U.S. patent application number 10/258150 was filed with the patent office on 2004-07-15 for method for measuring drug resistance.
Invention is credited to Bloor, Stuart, Dehertogh, Pascale Alfons Rosa, Hertogs, Kurt, Larder, Brendan, Mortier, Rudy Jean Marc.
Application Number | 20040137436 10/258150 |
Document ID | / |
Family ID | 26892992 |
Filed Date | 2004-07-15 |
United States Patent
Application |
20040137436 |
Kind Code |
A1 |
Larder, Brendan ; et
al. |
July 15, 2004 |
Method for measuring drug resistance
Abstract
The present invention concerns methods for measuring drug
resistance by correlating genotypic information with phenotypic
profiles. In one embodiment, a method for interpreting genotypic
information is described wherein a genetic code is generated from a
patient sample, a list of mutations known or suspect to play a role
in the development of resistance to one or more drugs is obtained
from the generated genetic code, a genotype database is
interrogated for previous samples with similar mutations relating
to said one or more drugs, a phenotype for said samples is located
in a phenotype database, the mean change in inhibition is
determined based on all the examples located in said phenotype
database, and a phenotype is determined for the patients sample.
Furthermore, methods are provided for predicting a phenotype from a
biological sample and for predicting drug or therapy resistance of
a patient, a pathogen or a malignant cell. Also methods and systems
are provided for designing, optimizing and assessing the efficiency
of a therapeutic regimen based upon the genotype of the disease
affecting the patient.
Inventors: |
Larder, Brendan; (Cambridge,
GB) ; Bloor, Stuart; (Bedfordshire, GB) ;
Hertogs, Kurt; (Antwerpen, BE) ; Dehertogh, Pascale
Alfons Rosa; (Puurs, BE) ; Mortier, Rudy Jean
Marc; (Laarne, BE) |
Correspondence
Address: |
PHILIP S. JOHNSON
JOHNSON & JOHNSON
ONE JOHNSON & JOHNSON PLAZA
NEW BRUNSWICK
NJ
08933-7003
US
|
Family ID: |
26892992 |
Appl. No.: |
10/258150 |
Filed: |
November 24, 2003 |
PCT Filed: |
April 18, 2001 |
PCT NO: |
PCT/EP01/04445 |
Current U.S.
Class: |
435/5 ; 435/6.13;
702/20 |
Current CPC
Class: |
G16B 30/10 20190201;
G16B 50/00 20190201; G16B 40/20 20190201; G16B 20/00 20190201; C12Q
1/6876 20130101; C12Q 2600/156 20130101; A61P 31/18 20180101; C12Q
1/707 20130101; C12Q 1/703 20130101; G16B 50/20 20190201; G16B
40/00 20190201; G16B 30/00 20190201 |
Class at
Publication: |
435/006 ;
702/020 |
International
Class: |
C12Q 001/68; G06F
019/00; G01N 033/48; G01N 033/50 |
Foreign Application Data
Date |
Code |
Application Number |
Apr 18, 2000 |
US |
60197606 |
Jun 22, 2000 |
US |
60213219 |
Claims
1. A method of determining a phenotype of a disease producing agent
comprising: a) obtaining a genetic sequence of said disease
producing agent, b) identifying at least one mutation pattern in
said genetic sequence wherein said genetic sequence comprises at
least one mutation, and wherein said at least one mutation or
mutation pattern is associated with resistance to at least one
therapy, c) searching a genotype database for at least one genotype
entry with a similar mutation pattern to at least one of the
mutation patterns identified in the genetic sequence in b), d)
correlating said at least one genotype entry with a similar
mutation pattern with a phenotype in a phenotype database, and, e)
determining the phenotype of said disease producing agent from the
database phenotype of the at least one genotype entry with a
similar mutation pattern.
2. A diagnostic method for assessing the effectiveness of a
patient's therapy comprising: a) providing a biological sample from
a patient, b) obtaining a genetic sequence from a disease producing
agent in said biological sample, c) identifying at least one
mutation pattern in said genetic sequence wherein said genetic
sequence comprises at least one mutation, and wherein said at least
one mutation or mutation pattern is associated with resistance to
at least one therapy currently being administered to the patient,
d) searching a genotype database for at least one genotype entry
with a similar mutation pattern to the at least one mutation
pattern identified in the genetic sequence in b), e) correlating
said at least one genotype entry with a similar mutation pattern
with a phenotype in a phenotype database, f) determining the
phenotype of disease producing agent from the database phenotype of
the at least one genotype entry with a similar mutation pattern, g)
obtaining a series of phenotypes by repeating steps b) through e)
for each therapy currently being administered to the patient, and,
h) evaluating the effectiveness of the patient's therapy from the
series of phenotypes.
3. A diagnostic method for optimizing therapy for a patient,
comprising: a) providing a biological sample from a patient, b)
obtaining a genetic sequence from a disease producing agent in said
biological sample, c) identifying at least one mutation pattern in
said genetic sequence wherein said genetic sequence comprises at
least one mutation, and wherein said at least one mutation or
mutation pattern is associated with resistance to at least one
therapy, d) searching a genotype database for at least one genotype
entry with a similar mutation pattern to the at least one mutation
pattern identified in the genetic sequence in b), e) correlating
said at least one genotype entry with a similar mutation pattern
with a phenotype in a phenotype database, f) determining the
phenotype of said disease producing agent from the database
phenotype of the at least one genotype entry with a similar
mutation pattern, g) obtaining a series of phenotypes by repeating
steps b) through e) for a group of therapies, and, h) optimizing
therapy for the patient from the series of phenotypes.
4. A diagnostic method for predicting resistance of a pathogen to
therapy comprising: a) providing a biological sample from a patient
containing a pathogen, b) obtaining a genetic sequence from said
pathogen, c) identifying at least one mutation pattern in said
genetic sequence wherein said genetic sequence comprises at least
one mutation, and wherein said at least one mutation or mutation
pattern is associated with resistance to at least one therapy, d)
searching a genotype database for at least one genotype entry with
a similar mutation pattern to the at least one mutation pattern
identified in the genetic sequence in b), e) correlating said at
least one genotype entry with a similar mutation pattern with a
phenotype in a phenotype database, f) obtaining a series of
phenotypes by repeating steps b) through e) for a group of
therapies, and, g) predicting resistance of the patient to therapy
from the series of phenotypes.
5. A diagnostic method for predicting resistance of a malignant
cell to therapy comprising: a) providing a biological sample from a
patient containing a malignant cell, b) obtaining a genetic
sequence from said malignant cell, c) identifying at least one
mutation pattern in said genetic sequence wherein said genetic
sequence comprises at least one mutation, and wherein said at least
one mutation or mutation pattern is associated with resistance to
at least one therapy, d) searching a genotype database for at least
one genotype entry with a similar mutation pattern to the at least
one mutation pattern identified in the genetic sequence in b), e)
correlating said at least one genotype entry with a similar
mutation pattern with a phenotype in a phenotype database, f)
obtaining a series of phenotypes by repeating steps b) through e)
for a group of therapies, and, g) predicting resistance of the
patient to therapy from the series of phenotypes.
6. The method of any of claims 1 to 5 wherein said disease
producing agent is obtained from a biological sample chosen from a
blood sample, a biopsy sample, a plasma sample, a saliva sample, a
tissue sample, and a bodily fluid or mucous sample.
7. The method of any of claims 1 to 4, wherein said disease
producing agent is a virus.
8. The method of claim 7 wherein said virus is chosen from HIV, HCV
and HBV.
9. The method of any of claims claim 1 to 4, and 6 to 8, wherein
the genetic sequence is obtained from a Human Immunodeficiency
Virus (HIV).
10. The method of claim 9, wherein the genetic sequence of HIV
comprises the genetic sequence of the protease region of the HIV
genome and/or the genetic sequence of the reverse transcriptase
region of the HIV genome
11. The method of any of claims 1 to 3 wherein said disease
producing agent is a malignant cell.
12. The method of any of claims claim 1 to 3, or 11, wherein the
genetic sequence of said malignant cell is from a molecular marker
for cancer
13. The method of any of claims 1 to 12, wherein the mutation
pattern comprises at least two mutations known or suspected to be
associated with resistance to at least one therapy.
14. The method of any of claims 1 to 13, wherein the similar
mutation pattern is identified by aligning the genetic sequence of
a cell or a pathogen in the biological sample with the WT genetic
sequence of said cell or pathogen.
15. The method of any of claims 1 to 13, wherein cluster searching
is used to determine similar mutation patterns.
16. The method of any of claims 1 to 15, wherein a relational
genotype/phenotype database is used for correlating the at least
one genotype entry with a similar mutation pattern with a phenotype
in said database.
17. The method of any of claims 1 to 16, wherein the phenotype of
said biological sample is (expressed as) a mean fold-change in
resistance towards at least one therapy, wherein said mean fold
resistance is calculated from the database phenotype of the at
least one genotype entry with a similar mutation pattern.
18. The method of any of claims 1 to 17, wherein the phenotype of
the cell or pathogen in said biological sample is expressed as an
IC.sub.50.
19. A method of generating a report, wherein said report comprises
the phenotype determined (predicted) using any of the methods of
claims 1 to 18.
20. A computer readable media comprising the phenotype determined
(predicted) using any method of claims 1 to 18.
21. A method of determining a phenotype of a disease producing
agent comprising: a) obtaining a genetic sequence of said disease
producing agent, b) identifying at least one mutation in said
genetic sequence wherein said mutation is comprised within at least
one mutation pattern, c) searching a genotype database for at least
one genotype entry comprising said mutation in said at least one
mutation pattern, d) correlating said at least one genotype entry
with a phenotype in a phenotype database, and e) determining the
phenotype of said biological sample.
22. A method according to claim 21, wherein said mutation pattern
is associated with resistance to one therapy.
23. A method according to claim 21 or 22, wherein said mutation
pattern comprises at least two mutations linked with a logical
operator.
24. A method according to claims 21-23, wherein at least two
mutation patterns are associated with resistance to one
therapy.
25. A method according to claim 24, wherein said mutation patterns
are linked with a logical operator defining a therapy profile.
26. A method according to claim 25, wherein said therapy profile is
represented by a sequence.
27. A method according to claim 26, wherein said sequence is
represented by a series of 1 and/or 0.
28. A method according to claim 27, wherein 1 represents the
presence of a mutation pattern in the therapy profile and 0 the
absence of a mutation pattern in the therapy profile.
29. A diagnostic system for determining a phenotype of a disease
producing agent comprising: a) means for obtaining a genetic
sequence of said disease producing agent, b) means for identifying
at least one mutation in said genetic sequence, c) genotype
database means comprising genotype entries, d) phenotype database
means comprising phenotypes, and e) correlation means correlating
said genotype entry with said phenotype, said genotype entry
corresponds with the obtained genetic sequence.
30. A diagnostic system according to claim 29 for use in the method
according to any of the previous claims 1-19 and 21-28.
31. A computer system comprising a first database comprising
genotype entries and a second database comprising phenotypes in
which the genotype entries correspond to at least one mutation
pattern and interface means linking the first database to the
second database.
32. A computer system comprising a relational genotype/phenotype
database wherein the genotype entries correspond to at least one
mutation pattern.
33. A computer program product stored on a computer usable medium
comprising computer readable program means for causing a computer
to control the execution of the method according to any of the
claims 1-19 and 21-28.
Description
FIELD OF THE INVENTION
[0001] The present invention concerns methods and systems for
predicting the resistance of a disease to a therapeutic agent. More
specifically, the invention provides methods for predicting drug
resistance by correlating genotypic information with phenotypic
profiles. The invention further relates to methods and systems for
designing, optimizing and assessing he efficiency of a therapeutic
regimen based upon the genotype of the disease affecting the
patient.
BACKGROUND TO THE INVENTION
[0002] Techniques to determine the resistance of a pathogen or
malignant cell to a therapeutic agent are becoming increasingly
important. For example, despite the great advantages of existing
treatments against viral infections such as HIV infection, cancer
and bacterial infections, many patients experience treatment
failure or reduced efficacy over time. In many instances this is
due to the pathogen, malignant cell, bacteria, virus or other
disease state mutating and/or developing a resistance to the
treatment.
[0003] For example, all the drugs in the HIV field were discovered
and developed over a period of 15 years, starting with AZT. By the
beginning of the year 2000, 15 different anti-HIV-1 agents had been
approved by the FDA. Initially, and due to a lack of alternative
drugs, these agents were administered alone, as monotherapy. Though
a temporary antiviral effect was observed, all the compounds lost
their effectiveness over time. In 1989, Larder et al. published a
paper in Science, 246, 1155-8, incorporated by reference herein,
that identified a number of mutations that caused HIV-1 resistance
to AZT. Since then, research has demonstrated that one of the main
reasons behind treatment failure for all the antiviral drugs is the
development of resistance of the virus to the drug.
