WO1999011822A1 - Procede de prediction de pronostic et de reponse a un traitement, destine a des anomalies genetiques - Google Patents

Procede de prediction de pronostic et de reponse a un traitement, destine a des anomalies genetiques Download PDF

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WO1999011822A1
WO1999011822A1 PCT/US1998/018261 US9818261W WO9911822A1 WO 1999011822 A1 WO1999011822 A1 WO 1999011822A1 US 9818261 W US9818261 W US 9818261W WO 9911822 A1 WO9911822 A1 WO 9911822A1
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disease
data
patient
exon
patients
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PCT/US1998/018261
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Steven Evans
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Gene Logic Inc.
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids

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  • the present invention relates to a method by which one can uncover patient and disease differentiations based on novel combinations of clinical and genotypical data (e.g., involving specific exon mutations within specific genes) which differentiate the patient population in ways that significantly enhance the ability to assess the prognosis and select suitable modes of treatment for the disease.
  • clinical and genotypical data e.g., involving specific exon mutations within specific genes
  • a single etiology such as an inherited genetic abnormality which, as a result, leads to a specific clinical manifestation reflective of the abnormality.
  • a specific treatment is available, and an associated prognosis possible.
  • such a simple model of a medical disorder is the exception rather than the rule.
  • the present invention relates to a method by which one can uncover patient and disease differentiations based on novel combinations of clinical and genotypical data which differentiate the patient population in ways that significantly enhance the ability to assess the prognosis and select suitable modes of treatment for the d
  • the process begins with a table of patient data that characterizes patients' medical clinical history in terms of specific gene's exon mutations or important polymorphism that patients have been found to carry, plus other clinical data, isease.
  • stage I Given a selection of clinical data including (a) some particular disease state and (b) one or more patient clinical data factors the first goal (stage I) is to characterize (i.e., predict) the selected patient clinical data for the particular disease state chosen in terms of the presence of specific characteristics of specific genes.
  • stage II The second goal (stage II) is to take these identified characterizations derived in stage I and use them to differentiate the patient population expressing the chosen disease state in terms of the prognosis of the particular disease and in the efficaciousness of specific treatment modalities.
  • the method of the present invention takes clinically observable, collectible data from selected patient populations with diseases arising from genetic abnormalities, and uses such information to differentiate classes of patients with regard to specific differences in their genetic makeup, which in turn then characterizes outcomes such as prognosis and treatment response.
  • Fig. 1 is a flow-chart showing a method of the present invention. DETAILED DESCRIPTION OF THE INVENTION
  • the process begins with a table of patient data that characterizes patients' medical clinical history in terms of specific gene's exon mutations or important polymorphism that patients have been found to carry, plus other clinical data which include but are not limited to: 1) any medical diagnoses they have and medical diagnoses their pertinent relatives have, particularly including first and second degree relatives (i.e., father and mother, siblings, and children, aunts, uncles, and grandparents on both sides of their family); 2) ages of onset of such diseases; 3) associated medical and other conditions such as presence of colon polyps or high blood pressure; 4) ethnic origin; 5) race; 6) sex; 7) habits such as smoking, exposure to toxic chemicals; 8) occupations carrying medical risks such as agricultural workers; and 9) other additional information which may be available or obtainable in a patient's and patient's relatives' medical chart or file.
  • additional information for each patient might also include any treatments received, the outcome(s) of the treatment(s), ongoing patient management issues such as "time until first re-
  • the first goal is to characterize (i.e., predict) the selected patient clinical data for the particular disease state chosen in terms of the presence of specific characteristics of specific genes, for example, specific mutated exons and/or over-expression.
  • the second goal is to take these identified characterizations derived in stage I and use them to differentiate the patient population expressing the chosen disease state in terms of the prognosis of the particular disease and in the efficaciousness of specific treatment modalities.
  • the method of the present invention takes clinically observable, collectible data from selected patient populations with diseases arising from genetic abnormalities, and uses such information to differentiate classes of patients with regard to specific differences in their genetic makeup, which in turn then characterizes outcomes such as prognosis and treatment response.
  • each row of the matrix represents an example or instance of the phenomenon of interest and each cell within the row (i.e., column of the matrix) represents some descriptor of the phenomenon.
