U.S. patent application number 15/918489 was filed with the patent office on 2018-09-20 for systems and methods for developing diagnostic tests based on biomarker information from legacy clinical sample sets.
The applicant listed for this patent is True Health IP LLC. Invention is credited to Michael P. McKenna, Michael S. Urdea.
Application Number | 20180267053 15/918489 |
Document ID | / |
Family ID | 38694451 |
Filed Date | 2018-09-20 |
United States Patent
Application |
20180267053 |
Kind Code |
A1 |
Urdea; Michael S. ; et
al. |
September 20, 2018 |
Systems and Methods for Developing Diagnostic Tests Based on
Biomarker Information from Legacy Clinical Sample Sets
Abstract
Disclosed are systems and methods for developing diagnostic
tests (e.g., detection, screening, monitoring, and prognostic
tests) based on biomarker information from legacy clinical sample
sets, for which only small sample volumes (e.g., about 0.05 to
about 1.0 mL or less per sample) are typically available. For
example, biomarkers (e.g., about 10, 50, 100, 150, 200, 300, or
more) may be detected in the clinical samples through the use of
single molecule detection and each biomarker may be detected in an
assay that includes about 1 or less of a legacy clinical
sample.
Inventors: |
Urdea; Michael S.; (Alamo,
CA) ; McKenna; Michael P.; (Branford, CT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
True Health IP LLC |
Frisco |
TX |
US |
|
|
Family ID: |
38694451 |
Appl. No.: |
15/918489 |
Filed: |
March 12, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15007786 |
Jan 27, 2016 |
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15918489 |
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13746216 |
Jan 21, 2013 |
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15007786 |
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13561913 |
Jul 30, 2012 |
8357497 |
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13746216 |
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12300019 |
Jun 29, 2009 |
8232065 |
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PCT/US07/11196 |
May 8, 2007 |
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13561913 |
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60798867 |
May 8, 2006 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 20/00 20190201;
G01N 33/5302 20130101; G01N 2800/60 20130101; G01N 33/6842
20130101; G01N 2800/56 20130101; G01N 2570/00 20130101; C12Q 1/6883
20130101 |
International
Class: |
G01N 33/68 20060101
G01N033/68; G06F 19/18 20060101 G06F019/18; C12Q 1/6883 20060101
C12Q001/6883; G01N 33/53 20060101 G01N033/53 |
Claims
1. A single detection means of evaluating the health state of a
human subject, comprising obtaining a measurement of a least one
clinical biomarker from at least one live clinical sample isolated
from said human subject, and inputting said measurement(s) into a
model that calculates an output value correlated to said health
state, the improvement comprising using as said model an algorithm
that was developed by measurement of multiple development
biomarkers comprising said clinical biomarker(s) from at least one
legacy clinical sample set annotated for said health state, said
measurement comprising use of legacy clinical samples having a
sample volume of 1 ml or less, and analyzing said measurement of
multiple biomarkers for an association with said health state.
2. The means of claim 1, wherein said measurement of multiple
development biomarkers comprises measuring at least two biomarkers
from said sample volume.
3. The means of claim 2, wherein said measurement of multiple
development biomarkers comprises measuring at least 10 biomarkers
from a sample volume less than about 0.5 milliliters.
4. The means of claim 2, wherein said measurement of multiple
development biomarkers comprises measuring at least 20 biomarkers
from said sample volume.
5. The means of claim 2, wherein said measurement of multiple
development biomarkers comprises measuring at least 100 biomarkers
from said sample volume.
6. The means of claim 2, wherein said measurement of multiple
development biomarkers comprises measuring at least 200 biomarkers
from said sample volume.
7. The means of claim 2, wherein said measurement of multiple
development biomarkers comprises measuring at least 300 biomarkers
from said sample volume.
8. The means of claim 1, wherein said measurement of multiple
development biomarkers comprises, for each biomarker, measuring
said biomarker in an assay of said legacy clinical sample, wherein
said assay used about 1 microliter (.mu.L) or less of said sample
volume for each biomarker.
9. The means of claim 8, wherein at least 10 development biomarkers
are measured per legacy clinical sample.
10. The means of claim 8, wherein said measurement of multiple
development biomarkers uses single molecule detection to measure
said multiple development biomarkers in said legacy clinical
sample.
11. The means of claim 10, wherein said measurement of biomarkers
in the legacy clinical sample by single molecule detection consists
of dynamic quantitation.
12. The means of claim 1, wherein said health state is the presence
or absence of a disease.
13. The means of claim 1, wherein said health state is the
pre-disease or pre-disease condition.
14. The means of claim 1, wherein said health state is the risk of
developing a disease.
15. The mans of claim 12, wherein said absence of a disease is
further defined to be a normal state or pre-disease state.
16. The means of claim 1, wherein said biomarkers comprise
traditional laboratory risk factors.
17. The means of claim 1, wherein said live clinical sample
isolated from said human subject is whole blood, serum, plasma,
blood cells, endothelial, cells, tissue biopsies, lymphatic fluid,
ascites fluid, interstitial fluid, bone marrow, cerebrospinal
fluid, saliva, sputum, sweat, or urine.
18. The means of claim 17, wherein said live clinical sample is
plasma or serum.
19. The means of claim 17, wherein said sample is from a human
subject undergoing one or more treatment regimens.
20. The means of claim 19, wherein said treatment regimens are
selected from a group consisting of therapeutics, prophylactics,
exercise regimens, dietary supplementation, weight loss, surgical
intervention, device implantation and exercise regimens.
Description
PRIORITY CLAIM
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/007,786, filed on Jan. 27, 2016, which is a
continuation of U.S. patent application Ser. No. 13/746,216, filed
on Jan. 21, 2013, which is a divisional of U.S. patent application
Ser. No. 13/561,913, filed Jul. 30, 2012 (now U.S. Pat. No.
8,357,497), which is a divisional of U.S. patent application Ser.
No. 12/300,019, filed Jun. 29, 2009 (now U.S. Pat. No. 8,232,065),
which is an U.S. 371 National Stage application of PCT Application
No. PCT/US2007/011196, filed May 8, 2007, which claims priority to
U.S. Provisional Patent Application No. 60/798,867, filed May 8,
2006, the entire contents of each which are incorporated herein by
reference and relied upon.
FIELD OF THE INVENTION
[0002] Embodiments of the present invention relate to systems and
methods for developing diagnostic tests and, more specifically, to
systems and methods for developing diagnostic tests based on
biomarker information from legacy clinical sample sets, for which
only small sample sizes (e.g., about 0.05 to 1.0 mL per sample) are
typically available. In a preferred embodiment, the biomarker
information is detected in the clinical samples through the use of
single molecule detection.
BACKGROUND OF THE INVENTION
[0003] Diagnostic tests have been provided for detecting,
screening, monitoring, and/or predicting the future development of
various health states (e.g., disease states) in a subject.
Typically, the detecting, screening, monitoring, or prognosis is
provided by a diagnostic test based, at least in part, on the
level(s) of one or more biological markers ("biomarkers") in a
clinical sample taken from the subject (e.g., the subject's blood),
or the presence thereof. Such biomarkers are selected because the
presence, absences, or levels of such biomarkers alone or in
combination are indicative of the presence, stage, or future
clinical course of the health state. Often times, but not
necessarily, the diagnostic test may additionally be based on
clinical information concerning the subject. Determining an
appropriate diagnosis or prognosis for a subject can, for example,
advantageously increase the subject's chances for survival and/or
recovery.
[0004] Diagnostic tests must undergo a development stage during
which the tests are formulated (and optionally tested/validated)
using previously collected samples stored for future research and
development needs. This process is prior to their use in diagnosing
or predicting the development of disease in subjects in real time.
The information used to formulate and validate the tests typically
comes from clinical samples for a cohort of subjects for whom at
least some biochemical and clinical data is known regarding the
presence or absence of the health state under consideration. Thus,
traditionally a party who is desirous of developing a diagnostic
test for a given health state is required to commit significant
resources to the collection of clinical samples (and optionally
clinical information such as medical history) from subjects who
have, and/or lack, the health state, often at various stages. This
data collection process can take many years, depending on the type
of disease being considered and the party's relative access to
suitable subjects.
[0005] Traditional approaches for developing diagnostic tests also
require the clinical samples that are collected to have
sufficiently large volumes, and such large samples cannot always be
readily obtained. Specifically, traditional biomolecular detection
approaches require large sample volumes in order to allow for the
selection of a set of biomarkers that will be useful in the
determination of a patient's health state. Of all the biomarkers
that are evaluated (e.g., 1-3, 150-300 biomarkers, or 1000 or
more), only those biomarkers that are determined to aid in the
determination of the health state in a patient are included in the
final diagnostic test. For example, according to one approach,
single-biomarker multiple ELISAs used to measure the presence or
level of 300 biomarkers typically require a serum or plasma sample
size of about 30 mL of specimen per individual (i.e., 100 uL per
assay times 300 biomarkers). The required sample volume becomes 90
mL of specimen per individual if the assays are done in triplicate.
This is a very large volume and is very impractical. In addition,
few studies have ever been conducted where so much clinical sample
was collected. Multiplexing, which involves measuring multiple
biomarkers in the same reaction vessel, can reduce the overall
required sample volume by way of conservation but requires
compatibility between all the assay components and typically
compromises sensitivity through increased background effects. As a
result, on an assay by assay basis, individual assays are typically
10 or more fold more sensitive than their counterpart within a
multiplexed assay.