[0004] Drug resistance and drug resistant mutations develop because
retroviruses such as HIV have no proofreading mechanism when
synthesizing new nucleic acid strands. This allows for the
continuous generation of a number of genetic variants in a
replicating viral population. More importantly, the genetic changes
may alter the configuration of the reverse transcriptase (RT) and
protease (PR) molecules in such a way that they are no longer
susceptible to inhibition by compounds developed to target them. If
antiretroviral therapy is ongoing and if viral replication is not
completely suppressed, the selection of genetic variants is
inevitable and the viral population becomes resistant to the
drug.
[0005] In the face of monotherapy failure and encouraged by a
number of clinical trials, in the early-mid 1990's treatment
strategy turned to combination therapy, i.e., administration of
mixtures of antiviral drugs. At the time there were still only one
class of drugs available--the nucleoside analogue reverse
transcriptase inhibitors (NRTIs). As a result, the standard of care
became two nucleosides, typically AZT+ddI, or AZT+ddC. Dual
combination therapy provided increased control of viral
replication, made it more difficult for the virus to develop
resistant strains or mutations and, as a result, provided extended
clinical benefit to patients.
[0006] In 1995, another milestone was reached with the approval of
the first of the protease inhibitors (PIs). These inhibitors showed
greater potency than the nucleosides, but again were prone to
resistance when used alone. Their combination with two nucleoside
analogues, however, seemed to provide the control over the virus
that everyone had been looking for. Triple combination therapy
using two nucleosides (most commonly AZT+3TC) plus a protease
inhibitor (typically indinavir) still remains the most common
standard of care in developed countries.
[0007] These highly active combinations have had an enormous effect
on the quality of life and on the survival of patients. This has
resulted in fewer hospitalizations and reintegration of the
patients in society. In a considerable number of patients, the
viral load has been reduced to below the detection limit for
prolonged periods.
[0008] In recent years, however, it has become clear that even
patients being treated with triple therapy including a protease
inhibitor often eventually experience treatment failure. Data
suggests that up to one half of patients on combination therapy do
not achieve or do not maintain suppression of virus replication. In
some cases, it may be that even state-of-the-art triple therapy is
insufficient to halt viral replication. As a result, drug resistant
strains of the virus develop.
[0009] Another factor contributing to the difficulty to maintain
suppression of virus replication has been the sheer burden of
taking up to 20 pills each day, at set times, with or without food,
day after day. It is simply unrealistic to expect people to adhere
to such stringent and demanding regimens indefinitely. But if
patients do not adhere, the price can be high. A dip in the blood
levels of any of the medications gives the virus an opportunity to
replicate and develop drug resistant strains. As such, during the
course of infection, drug resistant viral strains can emerge very
rapidly particularly for retroviral infections such as HIV-1. In
addition, not all HIV-1 infections originate with a wild type, drug
sensitive strain from which drug resistance will emerge. With the
increase in prevalence of drug resistant strains comes the increase
in infections that actually begin with drug resistant strains.
Infections with pre-existing drug resistance immediately reduce the
drug options for drug treatment and emphasize the importance of
drug resistance information to optimize initial therapy for these
patients. Moreover, as the number of available antiretroviral
agents has increased, so has the number of possible drug
combinations and combination therapies. However, it is not easy for
the physician to establish the optimal combination for an
individual. Previously, the only treatment guidelines that have
been in widespread use have been based on viral load and, where
available, the patient's treatment history. The physician's
objective is to keep the viral load as low as possible. An increase
in viral load is a warning that control of viral replication is
being lost and that a change in therapy is required. Viral load,
however, provides no information or guidance regarding which drugs
should be used.
[0010] Knowledge of the resistance patterns of different inhibitors
and the patient's treatment history can help. Resistance emergence
is highly predictive of treatment failure. In fact, while there are
a variety of factors that can contribute to the failure of drug
therapy, HIV-1 drug resistance is almost always involved. However,
the interactions between different viral mutations related to
different inhibitors is so complex that selecting the optimal
treatment combination with only a treatment history to go on is far
from ideal. Drugs can be ruled out unnecessarily and ineffective
drugs can be introduced. Even if the virus is resistant to just one
of three drugs in a treatment regimen, this can allow low-level
viral replication to take place and viral strains resistant to the
other two drugs to develop.
[0011] It is clear that although there are many drugs available for
use in combination therapy, the choices can quickly be exhausted
and the patient can rapidly experience clinical progression or
deterioration if the wrong treatment decisions are made. The key to
tailored, individualized therapy lies in the effective profiling of
the individual patient's virus population in terms of sensitivity
or resistance to the available drugs. This will mean the advent of
truly individualized therapy.
[0012] The aim of resistance monitoring is to provide the necessary
information to enable the physician to prescribe the most optimal
drug combination for the individual patient. At present, there are
two distinct approaches to measuring resistance:
[0013] The first approach involves phenotyping, which directly
measures the actual sensitivity of a patient's pathogen or
malignant cell to particular therapeutic agents. For example, HIV-1
phenotype testing directly measures HIV-1 drug resistance, detected
as the ability of HIV-1, taken from a patient, to grow in the
presence of a drug, in the laboratory. The phenotype is measured,
for example expressed an IC.sub.50 or as a fold resistance for a
particular drug, which is defined as the concentration of drug
required to kill half of the virions in a sample. This is compared
to the IC.sub.50 for the drug using wild type virus. The phenotype
is usually described or can be expressed in terms of the fold
increase in IC.sub.50 for each of the drugs.
[0014] There are three main types of methodology for phenotyping.
One such type is the plaque reduction assay. A drawback of this
method is that it does not detect NSI strains. Another method of
phenotyping includes PBMC p24 growth inhibition assays (Japour, A.
J., Mayers, T. L., Johnson, V. A., Kuritzkes, D. R., Beckett, L.
A., Arduino, J.-M., Lane, J., Black, R. J., Reichelderfer, P. S.,
D'Aquila, R. T., Crumpacker, C. S., The RV-43 Study Group & The
ACTG Virology Committee Resistance Working Group. 1993. Antimicrob.
Agents Chemother. 37, 1095-1101, incorporated by reference herein).
A problem with this technique is that virus culture from PBMCs is
very slow and labor-intensive. In addition, it lacks the precision
of other techniques and because it relies on primary human cells
for virus growth, assay automation and high throughput is virtually
impossible. Yet another method is the recombinant virus assay
(Kellam, P. & Larder, B. A. 1994. Antimicrob. Agents Chemother.
38, 23-30, incorporated by reference herein.). The recombinant
method has advantages over the previously mentioned assays in that
it reduces the amount of selection that takes place during growth
of the virus in the laboratory, it is faster, more reproducible,
amendable to automation and high throughput, and all available
drugs can be tested in one assay.
[0015] The second approach to measuring resistance involves
genotyping tests that detect specific genetic changes (mutations)
in the viral genome which lead to amino acid changes in at least
one of the viral proteins, known or suspected to be associated with
resistance.
[0016] There are a number of techniques for conducting genotyping,
such as hybridization-based point mutation assays and DNA
sequencing. Common point mutation assays include Primer-specific
PCR (Larder B A, Kellam P & Kemp, S D 1991. AIDS 5: 137-144,
incorporated by reference herein.), differential hybridization
(Eastman, P. S., Urdea, M., Besemer, D., Stempien, M. &
Kolberg, J. 1995. J. Acquir. Immune Defic. Syndr. Human Retrovirol.
9, 264-273, incorporated by reference herein.), Line Probe Assay
(LiPA.TM., Innogenetics) (Stuyver, L., Wyseur, A., Rombout, A.,
Louwagie, J., Scarcez, T., Verhofstede, C., Rimland, D., Schinazi,
R. F. & Rossau, R. 1997. Antimicrob. Agents Chemotherap. 41,
284-291, incorporated by reference herein.), and gene chip
sequencing (Affymetrix) (D'Aquila, R. T. 1995. Clin. Diagnost.
Virol. 3, 299-316, incorporated by reference herein). Point
mutation assays can only provide a small select part of the
resistance picture. DNA sequencing, however, provides information
on the nucleotides in the region of the genome sequenced. This
means that changes in the genome can be detected. However, at
present, it remains difficult to interpret the results of a
genotypic test to provide meaningful conclusions about therapeutic
agent resistance. The advantage of phenotyping over genotyping is
that phenotyping is a direct measure of any change in sensitivity
resulting from all the mutations that have occurred, and any
interactions between them. As such, it is the gold standard of
resistance testing. Disadvantages of phenotyping are that it is
complex, lengthy to perform, (usually 4 weeks) and, therefore, more
expensive than genotyping. Thus, phenotyping is not a practical way
of designing patient therapy. The importance of the speed by which
a physician can be informed of the patient's resistance profile can
be demonstrated by the following hypothetical but realistic
example, which highlights the need to reduce complexity and improve
performance time of assessing resistance. Suppose first-line triple
combination therapy reduces the viral load to undetectable limits
for a period of time. The viral load then begins to increase as a
result of the development of resistance. Without resistance
information, the physician can make a judgement based on the
patient's treatment history, and change one or more of the drugs.
As a result viral load is, again, reduced but the new treatment
regimen is sub-optimal so viral replication continues under
selection pressure from the drugs and resistance rapidly develops
once more. Consequently, control of viral replication is lost and
several of the 15 drugs available have been `used up`.
[0017] Although genotyping tests can be performed more rapidly, a
problem with genotyping is that there are now over 100 individual
mutations with evidence of an effect on susceptibility to HIV-1
drugs and new ones are constantly being discovered, in parallel
with the development of new drugs and treatment strategies. The
relationship between these point mutations, deletions and
insertions and the actual susceptibility of the virus to drug
therapy is extremely complex and interactive. An example of this
complexity is the M184V mutation that confers resistance to 3TC but
reverses AZT resistance. The 333D/E mutation, however, reverses
this effect and can lead to dual AZT/3TC resistance.
[0018] Consequently, the interpretation of genotypic data is both
highly complex and critically important. There have been a number
of different approaches to this challenge of interpretation. For
example, armed with the knowledge of the main resistance mutations
associated with each drug and the patient's recent treatment
history, a physician makes a decision as to the optimum treatment.
To assist physicians to make these judgments, various expert
opinion panels have been convened and have published guidelines,
e.g. the Resistance Collaborative Group. In addition, rules-based
algorithms constitute another approach. This is essentially a
formalized version of the above with tables giving the mutations
which are associated with resistance to each of the drugs. These
can be simple printed tables or the information can be used to
develop a rules-based computer algorithm. However, given the large
number of mutations that are involved in resistance to
antiretroviral drugs and given the complex interactions between the
mutations, the shortcoming of genotyping is the reliable
interpretation and clinical application of the results. As more
drugs become available and as more mutations are involved in the
development of resistance, the `manual` or rules-based
interpretation of raw genotype data is rapidly becoming impossible
due to an increase in complexity.
[0019] Therefore, the main challenge involved with genotyping is
improving the interpretation of the results. The technology will
identify some (i.e., point mutation assays) or all of the mutations
(i.e., DNA sequencing) that have occurred but it then requires
sophisticated interpretation to predict what the net effect of
these mutations might be on the susceptibility of the virus
population to the various therapeutic agents. A physician might
then have to combine this information with all the other
information relating to the patient and decide what all this means
in terms of selecting drugs for the treatment of their individual
patient.
[0020] It is therefore an aim of the present invention to provide
methods for improving the interpretation of genotypic results.
[0021] It is a further aim of the invention to provide methods for
determining (or predicting) a phenotype based on a genotype.
[0022] It is also a further aim of the invention to provide methods
for predicting the resistance of a pathogen or a malignant cell to
a therapy or a therapeutic agent.
[0023] It is also an aim of the invention to predict resistance of
a patient to therapy.
[0024] It is also an aim of the invention to provide methods to
assess the effectiveness or efficiency of a therapy or to optimize
a patient's therapy.
SUMMARY OF THE INVENTION
[0025] A solution to the problems set forth above involves new
methods for measuring drug resistance by correlating genotypic
information with phenotypic profiles.
[0026] In the present invention, the methods bring together the
knowledge of both a genotypic and a phenotypic database, and
determines a (virtual) phenotypic fold resistance value without
actually having to do phenotypic testing. The genotypic database
contains the mutations in the tested HIV viruses compared with the
reference HIV virus (wild type). The phenotypic database contains
phenotypic resistance values for the tested HIV viruses, with a
fold resistance determination compared to the reference HIV virus
(wild type). As described below, this analysis may be done by
comparing the sequence of the HIV virus sequence under test, e.g.
from a patient sample, against the stored sequences and by
selecting "similar sequences". Phenotypic data is then gathered for
those "similar sequences" and the mean or median fold resistance
may be calculated from the selected phenotypic values. This value
is called "Virtual Fold Resistance", which leads to the "Virtual
Phenotype."
