WO2008027912A2 - Prédiction de l'activité d'agents sur différents types de cellules et de tissus - Google Patents

Prédiction de l'activité d'agents sur différents types de cellules et de tissus Download PDF

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WO2008027912A2
WO2008027912A2 PCT/US2007/077022 US2007077022W WO2008027912A2 WO 2008027912 A2 WO2008027912 A2 WO 2008027912A2 US 2007077022 W US2007077022 W US 2007077022W WO 2008027912 A2 WO2008027912 A2 WO 2008027912A2
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cell
agent
molecular characteristics
cancer
activity against
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PCT/US2007/077022
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WO2008027912A3 (fr
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Dan Theodorescu
Jae Kyun Lee
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Dan Theodorescu
Jae Kyun Lee
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Priority to EP07841494A priority Critical patent/EP2062181A2/fr
Publication of WO2008027912A2 publication Critical patent/WO2008027912A2/fr
Publication of WO2008027912A3 publication Critical patent/WO2008027912A3/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Definitions

  • the present invention relates to a novel algorithm that uses molecular profile signatures to extrapolate the physiological processes of one type of cell set (e.g., cell line, tissue, normal or diseased) to predict the activity of an agent or agents against another type of cell set that has never been exposed to the agent in question (drug efficacy prediction).
  • the novel algorithm also allows one to predict the therapeutic response of a patient(s) to a therapeutic regimen even though the patient(s) may have never been exposed to that agent before, thereby allowing for selecting a therapeutic agent or combination of agents that would best suit the patient(s) (i.e., personalized medicine).
  • the present invention also relates to methods of using the agents identified by the novel algorithm to treat a variety of diseases, including cancer.
  • Tumors have traditionally been classified by descriptive characteristics such as organ of origin, histology, aggressiveness, and extent of spread. That empirical rubric is being challenged, however, as molecular-level classifications, made possible by microarrays and other high-throughput profiling technologies, become increasingly common and persuasive.
  • the reductionist program would suggest that, eventually, all differences among traditional tumor types will be reduced to statements about molecules in the tumors and about the interactions among those molecules. It might then be possible to study physiological processes in one type of cancer and extrapolate the results to predict another type through commonalities in their molecular constitutions. This concept forms the basis for the claimed invention.
  • the NCI-60 cell line screen which has been used by the Developmental Therapeutics Program (DTP) of the U.S. National Cancer Institute (NCI) to screen >100,000 chemically defined compounds plus a large number of natural product extracts for anticancer activity since 1990.
  • the NCI-60 panel comprises 60 diverse human cancers, including leukemias, melanomas, and cancers of renal, ovarian, lung, colon, breast, prostate, and central nervous system origin.
  • the NCI-60 have been comprehensively profiled at the DNA, RNA, protein, and functional levels, and the resulting information on molecular characteristics and their relationship to patterns of drug activity have proven fruitful for studies of drug mechanisms of action, resistance, and modulation.
  • the present invention provides novel methods for predicting the activity of at least one agent or combination of agents on cell lines or animal tumors, tissues, or organs either syngeneic or xenograft without the cell lines or animal tumors, tissues, or organs either syngeneic or xenograft ever having been exposed to the agent-the predicting being based on the sensitivity of other cell lines or animal tumors, tissues, or organs either syngeneic or xenograft to the agent.
  • the present invention also provides novel methods of predicting the therapeutic effectiveness of an agent or combination of agents in a human patient without that patient's tumor/organ/tissue ever having been exposed to the agent-the predicting being based on the sensitivity of other human patient/patient's tumor/organ/tissue to said agent.
  • one benefit of the present invention is the ability to predict a patient's response to an agent without having testing that agent on that patient or even a test set of patients.
  • the present invention also provides novel methods of predicting which cell lines or animal tumors, tissues, or organs either syngeneic or xenograft or human tumors that are sensitive to a specific therapeutic agents-thereby allowing for personalized therapy.
  • the present invention also provides a set of genes, the expression of which is important for the prediction of treatment responses for any cancer (e.g., cancers of the bladder and breast) to any agent with activity in cell lines, animal tumors, tissues, or organs either syngeneic or xenograft or human tumors.
  • cancer e.g., cancers of the bladder and breast
  • agent with activity in cell lines, animal tumors, tissues, or organs either syngeneic or xenograft or human tumors.
  • the present invention also provides a set of agents that have been found through use of the present invention to be effective in several human cancers including bladder, breast, prostate, pancreatic, and melanoma.
  • the present invention further provides methods of treating diseases with the agent(s) identified herein.
  • Figure 1 Application of the gene co-expression extrapolation signature (COXEN) to the BLA-40 bladder cell lines:
  • A Summary schematic diagram for chemosensitivity prediction model development and model validation.
  • B Direct comparison between the standardized MiPP prediction scores and the standardized log(GI50) values on the BLA-40 for cisplatin.
  • the sensitive (and resistant cell lines) are ordered based on their log(GI50) values (x-axis), which were obtained from in vitro chemosensitivity experiments.
  • the standardized predicted MIPP scores are also depicted next to the standardized log(GI50) values of corresponding cell lines.
  • the standardized scores were obtained by subtracting the overall mean divided by the standard deviation of the MiPP scores and log(GI50) values on the BLA-40.
  • FIG. 2 (A) Schematic illustration of co-expression extrapolation.
  • Probes 1 and 3 in Cell Set 1 (e.g., the NCI-60) show essentially the same patterns of co-expression correlation with other probes as do Probes 1 and 3 in Cell Set 2 (e.g., the BLA-40).
  • Probes 2, 4, and 5 show different patterns of co-expression correlation in the two Cell Sets. Therefore, Probes 1 and 3 (but not 2, 4, and 5) might be selected by the "co- expression extrapolation" algorithm (Step 5) for inclusion in the prediction signature for Step 6.
  • the co-expression correlations here are those calculated across cell types for a given pair of probes. (Step 5).
  • Bright red and blue bar indicates sensitive cells and resistant cells of the NCI-60 and BLA-40 as defined in Figures 1OB and D.
  • Bright yellow and cyan indicate the NCI-60 and the BLA-40 cell lines. Most cell lines clustered based on their origins — NCI-60 and BLA-40 and the sensitive (or resistant) cell lines are not intermixed between the two cell line panels.
  • E Co-clustering CEVI between the NCI-60 and the BLA-40 cell lines of the final 13 COXEN probes for paclitaxel (Supplemental Table Sl). The sensitives (and resistants) of the NCI-60 and the BLA-40 cell lines were closely clustered together despite of their differences in their tissue origins.
  • F Significance of COXEN biomarkers for BLA-40 sensitive and resistant cell lines to cisplatin and paclitaxel, respectively.
  • FIG. 3 Chemotherapeutic response prediction in patients with breast cancer:
  • A Schematic diagram for COXEN based chemotherapeutic response prediction model development and model validation for breast cancer patients.
  • C Kaplan-Meier survival curves for the COXEN predicted responder and nonresponder groups on the 60 breast cancer patients in the tamoxifen trial.
  • FIG. 4 Human bladder cancer drug discovery and validation: (A) Schematic diagram for computation drug screening of 45,545 compounds in the public NCI database available at the NCI website (dtp.nci.nih.gov).
  • NSC 637993 Effectiveness of NSC 637993 as a function of tumor histology of cancer cell lines is shown for the BLA-40 (Four cell lines are missing from panel due to difficulty growing them in culture). NSC 637993 is more effective at a lower dose (I x 10 "6 M) in bladder cancer than that in the nine tissue-specific cell line panels of the NCI-60 cell lines.
  • C Chemical structure of the lead novel compound NSC637993 discovered by COXEN. [0019]
  • Figure 5 Chemotherapeutic response prediction in the BLA-40 bladder cell lines and the patients with breast cancer: Continuous performance of top three MiPP prediction models (A) on the BLA-40 sensitive and resistant cell lines for cisplatin.
  • Figure 6 Figures A to D, graphically illustrate the classification of sensitive and resistant cancer cell lines to single drug chemotherapy.
  • A comprising six panels, illustrates growth-inhibition dose response curves for a) SLT4 and RT4 in respond to Cisplatin (upper two graphs); b) 253-JBV and RT4 to Paclitaxel (middle two graphs); and c) SW1710 and UMUC9 to Gemcitabine (lower two graphs.