  • each row may represent a patient, with the first column containing a code indicating which disease (if any) the patient has had, the second column indicating what age he or she had the disease, the third indicating whether the patient is a smoker ("yes" or "no" value), etc.
  • these rows are marked "true” examples of any phenomenon of interest on the part of the user of the software (e.g., certain patients are identified as true cases of "hereditary breast cancer,” or true cases of "patients who have survived a certain disease five or more years,” or true cases of patients “who do not buy life insurance,” etc.).
  • Data mining software uses computational techniques based on the mathematical theory of rough sets to select constituent elements (column values) for each row so that one or more boolean logical relationships among these elements characterizes (i.e., predicts) all the "true” examples that were marked, as noted above.
  • One off-the-shelf data mining product we have used in a part of our invention is Data Log ic/R+ 1.5 from Reduct Systems, Inc., Regina, Saskatchewan, Canada, although numerous software choices and vendors are available.
  • Disease includes but is not necessarily restricted to any measurable condition of an individual that is not normal in some statistically probabilistic sense for some subset of the population, any malfunction of an individual or dysfunctional condition or genomic defect for a subset of the population, a genomic abnormality such as a mutation in a gene or identifiable polymorphism or other significant abnormality in part of the genome, or a genomic variation for some subset of the population which is measurably different from another subset, where such disease(s) can be manifest in behavioral changes, physical changes, tissue conditions, tissue or genomic- level conditions and changes, or other performance changes as is distinguishable from a different subset of the population.
  • Exon Mutation Particular parts of the DNA structure, the genes, provide the blueprints for the construction of proteins, assembling each protein in steps. Each step is dictated by and active coding region of the gene, and exon, which is interrupted by spans of non-coding regions (introns). If one of these coding regions has a flaw and can not properly assist in its role in the formation of the protein under construction, then the flawed area represents an exon mutation.
  • Genotypical Data includes but is not necessarily restricted to data about the genome of an individual, cellular factors or substances that influence or affect or alter the genome, measurable alterations in the genome, data that reflect different levels of expression of the genome or parts of the genome or genome-related conditions, data that reflect the integrity or quality of the genome, or data that bear on the interrelationships between and among genomic elements of the cell or the cellular factors that influence the genomic elements.
  • Patient Clinical Data Factors includes but is not necessarily restricted to data that can measure or represent the status of an individual, any disease of that individual, or characteristics of that individual which might be included in a health-related database, with such data to also include information about an individual that distinguishes the individual from others
  • Prognosis includes but is not necessarily restricted to possible outcomes, results, implications or consequences of disease, a subsequent state or status of a patient which could be psychological, behavioral, physiological, genomic-related, or other measurable difference as a consequence of disease or an abnormal condition.
  • Treatment Outcome includes but is not necessarily restricted to outcomes or results as a consequence of the administration of a drug or other therapeutic substance, or the administration of a therapeutic regimen which may involve ingesting any substance, a physical procedure, a behavioral procedure, a surgical procedure, any procedure that may lead to an alteration in the individual's genome or genomic-related condition, or any other health- related action intended to alter the health status of an individual.
  • a treatment outcome may also be a measurable condition of the individual that distinguishes one state from a prior one even without knowing what agent created the transformation.
  • Step 1 The first step is to select a particular syndrome, disease or combination of diseases of interest, such as hereditary breast- ovarian cancer.
  • Data are assembled within a database as an array of patient information about patients who have exhibited the selected disease or syndrome which includes for each patient, data regarding other specific diseases he or she may have had, diseases any first and second degree relatives have had as well as other immediate relatives, the age when any of the diseases occurred, any genetic mutations that have been determined, itemized by gene and by exons within the gene, and other clinical data which might be found in a clinical or medical record including treatments, drugs taken, outcome data with regard to treatment or the course of the disease for each patient as well as any relatives, etc.
  • Step 2 Second, one selects a particular disease, syndrome, or combination of diseases of interest d;, such as hereditary breast- ovarian cancer.
  • Step 3a one or more medical database elements (patient characterizations C j ) are chosen for patients with disease d;, such as "had early onset of the disease” (for the example of hereditary breast-ovarian cancer this may be defined as the onset of the disease before the age of 40).
