Claims for Patent: 10,095,829

Summary for Patent: 10,095,829

Title:	Computer implemented methods of treating lung cancer
Abstract:	The present invention concerns a method for predicting the relative efficacy of a plurality of drugs for treating a tumor in an individual comprising the molecular characterization of the tumor, and the calculation of a score for the plurality of drugs essentially based on the percentage of deregulated target genes.
Inventor(s):	Lazar; Vladimir (Villejuif, FR), Soria; Jean-Charles (Igny, FR), Ducreux; Michel (Vanves, FR), Tursz; Thomas (Paris, FR)
Assignee:	WORLDWIDE INNOVATIVE NETWORK (Villejuif, FR)
Application Number:	13/382,585
Patent Claims:	1. A method for treating a patient having lung cancer, the method comprising: a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number; b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1; c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the lung cancer in the patient and wherein the step of characterizing molecular anomalies of the lung cancer sample comprises determining a fold change for the differentially expressed genes and optionally for the gain or loss of gene copy number; and d) selecting a drug with a high score for treating the patient, wherein the score (W) for a given drug is determined by the following algorithm: .times..SIGMA..times.>.times.> ##EQU00012## wherein: W is the score for the given drug; P is the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient; .SIGMA. is sum; F.sub.c>2 is the Fold Change of each deregulated target gene for the given drug with a Fold Change higher than 2; and n.sub.CF.sub.c>2 refers to the number of target genes for the given drug with a Fold Change higher than 2. 2. The method according to claim 1, wherein the target genes for each drug are classified in the database into: major target genes (CM) which have been demonstrated to have a clear cause and effect link with the drug's mechanism of action; minor target genes (Cm), the level of regulation of which is modified in the presence of the drug, without a direct link with the drug's mechanism of action; and resistance genes (CR) which induce a direct resistance to the drug or are associated with a major toxicity. 3. The method according to claim 1, wherein F.sub.c>2 is the Fold Change of each over-expressed target gene for the given drug with a Fold Change higher than 2 and n.sub.CF.sub.c>2 is either the number of target genes for the given drug with a Fold Change higher than 2, or the number of over-expressed target genes for the given drug with a Fold Change higher than 2. 4. The method according to claim 1, wherein said plurality of drugs are Axitinib, Capecitabine, Trastuzumab, Erlotinib, Gefitinib, Lapatinib, Bevacizumab, Imatinib, Temsirolimus, Dasatinib, Sorafenib, Nilotinib, Bosutinib, Sunitinib, Cetuximab, Vinorelbine, Dacarbazine, Docetaxel, Paclitaxel, Pemetrexed, Gemcitabine, Irinotecan and Topotecan. 5. The method according to claim 1, wherein the molecular anomalies of said lung cancer sample are determined by northern analysis, mRNA microarrays, cDNA microarrays or RT-PCR. 6. The method according to claim 1, wherein the step of characterizing molecular anomalies of the lung cancer sample further comprises determining the intensity of the gene transcription (Int) for the differentially expressed genes. 7. The method according to claim 1, wherein the patient has lung cancer. 8. A method for treating a patient having lung cancer the method comprising: a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number; b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1; c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the lung cancer in the patient and wherein the step of characterizing molecular anomalies of the lung cancer sample comprises determining a fold change for the differentially expressed genes and optionally for the gain or loss of gene copy number; and d) selecting a drug with a high score for treating the patient, and wherein the method further comprises in step a) the detection of the presence of a mutation in a gene in the lung cancer sample in comparison to the normal sample by sequencing and wherein the score (W) for a given drug is determined by the following algorithm: .function..SIGMA..times..times..times..times..SIGMA..times..times..times.- .times..SIGMA..times..times..times..times. ##EQU00013## wherein: W is the score for the given drug; P is the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient; .SIGMA. is sum; CM refers to major target genes for the given drug; Cm refers to minor target genes for the given drug; CR refers to resistance genes for the given drug; n.sub.1CM, n.sub.2Cm and n.sub.3CR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes; F.sub.CM, F.sub.Cm and F.sub.CR are the Fold Change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively; q.sub.1, q.sub.2 and q.sub.3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively; and z.sub.1, z.sub.2 and z.sub.3 are multiplication coefficients associated with the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively. 9. The method according to claim 8, wherein F.sub.CM, F.sub.Cm and F.sub.CR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and wherein n.sub.1CM, n.sub.2Cm and n.sub.3CR are either the number of target genes for the given drug with the defined threshold or the number of over-expressed target genes for the given drug with the defined threshold. 10. The method according to claim 9, wherein the defined threshold is a Fold Change of at least 2. 11. The method according to claim 8, wherein the multiplication coefficients for the target genes are between 10 and 1,000 for major target genes (q.sub.1), between 0.1 and 10 for minor target genes (q.sub.2) and between 10 to 1,000 for resistance genes (q.sub.3). 