DETAILED DESCRIPTION OF THE INVENTION
[0027] According to a first embodiment the present invention
relates to a method for determining or predicting a phenotype of a
disease producing agent, for example in a biological sample,
comprising:
[0028] a) obtaining a genetic sequence from said disease producing
agent,
[0029] b) identifying at least one mutation pattern in said genetic
sequence wherein said genetic sequence comprises at least one
mutation, and wherein said at least one mutation or mutation
pattern is to be associated with resistance to at least one therapy
or therapeutic agent,
[0030] c) searching a genotype database for at least one genotype
entry with a similar mutation pattern to at least one of the
mutation patterns identified in the genetic sequence in b),
[0031] d) correlating said at least one genotype entry with a
similar mutation pattern with a phenotype in a phenotype database,
and
[0032] e) determining the phenotype of said disease producing agent
from the database phenotype of the at least one genotype entry with
a similar mutation pattern.
[0033] The same methodology of the above described method can be
used for instance for evaluating currently applied therapies or for
predicting resistance of a patient to a therapy.
[0034] Therefore, according to another embodiment, the present
invention relates to a method for assessing the effectiveness of a
patient's therapy or for monitoring a patient's therapy
comprising:
[0035] a) providing a biological sample from a patient,
[0036] b) obtaining a genetic sequence from a disease producing
agent in said biological sample,
[0037] c) identifying at least one mutation pattern in said genetic
sequence wherein said genetic sequence comprises at least one
mutation, wherein said at least one mutation is associated with
resistance to at least one therapy currently being administered to
the patient,
[0038] d) searching a genotype database for at least one genotype
entry with a similar mutation pattern to at least one of the
mutation patterns identified in the genetic sequence in b),
[0039] e) correlating said at least one genotype entry with a
similar mutation pattern with a phenotype in a phenotype
database,
[0040] f) determining the phenotype of said disease producing agent
from the database phenotype of the at least one genotype entry with
a similar mutation pattern,
[0041] g) obtaining a series of phenotypes by repeating steps b)
through e) for each therapy currently being administered to the
patient, and,
[0042] h) evaluating the effectiveness of the patient's therapy
from the series of phenotypes.
[0043] Also, the invention relates to a method for optimizing
therapy for a patient, comprising:
[0044] a) providing a biological sample from a patient,
[0045] b) obtaining a genetic sequence from a disease producing
agent in said biological sample,
[0046] c) identifying at least one mutation pattern in said genetic
sequence wherein said genetic sequence comprises at least one
mutation, and wherein said at least one mutation is associated with
resistance to at least one therapy,
[0047] d) searching a genotype database for at least one genotype
entry with a similar mutation pattern to at least one of the
mutation pattern identified in the genetic sequence in b),
[0048] e) correlating said at least one genotype entry with a
similar mutation pattern with a phenotype in a phenotype
database,
[0049] f) determining the phenotype of said disease producing agent
from the database phenotype of the at least one genotype entry with
a similar mutation pattern,
[0050] g) obtaining a series of phenotypes by repeating steps b)
through e) for a grup of therapies, and,
[0051] h) optimizing therapy for the patient from the series of
phenotypes.
[0052] While described in the examples with respect to viruses,
particularly HIV, the present invention has broad applicability to
any disease state where it is desired to correlate genotypic
information with phenotypic profiles. One skilled in the art could
readily take the following discussion of the invention with the HIV
virus and through the exercise of routine skill apply this
invention to other diseases (such as other viral infections,
malignant cells, cancer, bacterial infections, other pathogens, and
the like) to correlate genotypic information to predict phenotypic
response, assess drug resistance, and eventually develop a
treatment regime of drugs for a particular patient. One skilled in
the art will also know that many virus species comprise many
strains for instance HIV comprise apart from HIV-1 also HIV-2 and
both groups are further divided into groups e.g. but not limited to
group O or M for HIV-1.
[0053] Therefore, according to another embodiment, the present
invention relates to a method for predicting resistance of a
pathogen to therapy comprising:
[0054] a) providing a biological sample from a patient containing a
pathogen,
[0055] b) obtaining a genetic sequence from said pathogen,
[0056] c) identifying at least one mutation pattern in said genetic
sequence wherein said genetic sequence comprises at least one
mutation, and wherein said at least one mutation is associated with
resistance to at least one therapy,
[0057] d) searching a genotype database for at least one genotype
entry with a similar mutation pattern to the mutation pattern
identified in the genetic sequence in b),
[0058] e) correlating said at least one genotype entry with a
similar mutation pattern with a phenotype in a phenotype
database,
[0059] f) obtaining a series of phenotypes by repeating steps b)
through e) for a group of therapies, and,
[0060] g) predicting resistance of the patient to therapy from the
series of phenotypes.
[0061] According to yet another embodiment, the present invention
relates to a method for predicting resistance of a malignant cell
to therapy comprising:
[0062] a) providing a biological sample from a patient containing a
malignant cell,
[0063] b) obtaining a genetic sequence from said malignant
cell,
[0064] c) identifying a mutation pattern in said genetic sequence
wherein said genetic sequence comprises at least one mutation, and
wherein said at least one mutation is associated with resistance to
at least one therapy,
[0065] d) searching a genotype database for at least one genotype
entry with a similar mutation pattern to the mutation pattern
identified in the genetic sequence in b),
[0066] e) correlating said at least one genotype entry with a
similar mutation pattern with at most one phenotype in a phenotype
database,
[0067] f) obtaining a series of phenotypes by repeating steps b)
through e) for a group of therapies, and,
[0068] g) predicting resistance of the patient to therapy from the
series of phenotypes.
[0069] The above methods should be interpreted as diagnostic
methods, therefore, the invention also provides diagnostic kits for
performing each of the methods of the invention.
[0070] It should be understood that the principles and methods
provided by this application are governed to provide the treating
physician a means to optimize or to select the therapy which will
be most successful. The principle is of particular relevance for
the treatment (or monitoring of therapy) of diseases like cancer,
bacterial and viral infections. These diseases states are subject
to complex and continuously varying therapy regimens and therefore
the patient under treatment needs to undergo frequent therapy
monitoring in order to follow the drug effect or in order to
optimize or select the optimal patient management.
[0071] The methods of the present invention determine a phenotype
without actually having to do phenotypic testing. Within this
meaning, the term "determining" is interchangeable with
"predicting" or "diagnosing".
[0072] A "patient" may be any organism, particularly a human or
other mammal, suffering from a disease or in need or desire of
treatment for a disease. A patient includes any mammal, including
farm animals or pets, and includes humans of any age or state of
development
[0073] A "biological sample" may be any material obtained in a
direct or indirect way from a patient comprising a disease
producing or a disease causing agent. Said disease producing agent
is able to be sequenced. In this respect the terms biological
sample and disease producing agents and disease causing agents are
interchangeable in the invention. A biological sample may be
obtained from, for example, saliva, semen, breast milk, blood,
plasma, feces, urine, tissue samples, mucous samples, cells in cell
culture, cells which may be further cultured, etc. Biological
samples also include biopsy samples. In one embodiment, for a
patient infected with HIV, any biological sample containing virus
may be used. In another embodiment, for a cancer patient, a sample
may include all of the above, and tumors, biopsy tissue, etc. from
which the sequence of In one embodiment, for a patient infected
with a virus, any biological sample containing virus may be used in
any of the methods of the invention. Preferably said virus is a
retrovirus. Preferably the biological sample contains a virus
chosen from HIV, HCV (Hepatitis C Virus) and HBV (Hepatitis B
virus).
[0074] "HIV" is the human immunodeficiency virus, which is a
retrovirus. "Retrovirus" is any RNA virus that utilizes reverse
transcriptase during its life cycle. "HCV" is the human hepatitis
virus, which is an RNA virus. "HBV" is the human hepatitis B virus,
which is a DNA virus, but which shares some characteristics of
retroviruses, in that is also displays a reverse transcriptase
activity by which genomic RNA is translated to DNA within the
virus.
[0075] According to yet another preferred embodiment, the
biological sample in any of the methods may contain cells, tissue
cells, mutated cells, malignant cells. For a cancer patient, a
biological sample may include all of the above, cancer cells, whole
or partial tumors, biopsy tissue, etc.
[0076] In one embodiment, a target nucleic acid or protein is
present from which a genetic sequence or protein sequence can be
derived is present in the biological sample.
[0077] A "genetic sequence" is any sequence containing at least one
nucleotide. A nucleotide, for example, may be represented by the
letters A, C, T or G. A combination of nucleotides, may be
represented, for example, by other letters such as R, Y, M, etc.
The amino acids are represented by their own code. An overview of
the abbreviations used for nucleic acids and amino acids can be
found in Alberts, B., Bay, D., Lewis, J., Raff, M., Roberts, K.,
Watson, J. The Molecular Biology of the Cell, Garland publishing,
New York, 1994.
[0078] Genetic sequences as used herein may refer to the complete
sequence of a disease producing agent or at least one segment of
the sequence of a disease producing agent. The sequence of a
particular target protein can be obtained by either sequencing the
nucleic acid coding for the target protein or by sequencing the
protein itself. Protein sequencing can be obtained for example but
not limited to classical Edman degradation chemistry ("Sequence
determination" Edman P. Mol. Biol. Biochem. Biophys. 1970, 8,
211-255.). This chemistry can also be fully automated. Novel
techniques including mass spectroscopy also enable the analysis of
the sequence of a protein under investigation ("Mass spectroscopy
from genomes to proteomics" Yates J. Trends in genetics 2000, 16,
5-8) Alternatively the sequence of a target protein can be obtained
using classical nucleic sequencing protocols e.g. extension chain
termination protocols (Sanger technique) (("DNA sequencing with
chain terminating inhibitors" Sanger F., Nichler., Coulson A. Proc.
Nat. Acad. Sci. 1977, 74, 5463-5467.) or chain cleavage protocols.
Particular sequencing methodologies were developed by e:g. Visible
Genetics. It should be understood that novel approaches have been
developed for unravelling the sequence of a target nucleic acid
including but not limited to mass spectrometry, MALDI-TOF (matrix
assisted laser desorption ionization time of flight spectroscopy)
("Differential sequencing with mass spectroscopy" Graber J, Smith
C., Cantor C. Genet. Anal. 1999, 14, 215-219.) chip analysis
(hybridization based techniques) (Multiplexed biochemical assays
with biological chips. Fodor S P; Rava R P; Huang X C; Pease A C;
Holmes C P; Adams C L Nature 1993, 364, 555-6.) It should be
appreciated that nucleic acid sequencing covers both DNA and RNA
sequencing.
[0079] The term "codon" whenever used in the present invention
relates to the position of the amino acid present at that specific
location of the gene investigated. E.g. a mutation at codon 90 of
the protease gene refers to the an altered amino acid at position
90 in the protein chain as compared to the wild type gene.
[0080] The nucleic acid can be present in the biological sample in
a free and/or soluble form, or can be encapsulated by proteins,
such as in viruses. In preferred embodiments of the invention, the
nucleic acid may be present in a cell, such as a tissue cell, a
malignant cell or a cancer cell. According to other preferred
embodiments the nucleic acid may be one of a microorganism,
protozoan or a multicellular organism. Preferred microorganisms
present in the biological sample for which a phenotype needs to be
determined are viruses or prions, or bacterial, algal or fungal
pathogens. The term "pathogen" also relates to mammalian or plant
parasites.
[0081] The term "pathogen" may relate to any bacterium, virus,
fungus or any other microorganism or multicellular organism which
causes a disease state in another organism. Said other organism
preferably is a mammal, most preferably a human mammal. However
said other organism can also be a plant or a plant cell wherein
said pathogen causes a disease state.
[0082] A "disease producing agent" or "disease causing agent" may
be any agent causing illness or disease that is amenable to therapy
resistance testing. Examples of disease producing agents are
already described above and may include, but are not limited to,
viruses such as retroviruses, cancer causing genes or mutant genes
such as p53-mutants and other oncogenes or tumor suppressor genes,
bacteria, viruses, prions, algae, fungi, parasites, protozoa and
other agents which result in an infectious disease
[0083] The term "malignant cell" relates to a cell showing an
abnormal structure or behavior in the organism containing it,
resulting in a severe disease state. Malignant cells in one
embodiment are cells containing mutations in their genome related
with the occurrence of a disease state or with cancer.
[0084] According to a preferred embodiment, the genetic sequence
obtained practicing the methods of the invention may be the genetic
sequence of a molecular marker for cancer, for instance the genetic
sequence of p53, oncogenes or tumor suppressor genes.