  • the left graphs of each group representative the sensitive cells and the right graphs of each group represent the resistant cells.
  • the percent of cell counts (divided by 100) is indicated on the Y axis.
  • Figure 7 2D scatter plots of expression intensities (Iog2 scale) of the first two genes of single-drug prediction models demonstrating their classification performance.
  • the genes listed are described in the examples: (7A) Cisplatin.
  • (7C) Gemcitabine Sensitive cells are indicated by blue dots (•) and resistant cells are indicated by red stars (*) cell lines were found to be separated by the two selected genes although some of them were still misclassified. Some of the misclassified ones were better separated by the additional genes, so the mean ERs were 0.069, 0.051, and 0.096 for Cisplatin, Paclitaxel, and Gemcitabine, respectively.
  • Figure 8 The scatter plot of the percent of cell counts compared to control (no drug) versus the posterior probability of sensitivity for the 15 cell lines randomly selected for the evaluation of chemotherapeutic sensitivity prediction for the three two-drug combinations shown.
  • the ordinate represents percent cell count and the abscissa represent probability of drug sensitivity.
  • Cis Cisplatin
  • Pac Paclitaxel
  • Gem Gemcitabine.
  • Figure 9 Classification of responder and nonresponder patients in the tamoxifen trial: Patients with recurrent disease had tumor recurrences within a relatively short time ( ⁇ 50 months) after the tamoxifen treatment, whereas no patient with durable survival falls in this time period. Hence, the assumption was made that such early recurrence patients were tamoxifen nonresponders (16 patients). In contrast, patients with long-term survival (>130 months) were considered responders (11 patients).
  • Figure 10 In vitro drug chemosensitivity of NCI-60 and BLA-40 cell lines.
  • A Ordered log(GI50) values of the NCI-60 cell line responses to cisplatin.
  • B Ordered log(GI50) values of the NCI-60 cell line responses to paclitaxel.
  • C Ordered log(GI50) values of the BLA-40 cell line responses to cisplatin.
  • D Ordered log(GI50) values of the BLA-40 cell line responses to paclitaxel.
  • Figure 11 Illustrated is the top- scoring pathway as defined by the Ingenuity analysis tool. Each pathway member is depicted by a symbol. Red symbols indicate those genes with down-regulated expression, green represents the genes with increased expression in the analysis, white symbols identifies pathway members not found altered in the tumor cells.
  • A Ingenuity generated interaction pathways of the identified COXEN biomarkers of response for the DOC-24 breast clinical trial of docetaxel.
  • B Ingenuity generated interaction pathways of the identified COXEN biomarkers of response for the human bladder cancer cell lines (BLA-40) to paclitaxel.
  • C Ingenuity generated interaction pathways of the identified COXEN biomarkers of response for the human bladder cancer cell lines (BLA-40) to cisplatinum.
  • Figure 12 Shows the COXEN combination chemosensitivity prediction on 43 lymphoma patients treated with CHOP-like regimen (cyclophosphamide, doxorubicin, vincristine, and prednisone).
  • the present invention encompasses a novel method for identifying the activity of an agent or combination of agents.
  • the invention is achieved by the creation and use of an algorithm termed "CO-eXpression ExtrapolatioN" (COXEN).
  • COXEN CO-eXpression ExtrapolatioN
  • the algorithm uses specialized molecular profile signatures for translating an agent(s) sensitivity signature from one set of cells to that of another set of cells (e.g., translating data from the NCI60 panel to a panel of cells not present in the NCI60 panel).
  • the present invention provides a potential solution to major problems in drug development as well as in the selection of optimal therapeutic regimens (personalized medicine). That is, while thousands of agents have been and are being synthesized, there are essentially no generally reliable ways to predict which of those agents will be active against a disease or disease model or potentially effective as a therapeutic agent. Cell and animal models have not been useful in this regard. Hence, many useful agents end up neglected ('leaky pipeline"), while others are only found to fail after expensive and time-consuming clinical trials. Together, this results in a "status quo" where long drug development timelines and huge costs are the norm.
  • the methods of the present invention address the above problem in drug discovery by accurate prediction of an agent(s) effectiveness in patients from in vitro sensitivity experiments on cell sets using the presently disclosed "CO-eXpression ExtrapolatioN" (COXEN) technique.
  • COXEN CO-eXpression ExtrapolatioN
  • the present invention has at least two applications: 1) selecting the optimal lead agents for Phase I human trials; and, 2) patient selection for Phase II and III clinical trials for agents that have already passed Phase I, markedly improving odds for success of these latter trials.
  • the present invention addresses the need for personalized medicine (or personalized selection of medicines) by accurate prediction of a single agent or combination of agents effectiveness in specific patients from in vitro agent sensitivity experiments on cell sets.
  • the invention addresses the problem of how to select combinations of therapeutic agents with therapeutic effectiveness, thereby allowing the medical practitioner to select a combination of agents that will provide the highest combination-agent activities to specific patients. In essence matching the patients disease/tumor etc. to the ideal treatment comprised of a combination of agents.
  • the COXEN method provided herein is useful for: 1) extrapolating agent sensitivity data obtained from in vitro screening of a cell set to predict the sensitivity/response of cell lines and diseases (e.g., cancers, diabetes, etc.) to agents; and, 2) testing and identifying agents for their ability to act as therapeutic agents for diseases (e.g., cancers, diabetes, etc.).
  • the basic protocol of the present invention is as follows (also see Figure IA):
  • STEP 1 Determine an agent's pattern of activity in cells of set 1.
  • STEP 3 Select a subset of those molecular characteristics that most accurately predicts the agent's activity in set 1 (chemosensitivity or agent activity signature selection).
  • STEP 5 Identify a subset among the molecular characteristics selected in (3) that are concordant (i.e., show a strong pattern of "co-expression” or "co- association") between sets 1 and 2. These molecular characteristics can be further reduced in number and data dimension by using a multivariate classification or dimension reduction algorithm.
  • STEP 6 Use a multivariate classification algorithm to predict an agent's activity in set 2 cells using the trained classification model on the basis of the drug's activity pattern and the molecular characteristics in set 1 selected in (5) and applying the trained classification model to set 2 on the same molecular characteristics in set 2 selected in (5).
  • the present invention provides a novel agent discovery methodology that was developed and validated in bladder cancer cells and breast cancer patients.
  • the method is useful, for example, for virtual screening of the approximately 45,545 compounds in the NCI drug database, and providing a list of compounds for human bladder cancer with putative activity in this tumor.
  • the method is also useful for screening other compounds and other diseases as well.
  • the use of at least one of the compounds of the NCI drug database is validated herein for its effectiveness in human bladder cancer. This paradigm shifting approach will greatly accelerate anticancer drug discovery and clinical care of patients (e.g. for patients with cancer).
  • the utility of the present invention has been demonstrated using a series of 40 human urothelial cancer cell lines (BLA-40), measuring the growth inhibition elicited by three widely-used chemotherapeutic agents: cisplatin, paclitaxel, and gemcitabine in the BLA-40, and correlating these GI50 (50% of growth inhibition) values with quantitative measures of global gene expression on these cell lines.
  • BLA-40 human urothelial cancer cell lines
  • GI50 50% of growth inhibition
  • the training set is comprised of compound activity data and molecular characteristic data from a first cell set.
  • the activity data allows one to determine which cells (or patients) are resistant and which are sensitive to a tested agent (e.g., drug substance or compound from a library) or group of agents (e.g., all approved cancer drug substances or a compound library) and what molecular characteristics are related to this resistance and sensitivity.
  • the validation set is comprised of molecular characteristics from a second, distinct cell set.
  • the data of the validation set is derived from cells (or other sources) that may not be present in the training set (e.g., the second set is derived from a series of bladder cancer cell lines and the first set is the NCI60 panel).
  • the validation set allows one to then select a set of molecular characteristics that are concordant to the training and validation sets. This concordant set of molecular characteristics allows one to then predict an agent' s activity against the cells of the validation set.
  • the present invention can use a third or more cell sets to further improve predictive accuracy that an agent will be more effective in a certain situation, cell or patient.
  • the source of the third or other additional cell sets is distinct from the first and second sets (e.g., human tissues for the third set and cell lines for the first and second cell sets).