12. The method according to claim 8, wherein the multiplication coefficients associated with a mutation z.sub.1, z.sub.2 and z.sub.3 are 1 when no mutation exists and, depending on the functional impact of the mutation, are between 10 and 1,000. 13. The method according to claim 8, wherein z.sub.1, z.sub.2, z.sub.3, q.sub.1, q.sub.2 and q.sub.3 are equal to 1. 14. A method for treating a patient having lung cancer the method comprising: a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number; b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1; c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the lung cancer in the patient and wherein the step of characterizing molecular anomalies of the lung cancer sample comprises determining a fold change for the differentially expressed genes and optionally for the gain or loss of gene copy number; and d) selecting a drug with a high score for treating the patient, and wherein the method further comprises in step a) the detection of the presence of a mutation in a gene in the lung cancer sample in comparison to the normal sample by sequencing and wherein the score (W) for a given drug is determined by the following algorithm: .function..SIGMA..times..times..times..times..function..SIGMA..times..tim- es..times..times..function..SIGMA..times..times..times..times. ##EQU00014## wherein W is the score for the given drug; .SIGMA. is sum; CM refers to major target genes for the given drug; Cm refers to minor target genes for the given drug; CR refers to resistance genes for the given drug; n.sub.1CM, n.sub.2Cm and n.sub.3CR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes; F.sub.CM, F.sub.Cm and F.sub.CR are the Fold Change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively; q.sub.1, q.sub.2 and q.sub.3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively; z.sub.1, z.sub.2 and z.sub.3 are multiplication coefficients associated with the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively; and P.sub.CM, P.sub.Cm and P.sub.CR are the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient for major target genes, minor target genes and resistance genes, respectively. 15. The method according to claim 14, wherein F.sub.CM, F.sub.Cm and F.sub.CR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and wherein n.sub.1CM, n.sub.2Cm and n.sub.3CR are either the number of target genes for the given drug with the defined threshold or the number of over-expressed target genes for the given drug with the defined threshold. 16. The method according to claim 15, wherein the defined threshold is a Fold Change of at least 2. 17. The method according to claim 14, wherein z.sub.1, z.sub.2, z.sub.3, q.sub.1, q.sub.2 and q.sub.3 are equal to 1. 18. The method according to claim 14, wherein the multiplication coefficients for the target genes are between 10 and 1,000 for major target genes (q.sub.1), between 0.1 and 10 for minor target genes (q.sub.2) and between 10 to 1,000 for resistance genes (q.sub.3). 19. The method according to claim 14, wherein the multiplication coefficients associated with a mutation z.sub.1, z.sub.2 and z.sub.3 are 1 when no mutation exists and, depending on the functional impact of the mutation, are between 10 and 1,000. 20. A method for treating a patient having lung cancer the method comprising: a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number; b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1; c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the lung cancer in the patient and wherein the step of characterizing molecular anomalies of the lung cancer sample comprises determining a fold change for the differentially expressed genes and optionally for the gain or loss of gene copy number; and d) selecting a drug with a high score for treating the patient, and wherein the method further comprises in step a) the detection of the presence of a mutation in a gene in the lung cancer sample in comparison to the normal sample by sequencing and wherein the score (W) for a given drug is determined by the following algorithms: .function..SIGMA..times..times..times..times..times..SIGMA..times..times.- .times..times..times..SIGMA..times..times..times..times..times. ##EQU00015## ##EQU00015.2## .function..SIGMA..times..times..times..times..times..function..SIGMA..tim- es..times..times..times..times..function..SIGMA..times..times..times..time- s..times. ##EQU00015.3## wherein W is the score for the given drug; P is the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient; .SIGMA. is sum; CM refers to major target genes for the given drug; Cm refers to minor target genes for the given drug; CR refers to resistance genes for the given drug; n.sub.1CM, n.sub.2Cm and n.sub.3CR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes; F.sub.CM, F.sub.Cm and F.sub.CR are the Fold Change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively; q.sub.1, q.sub.2 and q.sub.3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively; z.sub.1, z.sub.2 and z.sub.3 are multiplication coefficients associated with the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively; P.sub.CM, P.sub.Cm and P.sub.CR are the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient for major target genes, minor target genes and resistance genes, respectively; and Int.sub.CM Int.sub.Cm and Int.sub.CR are the intensity for major target genes, minor target genes and resistance genes, respectively. 21. The method according to claim 20, wherein F.sub.CM, F.sub.Cm and F.sub.CR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and wherein n.sub.1CM, n.sub.2Cm and n.sub.3CR are either the number of target genes for the given drug with the defined threshold or the number of over-expressed target genes for the given drug with the defined threshold. 