Claims for Patent: 10,460,831

Summary for Patent: 10,460,831

Title:	Predictive outcome assessment for chemotherapy with neoadjuvant bevacizumab
Abstract:	In a predictive outcome assessment test for predicting whether a patient undergoing a breast cancer treatment regimen will achieve pathological complete response (pCR), differential gene expression level information are generated for an input set of genes belonging to the TGF-.beta. signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample (70) of a breast tumor of a patient acquired before initiating (71) a breast cancer therapy regimen to the patient and response gene expression level information from a response sample (72) of the breast tumor acquired after initiating the breast cancer therapy regimen by administering a first dose of bevacizumab to the patient. A pCR prediction for the patient is computed based on the differential gene expression level information for the input set of genes belonging to the TGF-.beta. signaling pathway. Related predictive outcome assessment test development methods are also disclosed.
Inventor(s):	Varadan; Vinay (New York, NY), Kamalakaran; Sitharthan (Pelham, NY), Janevski; Angel (New York, NY), Banerjee; Nilanjana (Armonk, NY), Dimitrova; Nevenka (Pelham Manor, NY), Harris; Lyndsay (Briarcliff Manor, NY)
Assignee:	Koninklijke Philips N.V. (Eindhoven, NL)
Application Number:	14/649,321
Patent Claims:	1. A method comprising: providing a trained classifier configured to generate, from differential gene expression level information obtained from a patient's breast tumor, a pathological complete response (pCR) prediction, wherein the trained classifier is trained with differential gene expression level information calculated for a plurality of study subjects; acquiring a baseline sample of a breast tumor of a patient, the baseline sample acquired before initiating a breast cancer therapy regimen to the patient; determining, from the acquired baseline sample, baseline gene expression level information for a predetermined input set of genes comprising a first set of genes, wherein the baseline gene expression level information is generated by one or more of ribonucleic acid (RNA) sequencing, reverse transcription-polymerase chain reaction (RT-PCR) processing, and microarray processing; acquiring a response sample of the same breast tumor of the patient, the response sample acquired 10-14 days after administration of the breast cancer therapy regimen comprising at least a first dose bevacizumab to the patient; determining, from the acquired response sample, response gene expression level information for the predetermined input subset of genes, wherein the baseline gene expression level information is generated by one or more of ribonucleic acid (RNA) sequencing, reverse transcription-polymerase chain reaction (RT-PCR) processing, and microarray processing; generating differential expression level information for the predetermined input subset of genes, the differential gene expression level information comparing: (i) the determined baseline gene expression level information; and (ii) the determined response gene expression level information; computing, via the trained classifier, a pathological complete response (pCR) prediction for the patient based on the differential gene expression level information for the input subset of genes; determining, by a physician treating the patient, based on the computed pCR prediction for the patient, whether to: (i) continue a bevacizumab regimen; or (ii) modify the bevacizumab regimen; and treating the patient by: (i) continuing the bevacizumab regimen; or (ii) modifying the bevacizumab regimen; wherein the generating and computing are performed by an electronic data processing device comprising the trained classifier; and wherein the first set of genes comprises CDKN2B, ATL2, CTGF, INHBA, ID4, BMPR1A, CD1E, TFDP1, AMIGO2, DDIT4, TGFB2, SPP1, CD28, PMEPA1, FAT4, KDM6B, MAP3K4, FAM162A, MYH11, PPP2R1B, LTBP1, COL1A1, YIPF5, VEGFA, C18orf25, FNDC3B, MYBL1, CDKN1A, ARHGEF40, LARP6, PAIP2B, RBMS1, NR2F2, ANGEL2, LEMD3, PPP2CA, NDST1, ZNF395, RNASE4, SMURF1, EDN1, SSBP3, SKIL, TBPL1, ALOX5AP, JUN, RARA, LMCD1, SERTAD2, ETS2, ABTB2, BET1L, MYC, CDK17, DOPEY1, SERPINE1, PFKFB3, TBC1D2B, PKIA, BMPR2, and NCOR2. 2. The method of claim 1 wherein the breast cancer therapy regimen further includes administering at least one chemotherapy agent in addition to bevacizumab. 