step 800 a sample is acquired from the
subject. The sample could be any suitable sample such as a
peripheral blood sample, or the like, depending on the nature of
the biomarker values being determined. At step 805 the sample
undergoes preparation allowing this to be provided to the measuring
device 205 and used in a quantification process at step 810. For
the purpose of this example, the quantification process involves
PCR amplification, with the measuring device being a PCR machine,
although other suitable biomarker measurement devices and
techniques could be used. In this instance, amplifications times
At.sub.1, At.sub.2, At.sub.3, At.sub.4, are determined for each of
the four biomarkers Bm.sub.1, Bm.sub.2, Bm.sub.3, Bm.sub.4 at step
815, with the amplification times being transferred from the
measuring device 205 to the processing system 210 allowing the
processing system 210 to perform analysis of the corresponding
biomarker values.
[0529] Accordingly, at step 820 the processing system 210
calculates ratios using the amplifications times. In this regard,
as the amplification times represent a log value, the ratios are
determined by subtracting amplifications times as will be
appreciated by a person skilled in the art.
[0530] Accordingly, in this example the indicator and control
values would be determined as follows:
Ctrl.sub.1=(Log Bm.sub.1-Log Bm.sub.3)=(At.sub.1-At.sub.3)
Ctrl.sub.2=(Log Bm.sub.1-Log Bm.sub.4)=(At.sub.1-At.sub.4)
Ctrl.sub.3=(Log Bm.sub.2-Log Bm.sub.3)=(At.sub.2-At.sub.3)
Ctrl.sub.4=(Log Bm.sub.2-Log Bm.sub.4)=(At.sub.2-At.sub.4)
[0531] As previously mentioned, the indicator values can also be
used as control values, leading to two further control valves:
Ctrl.sub.5=(Log Bm.sub.1-Log Bm.sub.2)=(At.sub.1-At.sub.2)
Ctrl.sub.6=(Log Bm.sub.3-Log Bm.sub.4)=(At.sub.3-At.sub.4)
[0532] The processing system compares the ratios representing the
control values to respective control value threshold ranges
retrieved from the database 211, at step 825. Again, this can be
based on characteristics of the subject, with the control values
being derived from control values measured for a sample population
of individuals with similar characteristics.
[0533] At step 830, the processing system 210 determines if the
control ratios correspond to control values that are acceptable, in
other words if they fall within the defined threshold range. If
this is not the case then test failure is indicated, for example,
by having the processing system 210 generate a failure notification
and provide this to a client device 205 at step 835. The
notification could be of any suitable form and could include an
email, notification in a dash board of a test management software
application or the like. As part of this, any outlier ratios that
fall outside the control value ranges can be identified, allow an
operator to identify which if any of the biomarker values failed or
was inaccurately measured for any reason.
[0534] In the event that each of the control values are acceptable,
at step 840 the processing system 210 determines an indicator value
by combining the ratios for the indicator values, as follows:
In=(Log Bm.sub.1-Log Bm.sub.2)+(Log Bm.sub.3-Log
Bm.sub.4)=(At.sub.1-At.sub.2)+(At.sub.3-At.sub.4)
[0535] The processing system 210 then compares the indicator value
to one or more respective indicator thresholds at step 845.
[0536] As previously described, the indicator references are
derived for a sample population and are used to indicate the
likelihood of a subject suffering from ipSIRS or another condition.
To achieve this, the indicator reference is typically derived from
a sample population having similar characteristics to the subject.
The sample population is typically grouped based on a clinical
assessment into groups having/not having the conditions or a
measure of severity, risk or progression stage of the condition,
with this then being used to assess threshold indicator values that
can distinguish between the groups or provide a measure of
severity, risk or progression stage. The results of this comparison
are used by the processing system 210 to calculate a likelihood of
the subject having ipSIRS at step 850, with this being used to
generate a representation of the results at step 855, which is
transferred to the client device 203 for display at step 860, for
example as part of an email, dashboard indication or the like.
[0537] An example of the representation is shown in FIGS. 9A and
9B.
[0538] In this example, the representation 900 includes a pointer
910 that moves relative to a linear scale 930. The linear scale is
divided into regions 921, 922, 923, 924 which indicates the
probability of a subject having either SIRS or sepsis.
Corresponding indicator number values are displayed at step 930
with an indication of whether the corresponding value represents a
likelihood of inSIRS or ipSIRS being showing at step 940. An
alphanumeric indication of the score is shown at step 951 together
with an associated probability of the biological subject having
ipSIRS at step 952.
[0539] As shown in this example, regions of the linear scale where
the pointer is situated are highlighted with the diagnosis that is
most unlikely being greyed out to make it absolutely clear where
the subject sits on the scale. This results in a representation
which when displayed at step 860 is easy for a clinician to readily
understand and to make a rapid diagnosis.
[0540] Features of the benefits of using derived internal controls
over prior art will now be described with reference to FIGS. 10A,
10B and 10C.
[0541] Using the above example with four measured biomarkers used
in generating the indicator value, a standard control methodology
is shown in FIG. 10A. The work flow of the assay is divided up into
physical 1010 and algorithmic 1020 components, where the physical
components 1010 are inherent hardware, reagents and non-software
components and the algorithmic components 1020 are carried out on
an appropriate computing device 210. The values of the measured
biomarkers 1015 and external measured controls 1030 are generated
using the physical device 1010. External positive controls 1030 are
used to validate that the test is able to produce a result in the
reportable range by measuring against a reference range 1040, and
if no, then fail the test 1050, or if yes, output the indicator
value 1060 in a report 1070.
[0542] For comparison, the same device using internal relative
controls is shown in FIG. 10B. Again the workflow is divided up
into physical 1010 and algorithmic 1020 components and the measured
biomarkers 1015 are generated on the physical device 1010. Note
that the external positive controls 1030 shown in FIG. 10A are not
present, and there is an additional generation of internal relative
controls 1035 which are derived from the measured biomarkers 1015.
Otherwise the two methods are similar with the testing of controls
against reference thresholds 1040, and if outside these thresholds
a failure of the assay will be reported 1050, else the indicator
value 1060 will be output in the form of a report 1070.
[0543] It will be appreciated that by removing physical components
of a device in the form of external controls 1030, and replacing
their function with the use of internal relative controls 1035, the
control component of the test has been shifted from a physical
component, with fixed costs per unit, to the algorithmic component,
which is substantially more scalable as software. Therefore the use
of internal relative controls as shown in FIG. 10B has advantages
over the use of prior art measured controls FIG. 10A including
lower cost, complexity and risk for industrial manufacture or
processing risk.
[0544] An extension of this method is example in FIG. 10C, which
may be used in the event that the measured biomarkers 1015 are
unable or impractical to produce relative controls 1035 that
completely cover all instances where a failed sample must be
detected 1040. The use of such additional internal relative
controls is useful in the case where an indicator value may be
invalid even if no combinations of controls comprising the
indicator may be out of range. An example may be that a test is
designed for use in people with an inflammatory response due to
infection, and the indicator value may be a measure of the
likelihood that the patient will improve or deteriorate in the
following 24 hours. It may be that the indicator is highly
prognostic in this population, and that an internal relative
biomarker not related to this indicator can reliably distinguish
those with and without infection. In this example the internal
relative biomarker can be used as an additional control to ensure
that the patient is indeed infected, and therefore part of the
intended use population, and therefore that the indicator value is
valid for use on this patient. In this case an additional measured
biomarker 1031 can be run on the physical device 1010 in parallel
with the measured biomarkers 1015 that are used for the indicator
value 1060. Additional measured biomarkers 1031 need not be used in
the indicator value (test result), and are specifically selected to
ensure that any invalid samples, when tested for acceptable ranges
1040 will appropriately fail the test 1050. It will be appreciated
that multiple additional internal controls 1031 may be required to
cover all possible cases of invalid samples and that a plurality of
functions may be applied to additional measured internal controls
in combination with each other and the measured biomarkers
comprising the indicator value to meet this goal. It will further
be appreciated that generally it is simpler and cheaper to measure
additional internal controls 1031 in a medical device than to use
external controls 1030, and therefore even the addition of many
internal controls will generally still provide cost and complexity
advantages over the use of a small number of external controls.
[0545] An example will now be described with reference to FIGS. 12A
to 12C (a)-(f), which show kernel density plots for this
population. The distribution of Cycle Threshold (Ct) values for
each gene is shown in solid lines, and the Ct values for
synthesized In-Vitro Transcripts at known concentrations as
controls is shown as dashed lines.