[0085] The term "phenotype" may include any observable property of
an organism or disease producing agent that is produced by the
genotype in conjunction with the environment. In one embodiment,
phenotype refers to resistance of a disease producing agent to at
least one therapy. Therefore, the methods of the invention
determine a phenotype of a disease producing agent towards at least
one therapy or therapeutic agent.
[0086] The expression "virtual phenotype" relates to a phenotype of
a sample that is obtained through the determination of the genotype
of said sample, said genotype is used for correlation in a database
to search for matching genotypes for which a corresponding
phenotype is known. From this collection of phenotypes the
phenotype of the sample is calculated.
[0087] The methods of the invention can be repeated for each
possible therapy or therapeutic agent known or suspected to be
associated with resistance, or towards which a resistance can be
expected to appear. As such, according to another embodiment of the
invention, the phenotype of a biological sample can be presented as
a list of phenotypes against or in respect of individual therapies
or individual therapeutic agents. This is further illustrated in
the examples section.
[0088] The expression "phenotypic resistance" comprises resistance
of a cell, virus, or virally infected cell to a tested therapy,
therapeutic agent or drug.
[0089] The term "resistance" as used herein, pertains to the
capacity of resistance, sensitivity, susceptibility, or
effectiveness of a therapy against a disease.
[0090] The term "therapy" includes but is not limited to a drug,
pharmaceutical, bactericide, fungicide, antibiotic, or anticancer,
antiviral, anti-bacterial anti-fungal, anti-parasitical or any
other compound or composition that can be used in therapy or
therapeutic treatment. Therapy also includes treatment, such as
gene therapy or radiation therapy, useful for the treatment or
amelioration of a disease in a patient. Therapy, as used herein,
also includes combination therapies.
[0091] The present invention can also be applied to determine the
phenotype of normal (tissue) cells or non-malignant cells to
investigate their behavior towards a particular therapy or
therapeutic agent.
[0092] The term "mutation" as used herein, encompasses both genetic
and epigenetic mutations of the genetic sequence of the disease
causing agent. A genetic change includes, but is not limited to,
(i) base substitutions: single nucleotide polymorphisms,
transitions, transversions, substitutions and (ii) frame shift
mutations: insertions, repeats and deletions. Epigenetic changes
include, but are not limited to, alterations of nucleic acids,
e.g., methylation of nucleic acids. For instance (changes in)
methylation of cytosine residues in the whole or only part of the
genetic sequence. In the present invention, mutations may also be
considered at the level of the amino acid sequence, and comprise,
but are not limited to, substitutions, deletions or insertions of
amino acids.
[0093] The "control sequence" or "wild type" is the reference
sequence from which the existence of mutations is based. For
example, a control sequence for HIV is HXB2. This viral genome
comprises 9718 bp and has an accession number in Genbank at NCBI
M38432 or K03455 (gi number: 327742).
[0094] Reference or wild type sequences for use in the invention in
the field of specific diseases, infections or diseases caused by
specific pathogens can be easily obtained from publicly available
databases. For example, the influence of mutations on the etiology
of cancer can be exemplified by the mutations influencing the
effect of the tumor suppressor gene such as p53, TGF-beta, NF-1,
WT-1, and Rb. Also, mutations present in oncogenes such as Ras,
c-myc, c-raf, neu, and IL-2, and repair genes, e.g., methylguanosyl
and methyltransferase can cause changes in the phenotype and/or
drug effect.
[0095] In another embodiment, a mutation that is a methylation of
nucleic acids may occur at the 5-position of cytosine within the
CpG-dinucleotide. In general the CpG dinucleotide is greatly
under-represented throughout the mammalian genome, but it can be
found at close to its expected frequency in small genomic areas of
about one kilobase, called CpG islands. Although the CpG islands
account for only about 1% of the complete genome and for 15% of the
total genomic CpG sites, these regions contain approximately 50% of
the unmethylated CpG dinucleotides. Methylation, may for example,
impact disease states, such as Fragile X and Reft syndrome, and
also on drug profiling. See for example, Robertson et al., Nature
Reviews, 2000 vol 1, p. 11-19, and Esteller M. et al. New England
Journal of Medicine, 2000, Vol 343:19, p. 1350-1354, the
disclosures of which are hereby incorporated by reference.
[0096] The expression "at least one mutation that correlates to
resistance to at least one therapy" includes, but is not limited
to, mutations and combination of mutations in a genetic sequence
that influence sensitivity of a disease causing agent to a therapy.
The at least one mutation may influence sensitivity to a specific
therapy, e.g., a drug, or a group of therapies. The at least one
mutation may, for example, increase and/or decrease resistance of a
disease producing agent to a therapy. The at least one mutation,
may also, for example, enhance and/or decrease the influence of
other mutations present in a genetic sequence that effect
sensitivity of a disease producing agent to a therapy.
[0097] In one embodiment, the at least one mutation that correlates
to resistance to at least one therapy includes mutations or
combinations of mutations that are known or suspected to influence
the sensitivity to a therapy. Lists of mutations known or suspected
to influence the sensitivity of a disease producing agent to a
therapy may be found, for example, in the scientific literature,
patents, and patent applications, e.g. Schinazi, R. F., Larder, B.
A. & Mellors, J. W. 1997. Int. Antiviral News. 5, 129-142
(1997); WO 00/78996; WO 99/67427; WO 99/61658; U.S. Pat. No.
6,087,093; WO 00/73511; and U.S. patent application Ser. No.
09/580/491, U.S. patent application Ser. No. 09/589,167 and "Method
and system for predicting therapeutic agent resistance and for
defining the genetic basis of drug resistance using neural networks
Provisional Application filed, the disclosures of which are hereby
incorporated by reference. Examples of mutations known or suspected
to influence the sensitivity of a disease producing agent to a
therapy may also be found on the internet at
http://hiv-web.lanl.gov; http:I/hivdb.stanford.edu/hiv- /; or
http://www.viral-resistance.com. Additional examples of mutations
present in the RT domain of HIV conferring resistance to a reverse
transcriptase inhibitor include, but are not limited to, 69 C, 69
V, 69 T, 75A, 101I, 103T, 103N, 184T, 188H, 190E, 219 N, 219 Q,
221Y, 221I, and 233V. Additional examples of mutations present in
the PR domain of HIV conferring resistance to a reverse
transcriptase inhibitor include, but are not limited to, 24M, 48A,
and 53L. A mutation may effect resistance alone or in combination
with other mutations. The specific therapy, for example an
antiretroviral drug, for which a mutation may effect resistance may
be determined by one of skill in the art, for example, using the a
phenotypic resistance monitoring assay such as, the
ANTIVIROGRAM.RTM..
[0098] There are different possibilities to represent mutations in
sequences in the form of mutation patterns, some of which are
explained in detail in the examples section.
[0099] The expression "identifying a mutation pattern" in a genetic
sequence relates to the identification of mutations in a genetic
sequence under test compared to a wild type sequence which lead to
a change in nucleic acids or amino acids or which lead to altered
expression of the genetic sequence or altered expression of the
protein encoded by the genetic sequence or altered expression of
the protein under control of said genetic sequence.
[0100] A "mutation pattern" comprises at least one mutation
influencing sensitivity of at least one disease causing agent to at
least one therapy. As such, a mutation pattern may consist only one
single mutation. Alternatively a mutation pattern may consist of at
least two, at least three, at least four or at least five
mutations.
[0101] According to yet another embodiment a mutation pattern is a
list or combination of mutations or a list of combinations of
mutations that influence sensitivity of at least one disease
causing agent to at least one therapy. A mutation pattern may be
constructed, for example, by searching a genetic sequence for the
occurrence of each mutation of a series of mutations. The existence
of a mutation or the existence of one of a group of mutations may
then be noted. The mutation pattern is constructed, for example,
once a genetic sequence is searched for the occurrence of each
mutation in the series. In one embodiment, a mutation pattern is
constructed using a group of mutations that correlate to resistance
to a therapy, thereby constructing a mutation pattern that is
specific to a therapy. In a further embodiment, a mutation pattern
is constructed by searching for mutations in a genetic sequence
wherein the mutations are linked by at least one logical operator
chosen from AND, OR, NOT, and NOR. In one embodiment the invention
relates to any of the methods described in the invention wherein
the mutation pattern comprises at least two mutations known or
suspected to be associated with resistance to at least one
therapy.
[0102] Furthermore, the present invention also relates to the
identification of "at least one mutation pattern" in a sequence. It
should be clear from the following, that for each biological sample
(or for each genetic sequence derivable from said biological
sample) several (i.e. more than one) mutation patterns can be
identified towards a single therapy or a single therapeutic
agent.
[0103] In one embodiment of the invention, the sequence under test
is aligned with the wild type sequence and the alignment or
differences in the alignment are stored in a computer medium or a
database. Alternatively, a mutation pattern can be obtained from
the alignment, represented by the mutated amino acids and their
positions in the polypeptide(s). It should be clear that the man
skilled in the art knows different ways of representing and/or
handling information from sequence alignments including the use of
known computer programs and algorithms ("Bioinformatics: A
practical guide to the analysis of genes and proteins" Eds.
Baxevanis and Ouellette, 1998, John Wiley and Sons, New York.
Chapter 7 "Sequence alignment and database searching" G. Schuler,
Chapter 8 Practical "Aspects of multiple sequence alignment" A.
Baxevanis and Chapter 9 "Phylogenetic analysis" M. Hershkovitz and
D. Leipe). A practical example of multiple sequence alignment is
the construction of a phylogenetic tree. A phylogenetic tree
visualizes the relationship between different sequences and can be
used to predict future events and retrospectively to devise a
common origin. This type of analysis can be used to predict a
similar drug sensitivity for a sample but also can be used to
unravel the origin of different patient sample (i.c. the origin of
the viral strain).
[0104] According to preferred embodiments of any of the methods of
the invention, the similar mutation pattern is identified by
aligning the genetic sequence of a cell or a pathogen in the
biological sample with the WT genetic sequence of said cell or
pathogen.
[0105] According to another embodiment, "Discrete Clustering" is
used to determine when sequences are "similar". "Similar" in this
context does not mean "exactly" alike, since no single sequence
matches another. Rather, "similar", in this context, means "having
similar mutations", or "having mutations that have the same effect
towards resistance against inhibitor drugs." To be able to define
the patterns of mutations that were regarded as "having the same
effect", a pattern database that is drug related may be built. The
patterns of mutations referred to here are called "hot spots". The
term "hot-spot" is herein be defined as a combination of mutations
that confer resistance to a defined drug.
[0106] The hot spots describe mutations or clusters of mutations
(generally combined by "OR" (.vertline.) or "AND" (&) logical
operators) that are related to a certain inhibitor drug. A drug may
have 1, 2, 3, 4 or more hot spots attached to it. Other logical
operators may be "NOT", "NOR" etc. and the possibility to identify
INSERTS and DELETIONS in the DNA sequence.
[0107] A simplified example of, for instance, a hot spots table
used for testing resistance of HIV sequences towards different
drugs can be represented as follows:
1 Drug # Hot spot A 1 (mutationD .vertline. mutationE) &
(mutationF .vertline. mutationG) 2 mutationH .vertline. mutationI 3
mutationJ & mutationK 4 (mutationZ .vertline. mutationX) &
mutationV B 1 mutationL 2 mutationM & mutationN 3 (mutationO
& mutationP) .vertline. mutationQ C 1 mutationR 2 mutationS
.vertline. mutationT
[0108] Subsequently, every HIV virus sequence that is tested is
"profiled" by testing the sequence against all the available hot
spots, for all the inhibitor drugs involved. This analysis produces
a profile per drug for the sequence of interest.
[0109] In one embodiment, for every hot spot that matches, the
sequence receives a "1"; for every non-matching hot spot, it gets a
"0". For a given sequence under test, the result could be:
2 Drug Profile A 1010 hot spots 1 and 3 apply, hot spots 2 and 4 do
not for drug A. B 001 hot spot 3 applies, hot spots 1 and 2 do not
for drug B. C 10 hot spot 1 applies, hot spot 2 does not for drug
C.
[0110] As such, the expression "therapy profile" or "drug profile"
relates to the presentation of a genetic sequence as explained
above. The term "therapy or drug profile" is the combination of
mutation patterns corresponding to resistance to a single therapy
or drug.
[0111] In other words, a therapy profile can be given for each
drug. In the example of drug A above, hot spots 1 and 3 relate to
resistance to drug A and are assigned a value of 1. In contrast,
hot spots 2 and 4 do not and are assigned a value of 0, thus the
profile "1010". This procedure can be seen as a form of clustering.
However, since the elements of the cluster (0 and 1) are based on
pre-defined sets (hot spots) this method is usually referred to as
"discrete clustering."
[0112] The present invention thus relates to any of the methods of
the invention wherein discrete clustering is used to identify
similar sequences or wherein cluster searching is used to determine
similar mutation patterns.