  • patient characterizations C j patient characterizations
  • the subset of selected patients who have the disease of interest and who exhibit these medical factor(s) chosen is defined to be the "true" examples (for the data mining software) of the pattern to be investigated.
  • Step 3b A similar set of patients with the selected disease is also chosen and the factor(s) chosen in the first step are negated (-C ⁇ they do not exhibit C j ), and the combination is considered the "true” patterns. In this example, patients with hereditary breast- ovarian cancer who were late-onset would be the "true” examples of the pattern.
  • the subset of selected patients with the disease state but without exhibiting the negated factors is labeled as "false” examples of the pattern (for the data mining software).
  • Step 4a/b Data mining methods are applied using as the "true” and “false” patterns those identified in steps 3a and 3b.
  • the logical elements of the database which are specified for use by the data mining method i.e., the specific columns for each data entry row
  • the data about specific exon mutations of specific genes in individual patients For example, assume that we have in our database entries for any detected exon mutations for the genes BRCA1, BRCA2, MLH1 , and MSH2 for patients with hereditary breast-ovarian cancer. Then all of these are marked for the data mining process as the allowable elements (i.e., column values) which may be used to define the true set designated first in step 3a, then in step 3b.
  • Step 5a The output from the data mining software is a set of rules ("rule set") R[C
  • Step 5b Output from data mining software for step 4b is a set of rules involving the exons for the negation of the medical condition under consideration, Rt-CJ, which is an analogous set of rules as arise in step 4a.
  • Step 6 The two sets of rules in steps 5a and 5b are compared (as described below) to see if there is any logical overlap of constituent elements (i.e., exon mutations) comprising the two rule sets.
  • a logical overlap if one condition, such as [mutation of exon 2 of BRCA1], occurs as an element in both a rule in the rule set for 5a as well as in a rule in the rule set for 5b. If no such logical overlap exists for at least one exon, then the medical factor(s) (in our example, the condition "early onset” versus "late onset”) is said to be a splitting condition for the disease based on one or more of the elements of definition
  • the two sets of exon mutations arising in rules in steps 5a and 5b are the two splitting vectors (of exon mutations) for the splitting condition.
  • the set of exons unique to each of the splitting vectors are called the splitting exons for the splitting vectors. It is ultimately such significant splitting conditions and their associated splitting vectors and splitting exon mutations for a disease state and associated medical factor(s) that we desire to obtain.
  • This splitting condition means there are some differentiating clinical manifestation(s) for a disease state (in our example, "early” versus "late onset” for the disease hereditary breast-ovarian cancer) which can be reflected at the exon mutation level.
  • Step 6a If no significant splitting condition is found, another attribute is selected by the program from among the database attributes within the database (such as "there are two first degree relatives with the same disease"). This attribute is similarly tested for its prospects as a splitting condition. If one is obtained, then the second stage (step 7) of analysis is entered.
  • an otherwise apparently single umbrella disease e.g., hereditary breast-ovarian cancer
  • another attribute is selected by the program from among the database attributes within the database (such as "there are two first degree relatives with the same disease”). This attribute is similarly tested for its prospects as a splitting condition. If one is obtained, then the second stage (step 7) of analysis is entered.
  • each database attribute can be tested in a loop, one at a time, then two attributes taken together can be selected, and in a loop, tested as a splitting condition. Again, if none is found, three attributes taken together may be selected, etc. With a high speed computer, this cycling automatically considers all combinations in hopes of finding a splitting characteristic or a combination of attributes which will constitute a splitting condition.
  • Step 7 Once a splitting condition is obtained, (together with one or more associated combinations of splitting exon mutations comprising the associated splitting vector), a prognosis or treatment outcome attribute Tj and its negation -T. are selected where T, ⁇ C j .
  • T for the disease state "hereditary breast-ovarian cancer," and the attribute "early onset,” selected treatment and outcome parameters (such as "five years or more of remission” and "received radiation treatment") can be identified.
  • T can in fact be a boolean expression comprised of entries found in the database d j .
  • Step 8a The first of the two set(s) of genetic exon mutations in the two splitting vectors from step 6 is used as the permissible characteristics to define the patterns for patient records which exhibit the prognosis or treatment outcome selected in step 7.