[0006] In view of the foregoing, it would be desirable to provide
systems and methods for developing diagnostic tests in which access
to suitable clinical samples is improved and which rely on smaller
sample volumes.
SUMMARY OF THE INVENTION
[0007] The above and other objects and advantages of the present
invention are provided in accordance with the principles of the
present invention described herein. Embodiments of the present
invention relate to systems and methods for developing diagnostic
tests based on biomarker information from legacy clinical sample
sets, for which only small sample volumes (e.g., about 0.05 to 1.0
mL per individual) are typically available. As used herein, a
"legacy clinical sample set" is one or more clinical samples (e.g.,
10 to 5000 samples or more) collected in the past (i.e.,
retrospective sample collections). The use of legacy clinical
samples, as opposed to performing the process of collecting
clinical samples prospectively, reduces the resources and time that
must be committed to developing new diagnostic tests. Legacy
clinical samples may be from, for example, one or more past studies
that occurred over a span of 1 to 40 years or more, which studies
may be accompanied by tens to thousands of clinical parameters,
traditional laboratory measurements that are considered risk
factors or that provide additive information to enable a better
clinical decision to be made, and other previously measured
information (e.g., clinical data such as the subject's age, weight,
ethnicity, medical history, and/or other information). In most
cases, the legacy clinical samples are serum or plasma samples that
have been stored for years at -80 degrees Centigrade or -20 degrees
Centigrade. In other examples, a legacy clinical sample can
include, for example, blood cells, ascites fluid, interstitial
fluid, bone marrow, sputum, urine, or other biological sample.
Examples of such past studies, which are included for the purpose
of illustration and not limitation, are listed below: [0008] 1. DPP
(Diabetes Prevention Program)--An NIH sponsored trail that studied
the impact of lifestyle modifications, metformin vs. placebo. This
study had 2.8 years follow-up with diabetes outcomes. [0009] 2.
IRAS (Insulin Resistance Atherosclerosis Study)--Studied the impact
of insulin resistance on the development of cardiovascular disease.
[0010] 3. ARIC (Atherosclerosis Risk in Communities Study)--This
study includes CVD and cardiovascular outcomes. [0011] 4. Finnish
Diabetes Prevention Study--studied the impact of lifestyle changes
on the development of diabetes. [0012] 5. Israeli Diabetes Research
Group (MELANY)--Studied the development of diabetes in healthy
normal subjects from the Israeli military [0013] 6. HDDRISC (Heart
Disease and Diabetes Risk Indicators in a Screened
Cohort)--collection of diabetes and cardiovascular outcomes. [0014]
7. WSCOPS (West of Scotland Coronary Prevention Study)--studied the
impact of pravastatin on reduction of LDL and reduction in
myocardial events [0015] 8. ASCOT (Anglo-Scandinavian Cardiac
Outcomes Trial)--studied the impact of different medicines for
lowering blood pressure and cholesterol. CVD outcomes collected.
[0016] 9. SOF (Study of Osteoporotic Fractures)--Study looks for
predictors of fracture in women over 65 years of age [0017] 10.
NORA (National Osteoporosis Risk Assessment)--Studied fracture
outcomes in women with varying BMD levels. [0018] 11. Framingham
Heart Study--Related to identifying the common factors or
characteristics that contribute to CVD by following its development
over a long period of time in a large group of participants who had
not yet developed overt symptoms of CVD or suffered a heart attack
or stroke. [0019] 12. CARDIA--(Coronary Artery Risk Development in
Young Adults) A longitudinal study designed to trace the
development of risk factors for coronary heart disease in a cohort
of 18-30 year olds (1985) in four U.S. cities. [0020] 13. Reykjavik
Study--A long-term prospective population-based cardiovascular
study of 33-79 year olds with 4 to 20 year follow-up (1967-91), in
Iceland. [0021] 14. Malmo Preventive Project--A prospective,
population-based study of the effects of interventions on mortality
and cardiovascular morbidity in 32-51 year olds (1974-1992) in
Sweden [0022] 15. Heart Protection Study--A very large,
prospective, double-blind, randomized, controlled trial
investigating prolonged use (>5 years) of a statin and an
antioxidant vitamin cocktail in individuals 40 to 80 years old in
the United Kingdom who had an elevated risk for CHD. [0023] 16. 4S
(Scandinavian Simvastatin Survival Study)--Large double-blind,
randomized trial designed to evaluate the effect of a statin on
mortality and morbidity in patients with coronary heart disease
(CHD). [0024] 17. DREAM (Diabetes Reduction Assessment with
ramipril and rosiglitazone Medication) Study--A large,
double-blind, randomized, placebo-controlled trial evaluating the
effects of an ACE inhibitor and/or a thiazolidinedione on the
development of diabetes, death, or regression to normoglycaemia in
adults aged 30 years or more with impaired fasting glucose and/or
impaired glucose tolerance, and no previous cardiovascular disease.
[0025] 18. Physician's Health Study--a large cohort of apparently
healthy male U.S. physicians aged 40 to 84 years in 1982, followed
prospectively for an average of 60.2 months [0026] 19. WHI (Women's
Health Initiative)--A very large, prospective study, involving both
clinical trial and observational components, of women 50 to 79
years of age in the U.S., and is designed to examine the
relationship between health, lifestyle, and risk factors for a
variety of specific diseases, including CHD [0027] 20. WHS (Women's
Health Study) A very large, double-blind, randomized,
placebo-controlled trial to evaluate the effects of vitamin E and
low-dose aspirin on cardiovascular disease and cancer in apparently
healthy U.S. women, age 45 and older, which also included an
observational extension [0028] 21. NHS (Nurses' Health Study)--A
very large, prospective cohort study of nurses aged 30-55 (in 1976)
designed to assess the long term effects of oral contraceptive use
[0029] 22. NHS II (Nurses' Health Study II)--A very large,
prospective cohort study of nurses aged 25-42 (in 1989) designed to
assess the long term effects of oral contraceptives, diet and
lifestyle risks.
[0030] In an embodiment of the present invention, methods and
systems are provided for developing a diagnostic test for
determining a health state in a patient (e.g., a test for a
predicting or diagnosing disease such as diabetes, osteoporosis,
pre-osteoporosis, or any other disease), in which at least one
biomarker is detected in at least one legacy clinical sample. For
example, the biomarker may be detected in an immunoassay that
includes about 1 uL or less of the legacy clinical sample. The
detection may be performed by, for example, a single molecule
detector. Typically, although not necessarily, developing a new
diagnostic test comprises detecting multiple biomarkers from
multiple clinical samples, including samples from subjects known
have a given health state, or with respect to reference ranges from
a known normal population. The detected biomarker(s) are then
analyzed for an association with the health state. For example, a
statistical analysis may be performed to determine whether the
biomarker statistically correlates with the presence or absence of
the health state, or alternatively correlates with the existing
gold standard (whether biomarker, clinical parameter, or otherwise)
used for defining the presence of the health state (for example,
fasting glucose level for diabetes, blood pressure for hypertension
as a health state, or coronary imaging scores or percentage
occlusions/stenosis for coronary artery disease). Alternatively or
additionally, the analysis may involve determining whether the
inclusion of the biomarker in a formula or machine learning
analysis increases an ability of a mathematical function resulting
from the machine learning analysis to determine the health state in
a patient.
[0031] In another embodiment, clinical parameters (e.g., age,
weight, ethnicity, medical history, and/or other clinical
information) that accompany the legacy clinical sample(s) may also
be analyzed for an association with the health state.
[0032] In yet another embodiment, methods and systems are provided
for developing a diagnostic test for determining a health state in
a patient, in which a plurality of biomarkers (e.g., 10-300
biomarkers) are detected in a legacy clinical sample through the
use of a corresponding plurality of immunoassays, where the total
amount of the legacy clinical sample that is used across the
plurality of immunoassays is less than about 1 mL (e.g., less than
about 0.05 mL). Typically, multiple legacy clinical samples are
analyzed in the same fashion, and the detected biomarkers are then
analyzed for an association with the disease.
[0033] In another embodiment, a diagnostic test is used to screen
or monitor a patient for a given health state. The test is
developed using any of the methods disclosed herein for screening
legacy clinical samples. For example, at least one biomarker
indicative of the presence, absence, or likelihood of developing
the health state and identified by the methods described herein is
employed in the test and its presence, absence, or level is
determined.