  • the disease state of the cells can be the same or different from the first and second sets (e.g., the third set can be derived from human bladder cancer tissues, the second from bladder cancer cell lines, and the first the NCI60 panel (which does not contain bladder cancer cells).
  • a set of molecular characteristics concordant to the first and third cell sets is determined (i.e., a second concordant set).
  • a set of molecular characteristics common to the two concordant sets is then determined.
  • This common set of molecular characteristics can then be used to predict the activity of the agents both against the second and the third cell sets without physically conducting the experiments.
  • This dual prediction is particularly important in novel drug discovery. For example, one can determine new agent leads from a library of agents that have efficacy both on the second cell line set and the third human bladder cancer patient set. Once a lead agent is experimentally validated on the second cell line set, it has a high likelihood to be effective for the third human cancer patient set, which would not have been realized in the classical ways (current paradigms) until expensive human clinical trials has been performed. Thus, one can very efficiently discover and validate a drug or drugs that have the effectiveness against the disease of a patient, thereby significantly reducing the cost and risk of discovery of human therapeutic agents.
  • the present invention is useful for preparing and comparing molecular profiles for various kinds of cell sets. This information can be used in conjunction with current databases, or new databases, to predict the response of a test cell to an agent (e.g., a drug substance or a test compound).
  • an agent e.g., a drug substance or a test compound.
  • the present invention provides a novel method of treating a subject in need thereof with an agent identified by the methods of the invention.
  • the present invention provides a novel method of predicting the effectiveness of a known agent in a patient in need of treatment. For example, a tissue sample from a cancer patient can be used in the present invention to determine what cancer agent(s) will be effective against that patient's tumor without having the patient's tumor ever exposed to the agent.
  • the present invention can be used to determine what combination of agents will be effective against that patient's tumor without having the patient's tumor ever exposed to the agent.
  • the present methods are useful for agent screening (e.g., cancer agent screening).
  • Organizations such as the NCI and large pharmaceutical companies have been using the NCI-60 panel or similar panels to screen hundreds of thousands perhaps even millions of agents.
  • This information can be used with the methods of the present invention to select top agents candidates for every single human tumor, even those tumors that are not on the specific panel used for the screen.
  • the studies disclosed herein demonstrate how COXEN can be used in a screening mode and goes on to identify an agent that is potent and selective in bladder cancer.
  • the methods of the present invention are applicable for use in screening agent and agent combinations useful for treating any human tumor/cancer in patients.
  • the present invention further provides methods and compositions useful for therapeutic agent selection and discovery for patients with rare or orphan tumors. For example, most drug development and clinical trials in cancer have concentrated on common tumors. While this is understandable, many less common tumors have become "orphaned" and patients left without any guidance as to the optimal agents to use. Furthermore, few if any drug discovery efforts or clinical trials are being undertaken in these.
  • the COXEN technique can be used to 1) generate lists of optimal agents to use in patients among agents currently FDA approved for cancer; 2) provide new agents among those where sensitivity of said agents in cell line, animal tumors, tissues or organs either syngeneic or xenograft or patient tumor responses is known; and, 3) predict which individuals will be responsive to these identified agents (i.e., personalized medicine).
  • the present invention provides a novel method for predicting the activity of at least one agent, comprising:
  • MC-4 comprises: a set of molecular characteristics concordant to sets MC-2 and MC-3 (biomarker identification of concordantly- expressed or concordantly-associated (e.g., if SNP data is used) molecular networks between two different sets); and,
  • (f) predicting the agent's activity against each cell represented in CS-2 comprising: using a multivariate classification algorithm that compares the agent's determined activity against CS-I with MC-4.
  • step (f) comprises: (f-i) prior to predicting the agent's activity against CS-2, using a multivariate algorithm to reduce the number of molecular characteristics of MC-4 to form MC-4A, comprising: evaluating different combinations and selecting the best combinations of the molecular characteristics in MC-4 with a multivariate classification algorithm for their overall prediction performance of the agent's activity against CS-I, or alternatively, combining the information in MC-4 with a multivariate dimension reduction algorithm to form MC-4A; and,
  • (f-ii) predicting the agent's activity against each cell represented in CS-2, comprising: using a multivariate classification algorithm that compares the agent's determined activity against CS-I with MC-4A.
  • the present invention provides a novel method, wherein the activity against CS-2 is estimated by observing how closely the molecular characteristics MC-4A of each cell in CS-2 match, in terms of the presence and expression levels of the same characteristics, the molecular characteristics MC-4A of the sensitive and resistant cells in CS-I.
  • the present invention provides a novel method, wherein the method further comprises: replacing (f) with at least the following:
  • CS-3 contains cells that differ from those of CS-I and CS-2, which may differ by its source, e.g. in vitro vs. in vivo, or human patients vs. animal models; and;
  • MC-6 identifying a set of molecular characteristics (MC-6) that is a subset of MC-2 and MC-5, wherein MC-6, comprises: a set of molecular characteristics concordant to sets MC-2 and MC-5 (biomarker identification of concordantly- expressed or concordantly-associated molecular networks between MC-2 and MC-5);
  • MC-7 comprises: a set of molecular characteristics common to sets MC-4 and MC-6 (biomarker identification of concordantly-expressed or concordantly-associated molecular networks across all three sets MC-2, MC-3 and MC-5);
  • step (J) comprises:
  • CS-3 comprising: using a multivariate prediction algorithm that compares the agent's determined activity against CS-I with MC-7A.
  • the present invention provides a novel method, wherein the agent is from NCI-60 anticancer drug screening database.
  • the present invention provides a novel method, wherein the activity against CS-2 and CS-3 is estimated by observing how closely the molecular characteristics MC-7A of each cell in CS-2 and CS-3 match, in terms of the presence and expression level of the same characteristics, those of sensitive and resistant cells in CS-I.
  • the present invention provides a novel method, wherein the activity determined is the agent's cytostaticability (growth inhibition) and/or cytotoxicity (cell death) against each cell type in CS-I.
  • the present invention provides a novel method, wherein each cell set is a cancer cell set and the activity being tested is anti-cancer activity.
  • the present invention provides a novel method, wherein CS-I is a panel of cancer cells.
  • the present invention provides a novel method, wherein the panel of cancer cells is the NCI-60 panel.
  • the present invention provides a novel method, wherein CS-2 is a set of cells derived from human laboratory cell lines.
  • the present invention provides a novel method, wherein the human laboratory cell lines are cancer cell or endothelial cell lines.
  • the present invention provides a novel method, wherein the type of cancer is selected from bladder, lung, brain, breast, liver, colon, rectal, melanoma, pancreatic, leukemia, non-Hodgkin lymphoma, kidney, endometrial, prostate, thyroid, meningiomas, mixed tumors of salivary glands, adenomas, carcinomas, adenocarcinomas, sarcomas, dysgerminomas, retinoblastomas, Wilms' tumors, neuroblastomas, ovarian, squamous cell carcinoma, pancreatic, and mesotheliomas.
  • the type of cancer is selected from bladder, lung, brain, breast, liver, colon, rectal, melanoma, pancreatic, leukemia, non-Hodgkin lymphoma, kidney, endometrial, prostate, thyroid, meningiomas, mixed tumors of salivary glands, adenomas, carcinomas, adenocarcinomas, sar
  • the present invention provides a novel method, wherein wherein CS-3 is a set of cells derived from human tissue samples.
  • the present invention provides a novel method, wherein the human tissue samples were taken from cancerous tissues.
  • the present invention provides a novel method, wherein the type of cancer is selected from bladder, lung, brain, breast, liver, colon, rectal, melanoma, pancreatic, leukemia, non-Hodgkin lymphoma, kidney, endometrial, prostate, and thyroid.
  • the present invention provides a novel method, wherein CS-3 is a set of cancer cells derived from human tissue samples of the same type of cancer as that of CS-2.
  • the present invention provides a novel method wherein the molecular characteristics are selected from (i) profiling of gene expression, (ii) profiling of
  • SNPs single nucleotide polymorphisms
  • iii profiling of protein expression
  • the present invention provides a novel method, wherein the molecular characteristics are mRNA expression profiles.
  • the present invention provides a novel method, wherein the agent is at least one pharmaceutically active ingredient (API), at least one cancer API, or a group of APIs corresponding to all FDA approved cancer APIs.