[0546] Another advantage of the use of relative controls method
over standard measured controls will be described with reference to
FIGS. 11A and 11B, using the same example. In FIG. 11A, the Ct
value for each of the measured biomarkers (Bm.sub.1, Bm.sub.1,
Bm.sub.3, Bm.sub.4) is shown over a range of concentrations ranging
from 20 to 2000 ng input to the PCR reaction. In this example the
indicator value is measured as a sum of proportions of these
measured biomarkers:
In=In.sub.1+In.sub.2=(Bm.sub.1/Bm.sub.2)+(Bm.sub.3/Bm.sub.4)
The indicator value for these biomarkers at each concentration is
shown in FIG. 11B, which shows a flat profile despite very large
changes in concentration over the input range. These results
demonstrate that the indicator is stable over a range of input
concentrations because it measures the relative concentrations
between the biomarkers. If a reference threshold (in Ct units) were
applied over the stable indicator range (20 to 2000 ng input) to
each of the measured biomarkers in FIG. 11A, then the reference
ranges would be very wide. In this example, valid ranges for PLAC8
would range from 17 to 23 Ct units. Such wide control reference
ranges are generally not appropriate, so typically assays specify a
narrow input range so that reference ranges of measured biomarkers
can be made sufficiently narrow. This step in creating an input
concentration from a sample is an extra step in processing that can
be removed if the indicator value uses relative information derived
from measured biomarkers, and if the controls also use relative
information derived from measured biomarkers. Such an example shows
improved utility over controls using measured biomarkers in this
case by the removal of a processing step requiring diluting to a
specified input concentration, which saves time, cost and reduces
complexity.
[0547] In this example, the data shown in FIG. 12A is taken from
106 consecutive samples with 2 or more ipSIRS criteria, divided in
patients with or without infection, for biomarkers in the form of
signature genes LAMP1, CEACAM4, PLAC8 and PLA2G7.
[0548] Next a failure of the measurement of one of the four
signature genes will be described. Using sample number 13, and
artificially reducing reaction efficiency of the LAMP1 reaction to
89% (failed assay) we reduce the recorded Ct value from 25.71 to
22.88. The probability based on reference Ct observations for this
gene that the assay has failed is 32.5% as shown for LAMP1 in FIG.
12B (a)-(f), not sufficient to declare a failed assay. FIG. 12B
(a)-(f) show the Ct values recorded for each of the other
biomarkers (PLA2G7, PLAC8 and CEACAM4) for the failed sample, and
also that for both the high and low positive controls, the values
are in the expected ranges.
[0549] Table 2 shows that for each of the measured biomarkers and
both controls that the values are within the reference ranges and
there is not sufficient evidence (p<0.05) to identify the failed
sample.
TABLE-US-00002 TABLE 2 PLA2G7 PLAC8 CEACAM4 LAMP1 Hi Pos Cntl Low
Pos Cntl Sample Value 30 22 24 25.5 21 33 In range Yes Yes Yes Yes
Yes Yes P value 1.000 0.280 1.000 0.325 1.00 1.00
[0550] Now looking at ratios between the measured biomarkers in
FIG. 12C (a)-(f), the ratio PLAC8-LAMP1 clearly detects the failure
p<0.001, which is sufficient to correctly declare the assay
failed.
[0551] Table 3 shows that the failed assay is detected by a low p
value (<0.01) for the relative values of PLAC8 to LAMP1.
TABLE-US-00003 TABLE 3 PLA2G7 - PLA2G7 - PLA2G7 - PLAC8 - PLAC8 -
CEACAM4 - PLAC8 CEACAM4 LAMP1 CEACAM4 LAMP1 LAMP1 Sample Value 8 6
4.5 -2 -3.5 1.5 In range Yes Yes Yes Yes No Yes P value 0.204 0.927
0.291 0.133 <0.001 0.133
[0552] FIG. 12D plots the failed sample as a scatterplot of PLAC8
and LAMP1. It can be seen that although this sample is within the
maximum and minimum ranges of both PLAC8 and LAMP1, it is a clear
outlier when considering the relative position with respect to each
of the these biomarkers. This is reflected in the low p value for
this relative control, as it is outside of the reference range and
thus is correctly identified as a failed sample.
[0553] Accordingly, using this approach, multiple relative controls
that may not individually be sufficient to declare a failed assay
can be combined using a Bayes Rule or other probabilistic method to
give a joint probability of failure.
[0554] Accordingly, the above process described the use of controls
comprised of ratios of measured biomarker values, such as
expression of target genes, rather than the use of non-target
internal or external controls or spike-ins, in gene expression
experiments and analyses. Advantages of such an approach include no
additional measurements beyond those used for the indicator values,
the ability to analyse more targets in a single experiment and
reduced overall costs of performing gene expression analysis in
addition to higher sensitivity and the ability to skip an input
normalizing step during processing if the indicator values are also
comprised of ratios.
[0555] An example will now be described with reference to FIG. 13A
(a)-(d) which shows data from BUPA Medical Research Ltd in a study
of male patients for the detection of liver damage due to
alcoholism. In this study there were 144 enrolled volunteers
classified as alcoholic, and 200 enrolled volunteers classified as
non-alcoholic. Measurements of liver related proteins from
peripheral blood were taken for each volunteer and a diagnostic
combination of these proteins (the indicator) for alcohol related
liver damage yielded 94% accuracy of classification (Comak, Emre,
et al. "A new medical decision making system: Least square support
vector machine (LSSVM) with Fuzzy Weighting Pre-processing." Expert
Systems with Applications 32.2 (2007): 409-414.). The utility of
this test is that an accurate diagnosis of liver damage due to
alcoholism can be made with a multi-marker protein panel from
peripheral blood in preference to an invasive and more expensive
liver biopsy. Four proteins were measured from peripheral blood;
Alkaline Phosphatase (AP), Aminotransferase (AT), Aspartate
Aminotransferase (AspAT) and Gamma-glutamyl transpeptidase (GGT).
The reference population is the set of patients in the study, and
the reference data are the measured values for each of the protein
concentrations plus the relative controls defined as all pair-wise
ratios of abundance of each protein. In this example, consider a
hypothetical new sample for a patient with unknown liver status
with the following measurements AP=56, AT=49, AspAT=28 and GGT=6.
An indicator value for liver disease may be generated from these
measurements, but it is not yet known if the values are valid (if
there has been some failure in measurement). All of these
measurements are within the observed range of values for each of
these proteins, so this sample would traditionally be considered as
having satisfied the controls (being within the observed reference
distribution. FIG. 13A (a)-(d) shows the measured values for the
new sample against the reference distribution for each measured
biomarker (AP, AT, AspAT and GGT)). It can be seen that the sample
falls within the reference range for each measured biomarker and
therefore would traditionally be considered valid. Table 4 shows
the values for each measured biomarker against the reference
distribution and the probability of failure for each reference
distribution.
TABLE-US-00004 TABLE 4 AP AT AspAT GGT Sample Value 56 49 28 6 In
range Yes Yes Yes Yes P value 0.451 0.340 0.739 0.411
[0556] FIG. 13B (a)-(f) show the same sample against derived
control distributions. In the case of AT-GGT and Asp-GGT, there are
p values less than 0.05, indicating that this sample is unlikely to
have come from the population of samples for which this test was
designed, or there is some other failure, and therefore the
indicator value (result) for this test is invalid.
[0557] Table 5 shows the relative control values for this sample
and the specific controls capable of detecting this failure.
TABLE-US-00005 TABLE 5 AP - AP - AP - AT - AT - AspAT - AT AspAT
GGT AspAT GGT GGT Sample Value 7 28 50 21 43 22 In range Yes Yes
Yes Yes No No P value 0.208 0.381 0.653 0.157 0.023 0.006
[0558] Accordingly, the above described system introduces the use
of relative internal controls in the case of multi-biomarker
medical devices, such that the need for controls that are not
internal relative controls may be reduced or eliminated.
[0559] In one example, the relative internal controls are relative
biomarkers internal to the sample that are used to ensure that the
values used in establishing an indicator are valid. The relative
biomarkers can be derived from measured biomarker values, with
these being used by defining corresponding acceptable reference
thresholds for each relative biomarker. These relative biomarkers
may or may not include relative biomarkers used in determining the
indicator, and could include the same biomarker values used in
different or the same combinations. In one example, this provides a
set of relevant controls without the need for any additional
measured biomarkers being added to the assay.