[0034] Other features and advantages of the invention will be
apparent from the following detailed description and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0035] For a better understanding of the present invention, and not
intending to limit the scope of the invention in any way, reference
is made to the following description, taken in conjunction with the
accompanying drawings, in which like reference characters refer to
like parts throughout, and in which:
[0036] FIGS. 1 and 2 are illustrative diagrams of a single molecule
detector in accordance with an embodiment of the present
invention;
[0037] FIG. 3 is a flowchart of illustrative stages involved in
developing a diagnostic test in accordance with an embodiment of
the present invention;
[0038] FIG. 4 shows a typical result for a working standard curve
used in the development of immunoassays in accordance with an
embodiment of the present invention;
[0039] FIG. 5 shows illustrative single molecule detection data in
accordance with an embodiment of the present invention;
[0040] FIG. 6 shows a table indicating the actual number of analyte
molecules present in a sample across the ranges of various sample
sizes and starting analyte molar concentrations; and
[0041] FIG. 7 shows, without intending any limitation, the
detection limit of selected biomarker assay technologies that are
commercially available, indicating their typical analytical
reproducibility performance characteristic (coefficient of
variation) at these starting analyte concentrations. There are many
additional technologies being applied to improve the sensitivity of
single molecule detection, including microscopic techniques (atomic
force microscopy, magnetic resonance force microscopy, scanning
electrochemical microscopy, scanning tunneling microscopy) and
spectroscopic techniques (fluorescence correlation spectroscopy,
evanescent wave induced fluorescence spectroscopy, scanning
near-field optical microscopy, scanning enhanced raman
spectroscopy, surface plasma resonance).
DETAILED DESCRIPTION OF THE INVENTION
[0042] Embodiments of the present invention relate to systems and
methods for developing diagnostic tests for diagnosing, and
predicting the future development of, various health states (e.g.,
health states including disease-specific states as well as other
non-disease specific states)in a subject. Examples of diseases are
osteoporosis, pre-osteoporosis, diabetes, cancer, and any other
disease. In one embodiment of the present invention, systems and
methods are provided for developing diagnostic tests based on
biomarker information from legacy clinical sample sets, for which
only small sample sizes (e.g., about 0.05 to 1.0 mL or less) are
typically available. In a preferred embodiment, the biomarker
information is extracted from the clinical samples through the use
of single molecule detection.
Definitions
[0043] "Biomarker" in the context of the present invention
encompasses, without limitation, proteins, nucleic acids, and
metabolites, together with their polymorphisms, isoforms,
mutations, derivatives, variants, modifications, and precursors,
including nucleic acids and pro-proteins, cleavage products,
receptors (including soluble and transmembrane receptors),
subunits, fragments, ligands, protein-ligand complexes, mulitmeric
complexes, and degradation products, elements, related metabolites,
and other analytes or sample-derived measures. Biomarkers can also
include mutated proteins or mutated nucleic acids. Biomarkers also
include any calculated indices created mathematically or
combinations of any one or more of the foregoing measurements,
including temporal trends and differences. The term "analyte" as
used herein can mean any substance to be measured and can encompass
electrolytes and elements, such as calcium.
[0044] "Clinical parameters" encompasses all non-sample or
non-analyte markers of subject health status or other
characteristics, such as, without limitation, age (AGE), ethnicity
(RACE), gender (SEX), diastolic blood pressure (DBP) and systolic
blood pressure (SBP), family history (FHX), height (HT), weight
(WT), waist (Waist) and hip (Hip) circumference, body-mass index
(BMI), past Gestational Diabetes Mellitus (GDM), resting heart
rate, EMG, EEG, body temperature, and sleep states.
[0045] A "formula," "algorithm," or "model" is any mathematical
equation, algorithmic, analytical or programmed process, or
statistical technique that takes one or more continuous or
categorical inputs (herein called "parameters") and calculates an
output value, sometimes referred to as an "index" or "index value".
Non-limiting examples of "formulas" include sums, ratios, and
regression operators, such as coefficients or exponents, biomarker
value transformations and normalizations (including, without
limitation, those normalization schemes based on clinical
parameters, such as gender, age, or ethnicity), rules and
guidelines, statistical classification models, and neural networks
trained on historical populations. Of particular use in combining
markers are linear and non-linear equations and statistical
classification analyses to determine the relationship between
levels of the biomarkers detected in a subject sample and the
subject's risk of disease (for example). In panel and combination
construction, of particular interest are structural and synactic
statistical classification algorithms, and methods of risk index
construction, utilizing pattern recognition features, including
established techniques such as cross correlation, Principal
Components Analysis (PCA), factor rotation, Logistic Regression
(LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear
Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random
Forest (RF), Recursive Partitioning Tree (RPART), as well as other
related decision tree classification techniques, Shruken Centroids
(SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees,
Neural Networks, Bayesion Networks, Support Vector Machines, and
Hidden Markov Models, among others. Many of these techniques are
useful either combined with a biomarker selection technique, such
as forward selection, backwards selection, or stepwise selection,
complete enumeration of all potential panels of a given size,
genetic algorithms, or they may themselves include biomarker
selection methodologies in their own technique. These may be
coupled with information criteria, such as Akaike's Information
Criterion (AIC) or Bayes Information Criterion (BIC), in order to
quantify the tradeoff between additional biomarkers and model
improvement, and to aid in minimizing overfit. The resulting
predictive models may be validated in other studies, or
cross-validated in the study they were originally trained in, using
such techniques as Leave-One-Out (LOO) and 10-Fold cross-validation
(10-Fold-CV).
[0046] "Frank Disease" in the context of the present invention, is
a clearly manifest, unmistakable, evident, or symptomatic disease
state that unequivocally meets the definition of the disease set
forth by a professional medical organization, such as the World
Health Organization.
[0047] "Health state" encompasses disease states (e.g., presence,
absence, or risk of developing a disease and likely responses to
therapies for the disease) as well as other states not necessarily
related to a specific disease such as environmental exposure,
nutritional status, neurological function, immune status, organ
function, and blood chemistry. Generally, determining a health
state in a patient/subject involves determining that the patient
should be classified within a given one of a plurality of
populations (e.g., healthy vs. unhealthy, in a 2-population
example).
[0048] A "legacy subject" is a subject (defined below) for which
one or more clinical samples is included in a legacy clinical
sample set.
[0049] A "live subject" is a subject for whom a determination
(e.g., diagnosis or prognosis of disease) is made by a diagnostic
test that has been developed in accordance with the principles of
the present invention.
[0050] A "legacy clinical sample" is a clinical sample for an
individual from a legacy clinical sample set (which set may have
multiple samples for multiple individuals), where the volume of the
sample meets a sample requirement (defined below) and the biomarker
information from the sample may be used to develop a diagnostic
test in accordance with the principles of the present
invention.
[0051] A "live clinical sample" is a clinical sample from which
biomarker information is evaluated by a diagnostic test in order to
provide a determination (e.g., diagnosis or prognosis) for a
corresponding live subject.
[0052] "Measuring" or "measurement" means assessing the presence,
absence, quantity or amount (which can be an effective amount) of
either a given substance within a clinical or subject-derived
sample, including the derivation of qualitative or quantitative
concentration levels of such substances, or otherwise evaluating
the values or categorization of a subject's clinical parameters.
Alternatively, the term "detecting" or "detection" may be used and
is understood to cover all measuring or measurement as described
herein.
[0053] "Risk" in the context of the present invention, relates to
the probability that an event will occur over a specific time
period (e.g., conversion to frank Diabetes) and can can mean a
subject's "absolute" risk or "relative" risk. Absolute risk can be
measured with reference to either actual observation
post-measurement for the relevant time cohort, or with reference to
index values developed from statistically valid historical cohorts
that have been followed for the relevant time period. Relative risk
refers to the ratio of absolute risks of a subject compared either
to the absolute risks of low risk cohorts or an average population
risk, which can vary by how clinical risk factors are assessed.
Odds ratios, the proportion of positive events to negative events
for a given test result, are also commonly used (odds are according
to the formula p/(1-p) where p is the probability of event and
(1-p) is the probability of no event) to no-conversion. Alternative
continuous measures which may be assessed in the context of the
present invention include time to health state (e.g., disease)
conversion and therapeutic conversion risk reduction ratios.
[0054] "Pre-Disease" in the context of the present invention refers
to a state that is intermediate between that defined as the normal
homeostatic and metabolic state and states seen in Frank Disease.
Pre-disease states can include abnormalities of homeostatic
regulation, abnormal physiological measurements, abnormal
morphometric measurements, and/or states in which abnormal levels
of clinical parameters or biomarkers are present at a specific time
point. Abnormalities are measurement outside the normal range as
defined by professional medical organizations, such as the World
Health Organization. "Pre-Disease" states, in the context of the
present invention, are states, in an individual or in a population,
having a higher than normal expected rate of disease conversion to
frank disease. When a continuous measure of Pre-Disease conversion
risk is produced, having a "pre-disease condition" encompasses any
expected annual rate of conversion above that seen in a normal
reference or general unselected normal prevalence population.
[0055] "Risk evaluation," or "evaluation of risk" in the context of
the present invention encompasses making a prediction of the
probability, odds, or likelihood that an event or health state may
occur, the rate of occurrence of the event or conversion from one
health state to another (e.g., from a normoglycemic condition to a
pre-diabetic condition or pre-Diabetes, or from a pre-diabetic
condition to pre-Diabetes or Diabetes). Risk evaluation can also
comprise prediction of future levels, scores or other indices of
disease, either in absolute or relative terms in reference to a
previously measured population. The methods of the present
invention may be used to make continuous or categorical
measurements of the risk of conversion between health states.
Embodiments of the invention can also be used to discriminate
between normal and pre-diseased subject cohorts. In other
embodiments, the present invention may be used so as to
discriminate pre-diseased from diseased, or diseased from normal.
Such differing use may require different biomarker combinations in
individual panel, mathematical algorithm(s), and/or cut-off points,
but be subject to the same aforementioned measurements of accuracy
for the intended use.