  • API pharmaceutically active ingredient
  • the present invention provides a novel method, for selecting a patient-specific API, comprising:
  • MC-4 comprises: a set of molecular characteristics concordant to sets MC-2 and MC-3;
  • (f) using a multivariate classification algorithm to reduce the number of molecular characteristics of MC-4 to form MC-4A comprising: evaluating different combinations and selecting the best combinations of the molecular characteristics in MC-4 with a multivariate classification algorithm for their overall prediction performance of the API's activity against CS-I, or alternatively, combining the information in MC-4 with a multivariate dimension reduction algorithm to form MC-4A; and,
  • the present invention provides a novel method, wherein the activity against TS-I is estimated by observing how closely the molecular characteristics
  • MC-4A of each cell in TS-I match, in terms of the presence and expression levels of the same characteristics, those of sensitive and resistant cells in CS-I.
  • the present invention provides a novel method, wherein CS-I corresponds to the set of NCI-60 cancer cell lines or a similar set of cancer cell line panels.
  • the present invention provides a novel method, wherein CS-I corresponds to a set of patients and the data for (a) and (b) are collected from the response data and patient microarray data of the patients.
  • the present invention provides a novel method, wherein the patient response data and microarray data are from patients who have received therapy for a cancer or other disease.
  • the present invention provides a novel method, wherein the method further comprises:
  • the present invention provides a novel method, of treating cancer, comprising: administering a therapeutically effective amount of a compound of Table 3, 4, 5, 6, or 7 or a pharmaceutically acceptable salt thereof, wherein the cancer is selected from breast, bladder, prostate, melanoma, and pancreatic.
  • the present invention provides a novel hardware device, comprising: a machine readable storage device have stored thereon a computer program, comprising: a plurality of code sections executable by a machine for performing a process as described herein.
  • the present invention provides a novel method for predicting the activity of at least one agent, said method comprising: a hardware device having a machine readable storage, having stored thereon a computer program comprising a plurality of code sections executable by a machine, for performing the steps described herein.
  • the methods of the present invention can be used for determining toxicity profiles of agents used or in development for human disease. For example, by applying the COXEN technology between sets of cancer cells or other cells exposed to agents in vitro and normal cells or tissues, one could predict the toxicity profile of the various compounds in patients without the use of animal models.
  • compositions useful for practicing the invention may be administered to deliver a dose of between 1 ng/kg/day and 100 mg/kg/day.
  • compositions that are useful in the methods of the invention may be administered systemically in oral solid formulations, ophthalmic, suppository, aerosol, topical or other similar formulations.
  • Such pharmaceutical compositions may contain pharmaceutically-acceptable carriers and other ingredients known to enhance and facilitate drug administration.
  • Other possible formulations, such as nanoparticles, liposomes, resealed erythrocytes, and immunologically based systems may also be used to administer an appropriate agent according to the present invention.
  • compositions described herein may be prepared and administered to a mammal for treatment of a disease described herein.
  • the formulations of the pharmaceutical compositions described herein may be prepared by any method known or hereafter developed in the art of pharmacology. In general, such preparatory methods include the step of bringing the active ingredient into association with a carrier or one or more other accessory ingredients, and then, if necessary or desirable, shaping or packaging the product into a desired single- or multi-dose unit.
  • pharmaceutical compositions provided herein are principally directed to pharmaceutical compositions which are suitable for ethical administration to humans, it will be understood by the skilled artisan that such compositions are generally suitable for administration to animals of all sorts.
  • compositions suitable for administration to humans in order to render the compositions suitable for administration to various animals is well understood, and the ordinarily skilled veterinary pharmacologist can design and perform such modification with merely ordinary, if any, experimentation.
  • Subjects to which administration of the pharmaceutical compositions of the invention is contemplated include, but are not limited to, humans and other primates, mammals including commercially relevant mammals such as cattle, pigs, horses, sheep, cats, and dogs, birds including commercially relevant birds such as chickens, ducks, geese, and turkeys.
  • compositions that are useful in the methods of the invention may be prepared, packaged, or sold in formulations suitable for oral, rectal, vaginal, parenteral, topical, pulmonary, intranasal, buccal, ophthalmic, intrathecal, venous, or another route of administration.
  • Other contemplated formulations include projected nanoparticles, liposomal preparations, resealed erythrocytes containing the active ingredient, and immunologically- based formulations.
  • a pharmaceutical composition of the invention may be prepared, packaged, or sold in bulk, as a single unit dose, or as a plurality of single unit doses.
  • "Unit dose” is discrete amount of the pharmaceutical composition comprising a predetermined amount of the active ingredient.
  • the amount of the active ingredient is generally equal to the dosage of the active ingredient which would be administered to a subject or a convenient fraction of such a dosage such as, for example, one-half or one-third of such a dosage.
  • compositions of the invention will vary, depending upon the identity, size, and condition of the subject treated and further depending upon the route by which the composition is to be administered.
  • the composition may comprise between 0.1% and 100% (w/w) active ingredient.
  • a pharmaceutical composition of the invention may further comprise one or more additional pharmaceutically active agents.
  • additional agents include anti-emetics and scavengers such as cyanide and cyanate scavengers.
  • Controlled- or sustained-release formulations of a pharmaceutical composition of the invention may be made using conventional technology.
  • a formulation of a pharmaceutical composition of the invention suitable for oral administration may be prepared, packaged, or sold in the form of a discrete solid dose unit including a tablet, a hard or soft capsule, a cachet, a troche, or a lozenge, each containing a predetermined amount of the active ingredient.
  • Other formulations suitable for oral administration include, but are not limited to, a powdered or granular formulation, an aqueous or oily suspension, an aqueous or oily solution, or an emulsion.
  • An "oily" liquid is one which comprises a carbon-containing liquid molecule and which exhibits a less polar character than water.
  • Parenteral administration of a pharmaceutical composition includes any route of administration characterized by physical breaching of a tissue of a subject and administration of the pharmaceutical composition through the breach in the tissue.
  • Parenteral administration thus includes, but is not limited to, administration of a pharmaceutical composition by injection of the composition, by application of the composition through a surgical incision, by application of the composition through a tissue-penetrating non-surgical wound, and the like.
  • parenteral administration is contemplated to include, but is not limited to, subcutaneous, intraperitoneal, intramuscular, intrasternal injection, and kidney dialytic infusion techniques.
  • Formulations of a pharmaceutical composition suitable for parenteral administration comprise the active ingredient combined with a pharmaceutically acceptable carrier, such as sterile water or sterile isotonic saline. Such formulations may be prepared, packaged, or sold in a form suitable for bolus administration or for continuous administration. Injectable formulations may be prepared, packaged, or sold in unit dosage form, such as in ampules or in multi dose containers containing a preservative. Formulations for parenteral administration include, but are not limited to, suspensions, solutions, emulsions in oily or aqueous vehicles, pastes, and implantable sustained-release or biodegradable formulations. Such formulations may further comprise one or more additional ingredients including suspending, stabilizing, or dispersing agents.
  • a pharmaceutically acceptable carrier such as sterile water or sterile isotonic saline.
  • Such formulations may be prepared, packaged, or sold in a form suitable for bolus administration or for continuous administration.
  • injectable formulations may be prepared, packaged, or sold
  • the active ingredient is provided in dry (i.e. powder or granular) form for reconstitution with a suitable vehicle (e.g. sterile pyrogen free water) prior to parenteral administration of the reconstituted composition.
  • a suitable vehicle e.g. sterile pyrogen free water
  • the pharmaceutical compositions may be prepared, packaged, or sold in the form of a sterile injectable aqueous or oily suspension or solution.
  • This suspension or solution may be formulated according to the known art, and may comprise, in addition to the active ingredient, additional ingredients such as the dispersing agents, wetting agents, or suspending agents described herein.
  • Such sterile injectable formulations may be prepared using a non toxic parenterally acceptable diluent or solvent, such as water or 1,3 butane diol, for example.
  • Other acceptable diluents and solvents include, but are not limited to, Ringer's solution, isotonic sodium chloride solution, and fixed oils such as synthetic mono- or di-glycerides.