[0560] The system can further be used to provide the appropriate
use of these controls in a medical device using the relative
biomarkers used in establishing the indicator value.
[0561] The system can also provide a method by which additional
internal relative biomarkers may be added to the group of relative
controls to meet any arbitrarily stringent control requirement such
that a minimal set of additional measured biomarkers is required,
thus providing an optimal performance for a minimum cost.
[0562] Despite allowing additional markers to be avoided, the
system can successfully detect test failure in cases where prior
art methods are not able to, a critical advance in the case of
medical devices where acting on an invalid test results can have
potentially life threatening consequences.
[0563] The system is also shown by example to appropriately pass a
result in cases where prior art methods unnecessarily fail a
sample. Also an important advance for medical devices with
potentially life-critical consequences if a test is unnecessarily
failed (and the result is therefore unavailable).
[0564] A further example will now be described using in-house data
derived from the use of real-time polymerase chain reaction
(RT-PCR) on 546 blood samples taken from patients with suspected
sepsis. The results of the assay provide a probability of a patient
having sepsis (or SIRS) based on a formula that uses the PCR Ct
(cycle time) values for each of four target genes (PLA2G7, PLAC8,
LAMP1 and CEACAM4).
[0565] The method in brief was as follows. Patient blood was
collected directly into PAXgene tubes and total RNA extracted. The
RT-PCR assay was provided in kit form to a hospital laboratory
based in the Netherlands. The assay uses quantitative, real-time
determination of the amount of each four host immune cell RNA
transcripts in the sample based on the detection of fluorescence on
a qRT-PCR instrument (e.g. Applied Biosystems 7500 Fast Dx
Real-Time PCR Instrument, Applied Biosystems, Foster City, Calif.,
catalogue number 440685; K082562). Transcripts are each
reverse-transcribed, amplified, detected, and quantified in a
separate reaction well for each target gene using a probe that was
visualized in the FAM channel. Each of the four target genes has a
known Ct range and when assay results are obtained outside of these
ranges the test is failed. For each sample the following internal
controls were also run in separate reaction vessels--HIGH, LOW,
NEGATIVE and a no-template (NTC). The HIGH, LOW and NEGATIVE
internal controls contain a known quantity of an artificial DNA
template--each of these separate reactions must also fall within a
particular Ct range for the assay to pass, and the NTC must not
amplify a PCR product.
[0566] A summary table of the results from running the assay on
these 546 samples using both control methods ("Normal" and
"Relative") is shown below in Table 6. Full results are shown in
Tables 7, 8, 9 and 10.
TABLE-US-00006 TABLE 6 Relative Normal Fail Pass Fail (Controls) 0
23 Fail (Four Targets) 2 3 Pass 13 505
[0567] A brief summary, explanation and discussion of these results
follows.
[0568] 505 samples (92.5%) were passed using both control
methods.
[0569] Two (2) samples were failed using both control strategies.
Using the Normal controls method both samples failed because the Ct
values for the target gene PLA2G7 were out of the expected Ct
range. Using the Relative controls method these same two samples
were strongly failed because multiple Relative control p values
were obtained that were less than 0.001.
[0570] 26 samples were failed using Normal controls method but were
passed using Relative control method. Of these 26 samples, 23 were
failed because the LOW control was out of range. For these 23
samples, all individual gene target measurements (PLA2G7, PLAC8,
LAMP1 and CEACAM4) were within the expected Ct range, and all
Relative controls passed. Upon further inspection of the Ct values
for individual genes and other Normal controls the 23 samples that
were failed because of one out-of-range Normal control (LOW) should
not have been. The Relative control strategy did not fail these
samples. In practice this would mean that the use of the Relative
controls strategy would have `rescued` 23 valid diagnostic tests
that would be denied to the patient using a Normal control
method.
[0571] The other three (3) samples failed using the Normal method
because the Ct values for PLA2G7 were out of the expected range for
this gene. Of these three samples: [0572] 1) Sample IXP_128:
reported adjusted p values for the Relative controls above 0.045.
Such a p value suggests that this sample should not have been
failed. Upon further inspection of the Ct values all individual
gene measurements were comparatively high (in the high range for
values expected for the other three genes but not out of range).
Such a result suggests that the assay was run with a low input RNA
concentration or low quality RNA. Despite this, a valid probability
of sepsis was still able to be calculated. Further, the
retrospective clinical diagnosis of the patient matched the sepsis
probability from the assay, implying that the assay result was
valid and should not have been failed. [0573] 2) Sample 6869: The
Reported Relative control p values were as low as 0.0016. A result
like this can be expected in 16 in every thousand normal tests.
[0574] 3) Sample 1357: The Reported Relative control p values were
as low as 0.002. A result like this can be expected in one in every
five hundred normal tests.
[0575] The use of Relative control p values allows a clinical
interpretation of the relevance of an abnormality level (p value),
rather than an absolute call, allowing the treating physician (or a
procedure on behalf of the physician) to determine the optimal p
value at which to call a fail status on the test.
[0576] There were 13 instances where the Normal method passed
samples whereas the Relative method did not. In these instances all
measured gene markers were within the expected Ct range, and all
Normal controls were also within range. However, these samples
resulted in a low p value using the Relative method. In fact, the
probability that the Relative controls measurements would happen by
chance for any of these samples is less than one in one thousand.
These Relative control results suggests a high level of abnormality
for these 13 samples, and implies that these samples are not
similar to other samples observed, nor similar to the patient
population used for the development and interpretation of the
diagnostic. Based on the high level of abnormality using the
Relative control approach these 13 samples should be failed despite
the measured markers and Normal controls falling within expected Ct
range. In this instance the Relative control approach is especially
useful, as it has identified patients for whom the interpretation
of the diagnostic result using the Normal control approach is not
valid. Further, the Relative control approach provides a confidence
of the non-validity of the result. These latter two points are
discussed in more detail below.
[0577] Considering sample 3787: all Ct values of the genes are
within expected range, and the Normal internal controls all are
within range. Thus, this result would be considered valid using the
Normal control approach. However, the Relative control
CEACAM4/LAMP1 has a p value of 0.0007642 and the Relative control
LAMP1/PLAC8 has a p value of 3.96E-07 indicating that such a result
occurs in less than one in a million cases (based on the
distribution curve of expected results). Such a result can be
interpreted in two ways: [0578] 1). The test values are correct,
and this really is a one-in-a-million patient. [0579] 2). The test
values are incorrect and there is some problem with the assay.
[0580] Any patient sample that generates a test result so radically
different (1:1,000,000 chance) from all other patient samples
should not be diagnosed with reference to other patient sample
results that fit within the normal distribution--such a result
should at least be further investigated (e.g. repeat the assay
and/or investigate patient clinical notes).
[0581] Thus, when Relative controls approach reveal highly unlikely
results (based on p value) the test should be failed. In these 13
cases, through appropriate failure of the samples, the Relative
controls approach can 1) `protect` patients from diagnostic calls
that are unlikely to be actually valid, and 2) detect more
sensitively test results that do not reflect the true status of the
patient.
[0582] Full results of the 546 assays are shown in Tables 7, 8, 9
and 10.
[0583] Table 7 shows raw data results for 505 samples (of 546) that
passed using both the Normal and Relative controls method.