[0056] A "sample" in the context of the present invention is a
biological sample isolated from a subject and can include, by way
of example and not limitation, whole blood, serum, plasma, blood
cells, endothelial cells, tissue biopsies, lymphatic fluid, ascites
fluid, interstitial fluid (also known as "extracellular fluid" and
encompasses the fluid found in spaces between cells, including,
inter alia, gingival crevicular fluid), bone marrow, cerebrospinal
fluid (CSF), saliva, mucous, sputum, sweat, urine, or any other
secretion, excretion, or other bodily fluids.
[0057] A "sample requirement" is the volume of starting sample
required by a given assay technology in order to achieve an
acceptable level of performance (coefficient of variation).
[0058] A "subject" in the context of the present invention is
preferably a mammal. The mammal can be a human, non-human primate,
mouse, rat, dog, cat, horse, or cow, but are not limited to these
examples. Mammals other than humans can be advantageously used as
subjects that represent animal models of disease, pre-disease, or a
pre-disease condition. A subject can be male or female. A subject
can be one who has been previously diagnosed or identified as
having a health state (e.g., disease, pre-disease, or a pre-disease
condition), and optionally has already undergone, or is undergoing,
a therapeutic intervention for the health state. Alternatively, a
subject can also be one who has not been previously diagnosed as
having a given health state. For example, a subject can be one who
exhibits one or more risk factors for a disease, pre-disease, or a
pre-disease condition, or a subject who does not exhibit disease
risk factors, or a subject who is asymptomatic for a disease,
pre-disease, or pre-disease conditions. A subject can also be one
who is suffering from or at risk of developing disease,
pre-disease, or a pre-disease condition.
[0059] "Traditional laboratory risk factors" correspond to
biomarkers isolated or derived from subject samples and which are
currently evaluated in the clinical laboratory and used in
traditional global risk assessment algorithms (e.g., Stern,
Framingham, Finland Diabetes Risk Score, ARIC Diabetes, and
Archimedes). Traditional laboratory risk factors commonly tested
from subject blood samples include, but are not limited to, total
cholesterol (CHOL), LDL (LDL/LDLC), HDL (HDL/HDLC), VLDL (VLDLC),
triglycerides (TRIG), glucose (including, without limitation, the
fasting plasma glucose (Glucose) and the oral glucose tolerance
test (OGTT)) and HBA1c (HBA1C) levels.
INDICATIONS OF THE INVENTION
[0060] Embodiments of the present invention allow for the
determining of a health state in a patient. For example, the risk
of developing disease, pre-disease, or a pre-disease condition
typically can be detected with a pre-determined level of
predictability by measuring an "effective amount" of a biomarker in
a test sample (e.g., a subject derived sample), and comparing the
effective amounts to reference or index values, often utilizing
mathematical algorithms or formulas in order to combine information
from results of multiple individual biomarkers and from non-analyte
clinical parameters into a single measurement or index. When
appropriate, subjects identified as having an increased risk for a
health state can optionally be selected to receive treatment
regimens, such as administration of prophylactic or therapeutic
compounds, or implementation of exercise regimens or dietary
supplements to prevent or delay the onset of, for example, disease,
pre-disease, or a pre-disease condition or other adverse health
conditions.
[0061] The amount of the biomarker can be measured in a test sample
and compared to a normal control level, utilizing techniques such
as reference limits, discrimination limits, or risk defining
thresholds to define cutoff points and abnormal values for a health
state. The normal control level means the level of one or more
biomarkers or combined biomarker indices typically found in a
subject not having the health state. Such normal control level and
cutoff points may vary based on whether a biomarker is used alone
or in a formula combining with other biomarkers into an index.
Alternatively, the normal control level can be a database of
biomarker patterns from previously tested subjects who did not
convert to the health state over a clinically relevant time
horizon.
[0062] The present invention may be used to make continuous or
categorical measurements of the risk of conversion to an adverse
health state (e.g., disease), thus diagnosing and defining the risk
spectrum of a category of subjects defined as predisposed to the
adverse health state. In the categorical scenario, the methods of
the present invention can be used to discriminate between (for
example) normal and pre-diseased subject cohorts. In other
embodiments, the present invention may be used so as to
discriminate pre-disease from disease, or diseased from normal.
Other non-disease specific health states can also be determined.
Such differing use may require different biomarker combinations in
individual panel, mathematical algorithm, and/or cut-off points,
but be subject to the same aforementioned measurements of accuracy
for the intended use.
[0063] Identifying patients that are predisposed to adverse health
states (e.g., pre-disease states) enables the selection and
initiation of various therapeutic interventions or treatment
regimens in order to delay, reduce or prevent those patients'
conversion to the adverse health states (e.g., disease). Levels of
a specific amount of biomarker also may allow for the course of
treatment of the health state (e.g., disease, pre-disease, or a
pre-disease condition) to be monitored. For example, in this
method, a biological sample can be provided from a subject
undergoing treatment regimens, e.g., drug treatments, for a
disease. Such treatment regimens can include, but are not limited
to, exercise regimens, dietary supplementation, weight loss,
surgical intervention, device implantation, and treatment with
therapeutics or prophylactics used in subjects diagnosed or
identified with various health states. If desired, biological
samples are obtained from the subject at various time points
before, during, or after treatment.
[0064] The present invention can also be used to screen patient or
subject populations in any number of settings. For example, a
health maintenance organization, public health entity or school
health program can screen a group of subjects to identify those
requiring interventions, as described above, or for the collection
of epidemiological data. Insurance companies (e.g., health, life or
disability) may screen applicants in the process of determining
coverage or pricing, or existing clients for possible intervention.
Data collected in such population screens, particularly when tied
to any clinical progression to conditions like disease,
pre-disease, or a pre-disease condition, will be of value in the
operations of, for example, health maintenance organizations,
public health programs and insurance companies. Such data arrays or
collections can be stored in machine-readable media and used in any
number of health-related data management systems to provide
improved healthcare services, cost effective healthcare, improved
insurance operation, etc. See, for example, U.S. Patent Application
No.; U.S. Patent Application No. 2002/0038227; U.S. Patent
Application No. US 2004/0122296; U.S. Patent Application No. US
2004/0122297; and U.S. Pat. No. 5,018,067, which are hereby
incorporated by reference herein in their entireties. Such systems
can access the data directly from internal data storage or remotely
from one or more data storage sites. Thus, in a health-related data
management system, wherein risk of developing a diabetic condition
for a subject or a population comprises analyzing disease risk
factors, the present invention provides an improvement comprising
use of a data array encompassing the biomarker measurements as
defined herein and/or the resulting evaluation of risk from those
biomarker measurements.
[0065] A machine-readable storage medium can comprise a data
storage material encoded with machine readable data or data arrays
which, when using a machine programmed with instructions for using
said data, is capable of use for a variety of purposes, such as,
without limitation, subject information relating to health state
risk factors over time or in response to drug therapies, drug
discovery, and the like. Measurements of effective amounts of the
biomarkers of the invention and/or the resulting evaluation of risk
from those biomarkers can be implemented in computer programs
executing on programmable computers, comprising, inter alia, a
processor, a data storage system (including volatile and
non-volatile memory and/or storage elements), at least one input
device, and at least one output device. Program code can be applied
to input data to perform the functions described above and generate
output information. The output information can be applied to one or
more output devices, according to methods known in the art. The
computer may be, for example, a personal computer, microcomputer,
or workstation of conventional design.
[0066] Each program can be implemented in a high level procedural
or object oriented programming language to communicate with a
computer system. However, the programs can be implemented in
assembly or machine language, if desired. The language can be a
compiled or interpreted language. Each such computer program can be
stored on a storage media or device (e.g., ROM or magnetic diskette
or others as defined elsewhere in this disclosure) readable by a
general or special purpose programmable computer, for configuring
and operating the computer when the storage media or device is read
by the computer to perform the procedures described herein. The
health-related data management system of the invention may also be
considered to be implemented as a computer-readable storage medium,
configured with a computer program, where the storage medium so
configured causes a computer to operate in a specific and
predefined manner to perform various functions described herein.
Levels of a specific amount of one or more biomarkers can then be
determined and compared to a reference value, e.g. a control
subject or population whose state is known or an index value or
baseline value. The reference sample or index value or baseline
value may be taken or derived from one or more subjects who have
been exposed to the treatment, or may be taken or derived from one
or more subjects who are at low risk of developing a health state
(e.g., disease, pre-disease, or a pre-disease condition), or may be
taken or derived from subjects who have shown improvements in risk
factors (such as clinical parameters or traditional laboratory risk
factors as defined herein) as a result of exposure to treatment.
Alternatively, the reference sample or index value or baseline
value may be taken or derived from one or more subjects who have
not been exposed to the treatment. For example, samples may be
collected from subjects who have received initial treatment for
disease, pre-disease, or a pre-disease condition and subsequent
treatment for disease, pre-disease, or a pre-disease condition to
monitor the progress of the treatment. A reference value can also
comprise a value derived from risk prediction algorithms or
computed indices from population studies such as those disclosed
herein.