[0113] According to a preferred embodiment, the invention relates
to a method of determining a phenotype of a disease producing agent
comprising:
[0114] a) obtaining a genetic sequence of said disease producing
agent,
[0115] b) identifying at least one mutation in said genetic
sequence wherein said mutation is comprised within at least one
mutation pattern,
[0116] c) searching a genotype database for at least one genotype
entry comprising said mutation in said at least one mutation
pattern,
[0117] d) correlating said at least one genotype entry with a
phenotype in a phenotype database, and
[0118] e) determining the phenotype of said biological sample.
[0119] According to other preferred embodiments, the invention
relates to a method for assessing the efficiency of a patient's
therapy or for evaluating or optimizing a therapy comprising
obtaining a biological sample containing a disease causing agent
from a patient, further comprising at least steps a) to e) of the
above described method.
[0120] The invention further relates to the above described methods
wherein the mutation pattern is associated with resistance to one
therapy or drug. In the above methods, steps b) to e) can be
repeated to obtain a series of phenotypes for a group of therapies
or drugs.
[0121] The invention further relates to the above described methods
wherein said mutation pattern comprises at least two mutations
linked with a logical operator, further characterized in that the
at least two mutation patterns are associated with resistance to
one therapy.
[0122] The invention further relates to the above described methods
wherein said mutation patterns are linked with a logical operator
defining a therapy profile and wherein said therapy profile is
represented by a sequence, said sequence is represented by a series
of 1 and/or 0 wherein 1 represents the presence of a mutation
pattern in the therapy profile and 0 the absence of a mutation
pattern in the therapy profile.
[0123] It should be understood that the principles and methods as
outlined in the application are very dynamic. The databases are
frequently updated to incorporate new mutations which improve the
accuracy of the determination. The number and the combinations of
mutations present in the system are update on a regular basis
(every 3 to 4 months). This is necessary in order to incorporate
newly identified mutations or combinations which improve the
performance of the system. By taking less mutation (or hot-spots)
one will still be able to calculate the phenotype, however, from a
statistical perspective the performance of the system will lower.
In addition this regular update is required to anticipate the
effect of drugs which are added to the list and which may have
their own list of mutations causing resistance to that drug. The
person skilled in the art will be aware of those mutations or
combinations of mutations influencing the drug efficacy.
Information hereon can be found at the internet
http://hiv-web.lanl.gov, http://hivdb.stanford.edu/hiv/ or
http://www.viral-resistance.com. or in articles e.g. Schinazi, R.
F., Larder, B. A. & Mellors, J. W. 1997. Int. Antiviral News.
5, 129-142 (1997). In addition lists of mutations are provided in
several patent applications. (Means and methods for monitoring
protease inhibitor antiretroviral therapy and guiding therapeutic
decisions in the treatment of HIV/AIDS (WO 00/78996), Means and
methods for monitoring nucleoside reverse transcriptase inhibitor
antiretroviral therapy guiding therapeutic decisions in the
treatment of HIV/AIDS (WO 99/67427) Means and methods for
monitoring non-nucleoside reverse transcriptase inhibitor
antiretroviral therapy (WO 99/61658), Method for detection of
drug-induced mutations in the reverse transecriptase gene (U.S.
Pat. No. 6,087,093), New mutational profiles in HIV-1 reverse
transcriptase correlated with phenotypic drug resistance (WO
00/73511) and New mutational profiles in HIV-1 reverse
transcriptase correlated with phenotypic drug resistance (U.S. pat.
Ser. No: 09/580/491)
[0124] In the present invention, after determining the hot spots or
the therapy for a sequence under test, a genotype database may be
queried for sequences similar to the sequence under scrutiny. This
query may be done using cluster searches.
[0125] The expression "genotype database" relates to diverse types
of databases wherein sequence information is stored. According to
one embodiment of the invention, the genotype database stores
complete or partial nucleotide sequences. According to other
embodiments of the invention the genotype database stores
nucleotide sequences linked to their amino acid translations or
stores nucleotide sequences linked to at least one list of
particular mutations. These mutations are in respect of a reference
sequence. Also these mutations can be at the nucleotide level or at
the amino acid level. These lists can include all mutations in
respect of a reference sequence or can contain a selection of
mutations.
[0126] The information provided to the genotype database therefore
can also be in the form a complete or partial nucleic acid sequence
related with a biological sample or can be in the form of a list of
particular mutations representing a particular nucleic acid
sequence related with a biological sample. Therefore, the term
"genotype entry" relates to any form in which information is
provided to the genotype database.
[0127] For instance, according to the present invention, a
preferred way of listing mutations in a genotype database is
listing mutations which are known or suspected to be associated
with resistance to a particular therapy or therapeutic agent. As
such, each genotype entry in the genotype database can be linked to
several lists of mutations occurring in the genetic sequence
related to a biological sample, each of those lists representative
for mutations which are known or suspected to be associated with
resistance to a particular therapy or therapeutic agent towards
which a resistance is known or can be expected to appear.
[0128] Regardless of the method used to select "similar sequences",
once a selection of "similar sequences" is found, the application
queries the phenotypic database for phenotypic data belonging to
those sequences. The phenotype database may be constructed in such
a way that in a database entry a genetic sequence (related to a
biological sample) is linked to a phenotype. Alternatively,
phenotypes in a phenotype database may be linked to other means of
presenting nucleotide sequence information, for instance a mutation
pattern or mutation profile for a therapy, therapeutic agent or
drug. Alternatively relational genotype/phenotype databases may be
used in any of the methods of the invention to correlate genotypic
with phenotypic information. In one embodiment, this process is
done for each therapy, therapeutic agent or drug, again using
cluster searches. The query returns a selection of phenotypic
results for every therapy, therapeutic agent or drug listed. A
statistical analysis may be performed on the data to remove
outliers and the virtual fold resistance may be calculated. For
example, per drug, the mean of the log (fold resistance values) may
be used to calculate the virtual fold resistance and the
interpretation of these numbers will generate a Virtual Phenotype.
The virtual phenotype (Fold Resistance value) may then further be
used to classify the virus as Sensitive (S), Intermediate (I) or
Resistant (R).
[0129] "Resistance" is determined using the protocols described in
Antivirograme assay (WO 97/27480). Resistance is determined with
respect to a laboratory reference strain HIV LAI/IIIB. The
difference in IC.sub.50's between the patient sample and the
reference viral strain is determined as a quotient. This fold
change in IC.sub.50 is reported and indicative of the resistance
profile of a certain drug. Based on the changes in IC.sub.50,
cut-off values have been established to distinguish a sample from
being sensitive or resistant to a certain drug.
[0130] The expression "relational genotype/phenotype database"
refers to a database that brings together the knowledge of both a
genotypic and phenotypic database. The genotypic database, for
example, contains genetic sequence information regarding at least
one tested disease producing agent. The genetic sequence
information may vary from the entire sequence of a disease
producing agent to a segment of the sequence of a disease producing
agent, to a mutation pattern. For example, the genetic sequence of
tested HIV viruses or the mutation pattern of tested HIV viruses.
The phenotypic database contains phenotypic resistance values for
the at least one tested disease producing agent to at least one
therapy. For example, the phenotypic resistance values of tested
HIV viruses, with a fold resistance determination compared to the
reference HIV virus (wild type).
[0131] In one embodiment, for example, the methods may use
different genotype and phenotype databases. As a sample is run
during the analysis, the identified sequence entries and their
corresponding phenotypes are found and "transferred" to a "Call
Center Database". This call center is a third database, where the
pheno-genotype results are combined and used for the calculation of
the virtual fold resistance and the generation of the report. This
database is a relational database.
[0132] In one embodiment, in a relational gentoype/phenotype
database, the data entries are combined to yield a "2D"
representation for each sample: (x.sub.i, y.sub.i) where x.sub.i
represents the phenotypic result, y.sub.i the genotypic. In another
embodiment, the data entries are combined to yield a "3D"
representation for each sample: (x.sub.i, y.sub.i, z.sub.i) where
x.sub.i represents the phenotypic result, y.sub.i the genotypic
result, and z.sub.i other information regarding the sample, such as
a sample number.
[0133] Therefore, the present invention also relates to any of the
methods described wherein a relational genotype/phenotype database
is used for correlating the at least one genotype entry with a
similar mutation pattern with a phenotype in said database.
[0134] According to a preferred embodiment, the present invention
provides a thorough and reliable interpretation of genotypic
information by interrogating the genotype part of a relational
genotype/phenotype database for identical or similar patterns of
mutations to that of the patient sample under study. Once the
matches are found, the corresponding phenotypes are accessed and
the phenotypic information, the changes in IC.sub.50 to the various
drugs, is pooled and averaged to produce a phenotypic profile. This
profile, in one embodiment of the invention, may be based on data
from hundred or thousands of real phenotypes with the same patterns
of mutations. In another embodiment, the RT-PR region of the HIV-1
genome of a patient sample is sequenced and the sequence is used in
the methods of the invention to interpret the genotype information.
The virtual phenotype may then be used to design a therapy, which
may be one or more drugs. In a further embodiment, proprietary
software may be used to interpret the genotype information
according to the methods of the invention.
[0135] In one embodiment, a more accurate phenotype may be obtained
by constructing a mutation pattern using mutations that have been
validated. One of skill in the art will recognize that there are
numerous methods of validating whether a mutation correlates to
resistance to at least one therapy, including but not limited to
phenotype experiments, such as the ANTIVIROGRAM (Virco, Belgium)
and clinical studies. (WO 97/27480)
[0136] In another embodiment, the number and the combinations of
mutations used to construct a mutation pattern would be updated on
a regular basis. This may be done in order to incorporate newly
identified mutations or combinations which may improve the
performance of the system. In one embodiment, a phenotype may be
calculated from at least one mutation used to construct a mutation
pattern, however, from a statistical perspective a more accurate
phenotype may result from a greater number of mutations.
[0137] According to a further embodiment, in any of the methods of
the invention the phenotype of said biological sample can be
expressed as a mean fold-change in resistance towards at least one
therapy, wherein said mean fold-change resistance is calculated
from the database phenotype(s) of the at least one genotype entry
with a similar mutation pattern. Preferably, the phenotype of said
biological sample towards the at least one therapy or therapeutic
agent is expressed as an IC.sub.50. The IC values are inhibitory
concentrations, wherein the IC.sub.50 represents the concentration
of a defined drug yielding half of the signal output as compared to
a blank run comprising no drugs.
[0138] The invention further relates to a method for generating a
report wherein said report comprises the phenotype determined (or
predicted) using any of the methods of the invention. Several
examples of reports are illustrated in the examples section. The
report may contain the phenotype of a biological sample against at
least one therapy or therapeutic agent. Preferably the phenotype of
a biological sample against several therapies or therapeutic agents
are listed in said report.
[0139] According to yet another embodiment, the present invention
relates to a diagnostic system for determining a phenotype of a
disease producing agent comprising:
[0140] a) means for obtaining a genetic sequence of said disease
producing agent,
[0141] b) means for identifying at least one mutation in said
genetic sequence,
[0142] c) genotype database means comprising genotype entries,
[0143] d) phenotype database means comprising phenotypes, and
[0144] e) correlation means correlating said genotype entry with
said phenotype, said genotype entry corresponds with the obtained
genetic sequence.
[0145] The invention further relates to a diagnostic system as
herein described for use in any of the above described methods.
[0146] The invention also relates to a computer system comprising a
first database comprising genotype entries and a second database
comprising phenotypes in which the genotype entries correspond to
at least one mutation pattern and interface means linking the first
database to the second database. According to a preferred
embodiment, the genotype and phenotype database are integrated in a
relational genotype/phenotype database wherein the genotype entries
comprise, or are related with, at least one mutation pattern,
preferably comprising at least two mutations, or wherein the
genotype entries are related with a drug profile are a phenotype
profile.
[0147] The invention further relates to a computer program product
stored on a computer usable medium comprising computer readable
program means for causing a computer to control the execution of
the method according to any of the claims 1-19 and 21-28.
[0148] The invention further relates to systems, computer program
products, business methods, server side and client side systems and
methods for generating, providing, and transmitting the results of
the above methods.
[0149] According to a preferred embodiment, the invention relates
to a computer readable medium comprising the phenotype determined
or predicted using any of the methods of the invention.
[0150] The invention further relates to a computer program for
predicting resistance of a patient to therapy comprising:
[0151] a) receiving a genetic sequence from a disease producing
agent from said patient,
[0152] b) identifying at least one mutation pattern in the genetic
sequence comprising at least one mutation wherein said at least one
mutation or mutation pattern is associated with resistance to at
least one therapy,
[0153] c) searching a genotype database for at least one genotype
entry with a similar mutation pattern to the at least one mutation
pattern identified in b),
[0154] d) correlating said at least one genotype entry with a
similar mutation pattern with a phenotype in a phenotype
database,
[0155] e) obtaining a series of phenotypes by repeating steps b)
through e) for a group of therapies, and,
[0156] f) predicting resistance of the patient to therapy from the
series of phenotypes.