TABLE-US-00007 TABLE 7 Normal Controls Relative Controls External
CEACAM4/ CEACAM4/ CEACAM4/ LAMP1/ LAMP1/ PLAC8/ Sample CEACAM4
LAMP1 PLA2G7 PLAC8 Controls Status LAMP1 pVal PLAC8 pVal PLA2G7
pVal PLAC8 pVal PLA2G7 pVal PLA2G7 pVal Status 6607 21.3 23.7 25.0
21.6 P P 0.368 0.233 0.472 0.535 0.222 0.185 P 6617 22.5 24.3 28.0
19.8 P P 0.769 0.267 0.838 0.163 0.941 0.405 P 6629 21.6 24.0 27.3
21.1 P P 0.363 0.581 0.762 0.945 0.897 0.948 P 6636 22.0 24.7 32.7
22.0 P P 0.205 0.331 0.003 0.906 0.012 0.049 P 6648 23.7 23.6 29.2
22.3 P P 0.072 0.916 0.810 0.233 0.248 0.787 P 6650 21.6 24.3 29.5
17.9 P P 0.224 0.059 0.146 0.004 0.341 0.018 P 6653 21.2 24.0 26.8
18.3 P P 0.183 0.193 0.809 0.019 0.685 0.328 P 6656 24.0 24.3 28.5
22.3 P P 0.148 0.737 0.786 0.491 0.668 0.971 P 6657 22.4 23.2 26.9
21.2 P P 0.414 0.963 0.753 0.521 0.944 0.765 P 6658 22.0 24.3 31.1
21.4 P P 0.480 0.672 0.036 0.953 0.058 0.120 P IPX-098 21.5 23.8
30.5 19.3 P P 0.433 0.422 0.041 0.155 0.072 0.025 P IXP-080 23.9
24.4 25.2 20.0 P P 0.232 0.042 0.050 0.191 0.133 0.627 P IXP-081
23.6 24.3 27.2 22.0 P P 0.368 0.778 0.451 0.725 0.719 0.628 P
IXP-082 23.2 25.2 28.5 21.9 P P 0.623 0.984 0.921 0.706 0.890 0.922
P IXP-083 24.0 24.7 29.5 22.1 P P 0.317 0.592 0.820 0.879 0.458
0.608 P IXP-087 23.1 23.7 26.4 21.1 P P 0.265 0.595 0.363 0.810
0.674 0.638 P IXP-088 22.5 24.7 25.5 21.6 P P 0.489 0.816 0.263
0.800 0.122 0.270 P IXP-089 21.2 23.1 28.8 20.4 P P 0.705 0.738
0.188 0.935 0.221 0.348 P IXP-090 21.1 23.7 29.0 21.2 P P 0.256
0.319 0.142 0.810 0.313 0.498 P IXP-095 22.6 25.2 27.4 18.7 P P
0.246 0.043 0.870 0.003 0.450 0.292 P IXP-096 22.9 24.5 27.3 22.0 P
P 0.954 0.779 0.708 0.797 0.666 0.624 P IXP-097 24.0 25.0 28.8 21.9
P P 0.566 0.508 0.911 0.771 0.868 0.768 P 6681 25.2 26.0 30.3 23.1
P P 0.380 0.530 0.977 0.972 0.640 0.692 P 6694 21.4 24.3 27.3 21.7
P P 0.159 0.248 0.669 0.831 0.808 0.754 P 6706 22.7 24.4 28.2 20.7
P P 0.896 0.559 0.808 0.473 0.845 0.579 P 6709 21.5 24.5 26.9 21.3
P P 0.102 0.423 0.860 0.743 0.532 0.748 P 6713 21.4 24.2 26.1 19.5
P P 0.174 0.596 0.844 0.121 0.374 0.886 P 6718 23.2 24.3 29.7 19.6
P P 0.612 0.069 0.452 0.116 0.289 0.085 P 6735 23.1 25.7 27.4 23.2
P P 0.247 0.311 0.696 0.808 0.319 0.349 P 6744 20.5 23.7 31.1 19.1
P P 0.066 0.923 0.004 0.151 0.027 0.010 P 6746 21.1 23.2 26.1 18.2
P P 0.573 0.184 0.935 0.068 0.712 0.475 P 6750 22.3 23.6 26.1 21.0
P P 0.759 0.923 0.492 0.906 0.559 0.592 P 6888 23.4 24.5 26.3 22.2
P P 0.593 0.985 0.238 0.714 0.317 0.313 P IXP_099 23.0 24.6 25.5
21.2 P P 0.997 0.639 0.175 0.619 0.146 0.372 P IXP_102 24.8 26.2
28.2 22.8 P P 0.806 0.575 0.367 0.675 0.398 0.654 P IXP_104 24.4
25.7 28.9 22.5 P P 0.783 0.646 0.763 0.772 0.852 0.992 P IXP_105
22.4 24.2 28.1 19.6 P P 0.857 0.210 0.760 0.142 0.812 0.315 P
IXP_107 22.5 23.6 25.2 21.3 P P 0.532 0.996 0.214 0.648 0.307 0.282
P IXP_108 23.2 25.1 27.3 22.2 P P 0.759 0.852 0.597 0.982 0.471
0.571 P IXP_110 22.1 23.9 29.4 19.5 P P 0.829 0.291 0.261 0.200
0.272 0.111 P IXP_111 22.2 24.4 27.1 21.6 P P 0.472 0.630 0.907
0.994 0.629 0.700 P IXP_112 24.2 25.2 27.2 22.9 P P 0.486 0.949
0.271 0.663 0.405 0.362 P IXP_113 23.0 23.7 29.9 22.1 P P 0.306
0.802 0.333 0.314 0.121 0.492 P IXP_117 22.0 22.7 28.3 19.6 P P
0.327 0.371 0.540 0.807 0.252 0.290 P IXP_122 23.2 24.1 27.0 21.7 P
P 0.420 0.836 0.499 0.716 0.746 0.644 P 6762 25.1 24.9 29.0 20.4 P
P 0.050 0.008 0.531 0.164 0.761 0.310 P 6773 23.7 24.1 29.4 20.9 P
P 0.181 0.238 0.725 0.771 0.296 0.316 P 6776 22.7 24.3 28.8 20.1 P
P 0.998 0.298 0.587 0.266 0.561 0.278 P 6786 21.5 23.4 27.7 18.6 P
P 0.730 0.190 0.587 0.100 0.681 0.214 P 6796 25.1 25.4 29.9 20.5 P
P 0.169 0.011 0.889 0.085 0.592 0.166 P 6824 23.2 24.6 29.2 22.2 P
P 0.882 0.828 0.625 0.734 0.549 0.769 P 6825 22.2 24.4 27.8 23.4 P
P 0.517 0.065 0.779 0.135 0.982 0.397 P 6841 22.5 24.1 28.2 20.2 P
P 0.939 0.412 0.765 0.413 0.720 0.458 P 6844 22.2 23.7 29.1 20.4 P
P 0.973 0.690 0.329 0.688 0.287 0.281 P 6845 22.6 24.6 28.1 22.5 P
P 0.624 0.385 0.800 0.568 0.979 0.768 P 6867 21.0 23.2 24.5 20.2 P
P 0.474 0.731 0.411 0.880 0.215 0.361 P IXP_123 24.4 24.7 28.3 22.4
P P 0.161 0.546 0.519 0.711 0.994 0.842 P IXP_124 22.4 25.1 29.1
19.3 P P 0.200 0.174 0.398 0.017 0.789 0.125 P IXP_129 23.4 24.7
27.1 23.2 P P 0.728 0.417 0.455 0.264 0.530 0.261 P IXP_130 23.0
23.8 26.0 21.1 P P 0.404 0.607 0.278 0.956 0.454 0.527 P IXP_131
22.2 24.5 26.8 20.3 P P 0.427 0.609 0.816 0.263 0.518 0.919 P
IXP_132 22.9 24.7 25.4 20.4 P P 0.841 0.351 0.182 0.255 0.125 0.547
P IXP_135 22.0 24.5 28.4 19.4 P P 0.336 0.292 0.496 0.069 0.802
0.226 P IXP_137 23.4 24.7 30.1 22.1 P P 0.808 0.964 0.398 0.898
0.303 0.449 P IXP_142 23.3 24.3 27.3 21.2 P P 0.479 0.475 0.563
0.804 0.790 0.939 P IXP_145 22.9 25.0 28.2 22.5 P P 0.561 0.530
0.892 0.803 0.885 0.799 P 6890 22.1 22.8 28.2 19.2 P P 0.325 0.215
0.587 0.542 0.283 0.229 P 6650 22.3 22.7 29.8 18.6 P P 0.175 0.060
0.209 0.306 0.043 0.028 P 6869 23.2 24.7 26.9 23.2 P P 0.898 0.359
0.439 0.285 0.443 0.226 P IXP_128 25.6 26.7 30.8 24.1 P P 0.560
0.869 0.935 0.805 0.705 0.867 P 6592 22.4 24.3 26.0 20.4 P P 0.712
0.524 0.410 0.344 0.285 0.737 P 6598 21.3 22.6 26.0 20.5 P P 0.739