[0067] The biomarkers of the present invention can thus be used to
generate a reference biomarker profile of those subjects who do not
have a health state (e.g., impaired glucose tolerance in the case
of Diabetes), and would not be expected to develop the health
state. The biomarkers disclosed herein can also be used to generate
a subject biomarker profile taken from subjects who have a health
state such as disease, pre-disease, or a pre-disease condition. The
subject biomarker profiles can be compared to a reference biomarker
profile to diagnose or identify subjects at risk for developing the
health state, to monitor the progression of the health state (e.g.,
disease), as well as the rate of progression of the health state,
and to monitor the effectiveness of any treatments for the health
state. The reference and subject biomarker profiles of the present
invention can be contained in a machine-readable medium, such as
but not limited to, digital and analog media like those readable by
a VCR, CD-ROM, DVD-ROM, USB flash media, among others. Such
machine-readable media can also contain additional test results,
such as, without limitation, measurements of clinical parameters
and traditional laboratory risk factors. Alternatively or
additionally, the machine-readable media can also comprise subject
information such as medical history and any relevant family
history. The machine-readable media can also contain information
relating to other disease-risk algorithms and computed indices such
as those described herein.
[0068] A diagnostic test that is developed in accordance with the
principles of the present invention can be used to make a
determination for a live subject (e.g., a diagnosis or prognosis)
based, at least in part, on the presence or level(s) of one or more
biomarkers present in a live clinical sample from the live subject.
The levels are determined, as is understood to those of ordinary
skill in the art, within the sensitivity and specificity parameters
of the test format selected (e.g., a biomarker is "absent" if its
level is below the test's limit of detection or some other cut-off
value). For example, one such diagnostic test may involve comparing
the subject's biomarker level(s) to a reference value. As another
example, the diagnostic test may involve evaluating the live
subject's biomarker level(s) (and optionally other information for
the subject such as, for example, age, weight, ethnicity, medical
history, and/or other clinical information) with a formula or model
that produces a diagnostic or prognostic score for the live
subject.
[0069] A diagnostic test for a given health state may be developed,
at least in part, through the use of a legacy clinical sample set.
The legacy clinical sample set may include samples for a cohort of
legacy subjects, for whom at least some data is known regarding the
presence or absence of the health state. For example, a diagnostic
test may be developed based on samples for legacy subjects who are
known to have a given disease. Alternatively or additionally, the
diagnostic test may be developed based on clinical samples for
legacy subjects who are known to lack the disease or other health
state.
[0070] Theoretically, an almost limitless number of biomarkers are
available for selection within the process of developing a
diagnostic test. However, only a subset of all available biomarkers
(e.g., between 10 and 300) are typically selected per disease area,
which subset of biomarkers may be identified by physicians and/or
other sources of information (e.g., medical journals) with
expertise in the disease area. Biomarkers may also be derived from
de novo research using "open" proteomics profiling technologies
such as mass spectrometry, LC-LC mass spectrometry, 2-D gel
electrophoresis, protein arrays, western blots, reverse western
tissue blots, etc.
[0071] In an embodiment of the present invention, systems and
methods are provided for developing a diagnostic test, according to
which (i) a set of one or more legacy clinical samples is received
(e.g., 50 to 5000 legacy samples), (ii) the levels of a selected
subset of biomarkers are measured from the sample(s), and (iii) the
biomarker levels (and optionally clinical parameters) are analyzed
for an association with the health state under consideration. This
analyzing may involve, for example, using statistical analysis to
determine whether a particular one or more biomarkers (and
optionally particular level(s) of those biomarkers and/or clinical
parameters) is correlated statistically with the presence, absence,
or risk of developing the health state (e.g., progression to
disease states of different severities), and/or to select one or
more therapies or to monitor therapy response/efficacy. In some
embodiments, a biomarker panel can be constructed and a formula
derived specifically to enhance performance for use also in
subjects undergoing therapeutic interventions, or a separate panel
and formula may alternatively be used solely in such patient
populations. An aspect of the invention is the use of specific
known characteristics of biomarkers and their changes in such
subjects for such panel construction and formula derivation. Such
modifications may enhance the performance of various indications
noted above in prevention of adverse health states, and diagnosis,
therapy, monitoring, and prognosis of a health state. The
biomarkers may vary under therapeutic intervention for the health
state, whether lifestyle (e.g. diet and exercise), surgical (e.g.,
bariatric surgery) or pharmaceutical (e.g., one of the various
classes of drugs mentioned herein or known to modify common risk
factors or risk of disease) intervention. The biomarkers may also
vary based on environmental exposure, nutritional status,
neurological function, immune status, organ function, and/or blood
chemistry. Alternatively or additionally, the analyzing of the
biomarker may involve determining whether the inclusion of
particular biomarker(s) in a formula or machine learning analysis
(e.g., support vector or neural network analysis) increases the
relative ability of a mathematical function resulting from the
analysis to diagnose or predict the health state in a subject.
Generally, machine learning is a form of artificial intelligence
whereby information learned from a computer-assisted analysis of
data can be used to generate a function that describes dependencies
in data. This computer-assisted, machine learning analysis may be
performed by any suitable software, hardware, or combination
thereof (a "machine learning tool"). Suitable examples of machine
learning tools will be apparent to those of ordinary skill in the
art and therefore will not be described in detail.
[0072] A key feature of embodiments of the invention is the ability
to profile tens, hundreds or even thousands of biomarkers in a
single small legacy sample. It will be apparent that the invention
thus allows the profiling of several classes of biomarkers, and the
testing of multiple members of each class, in order to gain insight
into the biological mechanisms of a health state and the
interaction of such biomarkers. In the preferred embodiment, this
encompasses two or more biomarker members per class, more
preferably five or more, and most preferably ten or more. As will
be appreciated by one skilled in the art, such classes include,
without limitation, cytokines and chemokines, such as
chemoattractants and inflammatory molecules such as acute phase
reactants, signaling molecules, adhesion molecules, biomarkers of
immunity (including subclasses, such as those related to individual
immune cell lines such as macrophages, T-cells, neutrophils,
eiosinophils, etc), biomarkers of angiogenesis and endothelial
function, and biomarkers of glucose and lipid metabolism and energy
storage. Several of these classes overlap, in particular with
respect to the cytokine, chemokine, and growth factor members of
each. Selected representative examples of such classes and their
members are given in the table below, without limiting the
foregoing in any way.
TABLE-US-00001 Examples of Classes of Molecules Examples of Genes
and Molecules in the Class Acute Phase Reactants SAA1, CRP, IL1,
IL6, IL8, TNFA, FTL, A2M. MBL, SAP Angiogenesis & VEGF, CD36,
ANG1, ANG2/ANGPT2, ENG, FGF2, Endothelial Function PDGF Cell
Adhesion ICAM, DPP4/CD26, CD38, SELE, SELP, CD62L, VCAM, ITGA1,
ITGA2, ITGA4, ITGAL, ITGAX, ITGB1, ITGB2, ITGB3 Cell Proliferation
& Death AKT1, CASP2, CASP8, CASP9, IGF, TNF/TNFA, TNFR1,
TNFSF10, TNFSF11, CDK2, FAS, FASLG Chemokines CCL1, MCP-1/CCL2,
CCL3, CCL4, CCL5, CCL6, MCP- 3/CCL7, MCP-2/CCL8, CCL9, CCL11,
CCL12, MCP- 4/CCL13, CCL19, CCL21, CCL24, CCL26, CCL27, CXCL1,
CXCL2, IP10/CXCL10, IL8, CX3CL1 Cytokines IL1B, IL1RN, IL2, IL3,
IL4, IL5, IL6, IL8, IL10, IL12, IL12B, IL13, IL18, BTC, TGFA, TGFB,
TNF, CSF1, CSF2, CSF3, IFNG Coagulation C2, C3, C4, C5, C9, C1, F2,
F12, PROC, PROS1, SERPING1, FGA, VWF, D-dimer Growth Factors &
EGF, GH1, NGFB, ADIPOQ, IGF, CSF1, CSF2, CSF3, Hormones PDGF, EPO,
FGF2, GDF8, GDF9, GH1, IGF1, TGFB1, TPO, EFG, HGF, FGF, IGF, BMP1,
BMP2, BMP3, BMP7 Inflammation CSF1, CSF2, CSF3, IFNG, CD40LG, CD40,
C3, C5A, TNF, IL1, IL8, SELP, Lipid Metabolism Lipoprotein(a), LEP,
ADIPOQ, AGRP, NPY Energy Homeostasis INS, glucose, HBA1c,
C-peptide, IGF-1, AKT2 Proteolysis MMP2, MMP9, SERPINA1, heparin,
SERPIND1, PAI- 1/SERPINE1, TIMP1, TIMP2, CASP3
[0073] Another key aspect of the invention is, in a preferred
embodiment, utilizing a single molecule detector, with the ability
to range multiple orders of concentration magnitude by using the
stochastic and quantum nature of single molecule detection. In
particular, biomarkers within the plasma proteome, including many
of those cited above, are known to span many orders of magnitude in
their molar concentration, as seen in the literature. Without
limitation of the foregoing, a review of such concentrations cited
from literature for cardiovascular and cancer related plasma
proteins is described in Anderson, "Candidate-Based Proteomics in
the Search for Biomarkers of Cardiovascular Disease", J Physiol
563.1 pp 23-60 (2005), and Anderson, "A List of Candidate Cancer
Biomarkers for Targeted Proteomics", Biomarker Insights 2: 1-48
(2006), which are hereby incorporated by reference herein in their
entireties. As shown in the table below and in FIG. 6, this range
of concentrations rapidly approaches single molecule requirements,
particularly when combined with the smaller volume samples commonly
available in legacy clinical sample sets.