  • exons e e 2 , and e 3 in gene BRCA1 are the characterizing exon mutations (the splitting exons for the first vector) for the disease state "hereditary breast-ovarian cancer," and the splitting condition "early onset.”
  • the constituent elements e ⁇ e 2 , and e 3 in the splitting vector represent the eligible characteristics that may be used with data mining software to define or characterize a prognosis or treatment outcome T j for the disease condition and medical attribute(s) corresponding to the splitting condition (e.g., "early onset” and "hereditary breast-ovarian cancer”).
  • step 8b As in step 8a, the exons in the first splitting vector is used to characterize the negative form of T,.
  • step 8c Analogous to step 8a, the exons in the second splitting vector are used to characterize the "true" set defined to be the patterns with d, and -T,.
  • Step 8d As in step 8c, the exons in the second splitting vector are used to characterize the "true" patterns defined to be the patterns with d, and -T,. Step 9 If step 8a-d is successful, the "true" cases are characterized by rule sets from the data mining. Step 10 If step 9 is successful for significantly characterizing the true set, then the corresponding constituent exons contained in each rule set are defined to be a successful two-stage splitting result (i.e., working for both stage I and stage II).
  • Step 11 To cycle through the database, one returns to step 7 and other prognosis and treatment outcomes attributes are selected (e.g., all hereditary breast-ovarian patients with one year reoccurrence rates, all with two year, all who responded favorably to radiation, all who responded favorably to chemotherapy, etc.) one by one using the first two sets of exons in E ⁇ CJ and E 2 [-CJ. If a more than two-stage level is desired, we employ all four sets of exons derived in step 9 and enter step 7 with two pairs of exon sets rather than just two sets. Then in step 8, each pair is treated as each prior single set was treated. One may continue such a process for as many stages as desired.
  • other prognosis and treatment outcomes attributes are selected (e.g., all hereditary breast-ovarian patients with one year reoccurrence rates, all with two year, all who responded favorably to radiation, all who responded favorably to chemotherapy, etc.) one by one using the first two sets of exons in E ⁇ CJ and E 2 [-C
  • the constituent elements of the splitting vectors derived are used as the permissible elements to define logical rules to characterize the prognosis condition or treatment outcome conditions selected.
  • these attributes may be combined into more complex patterns and tested, taking first two attributes in combination, then three, etc (e.g., does the exon set [e1, e2, and e3] characterize five year remission cases treated with both radiation and chemotherapy).
  • step 4a or 4b may be null, i.e., one-sided splitting conditions, obtaining no results, wherein 5a or 5b would be null.
  • step 6 indicates that "one or both of [results] is not empty” (but it need not have been the case that both had to be “not empty”).
  • step 7m attribute T, and -T, is selected, and results obtained if possible in the four parts of step 8.
  • step 8 is selected, and results obtained if possible in the four parts of step 8.
  • step 8 is not productive in deriving results, the process continues, and any output derived is exhibited in step 9.
  • no T is even selected at step 7, the process by default goes through steps 8 and 9 in the null case, and terminates as it loops back to step 7.
  • a further method of the present invention includes the following steps, in addition to the steps of the above method:
  • steps 3a and 3b of the method above medical factors such as "early onset” are selected as interesting aspects of a medical disease (in this example, hereditary breast-ovarian cancer). Similarly in step 7, various prognosis or treatment outcomes are selected. In each case, exhaustive selection involving one or more combinations of factors may be chosen.
  • Step 1 We apply data mining to correlate mutational outcomes with patient attributes in order to guide choices for steps 3a, 3b and step 7.
  • the "true" pattern of interest is the clinical family history of the patients who have (a) the selected disease state and (b) who have tested positive for any specific intragenic mutation(s) in any gene(s).
  • step 7 All of the exons may be used by the data mining software to define the "true" set. All other patient records are labeled as “false.”
  • condition (b) becomes a positive occurrence of any one or more of these splitting exons.
  • Step 2 The data mining technology is then challenged to define what clinical medical attributes and what values for each attribute would define rules that characterized the disease state and mutations marked. This resulting set of rules might characterize the true set with such attributes such as "early onset” or "5 years of remission.” All constituent elements comprising the rules heuristically suggest interesting choices for steps 3a, 3b or step
  • Example 1 Clinical data are collected on family histories of patients with the selected disease of hereditary breast-ovarian cancer who manifested a variety of mutations in the BRCA1 and BRCA2 genes (which yield breast and/or ovarian cancers). First, patients are defined who exhibited a 5382- insertion C mutation in BRCA1 as true. Patients who carried some mutation other than 5382-insertion C were labeled as false.