[0157] The term "health care provider" is understood to include any
professional person authorized or trained to treat or take patient
data and/or samples. Such persons include but are not limited to
physicians, doctors, clinicians, health care workers, nurses,
technicians, laboratories, etc.
[0158] The present invention also relates to a business method,
comprising a method predicting resistance of a patient to therapy
comprising:
[0159] a) receiving from the health care provider a genetic
sequence from a disease causing agent for example from a biological
sample from said patient,
[0160] b) identifying at least one mutation pattern in said genetic
sequence comprising at least one mutation, and wherein said at
least one mutation or mutation pattern is associated with
resistance to at least one therapy,
[0161] c) searching a genotype database for at least one genotype
entry with a similar mutation pattern to said at least one mutation
pattern identified in the genetic sequence in b),
[0162] d) correlating said at least one genotype entry with a
similar mutation pattern with a phenotype in a phenotype
database,
[0163] e) determining a phenotype of the disease causing agent from
the database phenotype of the at least one genotype entry with a
similar mutation pattern,
[0164] f) obtaining a series of phenotypes by repeating steps b)
through e) for a group of therapies;
[0165] g) predicting resistance of the patient to therapy from the
series of phenotypes;
[0166] h) providing the health care provider with a prediction of
the resistance of the patient to therapy.
[0167] FIG. 10 provides an exemplary flowchart for determining a
virtual phenotype. In one embodiment, the various steps and
operations of FIG. 10 may be performed by the phenotype
determination system 40 in the system environment of FIG. 11 to
assess resistance of a patient to a therapy, or design or optimize
a therapy for a patient, for example, with HIV.
[0168] As illustrated in FIG. 10, in one embodiment the process
starts with obtaining at least one genetic sequence of a patient
(step 100). A genetic sequence may be obtained by a health care
provider, laboratory, or any other entity. In one embodiment, the
at least one genetic sequence, including genetic sequences taken at
various times or a history of sequence of a patient may be stored
in a database, such as local database 46 of phenotype determination
system 40 (see FIG. 11).
[0169] As part of computing a virtual phenotype, a mutation pattern
of the genetic sequence may be determined (step 110) for at least
one therapy. As part of this step, the phenotype determination
system 40 may include data of mutations that correlate to
resistance to at least one therapy. The mutation data may be
accessed from local database 46 and/or public database(s) 52.
[0170] A relational genotype/phenotype database is then searched
for at least one genetic sequence similar to the genetic sequence
of the patient (step 120). All similar sequences are identified.
This may be accomplished by searching for a mutation pattern
similar to the mutation pattern determined in step 110 or, for
example, by comparing the genetic sequence of the patient to
sequences of the relational genotype/phenotype database using
sequence alignment. The relational genotype/phenotype database may
be accessed from a local database 46 and/or 46 and/or public
database(s) 52.
[0171] As illustrated in FIG. 10, a database phenotype is obtained
for each similar genetic sequence identified from the relational
genotype/phenotype database (step 130). A phenotype for the genetic
sequence of the patient is then calculated from all of the database
phenotypes identified (step 140).
[0172] The information may then be transmitted back to the health
care provider or used in the determination of other information,
such as assess resistance of a patient to a therapy, or to design
or optimize a therapy for a patient. The resulting information may
then be transmitted back to the health care provider. FIG. 11 is an
exemplary system environment in which the features and methods of
the invention may be implemented (for example, the methods as shown
in FIG. 10). As illustrated in FIG. 11, a communication channel 30
is provided for facilitating the transfer of data between various
system components and entities. These components and entities may
include, for example, one or more health care providers 12A-12N who
interact with or treat patients (not shown), a phenotype
determination system 40, and one or more public databases 52.
[0173] Communication channel 30 may be implemented through any
single or combination of channels that allow communication between
different people, computers, or locations. The communication
channel may be any system that allows communication between the
different entities illustrated in FIG. 11.
[0174] Each of the health care providers 12A-12N, for example,
collects biological samples for each patient or patients, and
determines a genetic sequence or has a genetic sequence determined,
wherein such data is submitted for analysis by phenotype
determination system 40.
[0175] In one embodiment, the phenotype determination system 40 may
be implemented through any suitable combination of hardware,
software and/or firmware. For example, phenotype determination
system 40 may be implemented through the use of a personal
computer, a working station, a server or any other computing
platform. Software or programmed instructions may also be provided
for controlling the operations of the computing platform,
consistent with the principles of the invention. As illustrated in
FIG. 11, phenotype determination system 40 may also include a local
database 46 for storing patient data including genetic sequence
data. Local database 46 may also store mutation data and/or
relational genotype/phenotype data mutation data and/or relational
genotype/phenotype data may be accessed from one or more public
databases 52 by phenotype determination system 40.
[0176] Consistent with the methods of the present invention,
phenotype determination system 40 is configured to provide
information regarding at least one of: phenotype, assessment of
resistance of a patient to a therapy, and design or optimization of
a therapy for patients treated by physicians 12A-12N. The
information may be sent by system 40 to physicians 12A-12N in
numerous formats (e.g., written report, electronic file, graphical
display, etc.) and may be provided to physicians on fee basis or as
a free or ancillary service.
[0177] It should be understood that the method as outlined in the
Examples is apt to analyze the effect of genetic alterations, and
the consequent protein changes, in the protease and reverse
transcriptase gene of HIV. It should be appreciated that the method
is equally well adaptable to analyze different genes or sets of
genes present in HIV, or any other organism be it of viral,
prokaryotic or eukaryotic origin, implicated in clinical
diagnostics or in pharmacogenetics.
[0178] The following examples and figures are given by means of
illustration of the present invention and are in no way limiting.
All references, patents, and patent applications cited herein are
incorporated by reference in their entirety.
DESCRIPTION OF FIGURES
[0179] FIG. 1: The report of FIG. 1 provides the following
information to aid the physician to interpret the genotypic data
and develop a treatment regime:
[0180] 1. The first two columns give the trade and generic names of
the drugs.
[0181] 2. The top of the chart has a graphic representation of the
mutations in the protease region of the genome.
[0182] 3. Below this is the same information for the reverse
transcriptase region.
[0183] 4. The third column simply indicates whether or not
mutations affecting susceptibility for that particular drug were
found.
[0184] 5. The fourth column indicates the number of samples in the
database that match the pattern of mutations in the sample virus,
for each drug.
[0185] 6. The fifth column has a color-coded representation of the
range of phenotypic susceptibilities found in the database.
[0186] 7. Finally the average IC.sub.50 for all the matches in the
database is presented for each drug.
[0187] FIG. 2: A Prediction of a Phenotypic Report Using the
Present Invention.
[0188] FIG. 3: Predictive value of the present invention.
[0189] FIG. 4: Section of the HIV genome covered by the
Antivirogram.RTM. assay
[0190] FIG. 5: Schematic representation according to one embodiment
of resistance monitoring.
[0191] FIG. 6: is a schematic diagram of an exemplary pattern
search The numbers indicated for each mutation (N) indicate the N
observed in the database analysis illustrated in Table 1.
[0192] FIG. 7: depicts the phenotypic search results for virus with
different clusters of AZT resistance mutations. The graph shows the
mean (o), standard error (.THETA.) and 95% confidence limits (A)
for each cluster.
[0193] FIG. 8: is a correlation between the actual and computer
predicted virtual phenotype. A linear regression analysis is shown
for four independent random data sets comprising 500 samples
each.
[0194] FIGS. 9(a) & (b): are a depiction of the odds ratios of
failure to achieve a viral load reduction below 400 viral RNA
copies/ml.
[0195] FIGS. 10(a) & (b):
[0196] 10(a) is an exemplary flow chart for determining a
phenotype, in accordance with the methods of the invention
[0197] 10(b) is an exemplary flow chart of one embodiment for
performing step 110 to 130 of FIG. 10(a)
[0198] FIG. 11: an exemplary representation of a system environment
in which features and methods of the invention may be
implemented.
EXAMPLES
Example 1
[0199] Definition of a Sequence.
[0200] A sequence consists of a number of nucleotides. Nucleotides
are represented by the letters A, C, T and G. A, C, T and G are the
bases of a sequence. Other letters like R, Y, M etc. stand for a
combination of two or more bases.
3 Letter MPX Letter MPX R AG H ACT Y TC B GCT M AC V ACG K GT D AGT
S CG N GATC W AT
[0201] Groups of 3 nucleotides form a codon. These codons are
translated to amino acids and then compared to a reference sequence
in order to determine the mutations. A mutation is a difference
between the reference sequence and the test sequence. The raw
nucleotide reference sequence looks like this (the example shows
only the protease section which contains 99 amino acids or 297
nucleotides. The `reverse transcriptase` section contains 400 amino
acids or 1200 nucleotides.):
4 CCTCAGGTCACTCTTTGGCAACGACCCCTCGTCACAATAAAGATAGGGGG
GCAACTAAAGGAAGCTCTATTAGATACAGGAGCAGATGATACAGTATTAG
AAGAAATGAGTTTGCCAGGAAGATGGAAACCAAAAATGATAGGGGGAATT
GGAGGTTTTATCAAAGTAAGACAGTATGATCAGATACTCATAGAAATCTG
TGGACATAAAGCTATAGGTACAGTATTAGTAGGACCTACACCTGTCAACA
TAATTGGAAGAAATCTGTTGACTCAGATTGGTTGCACTTTAAATTTT
[0202] This is the protease section of the sequence under test:
5 CCTCAAATCACTCTTTGGCAACGACCCATCGTCACAATAAAAATAGGAGG
GCAACTAAGGGAAGCTCTATTAGACACAGGAGCAGATGATACAGTATTAG
AAGAAATAGATTTGCCAGGAAGATGGAAACCAAAAATCATAGGGGGAATT
GGAGGCTTTGTCAAAGTAAGAGAGTATGATCAARTACCCATAGAAATCTG
TGGAAAGAAAGTTATAGGTACAGTATTAGTAGGACCTACACCTGCCAACA
TAATTGGAAGAAATCTGATGACTCAGATGGGTTGCACTTTAAATTTT
[0203] The differences are underlined. After translation into amino
acids and comparison, the result looks like this:
6 1
[0204] The rows and figures with the dark grey background are the
positions within the protease section. The letters on the medium
grey background show the amino acids in the reference sequence. The
llight grey background shows the sequence under test: empty spaces
mean that the amino acid is the same as the one in the reference
sequence, bold and boxed amino acids are mutations that are known
or suspected to be associated with resistance to therapy or
drugs.
[0205] In this case, mutations would be: 10I, 20R, 36I etc. where
the number represents the position and the letter the amino acid
that has mutated.
[0206] Virtual Phenotype Calculation
[0207] To calculate the Virtual Phenotype, the concept of `similar
sequences` needs to be explained.
[0208] To determine similarity between sequences, one cannot just
match the nucleotides or amino acids, because they not always match
completely. This is due to a number of undocumented mutations that
can be found in any sequence and the fact that different
combinations of nucleotides lead to the same amino acid.
[0209] To be able to compare, we define anchor points, or
`Hot-spots` as they are called. For each drug, a number of
hot-spots is defined and continuously updated.
Example
[0210]
7 Drug A Mutation A .vertline. Mutation B .vertline. Mutation C
.vertline. Mutation D Mutation E .vertline. Mutation F Mutation G
& Mutation H (Mutation I .vertline. Mutation J) & (Mutation
K .vertline. Mutation L) Mutation M .vertline. Mutation N
.vertline. Mutation E .vertline. Mutation F (Mutation M .vertline.
Mutation N .vertline. Mutation E .vertline. Mutation F) &
Mutation G Mutation O & Mutation P Mutation Q .vertline.
Mutation R .vertline. Mutation F Mutation E & Mutation Q &
Mutation G Mutation R
[0211] In this example, there are 10 hot spot descriptions related
to the drug in question.
[0212] To compare the sequences, a list of profiles (one profile
per drug that is tested) is determined for every sequence. The
profile is determined by keeping count of matching and non-matching
hot spots per drug.
[0213] In the above example, if a sequence would match hot spot 2,
5, 6, 7 and 9, the sequence would have a profile for this drug
equal to `0100111010`. Every new profile is stored inside the
database.
[0214] Every hot-spot keeps count of the sequences that match the
mutations it states. Using this information, the system is able to
retrieve all the sequences that have exactly the same profile by
doing an intersection of the sets that match and by subsequently
subtracting the sets that don't match. In stead of using sets of
sequences, the systems uses the corresponding sets of phenotypic
data; this increases the performance of the system.