TABLE-US-00002 Concentration of 50 kDa molecule pg/m1 L amol/m1 L
Molecules/mL 50 mg/mL 50,000,000,000 1,000,000,000 6.02 .times.
10.sup.17 10 mg/mL 10,000,000,000 200,000,000 1.02 .times.
10.sup.17 1 mg/mL 1,000,000,000 20,000,000 1.02 .times. 10.sup.16
100 ug/mL 100,000,000 2,000,000 1.02 .times. 10.sup.14 10 ug/mL
10,000,000 200,000 1.02 .times. 10.sup.14 1 ug/mL 1,000,000 20,000
1.02 .times. 10.sup.13 100 ng/mL 100,000 2,000 1.02 .times.
10.sup.12 10 ng/mL 10,000 200 1.02 .times. 10.sup.11 1 ng/mL 1,000
20 1.02 .times. 10.sup.10 100 pg/mL 100 2 1.02 .times. 10.sup.9 10
pg/mL 10 0.2 1.02 .times. 10.sup.8 1 pg/mL 1 0.02 1.02 .times.
10.sup.7
[0074] Concentration ranges of common biomarkers within the plasma
proteome, indicating the disagreement of biomarker discovery
technology such as mass spec across sample sets in the literature
are also shown in Anderson et al., "The Human Plasma Proteome:
History, Character, and Diagnostic Prospects", Molecular &
Cellular Proteomics 1.11, pp. 845-867 (2002) and Anderson et al.,
"The Human Plasma Proteome: A Nonredundant List Developed By
Combination of Four Separate Sources", Molecular & Cellular
Proteomics 3.4, pp. 311-326 (2004), which are hereby incorporated
by reference herein in their entireties. Such disagreement further
demonstrates the different detection system needs inherent when
encountering broad concentration ranges, which may occur both
across many analytes and across many differing health states. FIG.
5 demonstrates the practice of the invention across multiple orders
of magnitude in concentration, and across representative biomarkers
of each of the aforementioned classes.
PRACTICE OF THE INVENTION
[0075] In a preferred embodiment, the biomarker levels are measured
from the clinical sample(s) through the use of a single molecule
detector. Suitable single molecule detection equipment is described
in U.S. Patent Application Publication Nos. 2004/0166514 A1,
2005/0164205 A1, and 2006/0003333 A1, the disclosures of which are
hereby incorporated by reference herein in their entireties. Other
examples of single molecule detectors that can be used in
accordance with preferred embodiments of the present invention are
described in U.S. Patent Application Publication No. 2005/0221408,
PCT Publication No. WO 2005/089524, and Richard Brown et al.,
"Review of Techniques for Single Molecule Detection in Biological
Applications, National Physical Laboratory Report, 2001, the
disclosures of which are hereby incorporated by reference herein in
their entireties. Generally, a single molecule detector operates
under the principle that the ultimate, and desired, detection of
biomarker information occurs at the level of individual molecules,
interactions between molecules, and molecular complexes. Such
individual molecules, molecular interactions, and/or molecular
complexes can be detected by flow cytometry, single molecule
electrophoresis, ion-channel switch membrane biosensor, or other
single-molecule analytical instrumentation. Single molecule
information can be cumulated over multiple molecular events,
providing dynamic quantification of biomarker levels within a
clinical sample, allowing the sparing use of very small samples.
Data acquisition of such events may be halted when a sufficient
number of events are received within a given sample volume to
reliably quantitate (e.g. reliably here meaning with a coefficient
of variation of 20% or less) a given biomarker's concentration
using a presumed Poisson or binomial probability distribution
function, as known by one skilled in the art. Such dynamic
quantitation of very small sample volumes is a key aspect of the
invention as practiced using single molecule detectors.
[0076] Accordingly, embodiments of the present invention
contemplate the specific application of single molecule detection
to the development of diagnostic tests based on legacy clinical
sample sets. Namely, it has been determined by the present
inventors that single molecule detection can detect the presence of
biomarker or levels thereof with a suitable sensitivity using only
about 1 uL or less of sample per single-biomarker immunoassay (for
example). Any suitable analyte recognition unit (e.g., antibodies,
aptamers, molecular imprints, probes, primers etc. which have
differentially greater affinity for a biomarker of interest) and
signal detection technique can be used with a single molecule
detection reader in accordance with the present invention.
Additionally, it will be understood that the present invention is
not limited to the use of immunoassays. Thus, for example, to
develop a diagnostic test based on an initial subset of (for
example) 300 biomarkers, the use of single molecule detection
allows requires a sample size of only about 0.3 mL (i.e., 1 uL per
assay*300 biomarkers), or about 0.9 mL if the assays are done in
triplicate. The assay may use a 96-well, 384-well format or any
other suitable assay configuration. Any multiplexing within the
assay will only further reduce the required sample size. The
present inventors have applied this knowledge to the discovery that
diagnostic tests can be developed based on legacy clinical samples
which, as described, are typically available in sizes of 0.05 to
1.0 mL or less. Additional details regarding an illustrative single
molecule detection system are provided below.
[0077] In some embodiments, the single molecule detection system
can rely on single-molecule fluorescence. Thus, in such
embodiments, no polymerases, enzymes or proteins, or any
amplification processes are necessary so sample preparation times
and complexity are minimal. In other embodiments, the single
molecule detection may utilize labeled antibodies. Such labels for
individual antibody (or other suitable biomarker recognition units)
may themselves be constructed of a plurality of individual
fluorescent molecules, further amplifying the signal derived from
each single complex multi-fold, and further reducing the detection
technique requirements for single molecule detection (such
multiplexing of fluorophores may be achieved using beads,
dedrimers, polysaccharides and other natural and synthetic
polymers, amongst other techniques well described in the art). In
one embodiment, the basic detection apparatus may comprise one or
two lasers (or a single laser source split into two beams),
focusing light-collection optics, one or two single photon
detectors, and detection electronics under computer control. FIGS.
1 and 2 are illustrative diagrams of a single molecule detector in
accordance with an illustrative, but non-limiting, embodiment of
the present invention. A sample compartment is also included and
may comprise two reservoirs that hold the solution being analyzed.
The reservoirs can be connected by tubing to a glass capillary
cell.
[0078] The system also may include a glass capillary flow cell. For
example, two laser beams (5 um in diameter) are optically focused
about 100 um apart and perpendicular to the length of the
sample-filled capillary tube. The lasers generally are operated at
particular wavelengths depending upon the nature of the detection
probe to be excited. An interrogation volume of the detection
system may be determined by the diameter of the laser beam and by
the segment of the laser beam selected by the optics that direct
light to the detectors. The interrogation volume is preferably set
such that, with an appropriate sample concentration, single
molecules (such as single biomarker-recognition unit hybrids,
single nucleic acid probes or single probe-target hybrids) are
present in the interrogation volume during each time interval over
which observations are made. Another embodiment of an apparatus for
use in accordance with the present invention uses the same
capillary flow cell and detection system, but only uses a single
laser beam and detector.
[0079] With the above-described instrument configuration (5 um
laser beam) approximately 0.25% of the fluorescent molecules in the
solution pass through the laser beams and are typically detectable.
This percentage can be increased by configuring each laser beam
such that it forms a narrow band perpendicular to the length of the
capillary. Such an arrangement can raise the percentage of
detectable molecules to approximately 5% of the molecules in the
solution. Other configurations illuminating larger areas of the
capillary have been calculated to enable detection of up to (for
example) 50% of the fluorescent molecules present in a sample. The
device has the capability of detecting single molecules in real
time, allowing the detection of a fixed number of counts
independent of time, and enabling dynamic quantification and
concentration range finding during the course of the initial
detection period. This feature allows faster readouts of samples as
setting a count threshold (for example, at 1000 molecular events or
such other effective level, giving a statistically valid
quantitation of a biomarker within a sample) is often much faster
than a fixed time point (1 minute). For higher biomarker
concentrations, preparatory sample dilution may nonetheless be
required in order to avoid reaching the count threshold too rapidly
in such single molecule detector configurations.
[0080] FIG. 3 is a flowchart of illustrative, exemplary stages
involved in developing a diagnostic test in accordance with some of
the embodiments of the present invention, including: identification
of biomarker candidates, sourcing of reagents, assay development,
procurement of clinical samples, interrogation of clinical samples
with biomarker assays, and analysis of the data to identify
predictive markers and incorporate the results into predictive
tests. These illustrative stages are described in greater detail
below.