  • compositions for sustained release or implantation may comprise pharmaceutically acceptable polymeric or hydrophobic materials such as an emulsion, an ion exchange resin, a sparingly soluble polymer, or a sparingly soluble salt.
  • Formulations suitable for topical administration include, but are not limited to, liquid or semi liquid preparations such as liniments, lotions, oil in water or water in oil emulsions such as creams, ointments or pastes, and solutions or suspensions.
  • Topically-administrable formulations may, for example, comprise from about 1% to about 10% (w/w) active ingredient, although the concentration of the active ingredient may be as high as the solubility limit of the active ingredient in the solvent.
  • Formulations for topical administration may further comprise one or more of the additional ingredients described herein.
  • dosages of the compound of the invention which may be administered to an animal, preferably a human, range in amount from 1 ⁇ g to about 100 g per kilogram of body weight of the animal. While the precise dosage administered will vary depending upon any number of factors, including the type of animal and type of disease state being treated, the age of the animal and the route of administration.
  • the dosage of the compound will vary from about 1 mg to about 1O g per kilogram of body weight of the animal. More preferably, the dosage will vary from about 10 mg to about 1 g per kilogram of body weight of the animal.
  • the compound may be administered to an animal as frequently as several times daily, or it may be administered less frequently, such as once a day, once a week, once every two weeks, once a month, or even less frequently, such as once every several months or even once a year or less.
  • the frequency of the dose will be readily apparent to the skilled artisan and will depend upon any number of factors, including the type and severity of the disease being treated, the type and age of the animal, etc.
  • the present invention also includes a kit comprising the composition of the invention and an instructional material which describes administering the composition to a cell or a tissue of a mammal.
  • this kit comprises a (preferably sterile) solvent suitable for dissolving or suspending the composition of the invention prior to administering the compound to the mammal.
  • the present invention further provides kits for use in administering or using compounds of the present invention.
  • API active pharmaceutical ingredient (aka, drug substance);
  • CEEC co-expression extrapolation coefficient
  • CIM co-clustering cluster image map
  • COXEN COeXpression ExtrapolatioN
  • MiPP misclassification-penalized posterior
  • Examples of multivariate classification/prediction algorithms include algorithms selected from linear discriminant analysis (LDA), quadratic discriminant analysis
  • QDA support vector machine
  • SVM support vector machine
  • Examples of multivariate dimension reduction algorithms include algorithms selected from principal component analysis and singular value decomposition.
  • Agent includes a pharmaceutically active ingredient (API) or drug substance
  • Agent also includes a compound that is a potential drug substance or a potential lead compound in the search for a drug substance.
  • APIs include cancer APIs (e.g., all FDA approved cancer APIs).
  • Agent also includes a library of compounds (e.g., a group of compounds used to screen for research leads).
  • a library of compounds can include 10, 100,
  • Compound refers to any type of substance that is commonly considered a chemical, biological (e.g., protein), drug, or a candidate for use as a therapeutic agent for use in a mammal (e.g., human).
  • the source of the compound can be natural (e.g., a natural product), synthetic (e.g., a man-made API), or semi- synthetic (e.g., a modified natural product).
  • Cell set includes groups (e.g., panels) of cells and/or tissues. Thus, when cells are referred to in the claims, tissues are also included. The cells and tissues can come from a variety of sources including cell lines and tissue samples (e.g., tissues from a patient or patients). Cell set also includes a group of patients (e.g., patient set) whose molecular characteristics and sensitivity or resistance to an API have previously been determined (e.g., publicly reported).
  • the cell sets are typically representative of a disease state (e.g., cancer or diabetes) and can be various cells of one type of disease (e.g., various bladder cell lines) or various cells of different types of the same disease (e.g., the NCI60 panel which contains cells of a wide variety of cancer types).
  • Cell sets also include cell lines and/or cell tissues derived from normal (i.e., non-diseased) human samples (e.g., endothelial cells, white blood cells, and other marrow components).
  • NCI60 panel An example of a panel of cancer cells is the NCI60 panel. Other similar panels would also be useful in the present invention.
  • Molecular characteristics are measurements of molecular components expressed and the levels of expression. [00115] Molecular characteristics include profiling of (i) gene expression, (ii) SNPs
  • the determining of each agent's pattern of activity against a 1 st cell set can be accomplished experimentally or, when available, by using data from a database (e.g., selecting data from a published database).
  • the data sought is the type that shows which cells are sensitive and resistant to the agent. When more than one agent is being tested, this activity data will need to be determined for each agent.
  • One of ordinary skill in the art can take advantage of published data when determining a agent's pattern of activity and measuring a set of molecular characteristics. For example, there is microarray data available for cancer patients who have received cancer therapy. This data can be used to measure molecular characteristics. There is also data available showing patient response to treatment with a drug substance. For example, there is patient response data for cancer agents. This data can be used to determine whether or not a patient is sensitive or resistant to a specific agent. Thus, there is publicly available data showing the molecular characteristics of patients that are sensitive or resistant to an agent
  • a cancer drug (e.g., a cancer drug).
  • Chemosensitivity signature selection means selecting a subset of molecular characteristics that most accurately predict an agent's activity against each cell represented in a cell set.
  • agent activity signature selection involves selecting 2, 3, 4 5, 6, 7,
  • Cancer is defined as proliferation of cells whose unique trait — loss of normal controls — results in unregulated growth, lack of differentiation, local tissue invasion, and metastasis.
  • an “effective amount” means an amount of a compound or agent sufficient to produce a selected or desired effect.
  • the term “effective amount” is used interchangeably with “effective concentration” herein.
  • “Pharmaceutically acceptable carrier” includes any of the standard pharmaceutical carriers, such as a phosphate buffered saline solution, water, emulsions such as an oil/water or water/oil emulsion, and various types of wetting agents. The term also encompasses any of the agents approved by a regulatory agency of the US Federal government or listed in the US Pharmacopeia for use in animals, including humans.
  • Treating covers the treatment of a disease-state in a mammal, and includes: (a) preventing the disease-state from occurring in a mammal, in particular, when such mammal is predisposed to the disease-state but has not yet been diagnosed as having it; (b) inhibiting the disease-state, i.e., arresting it development; and/or (c) relieving the disease-state, i.e., causing regression of the disease state until a desired endpoint is reached. Treating also includes the amelioration of a symptom of a disease (e.g., lessen the pain or discomfort), wherein such amelioration may or may not directly affect the disease (e.g., cause, transmission, expression, etc.).
  • a symptom of a disease e.g., lessen the pain or discomfort
  • “Pharmaceutically acceptable salts” refer to derivatives of the disclosed compounds wherein the parent compound is modified by making acid or base salts thereof.
  • pharmaceutically acceptable salts include, but are not limited to, mineral or organic acid salts of basic residues such as amines; alkali or organic salts of acidic residues such as carboxylic acids; and the like.
  • the pharmaceutically acceptable salts include the conventional non-toxic salts or the quaternary ammonium salts of the parent compound formed, for example, from non-toxic inorganic or organic acids.
  • such conventional non-toxic salts include, but are not limited to, those derived from inorganic and organic acids selected from 1, 2-ethanedisulfonic, 2-acetoxybenzoic, 2- hydroxyethanesulfonic, acetic, ascorbic, benzenesulfonic, benzoic, bicarbonic, carbonic, citric, edetic, ethane disulfonic, ethane sulfonic, fumaric, glucoheptonic, gluconic, glutamic, glycolic, glycollyarsanilic, hexylresorcinic, hydrabamic, hydrobromic, hydrochloric, hydroiodide, hydroxymaleic, hydroxynaphthoic, isethionic, lactic, lactobionic, lauryl sulfonic, maleic, malic, mandelic, methanesulfonic, napsylic, nitric, oxalic, pamoic, pantothenic
  • the pharmaceutically acceptable salts of the present invention can be synthesized from the parent compound that contains a basic or acidic moiety by conventional chemical methods. Generally, such salts can be prepared by reacting the free acid or base forms of these compounds with a stoichiometric amount of the appropriate base or acid in water or in an organic solvent, or in a mixture of the two; generally, non-aqueous media like ether, ethyl acetate, ethanol, isopropanol, or acetonitrile are useful. Lists of suitable salts are found in Remington's Pharmaceutical Sciences, 18th ed., Mack Publishing Company, Easton, PA, 1990, p 1445, the disclosure of which is hereby incorporated by reference.