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0.854 0.447 0.332 0.777 0.444 P 3187134 21.8 21.3 24.7 19.3 P P
0.018 0.332 0.238 0.497 0.932 0.656 P 2996445 22.0 24.2 28.2 20.5 P
P 0.533 0.844 0.585 0.508 0.783 0.557 P 2973087 21.8 23.3 25.3 19.5
P P 0.950 0.455 0.430 0.452 0.414 0.810 P 3007078 23.2 24.7 29.3
19.1 P P 0.892 0.028 0.610 0.024 0.538 0.081 P 3143971 21.7 24.0
27.2 19.5 P P 0.441 0.456 0.831 0.177 0.877 0.532 P 3241087 21.8
22.2 25.0 20.0 P P 0.161 0.658 0.328 0.587 0.727 0.560 P 2801327
24.5 25.3 26.7 22.3 P P 0.380 0.453 0.121 0.869 0.220 0.370 P
2774672 22.9 24.4 26.8 19.8 P P 0.902 0.146 0.541 0.144 0.552 0.740
P 3267724 24.1 24.1 26.8 20.1 P P 0.070 0.032 0.214 0.329 0.667
0.846 P 3219499 24.8 25.5 26.9 20.5 P P 0.367 0.021 0.128 0.070
0.237 0.958 P 3022381 21.8 21.9 25.0 19.4 P P 0.105 0.399 0.325
0.786 0.803 0.725 P 2973476 24.2 25.3 30.1 22.0 P P 0.600 0.501
0.637 0.736 0.443 0.421 P 3143818 22.3 24.0 26.6 21.2 P P 0.874
0.930 0.691 0.983 0.613 0.692 P 3118227 24.2 24.6 29.2 23.8 P P
0.179 0.516 0.967 0.096 0.532 0.674 P 3250409 22.5 24.2 27.5 20.5 P
P 0.878 0.583 0.954 0.486 0.890 0.783 P 2654918 24.0 25.2 26.7 21.5
P P 0.664 0.370 0.229 0.522 0.282 0.610 P 2163170 22.3 23.5 28.1
20.1 P P 0.695 0.489 0.678 0.650 0.522 0.443 P 3257803 23.4 24.7
28.6 21.8 P P 0.733 0.761 0.989 0.939 0.853 0.848 P 2911717 23.9
25.0 29.3 22.9 P P 0.563 0.875 0.873 0.558 0.645 0.964 P 3082897
24.7 25.3 30.7 20.3 P P 0.261 0.014 0.622 0.071 0.276 0.060 P
2893476 24.0 26.9 30.0 24.1 P P 0.167 0.306 0.668 0.928 0.818 0.814
P 3136847 22.1 23.2 26.1 21.7 P P 0.557 0.503 0.557 0.254 0.735
0.366 P 3161091 20.4 22.2 24.9 19.6 P P 0.761 0.740 0.762 0.893
0.634 0.647 P 3263354 23.2 24.0 26.9 20.1 P P 0.354 0.163 0.474
0.413 0.760 0.836 P 2156886 23.7 25.2 29.6 22.5 P P 0.999 0.958
0.655 0.956 0.632 0.723 P 3247954 20.6 24.1 26.8 20.8 P P 0.031
0.281 0.578 0.681 0.633 0.876 P 3042060 24.6 26.9 27.6 22.8 P P
0.373 0.670 0.289 0.272 0.114 0.507 P 2961182 25.1 26.5 32.4 23.7 P
P 0.859 0.896 0.258 0.992 0.192 0.292 P 3141749 21.0 23.0 24.0 20.0
P P 0.610 0.844 0.287 0.873 0.162 0.300 P 3174398 22.5 23.4 27.2
22.2 P P 0.466 0.492 0.842 0.207 0.881 0.563 P 2425352 21.3 23.2
24.4 20.0 P P 0.713 0.969 0.287 0.758 0.185 0.370 P 2628334 21.1
25.0 27.1 22.4 P P 0.009 0.054 0.642 0.870 0.421 0.461 P 2156857
22.4 23.5 27.7 20.7 P P 0.553 0.753 0.893 0.925 0.660 0.762 P
3222786 22.9 24.0 27.4 20.9 P P 0.622 0.586 0.770 0.824 0.946 0.945
P 3008502 23.1 24.4 28.1 19.3 P P 0.749 0.047 0.950 0.059 0.927
0.263 P 3007162 23.4 25.9 28.1 23.0 P P 0.335 0.536 0.845 0.969
0.490 0.593 P 3173057 24.3 24.8 31.1 21.7 P P 0.213 0.320 0.366
0.875 0.112 0.172 P 3111443 21.0 23.4 28.2 19.1 P P 0.353 0.625
0.265 0.233 0.463 0.211 P 2671979 24.0 25.8 29.6 22.7 P P 0.817
0.935 0.792 0.799 0.867 0.783 P 2627904 22.8 24.7 27.6 22.0 P P
0.686 0.703 0.900 0.909 0.737 0.739 P 2789061 22.2 25.3 29.6 24.1 P
P 0.074 0.016 0.210 0.204 0.649 0.735 P 3269868 21.6 24.6 27.0 22.1
P P 0.118 0.197 0.865 0.806 0.550 0.538 P 3033996 22.4 24.5 26.8
21.4 P P 0.576 0.864 0.706 0.824 0.494 0.670 P 2910981 23.4 27.5
29.8 24.4 P P 0.005 0.095 0.503 0.815 0.505 0.684 P 3018183 22.0
24.3 28.9 21.1 P P 0.420 0.808 0.346 0.745 0.543 0.503 P 3125147
23.1 24.7 27.4 22.8 P P 0.981 0.485 0.687 0.467 0.657 0.447 P
3129468 21.0 24.5 28.4 22.1 P P 0.030 0.075 0.224 0.748 0.826 0.999
P 2857601 22.5 23.8 26.8 21.9 P P 0.784 0.616 0.682 0.463 0.762
0.515 P 2636623 25.4 27.0 31.6 24.4 P P 0.953 0.806 0.558 0.826
0.549 0.719 P 2781026 24.2 27.5 31.4 25.3 P P 0.045 0.073 0.260
0.641 0.833 0.929 P 3255360 23.1 24.7 27.4 21.5 P P 0.951 0.803
0.698 0.756 0.655 0.851 P 3029652 23.8 26.8 27.0 22.7 P P 0.115
0.966 0.313 0.273 0.062 0.370 P 2912013 25.7 27.1 33.5 25.4 P P
0.819 0.455 0.146 0.336 0.094 0.415 P 2880471 22.9 25.3 29.5 20.8 P
P 0.419 0.497 0.442 0.190 0.673 0.286 P 3151473 22.3 23.1 25.3 20.5
P P 0.369 0.646 0.276 0.873 0.472 0.504 P 3131243 23.4 24.3 26.2
20.9 P P 0.444 0.328 0.234 0.625 0.371 0.653 P 2811174 22.3 26.2
27.9 21.9 P P 0.009 0.541 0.804 0.217 0.301 0.883 P 2768722 24.3
25.8 29.1 21.7 P P 0.953 0.324 0.895 0.314 0.911 0.639 P 2601748
22.3 23.1 27.1 20.2 P P 0.370 0.496 0.871 0.938 0.785 0.793 P
2793139 21.2 21.4 26.5 17.9 P P 0.103 0.114 0.900 0.615 0.343 0.297
P 175 22.8 23.6 28.7 18.0 P P 0.380 0.007 0.665 0.026 0.366 0.050 P
1528 23.9 24.2 29.4 20.6 P P 0.133 0.114 0.850 0.550 0.342 0.273 P
763 21.6 22.4 23.6 19.4 P P 0.402 0.467 0.104 0.866 0.185 0.330 P
2449113 23.7 25.2 28.5 22.7 P P 0.922 0.865 0.902 0.801 0.934 0.836
P 1239 23.7 25.9 27.6 23.1 P P 0.508 0.631 0.530 0.974 0.315 0.409
P 2240 20.9 25.2 30.4 19.5 P P 0.002 0.865 0.021 0.016 0.343 0.036
P 1894 23.3 25.5 27.6 23.2 P P 0.475 0.390 0.691 0.690 0.434 0.395
P 1143 23.8 25.6 30.4 20.3 P P 0.855 0.086 0.445 0.049 0.466 0.094
P 2888477 21.3 23.4 28.7 19.6 P P 0.591 0.678 0.228 0.406 0.305
0.199 P 2786127 22.2 23.7 27.0 20.8 P P 0.920 0.894 0.871 0.944
0.901 0.951 P 1782 26.3 25.0 30.0 21.2 P P 0.001 0.003 0.492 0.443
0.376 0.255 P 2774321 24.8 26.2 31.1 19.8 P P 0.887 0.005 0.509
0.004 0.435 0.025 P 2512541 21.6 23.6 27.8 18.1 P P 0.612 0.089