[0081] Identify biomarkers: Biomarkers may be identified by way of
a comprehensive search through scientific and patent literature,
supplemented with expert review. Based on an understanding of
biological mechanisms associated with progression in a given
disease area, standard search terms are developed to generate
disease-specific databases containing typically thousands of
journal articles and hundreds of patents. Cannonical pathways,
homology, and linkage studies are alternative means of identifying
putative biomarkers for a given disease state, as are cell line and
animal experiments utilizing mRNA expression under response to
stimuli, active agents (drugs, siRNAs, etc.), or in
disease-specific organisms (knock-outs, nude mice, ApoE deficient
mice, etc.) as are well known to those versed in the art of
biomarker discovery. Analytical techniques on larger sample
volumes, or pooled sample volumes, may also be used as in Granger,
et al. Discovery of Proteins Related to Coronary Artery Disease
Using Industrial-Scale Proteomics Analysis of Pooled Plasma,
American Heart Journal v152 (3) September 2006, which is hereby
incorporated by reference herein in its entirety. Each article and
patent is read to identify candidates which are organized in a
spreadsheet. For each biomarker, standardized nomenclature derived
from human genome databases is applied to eliminate redundancy and
enter standardized annotations.
[0082] A score for evidence level is assigned to prioritize the
potential value of each biomarker based on experimental data. The
evidence level may be combined with protein cellular expression
localization to create an overall prioritized list of biomarkers
for each disease. At the end of this process, the list of
candidates is typically 150-400 biomarkers, but may be more or
less. Illustrative lists of biomarkers for use in developing
diagnostic tests for diabetes and osteoporosis are described in
U.S. Provisional Patent Application Nos. 60/725,462, filed Oct. 11,
2005, 60/771,077, filed Feb. 6, 2006, Ser. No. 11/546,874, filed
Oct. 11, 2006, Ser. No. 11/703,400, filed Feb. 6, 2007, and U.S.
application Ser. No. 11/788,260, filed Apr. 18, 2007, titled
"Diabetes-Associated Markers and Methods of Use Thereof" and
bearing attorney docket no. 24748-502 CIP, which are all hereby
incorporated by reference herein in their entireties.
[0083] Source Reagents: Table 1 below shows a large and diverse
array of vendors that may be used to source immunoreagents as a
starting point for assay development. Using the prioritized list of
markers, a search for capture antibodies, detection antibodies, and
analytes may be performed that can be used to configure a working
sandwich immunoassay.
[0084] For example, in one disease area, diabetes, 156 of 208
biomarkers were successfully sourced. Depending on the specific
disease area, it is anticipated that anywhere from 50 to 80% of the
biomarkers on any list are available from commercial sources. The
reagents are ordered and received into inventory.
TABLE-US-00003 TABLE 1 Immunoreagent Vendors Company Abazyme AbCam
AbGent AbKem Abnova Absea Biotechnology Academy Biomed Accurate
Chemical and Scientific Corporation Acris Advanced Immunochemical,
Inc. Advanced Targeting Systems Affibody Affiniti Research Products
Limited Affinity Biologicals Affinity Bioreagents Alexis
Biochemicals Alomone Labs Alpha Diagnostic Intl. AlphaGenix
American Diagnostica Inc. American Qualex American Research
Products American Type Culture Collection Anaspec ANAWA Trading SA
Ancell AngioBio Angio-Proteomie Aniara Anogen Antibodies
Incorporated AntibodyBcn AntibodyShop Apotech APTEC Diagnostics
Araclon Biotech Assay Designs Athens Research and Technology
Austral Biologicals Aves Labs Aviva Antibody Axxora Babraham
Technix Bachem Beckman Coulter, Inc. Bender Medsystems Bethyl
Laboratories Bio Research Canada BioCore BioCytex Biodesign
International Biogenesis BioGenex BioLegend Biomarket Biomeda
Corporation Biomedical Technologies BIOMOL International
BioProcessing Biosense Laboratories BioSepra Biosonda BioSource
International BiosPacific Biostride Biotrend Biovendor Laboratory
Medicine Biovet BMA Biomedical Boston Biochem Brendan Scientific
Calbiochem Caltag Cambio CanAg Diagnostics Capralogics Capricorn
Products Cayman Chemical Company Cedarlane Laboratories Cell Marque
Cell Sciences Cell Signaling Technology Cemines Chang Bioscience
Chemicon International Chemokine Clonegene Clontech Cortex Biochem
Covance Research Products Cytolab Cytopulse Cytoshop CytoStore DAKO
Deltabiolabs Development Studies Hybridoma Bank Diaclone Diagnostic
BioSystems Diagnostic Systems Laboratory Diasorin Diatec Dolfin
Dutch Diagnostics East Coast Biologics eBioscience Echelon Research
Laboratorie ECM Biosciences EnCor Biotechnology Endocrine
Technologies Enzo Biochem Epitomics Euroclone Euro-Diagnostica
Eurogentec Everest Biotech Exalpha EXBIO Praha EY Laboratories
FabGennix Int. Fitzgerald Industries International Fortron Bio
Science Fusion Antibodies FutureImmune Immunologic Technical and
Consulting Services Gallus Immunotech G-Biosciences GEMAC Genesis
Biotech G-Biosciences Genex Genhot Laboratories Genway Biotech
GloboZymes Good Biotech Green Mountain Antibodies Groovy Blue Genes
Biotech Haematologic Technologies Hampton Research Histoline
Laboratoires HyCult Biotechnology HyTest IBL IBT IDS Imgenex IMMCO
Diagnostics Immunodetect Immunodiagnostik ImmunoGlobe
Antikoerpertechnik ImmunoKontact ImmunologicalsDirect Immunology
Consultants Laboratory Immunometrics Immuno-Precise Services
Immunostar Immunostep ImmunoTools Immunovision Immuquest Biogenex
Innova Biosciences Innovation Automation Innovex Insight
Biotechnology International Enzymes Invitek Invitrogen IQ Products
Isconova ISL (Immune Systems Ltd) Jackson ImmunoResearch Laboratory
KCH Scientific Kirkegaard & Perry Laboratorie KMI Diagnostics
Koma Biotech Kordia Laboratory Supplies Lab Vision Corporation
LabFrontier Life Science Institute LAE Biotechnology Company
Lampire Biological Laborator Lee Laboratories Leinco Technologies
Lifescreen Linco Research Maine Biotechnology Services MBL
International Mediclone Medix Biochemica MedSystems Diagnostics
GmbH MicroPharm Ltd. MilleGen MitoSciences MoBiTec ModiQuest
Molecular Innovations Molecular Probes MP Biomedicals Mubio
Products NatuTec Neoclone Neuromics New England Biolabs Nordic
Immunological Laboratories Norrin Laboratories Novocastra Novus
Biologicals OEM Concepts, Inc. Oncogene Research Products Open
Biosystems Orbigen Oxford Biotechnology Pacific Immunology Pall
Corporation Panvera PBL Biomedical Laboratories Peprotech, Inc.
PerkinElmer Life Sciences Perseus Proteomics Pharmingen Phoenix
Pharmaceuticals PickCell Laboratories Pierce Chemical Company
PlasmaLab International, Inc. Polymun Scientific Polysciences, Inc.
PRF&L Pro-Chem Progen Promab Biotechnologies Promega
Corporation ProSci Proteogenix Protos Immunoresearch QED
Biosciences, Inc. Quidel Corporation R&D Systems Randox
Repligen Research Diagnostics Roboscreen Rockland Immunochemicals
Rose Biotech Santa Cruz Biotechnology SCIpac Scottish Agricultural
Science
ScyTek Laboratories Seikagaku America Seramon Serological
Corporation Serotec SigmaAldrich Signature Immunologics Signet
Laboratories Silver Lake Research Southern Biotechnology Associates
SPI-BIO Statens Serum Institut StemCell Technologies Sterogene
Bioseparations Strategic Biosolutions Stressgen Structure Probe,
Inc. (SPI) SWant Synaptic Systems GmbH SynthOrg Biochemicals, Ltd.
Technopharm Terra Nova Biotechnology Tetra Link International The
Biotech Source TiterMax Transmissible Spongiform Encephalopothy
Research Center Trevigen Trillium Diagnostics Triple Point
Biologics Tulip Biolabs Union Stem Cell & Gene Engineering
Company Upstate Biotechnology US Biological Vector Laboratories
Ventana Medical Systems, Inc Vision BioSystems Wako Pure Chemical
Industrie WolwoBiotech Company Zeptometrix
[0085] Develop Immunoassays: Immunoassays are preferably developed
in three steps, Prototyping, Validation, and Kit Release.
[0086] Prototyping: Prototyping may be done using standard ELISA
formats if the two antibodies used in the assay are from different
host species. Using standard conditions, anti-host secondary
antibodies conjugated with horse radish peroxidase are evaluated in
a standard curve. If a good standard curve is detected, the assay
proceeds to the next step. Assays that have same host antibodies go
directly to the next step (i.e., mouse monoclonal sandwich
assays).
[0087] Validation: Validation of a working assay may be performed
using single molecule detection technology. The detection antibody
is first conjugated to fluorescent molecules, typically Alexa 647.
The conjugations use standard NHS ester chemistry, for example,
according to the manufacturer. Once the antibody is labeled, the
assay is tested in a sandwich assay format using standard
conditions. Each assay well is solubilized in a denaturing buffer,
and the material read on the single molecule detection
platform.
[0088] FIG. 2 shows a typical result for a working standard curve.
Once a working standard curve is demonstrated, the assay may be
applied to 24 serum samples (for example) to determine the normal
distribution of the target analyte across clinical samples. The
amount of serum required to measure the biomarker within the linear
dynamic range of the assay is determined, and the assay proceeds to
kit release. In the present example, based on 39 validated assays,
0.004 microliters are used per well on average.