  • Therapeutically effective amount includes an amount of a compound of the present invention that is effective when administered alone or in combination to treat an indication listed herein.
  • “Therapeutically effective amount” also includes an amount of the combination of compounds claimed that is effective to treat the desired indication.
  • the combination of compounds can be a synergistic combination. Synergy, as described, for example, by Chou and Talalay, Adv. Enzyme Regul. 1984, 22:27-55, occurs when the effect of the compounds when administered in combination is greater than the additive effect of the compounds when administered alone as a single agent. In general, a synergistic effect is most clearly demonstrated at sub-optimal concentrations of the compounds. Synergy can be in terms of lower cytotoxicity, increased effect, or some other beneficial effect of the combination compared with the individual components.
  • "Instructional material” includes a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of the peptide of the invention in the kit for effecting alleviation of the various diseases or disorders recited herein.
  • the instructional material may describe one or more methods of alleviation the diseases or disorders in a cell or a tissue of a mammal.
  • the instructional material of the kit of the invention may, for example, be affixed to a container which contains the peptide of the invention or be shipped together with a container which contains the peptide. Alternatively, the instructional material may be shipped separately from the container with the intention that the instructional material and the compound be used cooperatively by the recipient.
  • NCI-60 Publicly available drug sensitivity data, expressed in terms of 50% growth inhibition (GI50) for the NCI-60 were obtained from the NCI DTP web site (dtp.nci.nih.gov). NCI-60 transcript expression profiles were previously generated in a collaboration between the NCI Genomics & Bioinformatics Group and GeneLogic, Inc. (Gaithersburg, MD, U.S.A.) using HG-U133A GeneChip® arrays (Affymetrix, Santa Clara, CA, USA). BLA-40 transcript expression data were obtained using the HG-U 133 A chips as part of the present study (Supplementary Materials and Methods).
  • Step 3 For each compound in the public NCI-60 drug database, we identified the approximately 20% of the NCI-60 cells most sensitive to the compound and the 20% most resistant. Using slightly different percent cutoffs did not change the ultimate results appreciably (data not shown). For concreteness in describing the COXEN algorithm and its results, we used the examples of cisplatin and paclitaxel in the NCI-60 drug database, two drugs commonly used for clinical treatment of human bladder cancer (Calabro et al., 2002).
  • SAM Signal Analysis of Microarrays
  • FDR false discovery rate
  • chemosensitivity biomarkers can be selected by evaluating overall correlation between each molecular characteristic and agent activity values, e.g., GI50. That procedure identified 191 probe sets for cisplatin and 105 for paclitaxel.
  • Step 5 Identification of co-expression extrapolation signatures (Step 5) The co- expression extrapolation procedure is conceptually illustrated in Figure 2A. Each gene's concordant co-expression relationships between two studies can be mathematically evaluated by co-expression extrapolation coefficient (CEEC). This CEEC will be high if a probe' co- expression network relationships with the other genes on the first set (i.e. NCI-60) are concordant with those of the second set (i.e., BLA-40).
  • CEEC co-expression extrapolation coefficient
  • Step 6 We had identified candidate biomarker genes for each tested compound on the basis of significant differential expression for drug sensitivity in the NCI-60 and high CEEC between the NCI-60 and each of the target sets as described above. Next, we searched among those candidate biomarkers for ones that would form optimal parsimonious models for prediction of the compound's activity. For that purpose, we used the "Misclassification- Penalized Posterior" (MiPP) algorithm, which we introduced previously. This technique is described more in detail in Supplementary Materials and Methods. [00135] Sensitivity of human bladder cancer cells to cisplatin, paclitaxel and NSC
  • Cell lines were maintained in appropriate media, in a humidified atmosphere containing 5 % CO2 in air, except CRL2169 (SW780) which requires no CO2 for its growth. Cell lines were subcultured in an aqueous solution of 0.05% trypsin (Difco, 1:250) and 0.016% EDTA. Each cell line was used within 10 passages from its archival passage number in order to minimize any long term cell culture effects.
  • Gene expression analysis of bladder cell lines was carried out as previously described using the HG-U 133 A GeneChip® array (Affymetrix®, Santa Clara, CA, USA) (6, 7). The image file was analyzed with RMA, to obtain the expression intensity values of the microarray data (8).
  • Cisplatin 1000 cells/well. 24 hours later, cells were exposed to the drugs diluted in RPMI- 1640 medium, containing 10% FBS, concentration that is required by more than 75% of cell lines for their normal growth, at a total volume of 200 ⁇ L. Each drug dose was plated in triplicate, and the experiment was repeated four to seven times.
  • the doses for Cisplatin were 200, 400, 800, 1600, 3200, and 6400 ng/ml; for Paclitaxel 0.0001, 0.001, 0.002, 0.005, 0.01, and 0.1 ⁇ M; for Gemcitabine 0.001, 0.01, 0.1, 1, 10, 100 ⁇ M.
  • CR criterion dose
  • MiPP is based on stepwise incremental classification modeling discovery for the optimal, most parsimonious prediction models and double cross-validated evaluation for each trained prediction model.
  • Model training can be performed from several different classification modeling techniques such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), or logistic regression; LDA was used for most application in our current study.
  • LDA linear discriminant analysis
  • QDA quadratic discriminant analysis
  • SVMs support vector machines
  • logistic regression logistic regression
  • MiPP generates multiple independent splits, which, in turn, results in multiple prediction models.
  • the multiple models from different splits were re-evaluated on a large number of (e.g. 100) random splits of test and training sets to obtain their objective confidence bounds with the summary index, so- called sMiPP (standardized MiPP score), which varies between -1 to 1, from the worst to the best. From this confidence interval evaluation, mean and lower 5% sMiPP scores were obtained for each of the candidate prediction models, together with mean misclassification rates (ER).
  • the final prediction of sensitive (or resistant) cell lines was performed by averaging its (posterior) classification probabilities of the top three prediction models exceeding 5% sMiPP>0.5.
  • n.fold, p. test, n. split, and n.seq were 5, 1/3, 20, and 3, respectively.
  • we pre-selected the most significant top 1% genes by LPE and SAM, and did not use the MiPP gene selection option by setting percent.cut 0.
  • Statistical chemosensitivity prediction for combination drug treatments [00151] Prediction of combination drug efficacy was obtained based on the final single- drug prediction models, directly utilizing each cell line's classification probabilities from these models. That is, assuming two different drug compounds acted independently, the combination chemosensitivity probability PAB of their combination treatment was derived as: [00152] 1 - PA[resistant for drug A] x PB [resistant for drug B] .
  • RESULTS Below we will provide the results using COXEN for single and combination agents. These sections are kept separate for clarity here, but in practice, will be used in an integrated and inter related manner to provide information. [00155] RESULTS (SINGLE AGENTS):To describe the use and demonstrate the capability of COXEN, three proof-of-principle test applications were addressed for single agents. First, a panel of 40 human urothelial bladder carcinomas (BLA-40) was assembled, profiled at the mRNA level as had been done with NCI-60, and the mRNA profiles of the two cell line panels were used to obtain a COXEN "Rosetta Stone" profile for prediction of drug sensitivities of the BLA-40 from those of the NCI-60.
  • BLA-40 human urothelial bladder carcinomas
  • COXEN was used to carry out in silico screening of 45,545 compounds to identify new candidate agents that might be selectively active against bladder cancer cells in the BLA-40.
  • Step 2 were the NCI-60 and BLA-40 cell panels, respectively; the Step 1 drug activities were those assessed by DTP in the NCI-60 using a 48-hour sulforhodamine B assay; the "molecular characteristic" in Steps 2 and 4 was transcript expression level, as assessed using Affymetrix HG-U133A microarrays; the algorithm in Step 3 was "Significance Analysis of Microarrays (SAM)" or two-sample t-test; Step 5 was a novel "co-expression extrapolation” algorithm; Step 6 was another novel algorithm, “Misclassification-Penalized Posterior” (MiPP), which we recently introduced for selection of the best mathematical "models” for the prediction; and applying the prediction models obtained in Step 6, independent testing of the predictions on BLA-40 cells was performed, mimicking the way the assay for the NCI-60 by DTP.