0.571 0.029 0.723 0.135 P 2109 23.1 25.3 28.1 21.4 P P 0.514 0.728
0.934 0.399 0.679 0.893 P 2005 22.1 24.2 27.8 21.7 P P 0.543 0.518
0.768 0.803 0.989 0.899 P 1145 22.8 25.6 31.0 19.3 P P 0.168 0.088
0.097 0.005 0.275 0.015 P 2545687 19.7 21.6 28.4 19.0 P P 0.748
0.710 0.061 0.869 0.064 0.161 P 1824 26.1 25.2 30.0 21.0 P P 0.004
0.003 0.539 0.266 0.443 0.217 P 554 22.3 22.6 25.8 19.1 P P 0.150
0.131 0.405 0.572 0.860 0.864 P 2152399 25.2 25.0 31.4 20.1 P P
0.047 0.003 0.573 0.083 0.111 0.025 P 1630 23.9 24.9 26.2 23.0 P P
0.499 0.756 0.146 0.412 0.221 0.150 P 2089 22.5 24.3 28.3 20.3 P P
0.812 0.467 0.732 0.343 0.803 0.468 P 1914 21.0 22.0 25.5 18.5 P P
0.519 0.346 0.774 0.591 0.990 0.758 P 1189 20.9 24.5 28.0 20.3 P P
0.024 0.613 0.289 0.275 0.989 0.538 P 2677542 25.1 26.7 30.1 22.9 P
P 0.989 0.472 0.952 0.438 0.943 0.710 P 2073538 23.5 23.5 29.5 20.4
P P 0.074 0.152 0.656 0.814 0.171 0.218 P 1967 21.6 23.1 26.2 19.5
P P 0.905 0.533 0.810 0.563 0.842 0.873 P 2550576 22.5 24.2 27.2
22.5 P P 0.911 0.372 0.810 0.384 0.754 0.462 P 438 22.7 22.3 26.9
18.0 P P 0.024 0.008 0.659 0.241 0.514 0.240 P 1550 25.1 26.0 30.1
23.2 P P 0.420 0.635 0.970 0.938 0.718 0.804 P 2087 21.4 23.4 26.7
19.4 P P 0.655 0.538 0.941 0.327 0.886 0.668 P 1902 22.4 24.4 26.5
21.4 P P 0.591 0.820 0.619 0.884 0.423 0.573 P 3485126 24.1 27.1
29.8 23.9 P P 0.108 0.421 0.720 0.761 0.678 0.868 P 1957 23.8 24.5
28.6 20.7 P P 0.306 0.138 0.848 0.400 0.761 0.477 P 278 24.3 25.4
31.7 21.3 P P 0.528 0.155 0.247 0.289 0.120 0.066 P 2276373 23.6
24.2 28.4 21.0 P P 0.312 0.297 0.904 0.705 0.708 0.609 P 2318175
21.7 23.9 28.5 21.0 P P 0.485 0.699 0.369 0.925 0.538 0.584 P
2079564 23.4 24.3 27.6 18.0 P P 0.426 0.001 0.647 0.005 0.925 0.136
P 2295511 26.3 26.1 29.3 22.7 P P 0.041 0.063 0.274 0.612 0.875
0.877 P 2495 23.5 25.5 27.0 21.3 P P 0.575 0.466 0.420 0.237 0.252
0.791 P 1837 25.2 26.9 32.1 23.3 P P 0.924 0.634 0.346 0.564 0.335
0.273 P 1223 24.8 26.3 28.9 23.1 P P 0.934 0.719 0.575 0.747 0.576
0.786 P 2193 25.1 26.4 31.2 23.4 P P 0.762 0.700 0.593 0.849 0.468
0.490 P 2045301 25.0 25.3 31.1 21.0 P P 0.176 0.041 0.570 0.229
0.199 0.089 P 2469217 21.2 22.5 27.3 19.9 P P 0.681 0.933 0.630
0.835 0.470 0.641 P 2351284 20.9 21.9 22.4 19.2 P P 0.466 0.733
0.057 0.870 0.094 0.150 P 2712378 21.6 23.2 26.5 19.3 P P 0.949
0.415 0.921 0.411 0.941 0.692 P 2739656 24.6 26.2 29.0 23.6 P P
0.984 0.856 0.704 0.858 0.676 0.664 P 2462 20.0 24.1 27.7 21.5 P P
0.006 0.041 0.186 0.834 0.953 0.945 P 1835 22.1 23.5 26.6 21.1 P P
0.848 0.837 0.767 0.720 0.824 0.705 P 1400 22.0 23.9 25.0 20.7 P P
0.694 0.990 0.286 0.766 0.180 0.361 P 2662486 24.7 24.7 27.6 22.1 P
P 0.071 0.279 0.247 0.879 0.732 0.719 P 2654139 20.8 22.4 24.0 20.1
P P 0.905 0.679 0.339 0.723 0.278 0.284 P 2309104 23.0 25.8 28.6
20.0 P P 0.192 0.182 0.797 0.018 0.709 0.312 P 1426 23.4 24.1 28.9
21.7 P P 0.329 0.767 0.810 0.696 0.457 0.702 P 2661366 26.4 25.9
29.9 22.2 P P 0.018 0.022 0.410 0.466 0.765 0.519 P 2143801 25.0
25.6 28.9 22.6 P P 0.255 0.363 0.542 0.884 0.931 0.991 P 1717 23.3
24.7 28.5 20.9 P P 0.849 0.380 0.943 0.424 0.864 0.561 P 1863 23.7
25.6 26.9 22.4 P P 0.771 0.970 0.310 0.801 0.216 0.392 P 1218 24.9
26.4 29.3 23.1 P P 0.870 0.622 0.689 0.683 0.728 0.957 P 2566446
21.9 22.0 25.5 20.0 P P 0.080 0.589 0.431 0.490 0.979 0.718 P
2355070 23.3 26.3 30.2 20.0 P P 0.105 0.111 0.325 0.004 0.803 0.073
P 2512233 24.8 26.2 31.2 20.6 P P 0.808 0.023 0.495 0.024 0.393
0.053 P 2317 22.1 25.1 25.8 21.9 P P 0.111 0.428 0.489 0.757 0.124
0.287 P 2286 22.9 25.7 28.6 22.3 P P 0.179 0.636 0.761 0.639 0.731
0.986 P 1128 21.6 25.0 27.7 22.3 P P 0.037 0.131 0.605 0.923 0.626
0.656 P 1536 19.4 23.4 24.9 17.3 P P 0.008 0.492 0.840 0.008 0.266
0.561 P 2257043 21.1 22.5 27.0 17.7 P P 0.786 0.098 0.660 0.117
0.544 0.174 P 2231993 22.7 23.1 25.9 20.1 P P 0.190 0.287 0.319
0.852 0.677 0.817 P 1521 25.5 24.8 27.5 22.3 P P 0.011 0.142 0.111
0.785 0.657 0.611 P 220 24.1 24.8 27.5 20.0 P P 0.338 0.027 0.360
0.097 0.614 0.606 P 893 23.8 24.8 28.5 20.8 P P 0.490 0.177 0.826
0.348 0.913 0.543 P 2138 23.6 25.5 30.3 23.0 P P 0.705 0.595 0.389
0.770 0.462 0.667 P 2076 22.3 24.3 25.8 22.6 P P 0.660 0.250 0.400
0.364 0.261 0.159 P 1142 23.1 26.8 28.7 24.6 P P 0.016 0.036 0.794
0.632 0.355 0.312 P 1539 22.8 24.3 30.3 20.8 P P 0.923 0.552 0.211
0.572 0.165 0.152 P 349 21.9 23.6 26.2 20.1 P P 0.887 0.652 0.672
0.560 0.599 0.921 P 1669 26.0 25.8 28.1 23.6 P P 0.042 0.367 0.116
0.610 0.501 0.410 P 2740095 21.3 24.7 28.6 22.1 P P 0.050 0.119
0.269 0.805 0.833 0.974 P 2855706 21.2 22.8 26.8 19.9 P P 0.962
0.982 0.793 0.954 0.796 0.810 P 2259 20.6 22.1 27.0 16.6 P P 0.908
0.032 0.502 0.027 0.436 0.064 P 2593636 22.0 22.6 28.3 17.2 P P
0.230 0.006 0.531 0.042 0.204 0.032 P 2937810 22.8 24.3 30.0 20.8 P
P 0.862 0.540 0.270 0.597 0.204 0.189 P 2792222 24.2 25.4 29.2 23.2
P P 0.656 0.846 0.931 0.596 0.896 0.850 P 1853 22.2 23.3 26.3 19.6
P P 0.582 0.312 0.639 0.496 0.818 0.847 P 2802981 22.6 25.2 28.1
19.1 P P 0.225 0.092 0.797 0.008 0.743 0.223 P 2060742 21.4 22.3
25.5 18.8 P P 0.406 0.277 0.584 0.577 0.863 0.865 P 820 20.8 21.2
25.5 17.8 P P 0.213 0.181 0.866 0.599 0.660 0.518 P 922 21.7 22.1