[0089] Kit Release: Each component of the kit including
manufacturer, catalog numbers, lot numbers, stock and working
concentrations, standard curve, and serum requirements may be
compiled into a standard operating procedures for each biomarker
assay. This kit may then be released for use in testing clinical
research samples.
[0090] Acquiring Clinical Samples: Depending on the specification
of the diagnostic test being developed, the clinical samples
preferably have (for example) clinical annotations that track
progression of disease, and preferably also include measurements of
underlying mechanisms or disease phenotypes, and/or have disease
outcomes using longitudinal samples over time. Relationships with
the investigators may then be developed, and a contractual
agreement is put into place. For each clinical study, the typical
volumes range from 0.1 to 1 mL.
[0091] Import Clinical Annotations: Samples arrive frozen on dry
ice, and each sample is stored at -80 C. Each sample typically has
tens to hundreds of clinical annotations associated with it. The
clinical annotations associated with each sample set may be brought
into a standardized nomenclature prior to import. All of the
clinical annotations associated with each sample are then imported
into a relational database.
[0092] Prepare Clinical Samples: The frozen aliquots are thawed and
aliquotted for use in the laboratory. Each clinical sample is
thawed on ice, and aliquots are dispensed into barcoded tubes
(daughter tubes). Each daughter tube is stored at -80 C until it is
needed for immunoassays. The daughter tubes are then arrayed into
sample plates. Each barcoded daughter tube to be assayed is arrayed
into barcoded 96 or 384 well plates (sample plates). This daughter
tube to sample plate well mapping is tracked by the relational
database.
[0093] Run Immunoassays: Each sample plate is now prepared for
immunoassays. In one example, 384 well barcoded assay plates may be
dedicated to one biomarker per plate. Typically, 4-12 assay plates
are derived from each sample plate dependent on the amount of serum
required for each assay. The sample plate goes through a series of
dilutions to ensure that the clinical samples are at an appropriate
dilution for each immunoassay. The clinical samples are then
deposited into the assay plate wells in triplicate for each marker.
Again, tracking of each sample plate well to assay plate well is
tracked in the relational database. The assays may then be
processed using standard immunoassay procedures, and the assay
plate is read on a single molecule detection instrument. Each run
contains data for a single biomarker across multiple clinical
samples, typically around one hundred. The resulting data files may
then be imported back into the relational database, where standard
curves can be calculated and the concentration values for each
biomarker for each sample can be calculated. FIG. 3 shows an
example of single molecule detection data across 92 samples for 25
biomarkers.
[0094] Analyze Data: The quantitative biomarker data can now be
correlated to the clinical annotations associated with each sample.
Any number of statistical formula or machine learning approaches on
single or multiple markers can be used to identify disease states
or risk for disease or biomarker patterns that have commercial
potential to diagnose or prognose disease state (for example).
[0095] The following is an illustrative example of a Standard
Operating Procedure (SOP) for use in developing diagnostic tests in
accordance with an embodiment of the present invention.
Assay Analyte: C-Reactive Protein
Components:
TABLE-US-00004 [0096] Component Vendor Catalog Number Lot Number
C-Reactive Protein US Biologicals C7907-26A L5042910 Capture
Antibody US Biologicals C7907-09 L4030562 Detection Antibody US
Biologicals C7907-10 L2121306M
[0097] 1. Plate Coating: Coat and Block immunoassay plates for
analyte capture [0098] 1.1 Materials [0099] 1.1.1. NUNC Maxisorp
384 well plates, Cat. No. 460518 [0100] 1.1.2. NUNC Acetate
Sealers, Cat. No. 235306 [0101] 1.1.3. Coating buffer [0102]
1.1.3.1. 0.05 M carbonate, pH 9.6 [0103] 1.1.3.2. Store at
4.degree. C. for up to 2 months [0104] 1.1.4. Capture Antibody
[0105] 1.1.5. Wash buffer A [0106] 1.1.5.1. PBS with 0.1% TWEEN 20
[0107] 1.1.5.2. Store at room temperature for up to 2 months [0108]
1.1.6. Blocking buffer [0109] 1.1.6.1. 1% BSA, 5% sucrose, 0.05%
NaN.sub.3 in PBS [0110] 1.1.6.2. Store at 4.degree. C. for up to 1
month [0111] 1.1.7. Microplate washer [0112] 1.2. Procedure [0113]
1.2.1. Dilute capture antibody to 1 microgram/mL in coating buffer.
(Prepare immediately before use) [0114] 1.2.2. Add 20 microliters
of diluted capture antibody per well [0115] 1.2.3. Seal and shake
for 2 minutes on plate shaker [0116] 1.2.4. Centrifuge 1000rpm 2
min, 25.degree. C. [0117] 1.2.5. Incubate overnight at room
temperature (no shaking) [0118] 1.2.6. Wash 3.times. with 100
microliters wash buffer A [0119] 1.2.7. Add 30 microliters blocking
buffer per well [0120] 1.2.8. Seal and shake for 2 minutes on
Jitterbug setting 7 [0121] 1.2.9. Centrifuge 1000rpm 2 min,
25.degree. C. [0122] 1.2.10. Incubate at least 2 hour at room
temperature (no shaking) [0123] 1.2.11. Dump plate and blot upside
down (no wash) [0124] 1.2.12. Air dry the blocked plates
(uncovered) at least 5 hours at room temperature [0125] 1.2.13.
Cover the dry plates with acetate sealer [0126] 1.2.14. Store at
4.degree. C. for up to one month [0127] 2. Single Molecule
Detection Assay: Add clinical samples to coated plates and quantify
[0128] 2.1. Materials [0129] 2.1.1. Coated, blocked NUNC Maxisorp
384 well plate [0130] 2.1.2. NUNC Acetate Sealers, Cat. No. 235306
[0131] 2.1.3. Assay buffer [0132] 2.1.3.1. BS* with 1% BSA, 0.1%
TRITON X-100 [0133] 2.1.3.2. Store 4.degree. C. for up to 1 month
[0134] 2.1.4. Standard Calibrator diluent [0135] 2.1.4.1. Assay
buffer+additional 5% BSA, [0136] 2.1.4.2. Enough volume for
standard curve, including 0 pg/ml. [0137] 2.1.4.3. Make fresh for
use. [0138] 2.1.5. Standard Molecule Control [0139] 2.1.6.
Detection Antibody: A647 labeled antibody [0140] 2.1.7. Assay Wash
buffer B [0141] 2.1.7.1. BS* with 0.02% TRITON X-100 and 0.001% BSA
[0142] 2.1.7.2. 500 ml per assay plate [0143] 2.1.7.3. Store at
4.degree. C. for up to 1 month [0144] 2.1.8. Elution Buffer [0145]
2.1.8.1. 4 M urea, 1.times. BS with 0.02% TRITON X-100 and 0.001%
BSA [0146] 2.1.8.2. Approx 8 ml per assay plate [0147] 2.1.9.
Microplate shaker (Jitterbug), set at "7" [0148] 2.1.10. Microplate
washer [0149] 2.1.11. Centrifuge [0150] 2.2. Procedure [0151] 2.3.
Record [0152] 2.3.1. Plate assay plate number, kit lot number, and
sample plates used [0153] 2.4. Standard Curve [0154] 2.4.1. Dilute
control to 100 ng/ml in calibrator [0155] 2.4.2. Prepare 1/2 serial
dilutions from 100 ng/ml to 0.01 pg/ml in calibrator diluent [0156]
2.5. Sample Dilution [0157] 2.5.1. Dilute samples 1:400 in assay
buffer [0158] 2.6. Capture and Detection [0159] 2.6.1. Add 20
microliters/well of standards [0160] 2.6.2. Add 20 microliters/well
diluted unknowns [0161] 2.6.3. Seal w/ acetate sealing tape. Shake
for 2 minutes on plate shaker [0162] 2.6.4. Incubate overnight at
room temperature [0163] 2.6.5. Dilute Detection antibody labeled
A647 antibody to 50 ng/ml in assay buffer. [0164] 2.6.6. Aspirate.
Wash 5.times. with 100 ul wash buffer B [0165] 2.6.7. Blot upside
down [0166] 2.6.8. Add 20 microliters/well diluted detection
antibody [0167] 2.6.9. Seal w/ acetate sealing tape. Shake for 2
minutes on plate shaker [0168] 2.6.10. Incubate 2 hours at room
temperature [0169] 2.6.11. Aspirate. Wash 5.times. with 100 ul wash
buffer B [0170] 2.6.12. Blot upside down [0171] 2.6.13. Add 20
microliters/well elution buffer. [0172] 2.6.14. Seal w/ acetate
sealing tape. Shake for 2 minutes on plate shaker. [0173] 2.6.15.
Incubate 1/2 hour at 25.degree. C. [0174] 2.6.16. Centrifuge on
1000 rpm for 2 min, 25.degree. C. [0175] 2.7. Analyze on Single
Molecule Detection instrument
Other Embodiments
[0176] It is to be understood that while the invention has been
described in conjunction with the detailed description thereof, the
foregoing description is intended to illustrate and not limit the
scope of the invention, which is defined by the scope of the
appended claims. Other aspects, advantages, and modifications are
within the scope of the following claims.
* * * * *