[0215] The next step is to retrieve the phenotypic results for
those sequences. They vary between none and well over 20.000. On
these phenotypic results, a few calculations are executed, e.g.
mean or median fold resistances can be calculated: 1 n x 2 - ( x )
2 n ( n - 1 ) . 1
[0216] 2. The log of the standard deviation of all the Fold
Resistance values is calculated: Where n is the amount of
phenotypic determinations and x contains the individual-fold
resistance values.
[0217] 3. The mean of all the Fold Resistance values is
calculated
[0218] 4. The outliers are determined using a value of 3 O; these
are the Fold Resistance value that are greater than
(mean+(3.times.STD)) or smaller than (mean-(3.times.STD))
[0219] 5. The corrected mean Fold Resistance is calculated on all
the data minus the outliers
[0220] This corrected value is reported and used to determine
resistance together with the cut-off values corresponding to that
drug. All the calculated values are stored together with the
profiles they were calculated for.
Example 2
[0221] One example of an embodiment of the present invention can be
described by the following steps:
[0222] 1. The gag-RT-PR sequence is entered into a computer as a
text string;
[0223] 2. The computer program scans the sequence for all
mutations, and `lists` all those that are known or suspected to
play a role in the development of drug resistance;
[0224] 3. The mutations are then listed against each of the drugs
for which they affect sensitivity;
[0225] 4. For each drug, the computer program interrogates a
genotype database for previous samples with the same or similar
mutations or sequences, relating to that drug. Primary mutations,
those initial mutations that have a discernable effect on drug
resistance, are searched in the database individually first.
Secondary mutations, those that have subtle effects on resistance
or increase viral fitness, are searched in groups. Typically there
will be several hundred records that match the pattern of mutations
for each drug;
[0226] 5. Every time a match is found, for example, a previous
sample with the same pr similar pattern of AZT mutations, the
computer program locates the phenotype for that sample in the Virco
phenotype database and stores it (expressed as a change in
IC.sub.50)
[0227] 6. Finally, again for each drug, the program calculates the
mean change in IC.sub.50 from all the examples it has found and
summarizes the distribution of sensitivities as the percentage that
were sensitive (resistance is unlikely), intermediate (resistance
is uncertain) or resistant (resistance is likely); and
[0228] 7. The program may then generate a final report that lists,
for each drug in turn:
[0229] A) The drug names
[0230] B) The mutations found in the genotype that affect
sensitivity to that drug
[0231] C) The number of genotypes in the Virco data base for which
phenotype data is available
[0232] D) The proportion of these that were sensitive, intermediate
or resistant to that drug
[0233] E) The mean sensitivity score--as a change in IC.sub.50.
[0234] The invention also provides, in one embodiment, a method of
assessing effectiveness of a therapy on a patient by determining
whether the phenotype of a biological sample is in a
therapeutically effect range. A therapeutically effective range
takes into account, among other variables, the therapy or therapies
being examined, individual patient characteristics such as a
patient's pharmacokinetics, and resistance of the disease causing
agent. One of skill in the art may calculate a therapeutically
effective range by using, for example, published therapy
effectiveness ranges and pharmacokinetic models. (See e.g.,
European Patent Application No. 00/203200.1, filed on Sep. 15,
2000, the disclosure of which is hereby incorporated by reference.)
The invention also provides methods of optimizing therapy for a
patient and designing therapy for a patient. In one embodiment, the
skilled artisan may optimize and/or design a therapy by comparing
the phenotypes determined using the methods of the invention and
choosing the therapy or therapies that would be most effective for
treating a patient.
[0235] FIG. 1 represents a sample report produced using the present
invention.
[0236] Studies have shown the present inventive method to be more
than 90% accurate in predicting the actual phenotype using a
current genotype and phenotype database. As more data is added to a
database, the chances of finding large numbers of exact matches for
the mutational pattern of an individual will increase and the level
of accuracy can be even higher.
Example 3
[0237] In the case shown in FIG. 2, for example, the virus
population is likely to respond to didanosine, zalcitabine, and
stavudine (from the NRTIs), not AZT, 3TC and possibly not abacavir.
A response is likely to any of the NNRTIs but the drug most likely
to be effective is efavirenz. The patient's virus will very likely
be resistant to the protease inhibitor nelfinavir and most likely
to be sensitive to amprenavir.
[0238] The distribution of the sensitivities of the phenotype
matches can generally enable the physician, regardless of the
disease studied, to select among alternative drugs that the system
predicts will be effective to minimize the chances of resistance.
With regard to HIV, for example, two protease inhibitors may have
an identical score for the predicted change in IC.sub.50,
suggesting sensitivity, but one may have a wider spread of data,
including some examples where there was resistance. The physician
can then choose the drug with no evidence of resistance in the
database.
[0239] This mean sensitivity score is highly predictive of the
actual phenotype and is therefore a reliable predictor of which
drugs the patient will or will not respond to in the clinical
setting. See FIG. 3
Example 4
[0240] In another embodiment, the present invention can be used
with phenotypic resistance monitoring assays, such as known
recombinant assays, in the clinical management of resistance
developing diseases, including HIV and other viral infections,
cancer, bacterial infections, and the like. A particularly useful
resistance monitoring system is a recombinant assay known as the
Antivirogram.RTM.. The Antivirogram.RTM. is a highly automated,
high throughput, second generation, recombinant assay that can
measure susceptibility, especially viral susceptibility, to all the
available drugs, particularly antiretroviral drugs (reverse
transcriptase inhibitors and protease inhibitors) at the same time.
(Hertogs K, de Bethune M P, Miller V et al. Antimicrob Agents
Chemother, 1998; 42(2):269-276, incorporated by reference).
[0241] The whole process can be divided into three phases:
molecular biology, transfection and susceptibility testing. The
process is summarized below and in FIG. 4.
[0242] Molecular Biology
[0243] Viral RNA fragments extracted from patient's blood
sample
[0244] Complementary DNA (cDNA) of the gag-PR-RT sequence, through
to codon 400 formed via reverse transcription
[0245] Gag-PT-RT sequence multiplied using two rounds of PCR
[0246] Purification of the DNA fragments
[0247] Creation of laboratory proviral clone with gag-PR-RT
sequence deleted
[0248] Insertion of the clone into bacterial plasmids for
reproduction of large quantities
[0249] Transfection
[0250] This is the process by which viral genes are transferred to
a cell.
[0251] 1. The gag-PR-RT sequences from the patient sample and the
plasmid fragments are mixed with CD4+, MT4 cells.
[0252] 2. Electroporation takes place: the cells are subject to a
short (milliseconds), but strong current in a cuvette producing
transient openings in the cell membrane, through which both the
gag-PR-RT DNA fragment and the plasmid fragment enter.
[0253] 3. In a relatively small proportion of the cells, both
fragments will meet up and, probably supported by a cellular
enzyme, recombine to form a complete HIV-1 genome that can now be
converted into infectious virus particles.
[0254] 4. The recombinant virus is then grown in this cell culture
for approximately 8 days, until the cytopathogenic effect or CPE
reaches a sufficient level.
[0255] 5. The medium is then centrifuged to separate out the cells
and the supernatant contains large quantities of recombinant
virus--the virus stock harvest.
[0256] 6. The virus is then titrated to achieve a known
concentration.
[0257] Susceptibility Testing
[0258] In this phase, it is determined if the different HIV-1
inhibitors are still capable of inhibiting replication of the
recombinant viruses mentioned above.
[0259] 1. Different concentrations of the antiviral agents are
placed in the 384 microwells of a microtiter test plate. Several
wells are used for each concentration and the mean results used to
increase reliability.
[0260] 2. A set dilution of the recombinant virus stock or wild
type control virus is added to each microwell.
[0261] 3. A set dilution of MT4 cells containing a fluorescent
reporter gene system is also added to each microwell.
[0262] 4. The plate is incubated for 3 days during which time the
recombinant virus will replicate in the MT4 cells unless inhibited
by the antiviral drug. Replication triggers the reporter gene,
which produces proteins which fluoresce.
[0263] 5. The amount of viral replication at each concentration of
drug is measured by computerized spectrophotometry, relative to the
wild type virus controls.
[0264] 6. The susceptibility of the virus to each drug is expressed
as a fold change in IC.sub.50 relative to wild type virus.
[0265] 7. A report is prepared which provides these data for each
drug with an increase in IC.sub.50 of less than 4 classified as
sensitive between 4 and 10 classified as intermediate and over 10
as resistant.
[0266] The whole process is highly automated and uses state of the
art robotics to ensure consistency and high throughput.
[0267] Another assay exists that allows for simultaneous testing of
susceptibility to reverse transcriptase inhibitors and protease
inhibitors on a large scale: Virologics's `Phenosense` assay
(Petropoulos, C J, Parkin N T, Limoli K L, et al. Antimicrob Agents
Chemother, 2000; 44(4):920-928, incorporated by reference herein.).
The assay can be described as follows:
[0268] 1. Viral RNA fragments are extracted from the patient's
blood sample.
[0269] 2. Complementary DNA (cDNA) of the gag-Pr-RT sequence to
codon 300 is formed via reverse transcription.
[0270] 3. Reverse transcriptase (RT) and protease (Pr)sequences are
multiplied using PCR.
[0271] 4. Sample RT-Pr sequences are ligated (joined) to provirus
with the RT-Pr sequences deleted and an indicator gene, luciferase
inserted in the deleted HIV-1 envelope gene.
[0272] 5. These recombinant viral vectors, together with a plasmid
carrying the envelope proteins of murine leukemia virus, are
transfected into humans cells in the presence of varying
concentrations of protease inhibitors.
[0273] 6. Viral particles that are formed are harvested and allowed
to infect target cells for a second time in the presence of various
concentrations of RT inhibitors.
[0274] Susceptibility of the viral sequences to RT inhibitors and
protease inhibitors is calculated by measurement of luciferase
activity.
Example 5
[0275] It is desired to provide physicians and people living with
diseases, in particular HIV/AIDS, with the most accurate, reliable
and useful information about the individual person's disease to
help them make the most informed decision about the optimal
treatment strategy and to design treatment strategies. The methods
of the present invention represented in one embodiment by the
VircoGEN.TM. II, and the Antivirogram.TM., have a place in the
clinical management of diseases, such as HIV/AIDS. The selection of
which diagnostic test(s) to use and when is for the physician and
his patient to make and depends on a number of different factors.
Recommendations for resistance testing are included in various
treatment guidelines including those of the US Department of Health
and Human Services and the International AIDS Society. They make no
recommendations for which test to use other than the DHHS
guidelines stating that the use of both tests is useful for people
with complex treatment histories. The use of both phenotyping and
genotyping is generally regarded as the most reliable approach to
resistance testing.
[0276] Some clinical situations where resistance testing could be
of value are listed below with some rational for the type of test
to use.
[0277] The following table gives examples of clinical situations
where resistance testing might be considered.
8TABLE 1 Clinical situation Assay/service Rationale Acute infection
VircoGEN II .TM. At this point there is usually a high viral titer
and any mutant virus that has been transmitted can be readily
detected. Initiation of VircoGEN II .TM. At this point the patient
is likely to have virus that is therapy predominantly wild type or
has a few mutations. It is, therefore, likely that the Virco
database will have large numbers of matching records and that a
Virtua/Phenotype .TM. will be highly reliable. Sub-optimal VircoGEN
II .TM. If the initial regimen was selected on the basis of
response to or BOTH genotypic information, then an Antivirogram
.TM. potent should be run. If the initial selection was made
combination without resistance information then a VircoGEN II
therapy may be sufficient. Treatment failure VircoGEN II .TM.
Again, when a patient's treatment regimen begins to fail, in most
cases the number and complexity of the mutations are likely to be
similar to samples run by Virco in the past, so the number of
matches and the predictability of the Virtua/Phenotype .TM. will be
high. Treatment failure BOTH In this situation an Antivirogram .TM.
is essential and in patients with running both tests would be best.
Conducting both very complex tests means that the one can act as a
check for the treatment other. This combination will give how
viruses with histories that pattern of mutations have `behaved` in
the pas and how this particular virus `behaves` in the presence of
drugs under controlled laboratory conditions. When new drugs BOTH
In this situation there is likely to be a scarcity of are
introduced information about the patterns of mutations involved in
resistance - an Antivirogram .TM. would be essential and running
both tests would be best. This would provide as much information as
possible about the molecular basis of resistance to the new drug as
well as informing clinical decision-making. Few matches for
Antivirogram .TM. In a small minority of cases a genotype may
reveal the individual's a novel pattern of mutations such that
there are genotype insufficient matches in the Virco database to
produce a statistically reliable Virtua/Phenotype .TM.. In these
cases, an Antivirogram is recommended.
Example 6
[0278] Sample Source and Susceptibility Analysis.