28.0 18.7 P P 0.180 0.158 0.556 0.592 0.194 0.179 P 2319 25.9 25.8
34.3 21.2 P P 0.059 0.008 0.088 0.143 0.006 0.002 P 1532 23.9 25.9
30.4 24.1 P P 0.659 0.300 0.474 0.432 0.583 0.995 P 632 24.4 25.4
31.0 23.7 P P 0.483 0.734 0.438 0.384 0.238 0.640 P 2226 24.1 25.1
29.6 22.4 P P 0.507 0.721 0.830 0.920 0.575 0.692 P 2332187 21.5
23.7 26.8 18.3 P P 0.497 0.134 0.915 0.037 0.822 0.328 P 2423724
23.7 26.1 29.6 20.9 P P 0.391 0.222 0.692 0.055 0.998 0.287 P 2269
25.5 26.5 29.1 23.1 P P 0.574 0.381 0.455 0.598 0.602 0.899 P 893_1
24.8 26.3 28.5 21.0 P P 0.878 0.045 0.455 0.043 0.468 0.589 P 1016
24.8 24.9 26.8 21.4 P P 0.113 0.106 0.107 0.565 0.347 0.662 P
2914265 24.4 25.8 28.7 21.1 P P 0.912 0.130 0.687 0.125 0.705 0.585
P 2235173 23.6 24.5 31.8 19.8 P P 0.454 0.055 0.098 0.133 0.032
0.010 P 615 24.3 25.3 30.3 23.8 P P 0.525 0.592 0.629 0.302 0.404
0.920 P 2555715 24.5 26.5 30.7 21.5 P P 0.664 0.173 0.557 0.077
0.679 0.189 P 3032 25.0 26.3 31.2 21.1 P P 0.725 0.036 0.561 0.048
0.424 0.082 P 2533160 23.9 26.6 31.9 24.4 P P 0.203 0.181 0.138
0.614 0.338 0.623 P 2108553 25.0 25.6 30.4 21.1 P P 0.280 0.039
0.889 0.157 0.491 0.181 P 2855460 24.3 25.6 26.6 21.5 P P 0.718
0.223 0.149 0.299 0.171 0.598 P 2505890 25.3 26.9 30.1 21.5 P P
0.945 0.046 0.851 0.037 0.867 0.308 P 438_1 24.9 24.5 28.7 21.4 P P
0.028 0.089 0.522 0.820 0.685 0.651 P 1670 24.1 24.6 29.1 21.7 P P
0.196 0.344 0.939 0.941 0.574 0.622 P 2895821 26.2 26.8 30.2 23.4 P
P 0.271 0.241 0.563 0.651 0.942 0.847 P 382 24.8 24.3 27.6 22.3 P P
0.020 0.322 0.222 0.533 0.878 0.639 P 923 23.4 24.2 26.0 19.6 P P
0.406 0.051 0.188 0.138 0.318 0.986 P 739 20.6 21.3 25.1 17.2 P P
0.319 0.109 0.775 0.324 0.850 0.484 P 1023 25.4 25.7 29.0 21.7 P P
0.127 0.058 0.431 0.358 0.932 0.659 P 2197135 26.2 26.7 33.0 22.2 P
P 0.234 0.033 0.365 0.160 0.117 0.041 P 2442 23.8 24.7 29.8 20.3 P
P 0.421 0.075 0.645 0.187 0.370 0.147 P 3245 23.0 25.3 29.1 21.8 P
P 0.486 0.967 0.607 0.584 0.838 0.640 P 2214618 22.5 23.5 27.9 20.1
P P 0.502 0.375 0.865 0.645 0.605 0.502 P 2923104 25.6 28.3 31.1
24.1 P P 0.216 0.829 0.836 0.260 0.693 0.759 P 2577185 25.3 25.9
26.8 23.8 P P 0.229 0.839 0.058 0.513 0.152 0.130 P 2691741 25.6
25.9 29.1 23.6 P P 0.147 0.583 0.394 0.642 0.848 0.681 P 2067 26.1
27.6 29.9 23.7 P P 0.940 0.360 0.497 0.357 0.490 0.964 P 2866838
24.6 27.4 29.7 24.1 P P 0.193 0.592 0.993 0.711 0.510 0.746 P 651
24.2 25.8 25.2 19.1 P P 0.979 0.003 0.033 0.002 0.023 0.935 P 784
22.8 24.4 27.8 21.8 P P 0.991 0.854 0.966 0.851 0.959 0.884 P 2538
22.5 22.5 24.8 20.5 P P 0.072 0.530 0.135 0.526 0.479 0.357 P 243
22.6 22.8 25.3 18.1 P P 0.134 0.014 0.210 0.126 0.551 0.713 P
2024940 23.7 26.4 30.3 25.1 P P 0.195 0.043 0.428 0.222 0.839 0.608
P 2473497 25.3 26.5 29.8 21.3 P P 0.657 0.037 0.776 0.058 0.934
0.325 P 2727912 24.6 25.6 27.0 21.1 P P 0.569 0.093 0.161 0.168
0.222 0.828 P 687 24.3 25.5 31.0 20.4 P P 0.662 0.039 0.400 0.059
0.262 0.051 P 160 24.3 24.8 29.0 21.2 P P 0.245 0.167 0.858 0.527
0.698 0.507 P 670 27.3 27.5 29.9 24.9 P P 0.109 0.394 0.200 0.802
0.565 0.547 P 2629101 26.5 26.9 30.3 24.4 P P 0.186 0.526 0.513
0.778 0.967 0.850 P 2532 23.1 24.3 28.2 17.7 P P 0.706 0.001 0.999
0.002 0.852 0.060 P 2071896 24.0 25.9 29.8 20.8 P P 0.713 0.132
0.725 0.061 0.846 0.233 P 2788747 25.9 27.6 29.8 22.6 P P 0.903
0.108 0.550 0.072 0.482 0.665 P 1611 22.8 24.3 27.6 17.9 P P 0.919
0.005 0.904 0.004 0.937 0.121 P 1248 21.8 23.1 28.7 18.9 P P 0.738
0.215 0.346 0.280 0.239 0.122 P 790 23.6 23.7 27.3 19.4 P P 0.091
0.021 0.454 0.216 0.969 0.474 P 1079 24.3 25.2 30.3 24.0 P P 0.483
0.438 0.624 0.182 0.381 0.972 P 3063 24.7 25.9 29.9 20.2 P P 0.651
0.013 0.947 0.020 0.767 0.127 P 3117 25.0 25.3 29.7 20.8 P P 0.143
0.024 0.865 0.176 0.583 0.234 P 852 25.5 26.2 29.4 22.6 P P 0.351
0.223 0.527 0.532 0.831 0.862 P 2295 23.8 23.8 25.1 20.4 P P 0.072
0.096 0.046 0.637 0.214 0.460 P 2912 25.1 26.1 31.0 20.9 P P 0.488
0.022 0.672 0.053 0.423 0.086 P 3137 24.9 25.8 29.1 22.5 P P 0.398
0.359 0.621 0.715 0.914 0.908 P 1206 22.0 22.5 25.0 20.1 P P 0.200
0.581 0.271 0.734 0.589 0.533 P 2974887 23.0 24.6 29.5 20.8 P P
0.972 0.464 0.446 0.450 0.404 0.275 P 2526 23.1 25.3 30.7 21.6 P P
0.495 0.809 0.190 0.452 0.286 0.202 P 3044 22.0 22.8 27.0 20.1 P P
0.336 0.586 0.960 0.907 0.669 0.780 P
2907847 22.8 23.6 26.6 19.1 P P 0.337 0.060 0.476 0.191 0.775 0.620
P 3210 23.3 25.0 26.2 22.1 P P 0.913 0.994 0.235 0.943 0.184 0.303
P 3199 25.4 25.1 30.0 21.4 P P 0.033 0.039 0.801 0.508 0.429 0.316
P 3318 26.5 27.4 32.4 22.5 P P 0.431 0.037 0.659 0.098 0.386 0.106
P 3111 24.5 26.1 29.1 21.3 P P 0.990 0.140 0.803 0.113 0.784 0.511
P 3210_1 26.4 26.7 29.5 23.2 P P 0.160 0.129 0.310 0.547 0.698
0.983 P 3088 24.0 24.8 26.9 20.0 P P 0.398 0.037 0.246 0.107 0.410
0.818 P 2912876 27.2 28.4 32.7 26.0 P P 0.635 0.995 0.831 0.726
0.642 0.857 P 2825793 25.6 25.4 30.2 22.1 P P 0.044 0.083 0.769
0.695 0.491 0.440 P 2761 20.7 21.2 25.9 17.4 P P 0.233 0.127 0.936
0.444 0.496 0.331 P 2440 22.1 22.4 26.1 20.0 P P 0.128 0.473 0.542
0.737 0.917 0.919 P
[0584] Table 8 shows raw data results for 2 samples (of 546) that
failed both the Normal controls and Relative controls method.