  • SAM Signal Analysis of Microarrays
  • Step 6 was another novel algorithm, “Misclassification-Penalized Posterior” (
  • step in 3 can be performed with other methods instead of SAM, or a two-sample t-test, or modifications thereof, which instead can be referred to a s "statistical identification of agent activity biomarkers of interest.”
  • steps 3 and 5 cannot be omitted; the algorithm uses, not the entire molecular signature, but those aspects of the signature that most strongly predict the drug's activity and that also reflect a pattern of co-expression between the two sets of cancer cells. As will be shown below, simply using the entire molecular signature (or even the entire drug activity molecular signature portion of it) does not work well.
  • the ROC formulation permits free choice of a set-point to use in balancing the costs of false- positive and false-negative predictions.
  • Non-parametric tests such as Wilcoxon rank-sum test can be calculated for comparing two different ROC curves.
  • Figure ID contrasts the ROC curves obtained for cisplatin from the full COXEN algorithm with those obtained by leaving out either the drug chemosensitivity signature step (Step 3) or the co-occurrence step (Step 5).
  • the predictions were far superior when the entire algorithm was used.
  • no chemosensitivity data on the BLA-40 cells were used to "tune" any part of the COXEN algorithm to obtain the results for described here or elsewhere in the study.
  • FIG. 2B-C The Clustered Image Maps (heat maps) in Figures 2B-C illustrates graphically the raison d'etre for the "co-occurrence" step (Step 5) in COXEN. Without that step ( Figure 2B), the cell types tend to sort themselves out according to whether they are NCI-60 or BLA- 40; with that step ( Figure 2C), the cells of the two panels tend to intermingle and (as one would wish) to cluster according to their sensitivity to the drug.
  • Figures 2D and 2E show similar results for paclitaxel. In all cases, the co-occurrence step makes the difference between clustering by cell panel and clustering by sensitivity to the drug.
  • GI50's 50% growth inhibitory concentrations
  • >50% of the BLA-40 were predicted to have submicromolar GI50's.
  • Not all of the candidate compounds were available from the DTP but, notably, our top hit, NSC637993 was, and we were able to assay it for growth inhibition in the BLA-40 panel.
  • the measured GI50 values were less than 10-6M for >60% of the cell types, consistent with prediction 61.8% ( Figure 4B).
  • NSC637993 was more potent overall in the BLA-40 bladder cancers than in any of the organ-of-origin types included in the NCI-60 (data not shown). It was even more potent in the BLA-40 than in the NCI-60 leukemias, which are generally the most sensitive cells.
  • Table 1 Top MIPP classification models on chemosensitivity response prediction on sensitive and resistant cell lines for cisplatin and paclitaxel. A) Top three MiPP models and their independent-set validated prediction performance on the NCI-60, B) Predicted and actual performance of the models shown in (A) in the BLA-40 panel. [00170] Table IA
  • Table 2 Evaluation of predictive performance of top three MIPP classification models on chemotherapeutic response of the breast cancer patients in the docetaxel (DOC-24) and tamoxifen trials (TAM-60).
  • # Classification of patients as responders and nonresponders is based on their posterior classification probabilities (CP) from each model, i.e., responder if CP>0.5 and nonresponder if CP ⁇ 0.5.
  • Affymetrix GeneChip® array files of the NCI-60 and breast cancer patients were analyzed with the RMA analysis software to obtain the expression intensity values of the microarray data.
  • the identified compounds relevant to breast cancer in particular are provided in Table 3.
  • Novel anticancer drug discovery for bladder cancer The Bladder cancer drug discovery was performed using BLA-40 and our internal microarray data set of 85 human bladder cancer patients (BLA-85) (Two validation/prediction sets; Table 4).
  • pancreatic cancer drug discovery was performed using the data set of 49 patients (Table 7). Ishikawa
  • Figures 6B-6D show the loglO(GBO), loglO(GI50), and loglO(GI70) of the 40 cell lines for each of the agents.
  • Figure 6B For cisplatin, we identified 16 sensitive and 11 resistant cell lines (Figure 6B); 17 sensitive and 11 resistant cell lines for Paclitaxel ( Figure 6C), and 8 sensitive and 11 resistant for Gemcitabine ( Figure 6D). Cell lines that did not meet the "sensitive/resistant" criteria were excluded from further analyses. For some cell lines, log(GI) values could not be estimated due to flat response curves in nonlinear regression model fitting; thus, these cell lines' log(GI) values were thresholded at the maximum dose concentration and were classified as resistant.
  • the prediction performance of Paclitaxel models was similar to that of Cisplatin with mean misclassification rates of between 4.1-7.1% and mean sMiPPs of 0.830 to 0.910.
  • Table 8A Best gene prediction models for single drug chemosensitivity response prediction to cisplatin, paclitaxel, and gemcitabine. Up to three models were selected with the selection criterion 5% sMiPP > 0.5.
  • Table 8B Predicted sensitivity probabilities to combination therapy and validation in fifteen urothelial cancer cell lines.
  • the growth inhibition of the combination drug treatment experiments (% of cell count in cells not exposed to drug) was obtained using the dose concentrations: Cisplatin logio(400 ng/ml), Paclitaxel logio(0.005 ⁇ M), and Gemcitabine Iog 10 (0.1 ⁇ M).
  • a cell line with the larger posterior probability (PP) is more likely to be a sensitive.
  • Single-drug posterior probabilities were obtained by averaging posterior probabilities if there were more than one model, and the combined posterior probability is 1-Pr (Resistant by Cisplatin) x Pr (Resistant by Paclitaxel).
  • DISCUSSION Below we will discuss results using COXEN for single and combination agents. These sections are kept separate for clarity here.
  • DISCUSSION SINGLE AGENT: The present invention provides a new algorithm, COXEN, for in silico prediction of chemosensitivity. Disclosed herein are illustrative studies in which COXEN was used (i) to extrapolate from chemosensitivity data on the NCI-60 cancer cell panel to an analogous cell line panel of bladder cancers, (ii) to extrapolate from the NCI-60 to clinical data on a panel of breast cancers, and (iii) to predict sensitivity of the bladder cancers to 45,545 candidate agents on the basis of NCI- 60 data. Importantly, in each case the algorithm was run independently of the validating experimental results and not further tuned thereafter. We expect that it will be possible in the future to improve the algorithm and its predictions by learning from the experience gained in applications such as those described here.
  • the lead hit identified, NSC637993 was an imidazoacridinone, with structural similarities to such drug classes as the anthracyclines (e.g., doxorubicin), the anthracenediones (e.g., mitoxantrone), and the anthrapyrazoles (e.g., oxantrazole and biantrazole), which are known to intercalate in DNA and inhibit DNA topoisomerase II.
  • anthracyclines e.g., doxorubicin
  • the anthracenediones e.g., mitoxantrone
  • anthrapyrazoles e.g., oxantrazole and biantrazole
  • COXEN might also prove useful for subsetting patients or for "personalizing" their treatment.
  • gene expression profiles obtained from a patient's tumor can be compared with the expression profiles from other tumors of the same organ, grade, and stage to assist in prognosis and selection of therapy.
  • the results described here for COXEN reinforce the idea that it is best to focus on the subset of genes that constitutes a signature of drug sensitivity.
  • COXEN is potentially useful whenever one has a combination of drug sensitivity and molecular profile data on one panel of cell types (or on a panel of molecular screens) and wants to use that information to predict chemosensitivity in a panel for which there are only the molecular profile data.
  • the essential inputs to the algorithm for each compound were (i) a vector consisting of the compound's pattern of activity against the NCI-60 cell lines; (ii) a matrix consisting of gene expression profiles of the NCI-60.
  • any matrix of cell characteristics e.g., protein expression, DNA copy number, occurrence of mutations, etc.
  • a matrix consisting of gene expression data for the panel for which sensitivities are to be predicted e.g., the BLA-40 or the breast sample set.
  • the two gene expression sets must include a sufficient number of genes in common. Preferably, they would have been obtained using the same microarray or other platform but, as in the clinical example here, not necessarily so.
  • DISCUSSION (COMBINATION AGENTS): Herein, we combined a novel mathematical approach (misclassification penalized posterior probabilities) with comprehensive gene expression profiles of 40 urothelial cell lines, to discover high- performance molecular prediction models for single and combination chemotherapeutic sensitivity.