22. The method according to claim 21, wherein the defined threshold is a Fold Change of at least 2. 23. The method according to claim 20, wherein z.sub.1, z.sub.2, z.sub.3, q.sub.1, q.sub.2 and q.sub.3 are equal to 1. 24. The method according to claim 20, wherein the multiplication coefficients for the target genes are between 10 and 1,000 for major target genes (q.sub.1), between 0.1 and 10 for minor target genes (q.sub.2) and between 10 to 1,000 for resistance genes (q.sub.3). 25. The method according to claim 20, wherein the multiplication coefficients associated with a mutation z.sub.1, z.sub.2 and z.sub.3 are 1 when no mutation exists and, depending on the functional impact of the mutation, are between 10 and 1,000. 26. A method for treating a patient having lung cancer the method comprising: a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number; b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1; c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the lung cancer in the patient and wherein the step of characterizing molecular anomalies of the lung cancer sample comprises determining a fold change for the differentially expressed genes and optionally for the gain or loss of gene copy number; and d) selecting a drug with a high score for treating the patient, and wherein the method further comprises in step a) the detection of the presence of a mutation in a gene in the lung cancer sample in comparison to the normal sample by sequencing and wherein the score (W) for a given drug is determined by one of the following algorithms: .function..SIGMA..times..times..times..times..SIGMA..times..times..times.- .times. ##EQU00016## ##EQU00016.2## .function..SIGMA..times..times..times..times..function..SIGMA..times..tim- es..times..times. ##EQU00016.3## ##EQU00016.4## .function..SIGMA..times..times..times..times..times..SIGMA..times..times.- .times..times..times. ##EQU00016.5## ##EQU00016.6## .function..SIGMA..times..times..times..times..times..function..SIGMA..tim- es..times..times..times..times. ##EQU00016.7## wherein W is the score for the given drug; P is the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient; .SIGMA. is sum; CM refers to major target genes for the given drug; CR refers to resistance genes for the given drug; n.sub.1CM and n.sub.3CR are respectively the number of deregulated target genes with a defined threshold for major target genes and resistance genes; F.sub.CM and F.sub.CR are the Fold Change of each gene higher than the defined threshold for major target genes and resistance genes, respectively; q.sub.1 and q.sub.3 are multiplication coefficients for major target genes and resistance genes, respectively; z.sub.1 and z.sub.3 are multiplication coefficients associated with the presence of a mutation in a major target gene and a resistance gene, respectively; P.sub.CM, and P.sub.CR are the percentage of genes for the given drug which are deregulated in the lung cancer of the patient for major target genes and resistance genes, respectively; and Int.sub.CM and Int.sub.CR are the intensity for major target genes and resistance genes, respectively. 27. The method according to claim 26, wherein F.sub.CM and F.sub.CR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and wherein n.sub.1CM and n.sub.3CR are either the number of target genes for the given drug with the defined threshold or the number of over-expressed target genes for the given drug with the defined threshold. 28. The method according to claim 27, wherein the defined threshold is a Fold Change of at least 2. 29. The method according to claim 26, wherein z.sub.1, z.sub.3, q.sub.1 and q.sub.3 are equal to 1. 30. The method according to claim 26, wherein the multiplication coefficients for the target genes are between 10 and 1,000 for major target genes (q.sub.1) and between 10 to 1,000 for resistance genes (q.sub.3). 31. The method according to claim 26, wherein the multiplication coefficients associated with a mutation z.sub.1 and z.sub.3 are 1 when no mutation exists and, depending on the functional impact of the mutation, are between 10 and 1,000. 32. A method for treating a patient having lung cancer the method comprising: a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array, and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization in the lung cancer sample in comparison to the normal sample by sequencing and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number; b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1; c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating lung cancer in the patient; d) selecting a drug with a high score for treating the patient; and e) administering the drug with the high score to the patient and treating the patient's lung cancer, wherein the score (W) for a given drug is determined by the following algorithm: .times..SIGMA..times.>.times.> ##EQU00017## wherein: W is the score for the given drug; P is the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient; .SIGMA. is sum; F.sub.c>2 is the Fold Change of each deregulated target gene for the given drug with a Fold Change higher than 2; and n.sub.CF.sub.c>2 refers to the number of target genes for the given drug with a Fold Change higher than 2.

Details for Patent 10,095,829

Applicant	Tradename	Biologic Ingredient	Dosage Form	BLA	Approval Date	Patent No.	Expiredate
Genentech, Inc.	HERCEPTIN	trastuzumab	For Injection	103792	09/25/1998		2029-07-08
Genentech, Inc.	HERCEPTIN	trastuzumab	For Injection	103792	02/10/2017		2029-07-08
Eli Lilly And Company	ERBITUX	cetuximab	Injection	125084	02/12/2004		2029-07-08
Eli Lilly And Company	ERBITUX	cetuximab	Injection	125084	03/28/2007		2029-07-08
Genentech, Inc.	AVASTIN	bevacizumab	Injection	125085	02/26/2004		2029-07-08