  • Ail rules contained the attribute "early age of onset” with very high values.
  • Ca32a is the code for first degree relatives with breast cancer (which in this rule must have at least four such cases).
  • Early refers to early onset of breast cancer, where three points are assigned if any case arises at or before the age of 35, two points are assigned if a case arises between age 36 and age 45, and one point is assigned for a case arising between 46 and 50.
  • Gen refers to the presence of cancers in the same generation, which for this rule requires at least two cases in the same generation. Using our heuristic module, any of these three attributes (Ca32a, Early, and Gen) would be suitable as a choice in steps 3a and 3b.
  • Gatekeeper genes directly regulate the growth of tumors by inhibiting growth or promoting death. Predisposed individuals who inherit one mutant copy of a gatekeeper gene need only one additional (somatic) mutation to initiate neoplasia. Moreover because the probability of acquiring a single somatic mutation is exponentially larger than the probability of acquiring two such mutations, people with a gatekeeper gene mutation are not only at greater risk of developing tumors than the general population but since only one more "hit" is needed, they can be expected to express such tumors earlier in life (early onset).
  • BRCA1 and BRCA2 contain a region that act as a transcriptional-activation domain when it is fused to a DNA-binding domain from another gene (Milner, et al., 1997; Chapman, et al., 1996; Monteiro, et al., 1996), and transcription factors are often found among the gatekeeper class of cancer-susceptibility genes, this property indicates BRCA1 and BRCA2 may directly control cellular proliferation.
  • BRCA1 can inhibit the growth of cells in which it is over-expressed (Holt, et al., 1996), and there is also a link between an inhibitor of cell-cycle-dependent kinase and BRCA1 protein (Hakem, et al., 1996).
  • BRCA1 can inhibit the growth of cells in which it is over-expressed (Holt, et al., 1996), and there is also a link between an inhibitor of cell-cycle-dependent kinase and BRCA1 protein (Hakem, et al., 1996).
  • BRCA genes are caretaker genes. Mutations in caretaker genes do not promote tumor initiation directly. Rather neoplasia occurs indirectly, since inactivation leads to genetic instabilities which result in increased mutations in all genes including gatekeeper genes, which then as noted above, may progress rapidly to tumor genesis. So if a patient inherited a caretaker gene defect, three somatic mutations would be needed to initiate cancer: mutation of the normal caretaker allele inherited from the unaffected parent, followed by mutations of both alleles of a gatekeeper gene.
  • BRCA genes might be added to the caretaker list since mutations in BRCA1 and BRCA2 are rarely found in sporadic cancers. Thus we have two contending roles for BRCA1 and BRCA2, gatekeeper and caretaker. Before proposing a resolution of this issue, we may take note of the fact that both BRCA genes bind to Rad51 , a protein that is involved in maintaining the integrity of the genome (-Sharan, et al., 1997; Scully, et al., 1997). Moreover Sharan et al.
  • BRCA2 knockout mice show early embryonic lethality and hypersensitivity to irradiation, similar to that observed in RAD51 knockout mice.
  • BRCA1 was found to bind to RAD51 (Scully, et al., 1997), and BRCA1 knockout mice also show early embryonic lethality (Hakem, et al., 1996; Liu, et al. 1996).
  • RAD51 is involved in resolving double-stranded DNA breaks in recombination-linked repair, and thus we can expect that disruption of the BRCA/Rad51 pathway might be expected to lead to genetic instability.
  • early versus late stage onset constitutes a total splitting condition, and the exons found would distinguish early from late onset.
  • Prognosis and treatment outcomes were then characterized from these two different sets of exon mutations, completing the application of the method.
  • the prognosis condition "P" indicating high penetrance of the cancer (many generations)
  • the output was 4 rule sets, which constitute the results of the process.
  • Example 3 Although specific exon mutations within specific genes may be utilized for the genotypical (i.e., genome-related) data, our method is not exclusively or restrictively constrained just to exon mutations, this merely being one type of genomic-related data, but rather genotypical data of other formulations as well.