[0279] Plasma samples were obtained from patients and submitted to
laboratories for routine assessment of drug susceptibility. These
were collected mainly from the USA, Canada and Europe, although
samples from South America, South East Asia and South Africa are
also represented in the database. Due to the nature of collection
of these samples, we were unable to obtain comprehensive therapy
and clinical histories from the majority of the patients
involved--although most were from different individual patients.
Viral RNA was extracted from these samples and converted to cDNA by
reverse transcription. Subsequently, a 1.7 kb fragment of the HIV-1
genome that encompassed part of gag, the protease and the first 400
codons of RT was amplified by PCR.sup.1. These amplicons were
directly sequenced by ABI automated sequencing and the drug
susceptibility phenotype was determined for 14 ndividual
antiretroviral drugs, using a recombinant virus assay. Text
sequences were imported directly into the database, as were the
IC.sub.50 and fold resistance values for each drug.
[0280] Database Development and Derivation of Virtual
Phenotype.
[0281] The genotype-phenotype database was developed in a RAD
(Rapid Application Development) environment using Apple Macintosh.
Programming was in "4.sup.th Dimension" (4D); a 32-bit, graphical,
multi-threaded relational database. The database currently runs on
a PowerMac G4, 400 MHz, 256 MB RAM. For the purposes of the
analysis, the software assumed that the mixture of a wild type and
mutant amino acid at a particular residue was mutant. A total of
108 individual, different amino acid changes were used in the
search procedure (at a total of 56 unique positions). This was
broken down into 39 changes in the protease and 69 in the RT (32
for the non-nucleoside RT inhibitors and 37 for the nucleoside
analogues). The following mutations, grouped by drug class, were
included in the search engine. Protease inhibitors: 10F/I/R/V,
20I/M/R/T, 24I, 30N, 32I, 33F/I/M/V, 36I, 46I/L, 47L, 48V, 50V,
54L/M/V, 71T/V, 73A/C/S, 77I, 82A/F/S/T, 84A/V, 88D/S, 90M.
Nucleoside analogues: 41L, 44A/D, 62A, 65R, 67N, 69D/N, 69
insertion, 70R, 74V/I, 75A/I/M/T, 77L, 100I, 115F, 116Y, 118I,
151M, 181C, 184I/T/V, 208Y, 210W, 211K/Q, 215F/Y, 219E/N/Q, 333D/E.
NNRTIs: 98G/S, 100I, 101E/I/P/Q, 103N/Q/R/S/T, 106A/I/L, 108I,
179D/E, 181C/I/V, 188C/H/L, 189I, 190A/E/S, 225H, 233V, 236L, 238T.
At the time of the study, the database comprised .about.45,000
phenotyped and .about.35,000 genotyped samples, of which >15,000
had both a genotype and phenotype.
[0282] DAP Analysis of Clinical Samples.
[0283] Viral load data of clinical samples from 191 patients who
participated in the VIRA 3001 prospective HIV-1 phenotyping study
were analysed according to the data analysis plan of the
international resistance collaborative group. Complete phenotypic
and genotypic data were available for these patients, who received
a total of 635 antiretroviral drugs. The analysis parameter was
virological failure at week 16, defined as plasma HIV-1 RNA above
400 copies/ml. Logistic regression was used to model this
parameter. In the univariate models, the total genotypic
sensitivity score (genotype analysis) or the phenotypic sensitivity
score (real phenotype and virtual phenotype analysis) were the only
factors in the model. Whereas, in the multivariate models, baseline
HIV-1 plasma viral load and number of new drugs in the treatment
regimen were added as extra covariates. To calculate the genotypic
sensitivity score, particular mutations, or groups of mutations,
were used to designate resistance or susceptibility to each
antiretroviral drug in the regimen (these were pre-defined by the
resistance collaborative group). Phenotypic sensitivity scores for
both the actual phenotypes and virtual phenotypes were based on the
fold change in IC.sub.50 relative to a wild type, susceptible virus
control. The total phenotypic score was defined as the number of
susceptible drugs in the regimen.
[0284] Derivation of the `Virtual Phenotype`
[0285] Firstly, the protease and reverse transcriptase (RT) regions
of the HIV-1 genome were sequenced by standard methods. These
regions code for the enzymes targeted by the current antiretroviral
drugs and mutations here can confer drug resistance. Mutations
associated with resistance present in the sequence were identified
and then software searched a relational genotype/phenotype database
for archived samples with a similar mutation pattern for each drug
(a mixture of wild type and mutant amino acid is treated as fully
mutant). Because of the substantial size of the database, typically
hundreds or thousands of matches were found. The software then
retrieved the phenotypic data for each of the matching genotypes
drug by drug, performed a logarithmic transformation and calculated
a transformed mean fold-change in resistance.
[0286] As with the actual phenotype on which it is based, this was
expressed as a fold change in the 50% inhibitory concentration
(IC.sub.50) compared with a value of 1.0 for fully sensitive, wild
type virus. FIG. 6 shows diagrammatically how such a search was
performed, using mutations that influence resistance to zidovudine
(AZT) as an example. This illustration is for a virus that has any
combination of the 41 L, 184V or I and 215Y or F mutations. A
series of searches first find all samples that individually contain
each of the mutations and then by an inclusion process, all samples
containing the three illustrated mutations are identified.
[0287] Corresponding information from the database for these
specific AZT resistance mutations is shown in Table 2. This
illustrates examples of the first 13255 genotypically-matched
samples found in the database for single and multiple mutations at
HIV-1 RT codons 41, 184 and 215. A number of interesting
characteristics are indicated in this Table. In particular, the
phenotypic effect of a mutation depends upon the genetic context in
which it occurs. In this simple example of only these three
mutations, viruses with 41L can have an average increase in
resistance ranging from 1.3-fold to >27-fold. Thus, simple
detection of the presence (or absence) of a given mutation can be
uninformative or even misleading. Further, the effect of mutations
is not simply additive--the modulating effects of the M184V or I
mutations (decreasing AZT susceptibility) and/or the 41L mutation
(increasing AZT susceptibility) on viruses with the 215Y or F
mutations can be discerned from Table X (range 6.2 to 27.7-fold).
This analysis is considerably less sophisticated than the virtual
phenotype system as it represents groups of samples where only the
inclusion of three specific mutations has occurred, rather than the
additional inclusion and exclusion of other mutations.
9TABLE 2 Example of Method for Deriving AZT Virtual Phenotypes
(using only three mutations). Geometric Average Standard Codon
Codon Codon Mean Phenotype Deviation 41 184 215 Phenotype (log)
(log) N ANY ANY ANY 3.9 0.59 0.78 13255 WT WT WT 1.3 0.12 0.38 4826
WT WT F/Y 13.4 1.13 0.73 695 WT V/I WT 1.3 0.10 0.47 2172 WT V/I
F/Y 6.2 0.79 0.61 673 L WT WT 1.7 0.24 0.36 54 L WT F/Y 27.7 1.44
0.69 1783 L V/I WT 1.3 0.13 0.45 75 L V/I F/Y 15.2 1.18 0.69
2693
[0288] In the actual derivation of a Virtual Phenotype for AZT, a
total of 18 mutations was examined in this fashion.
[0289] Identification of Genetic Clusters with Distinct
Phenotypes
[0290] If the search process were functioning appropriately, a
large series of phenotypically distinct genetic clusters should be
identified. Each of these should have distinguishable phenotypes
with only modest variability in susceptibility. This was evaluated
by examining the genetic clusters formed by the combinations of AZT
mutations described in Table 2. In addition to these mutations,
clusters were identified that also contained additional
AZT-resistance mutations (FIG. 7). Searches of the database were
performed using samples with specific AZT resistance mutations,
with or without the 3TC resistance mutations, 184V or I. The
numbers of samples in each genetic cluster were as follows: WT
(wild type, susceptible), 3798; 184 (184V/I), 777; 215 (215Y/F),
175; 215 184 (215Y/F and 184V/I), 70; 2M (41L and 215Y/F), 243;
[0291] 2M 184 (41L, 215Y/F and 184V/I), 186; 3M (41L, 210W and
215Y/F), 289; 3M 184 (41L, 210W, 215Y/F and 184V/I); 4M (41L, 67N,
210W and 215Y/F), 358; 4M 184 (41L, 67N, 210W, 215Y/F and 184V/I),
84.
[0292] This illustrates a number of important points regarding the
database searches. Firstly, different genetic clusters have
distinct susceptibility profiles (indicated by mean fold resistance
values, together with the standard error and 95% confidence
intervals). These values range from a slightly reduced level of
susceptibility (virus harbouring the 184V mutation) to almost
100-fold increases, due to multiple mutations conferring AZT
resistance. Secondly, in each case, the inclusion of the 184V
mutation together with AZT resistance mutations, caused a
substantial reduction in the predicted magnitude of AZT resistance.
The data clearly shows that the pattern recognition system can
predict altered susceptibility due to interactions of
mutations.
[0293] Correlation Between Predicted and Actual Phenotype
[0294] The virtual phenotype was validated in a number of ways.
Firstly, between 2700 and 8700 genotypically wild type samples were
tested for each drug. As anticipated, the predicted fold change was
close to one for all drugs examined, with a range of 0.66-1.69
fold. Next, the quantitative relationship between the predicted
phenotypes and actual phenotypes was investigated. 5000
clinically-derived samples from the USA were randomly selected from
the resistance database from 1999 onwards and the phenotypic
predictions obtained from the genotypic profiles for each drug were
compared to actual phenotypes in 10 random subsets of 500 samples
each. This resulted in approximately 70,000 determinations in
total. Independent linear regression analyses were then performed
on each of these data sets (four of these analyses are shown in
FIG. 8). These showed a good correlation between the virtual
phenotype (mean fold change in IC.sub.50 value) and actual drug
susceptibility phenotype, with an average slope of 0.83 (range
0.81-0.85), intercept of 0.05 (range 0.02-0.07) and average
correlation coefficient of 0.87 (range 0.86-0.89) across the ten
groups of 500 clinical samples.
[0295] The Virtual Phenotype Predicts Clinical Response
[0296] The predictive value of the virtual phenotype was also
tested. To address this, we performed a retrospective analysis of
clinical and virological data from the clinical study, VIRA 3001.
Cohen, C., et al., XIII International AIDS Conference. Durban.
(2000). This is a recently completed prospective, randomized,
clinical trial that demonstrated the positive effect of phenotypic
drug resistance information on virological response in patients who
had failed a PI-containing therapeutic regimen. Samples from 191
patients in this study were re-analysed to test the relationship
between the virtual phenotype (from genetic sequence) and
virological outcome at 16 weeks. The predictive values of
phenotype, virtual phenotype and genotype with `rules-based`
interpretation, were analysed according to a data analysis plan
(DAP) used by the international resistance collaborative group to
re-analyse clinical trials. DeGruttola V., et al., Antiviral
Therapy 5, 41-48 (2000). This analysis system comprises univariate
and multivariate statistical approaches and requires the use of a
`rules-based` mutation list for genotypic interpretation. The
results of this analysis are shown in FIG. 9. Logistic regression
was used to model the parameter of virological failure at week 16
(defined as plasma HIV-1 RNA above 400 copies/ml). Univariate (a)
or multivariate (b) models were used for the drug susceptibility
phenotype (phenotype), virtual phenotype (virtual) or genotype. The
calculated phenotypic sensitivity score (PSS) or genotypic
sensitivity score (GSS) were derived separately for a drop outs as
censored (DAC) or drop outs as failures (DAF) analysis. Results of
the regression analysis are shown on the FIG. 9 as an odds ratio
(OR) of failure to achieve a viral load reduction below 400
copies/ml, with the 95% confidence interval (CI).
[0297] In the univariate model, the genotype analysis (dropouts as
censored, DAC) was a significant predictor of response with an odds
ratio (OR) of 0.69 (CI=0.51-0.93), p=0.015 (FIG. 9a). However, the
genotype was not a significant predictor of response in the
multivariate model, OR=0.81 (CI=0.57-1.14), p=0.22 (FIG. 9b). In
contrast, the virtual phenotype was highly significant in both
models, also using the DAC analysis. With a 4-fold susceptibility
cut-off for all drugs in the univariate model, the OR=0.38
(CI=0.25-0.6), p<0.0001 and in the multivariate model the
OR=0.52 (CI=0.31-0.87), p=0.013. Using recently defined,
drug-specific, biological cut offs, the predictive power of the
virtual phenotype was even more significant. Larder, B. A. &
Harrigan, P. R., Fifth International Congress on Drug Therapy in
HIV Infection, Glasgow (2000). The OR in the univariate model was
0.39 (CI=0.26-0.58), p<0.0001, and in the multivariate model the
OR=0.49 (CI=0.31-0.76), p=0.0014. The DAF (dropouts as failures)
analyses showed consistent superiority for the predicted phenotype
over genotype although the level of significance was
correspondingly lower for all of the categories.
* * * * *
References