  • the high performance characteristics of the predictive models obtained in this study may be due to several factors.
  • a single anatomic origin should eliminate confounding and biased gene expression signals that represent tissue-dependent sensitivity to different chemotherapy agents.
  • the MiPP method combines the best of both approaches by maintaining excellent predictive accuracy with a small set of genes that are easy to evaluate in human tumors using currently available techniques, such as real time RT-PCR. This feature is a significant advantage as we begin to prospectively evaluate these genes for their ability to predict tumor response in patients treated with drug combinations. [00209] The approach taken here led to the identification of predictive gene models for each of the three drugs.
  • Cisplatin model 1 is comprised of TGM2, MOAPl, HIST2H2AA, MRPS30; Model 2 contains CAV2, LCPl, and MOAPl and Model 3 includes CCNG2, PEGlO, and WNT5B.
  • TGM2, MOAPl, and CAV2 direct and indirect
  • HIST2H2AA, and LCPl indirect
  • WNT5B WNT5B
  • Caveolin 2 (CA V2) is a major component of the inner surface of caveolae, and is implicated in the control of cellular growth, signal transduction, lipid metabolism, and apoptosis.
  • LCPl or lymphocyte cytosolic protein 1 is found in hemopoietic cell lineages and also in many types of malignant human cells of non-hemopoietic origin.
  • Cyclin G2 (CCNG2) is a member of the Cyclin family.
  • Northern blot analysis revealed that cyclin G2 mRNA fluctuates throughout the cell cycle with peak expression in late S phase. Furthermore, cyclin G2 is induced by the DNA damaging agent actinomycin D.
  • Models for Paclitaxel included several genes involved in essential eukaryotic cell functions such as protein modification (PLAT), spermatogenesis and cell differentiation (DZIPl) and negative autocrine growth factor regulation (LGALSl).
  • PLAT protein modification
  • DZIPl spermatogenesis and cell differentiation
  • LGALSl negative autocrine growth factor regulation
  • KIF14 This gene is responsible for microtubule motor activity and is expressed at very low levels in normal tissue samples, compared to significantly increased expression in the majority of tumor samples. Its overexpression may lead to rapid mitoses, potentially leading to aneuploidy.
  • KIF14 overexpression is most striking in retinoblastoma, lung, breast, thymus, and tumors and associated with decreased survival in lung cancer.
  • NCI-60 panel and drug potency data The NCI-60 panel consists of 60 cancer cell lines across nine different types of human cancer: breast (6), colon (7), central nerve system (6) leukemia (6), lung (9), melanoma (10), ovarian (6), prostate (2), and renal (8).
  • the in vitro drug screening potency data of NCI-60 provide information-rich pharmacological profiles of the compounds in terms of 60 potency values for each compound.
  • the potency of each drug compound is summarized with several dose concentrations on the 60 cell lines such as GI50 (Growth Inhibition 50), the minimum dose concentration that inhibits the growth of each cell line 50% in comparison with untreated control under the in vitro 48 hr microtiter plate assay used.
  • GI50 Rowth Inhibition 50
  • NCI-60 gene expression profiling Our protocols for cell culture, cell harvests, and RNA purification, and microarray studies are being described in detail elsewhere (Shankavaram, et al., manuscript in preparation). Briefly, seed cultures of the 60 cell lines were drawn from aliquoted stocks, passaged once in T- 162 flasks, and monitored frequently for degree of confluence. The medium was RPMI- 1640 with phenol red, 2 mM glutamine, and 5% fetal bovine serum. For compatibility with our other profiling studies, all fetal bovine serum was obtained from the same large batches as were used by DTP for the drug screen. One day before harvest, the cells were re-fed.
  • BLA-40 gene expression profiling Applicants recently collected 40 commonly used human bladder cancer cell lines 20, here designated the "BLA-40 cell panel.” Gene expression profiling for the BLA-40 was also carried out using HG-U133A arrays on duplicate samples generated from independent cell cultures as described 20. When the image files of the NCI-60 and BLA-40 cell lines passed quality-control checks, they were analyzed using the RMA analysis software for GeneChip® data to obtain expression levels.
  • U k and V k are the mean correlation coefficients of the row-k correlation coefficient vectors for the NCI-60 and BLA-40.
  • a cut-off criterion e.g., 98th percentile of the corresponding random distribution generated by randomly shuffling the gene identities between the two sets
  • gene j was selected as a gene for co-expression extrapolation between the two panels. Since gene j was selected from the set of n candidate chemosensitivity predictors, it had that pharmacological characteristic as well.
  • Misclassification-Penalized Posterior classification for chemosensitivity prediction The CEEC probes (e.g. Table SlA) were then used to develop chemosensitivity prediction models by searching for the most parsimonious prediction models that best classified NCI-60 cell lines as sensitive or resistant to the drug (e.g., cisplatin).
  • the drug e.g., cisplatin
  • MiPP Misclassification-Penalized Posterior
  • the first cross-validation is based on random splitting of the whole data set into a training set and an independent test set for external model validation; the second is an n-fold cross-validation on the training set in order to avoid the pitfalls of a large- screening search and to obtain the most parsimonious optimal prediction model(s).
  • Multiple independent splits of the training and test set combinations are generated. Those independent splits result in multiple prediction models.
  • the multiple models are then re-evaluated using a large number (e.g., 100) of random splits of test and training sets to obtain their objective prediction accuracy confidence bounds. From that confidence interval evaluation on the prediction performance, together with mean misclassification error rates (ER), were obtained for each of the candidate prediction models.
  • ER mean misclassification error rates
  • the final prediction of a cell line as "sensitive” or “resistant” was based on the cell's (posterior) classification probability of being sensitive from (3-5) top prediction models based on these confidence bounds away from 0.5, i.e. random coin tossing.
  • MiPP is particularly useful in our COXEN algorithm since it searches for the most parsimonious gene prediction models, especially based on the small number of co- expression extrapolated genes between the NCI-60 and each of target validation sets by efficiently utilizing non-redundant predictive information from the candidate modeling genes.
  • the open-source MiPP package in R is available at the Bioconductor website (www.bioconductor.org). See the original studies for technical details.
  • NCI-60 and BLA-40 cell types separate almost completely, irrespective of cisplatin response, when they were hierarchically clustered on the basis of gene profiles not selected with relation to drug sensitivity. This is shown in Figure 2B where clustering was performed on the basis of the top 50 differentially expressed genes. Results similar to those in Figures 2B and C were obtained for paclitaxel on the BLA-40 cell lines (Figure 2D-E) and the docetaxel (DOC-24) and tamoxifen (TAM-60) clinical trials (data not shown).
  • Table Sl Co-expression extrapolation signature probes for chemosensitivity prediction of cisplatin and paclitaxel between NCI-60 and BLA-40 panels. 18 probes for cisplatin and 13 for paclitaxel identified as a function of significant differential expression between NCI-60 sensitive and resistant cell lines and with their high co-expression extrapolation coefficients between NCI-60 and BLA-40 cell line panels.
  • Table S2 Co-expression extrapolation signature probes for chemosensitivity prediction of paclitaxel and tamoxifen between the NCI-60 panel and breast cancer tissues. Probes identified as a function of significant differential expression between NCI-60 responder and nonresponder cell lines, and then with their high co-expression extrapolation coefficients between NCI-60 and each of the two patient populations from the docetaxel (14 probes) and tamoxifen (8 probes) breast cancer clinical trials.

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Abstract

L'invention porte sur un nouvel algorithme utilisant les signatures de profils moléculaires pour extrapoler le processus physiologique d'un type d'ensemble de cellules (par exemple lignée de cellules ou tissu sain ou malade) et prédire l'activité d'un ou de plusieurs agents contre un autre ensemble de cellules n'ayant jamais été exposé à l'agent en question (prédiction de l'efficacité de médicaments). Le nouvel algorithme permet également de prédire la réponse thérapeutique d'un patient à une thérapie même s'il n'a jamais été exposé antérieurement audit agent. On peut ainsi sélectionner le ou les agents thérapeutiques les mieux adaptés au cas du patient (médecine personnalisée). L'invention porte également sur des méthodes d'utilisation d'agents identifiés par le nouvel algorithme pour traiter différentes maladies dont le cancer.
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