  • a prognosis may include a specific outcome or condition or state for a patient, and also may include a level of physiological status (measured by laboratory values), or a genomic-related status such as a measure of the degree of over-expression of a gene which can characterize an organism.
  • the outcomes in step 5a and 5b may not be necessarily so simple as the absence or presence of just an exon, but also may be the absence or presence of any factor used in the database which was stated to be comprised of clinical and genotypical data.
  • steps 5a and 5b may not be merely so simple as the presence or absence of a data element but also may be a function involving a data element such as element e1 is less than or equal to a certain value, greater than or equal to a certain value, or in general, any arithmetic function of the data element.
  • a data element such as element e1 is less than or equal to a certain value, greater than or equal to a certain value, or in general, any arithmetic function of the data element.
  • Such variations of genotypical descriptions, disease states, prognosis states, and presence or value of a data element are all within the purview of the method's arena of application, although these variations are not inclusive of all variations, but only articulate some of the additional possible variations.
  • specific over-expressed gene for a specific tissue is selected as the "abnormal" state (i.e., the "disease” for the method).
  • a specific range for the over-expression of the gene as the medical factor of interest is selected, with the specific range as measured by some laboratory method.
  • the database includes the specific ranges of expression of this gene for each patient plus clinical data to describe patients, typical of data to be found in any comprehensive medical record.
  • step 3a becomes: the true set are those patients with the over- expressed gene (their abnormal condition) with the value of the over- expression within some specifically designated range (as the medical factor of interest) along with their associated clinical and genotypical descriptors which may include exon mutation data, as well as also include other medical descriptors as well.
  • step 3b constitutes the contrasting group of what amounts to those patients who do NOT have the same range of the gene over-expression, but otherwise are the same.
  • step 4a/b what characterizes the two different groups in step 3a and 3b.
  • the items that characterize the two groups can be the presence or absence of descriptor factors or arithmetic measures of elements such as "patient's age is less than some value.”
  • Example 4 The method of the present invention can also be applied where a disease d : is a medical condition such as stroke, the clinical attribute C, is a drug X is given to the patient, and its negation -Cj is drug X not given (i.e., a placebo or null drug is given), and the rest of the characterizing elements are a mixture of clinical data and genotypical data. Steps 4a and 4b, 5a and 5b, and step 6 are then followed.
  • step 7 the outcome T, selected is "a positive response to drug X as measured by the results of a trial for drug X" while its negation is "no positive response by the results of a trial for drug X.”
  • step 8 characterizes the drug-taking positive responders, the placebo positive-responders, and characterize the drug-taking non-responders and the placebo-taking non-responders.
  • Example 5 The method of the present invention can further be applied where a disease to be abnormal tissue of some type, the attributes C j is a series of genes differentially expressed for the abnormal tissue, and then characterize the tissue in step 4 with the set of said genes in terms of clinical and genotypical data available for the tissue.
  • the treatment outcome can be measured in terms of some transformation of the gene series selected as C j .
  • step 8 obtains the data elements which characterize the abnormal tissue with a baseline genetic characterization that has undergone a genetic transformation, and that has failed to undergo a genetic transformation.
  • One can also obtain the characterization of the tissue which did not exhibit the genetic series C j but which underwent a transformation as well as did not undergo a transformation.

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Abstract

La présente invention se rapporte à un procédé de prédiction de pronostic et de réponse à un traitement, destiné à des anomalies génétiques. Dans ce procédé, on utilise des données pouvant être observées et collectées cliniquement, à partir de populations de patients présentant des maladies provoquées par des anomalies génétiques, et on utilise ces informations pour différentier des classes de patients en fonction de différences spécifiques constatées dans leur patrimoine génétique, ce qui permet alors de caractériser les évolutions, comme le pronostic et la réponse à un traitement.
PCT/US1998/018261 1997-09-03 1998-09-03 Procede de prediction de pronostic et de reponse a un traitement, destine a des anomalies genetiques WO1999011822A1 (fr)

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US7343247B2 (en) 2001-07-30 2008-03-11 The Institute For Systems Biology Methods of classifying drug responsiveness using multiparameter analysis
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US8055452B2 (en) 2001-11-09 2011-11-08 Life Technologies Corporation Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles
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