TABLE-US-00008 TABLE 8 Relative Controls Normal Controls CEACAM4/
CEACAM4/ Sample CEACAM4 LAMP1 PLA2G7 PLAC8 Status LAMP1 pVal PLAC8
pVal 2712753 21.30 22.23 36.06 18.67 F 0.460 0.286 2023537 25.29
25.22 37.02 21.26 F 0.062 0.032 Relative Controls CEACAM4/ LAMP1/
LAMP1/ PLAC8/ Sample PLA2G7 pVal PLAC8 pVal PLA2G7 pVal PLA2G7 pVal
Status 2712753 0.000 0.547 0.000 0.000 F 2023537 0.001 0.351 0.000
0.000 F
[0585] Table 9 shows raw data results for 26 samples (of 546) that
failed both the Normal controls but passed the Relative controls
method.
TABLE-US-00009 TABLE 9 Normal Controls Relative Controls External
CEACAM4/ CEACAM4/ Sample CEACAM4 LAMP1 PLA2G7 PLAC8 Control Status
LAMP1 pVal PLAC8 pVal 6759 22.50 23.66 26.51 22.02 F F 0.629 0.566
6769 23.89 24.05 27.32 21.34 F F 0.107 0.317 6789 22.52 23.26 26.83
21.76 F F 0.338 0.717 6812 21.97 23.71 26.12 22.12 F F 0.864 0.290
6829 22.04 23.95 26.75 22.49 F F 0.721 0.197 6834 21.29 23.97 29.11
20.88 F F 0.221 0.526 6836 21.20 23.52 27.88 20.38 F F 0.412 0.753
6843 21.42 22.58 24.07 20.46 F F 0.632 0.833 6852 21.90 24.03 26.33
22.64 F F 0.546 0.132 6853 23.87 24.49 25.97 21.25 F F 0.273 0.289
6870 24.10 25.35 28.17 22.11 F F 0.702 0.564 IXP-109 22.61 24.05
27.79 20.50 F F 0.866 0.503 ixp-136 23.04 24.41 25.56 19.59 F F
0.808 0.090 IXP-138 23.47 24.12 28.75 20.79 F F 0.291 0.272 IXP-139
22.12 23.35 26.09 20.97 F F 0.688 0.947 IXP-140 21.48 23.36 27.55
22.04 F F 0.744 0.168 IXP-141 23.95 25.12 27.55 20.63 F F 0.637
0.111 IXP-143 23.24 24.40 26.09 22.16 F F 0.635 0.903 IXP-144 22.52
24.89 27.06 22.01 F F 0.378 0.579 IXP-146 22.05 24.19 27.20 21.35 F
F 0.531 0.682 IXP-147 24.99 25.72 26.24 21.69 F F 0.332 0.115
IXP-148 21.46 23.18 25.75 21.80 F F 0.887 0.228 IXP-149 23.67 24.19
25.50 22.14 F F 0.226 0.821 6869 31.60 35.99 37.22 33.46 P F 0.002
0.018 IXP_128 31.87 33.39 38.03 28.11 P F 0.940 0.053 1357 27.27
27.05 35.06 22.12 P F 0.042 0.003 Relative Controls CEACAM4/ LAMP1/
LAMP1/ PLAC8/ Sample PLA2G7 pVal PLAC8 pVal PLA2G7 pVal PLA2G7 pVal
Status 6759 0.570 0.337 0.712 0.407 P 6769 0.385 0.921 0.897 0.873
P 6789 0.681 0.280 0.971 0.569 P 6812 0.622 0.315 0.539 0.293 P
6829 0.842 0.264 0.695 0.350 P 6834 0.153 0.834 0.356 0.391 P 6836
0.405 0.796 0.627 0.594 P 6843 0.200 0.568 0.256 0.218 P 6852 0.726
0.242 0.498 0.233 P 6853 0.117 0.736 0.256 0.467 P 6870 0.593 0.735
0.702 0.904 P IXP-109 0.961 0.554 0.891 0.661 P ixp-136 0.178 0.103
0.185 0.871 P IXP-138 0.920 0.683 0.526 0.461 P IXP-139 0.555 0.718
0.665 0.583 P IXP-140 0.607 0.218 0.697 0.711 P IXP-141 0.434 0.174
0.546 0.789 P IXP-143 0.240 0.635 0.306 0.277 P IXP-144 0.774 0.963
0.455 0.564 P IXP-146 0.974 0.986 0.781 0.830 P IXP-147 0.044 0.329
0.094 0.420 P IXP-148 0.674 0.237 0.602 0.282 P IXP-149 0.087 0.526
0.218 0.179 P 6869 0.784 0.809 0.201 0.245 P IXP_128 0.578 0.045
0.526 0.105 P 1357 0.157 0.085 0.011 0.003 P
[0586] Table 10 shows raw data results for 13 samples (of 546) that
passed the Normal controls but failed the Relative controls
method.
TABLE-US-00010 TABLE 10 Normal Controls Relative Controls External
CEACAM4/ CEACAM4/ Sample CEACAM4 LAMP1 PLA2G7 PLAC8 Control Status
LAMP1 pVal PLAC8 pVal 2400065 21.11 27.01 29.58 23.37 P P 0.000
0.008 2491930 21.48 26.42 28.04 23.01 P P 0.000 0.035 2283614 22.05
26.95 30.13 24.69 P P 0.000 0.003 2541845 20.28 25.17 27.53 22.41 P
P 0.000 0.010 3787 26.15 30.73 31.07 21.69 P P 0.001 0.013 2781056
22.21 27.49 29.08 24.38 P P 0.000 0.009 2636948 19.62 24.71 27.18
21.20 P P 0.000 0.031 2580739 27.27 26.55 29.78 20.66 P P 0.009
0.000 1329 26.14 24.03 30.86 22.36 P P 0.000 0.052 2423113 23.90
26.72 32.41 19.80 P P 0.164 0.029 2791 25.57 23.59 31.40 19.13 P P
0.000 0.000 1914 24.65 26.29 29.91 17.72 P P 0.958 0.000 2452 23.48
22.98 26.30 17.56 P P 0.018 0.000 Relative Controls CEACAM4/ LAMP1/
LAMP1/ PLAC8/ Sample PLA2G7 pVal PLAC8 pVal PLA2G7 pVal PLA2G7 pVal
Status 2400065 0.077 0.508 0.600 0.956 F 2491930 0.443 0.628 0.287
0.557 F 2283614 0.118 0.651 0.852 0.688 F 2541845 0.259 0.956 0.521
0.585 F 3787 0.929 0.000 0.076 0.166 F 2781056 0.352 0.818 0.283
0.461 F 2636948 0.196 0.574 0.563 0.877 F 2580739 0.177 0.012 0.880
0.206 F 1329 0.847 0.346 0.061 0.326 F 2423113 0.073 0.001 0.219
0.004 F 2791 0.701 0.180 0.016 0.007 F 1914 0.933 0.000 0.948 0.008
F 2452 0.234 0.035 0.920 0.276 F
[0587] Throughout this specification and claims which follow,
unless the context requires otherwise, the word "comprise", and
variations such as "comprises" or "comprising", will be understood
to imply the inclusion of a stated integer or group of integers or
steps but not the exclusion of any other integer or group of
integers.
[0588] Persons skilled in the art will appreciate that numerous
variations and modifications will become apparent. All such
variations and modifications which become apparent to persons
skilled in the art, should be considered to fall within the spirit
and scope that the invention broadly appearing before
described.
* * * * *