3. The method of claim 1, wherein the initiating comprises administering the first dose of bevacizumab to the patient without administering another chemotherapy agent to the patient. 4. The method of claim 1, wherein the wherein the predetermined input set of genes consists of CDKN2B, ATL2, CTGF, INHBA, ID4, BMPR1A, CD1E, TFDP1, AMIGO2, DDIT4, TGFB2, SPP1, CD28, PMEPA1, FAT4, KDM6B, MAP3K4, FAM162A, MYH11, PPP2R1B, LTBP1, COL1A1, YIPF5, VEGFA, C18orf25, FNDC3B, MYBL1, CDKN1A, ARHGEF40, LARP6, PAIP2B, RBMS1, NR2F2, ANGEL2, LEMD3, PPP2CA, NDST1, ZNF395, RNASE4, SMURF1, EDN1, SSBP3, SKIL, TBPL1, ALOX5AP, JUN, RARA, LMCD1, SERTAD2, ETS2, ABTB2, BET1L, MYC, CDK17, DOPEY1, SERPINE1, PFKFB3, TBC1D2B, PKIA, BMPR2, and NCOR2. 5. The method of claim 1, wherein the computing of the pCR prediction comprises inputting the input subset of genes to the trained classifier. 6. The method of claim 1, wherein the trained classifier is a shrunken centroid classifier. 7. A method comprising: generating differential gene expression level information comparing: (i) baseline gene expression level information from a baseline sample of a breast tumor of a patient acquired before initiating a breast cancer therapy regimen to the patient; and (ii) response gene expression level information from a response sample of the breast tumor acquired after initiating the breast cancer therapy regimen by administering of bevacizumab to the patient for an input set of genes, at least some of the input set of genes belonging to the transforming growth factor 13 (TGF-.beta.) signaling pathway; computing a pathological complete response (pCR) prediction for the patient based on the differential gene expression level information for the input set of genes; computing, via a trained classifier, a pathological complete response (pCR) prediction for the patient based on the differential gene expression level information for the input subset of genes, wherein the trained classifier is trained with differential gene expression level information calculated for a plurality of study subjects; determining, by a physician treating the patient, based on the computed pCR prediction for the patient, whether to: (i) continue a bevacizumab regimen; or (ii) modify the bevacizumab regimen; and treating the patient by: (i) continuing the bevacizumab regimen; or (ii) modifying the bevacizumab regimen; wherein the input set of genes comprises at least CDKN2B, ATL2, CTGF, INHBA, ID4, BMPR1A, CD1 E, TFDP1, AMIGO2, DDIT4, TGFB2, SPP1, CD28, PMEPA1, FAT4, KDM6B, MAP3K4, FAM162A, MYH11, PPP2R1B, LTBP1, COL1A1, YIPF5, VEGFA, C18orf25, FNDC3B, MYBL1, CDKN1A, ARHGEF40, LARP6, PAIP2B, RBMS1, NR2F2, ANGEL2, LEMD3, PPP2CA, NDST1, ZNF395, RNASE4, SMURF1, EDN1, SSBP3, SKIL, TBPL1, ALOX5AP, JUN, RARA, LMCD1, SERTAD2, ETS2, ABTB2, BET1L, MYC, CDK17, DOPEY1, SERPINE1, PFKFB3, TBC1D2B, PKIA, BMPR2, and NCOR2. 8. A method comprising: initiating an breast cancer therapy regimen comprising at least bevacizumab by administering a first dose of bevacizumab to a patient; before the initiating, acquiring a baseline sample of a malignant tumor in the patient; after the initiating, acquiring a response sample of the malignant tumor in the patient; generating baseline gene expression level information from the baseline sample for an input set of genes; generating response gene expression level information from the response sample for the input set of genes; generating differential gene expression level information comparing the baseline and response gene expression level information for the input set of genes; computing, via a trained classifier, a pathological complete response (pCR) prediction for the patient based on the differential gene expression level information for the input set of genes, wherein the trained classifier is trained with differential gene expression level information calculated for a plurality of study subjects; determining, by a physician treating the patient, based on the computed pCR prediction for the patient, whether to: (i) continue the breast cancer therapy regimen; or (ii) modify the breast cancer therapy regimen; and treating the patient by: (i) continuing the breast cancer therapy regimen; or (ii) modifying the breast cancer therapy regimen; wherein the input set of genes comprises at least CDKN2B, ATL2, CTGF, INHBA, ID4, BMPR1A, CD1 E, TFDP1, AMIGO2, DDIT4, TGFB2, SPP1, CD28, PMEPA1, FAT4, KDM6B, MAP3K4, FAM162A, MYH11, PPP2R1B, LTBP1, COL1A1, YIPF5, VEGFA, C18orf25, FNDC3B, MYBL1, CDKN1A, ARHGEF40, LARP6, PAIP2B, RBMS1, NR2F2, ANGEL2, LEMD3, PPP2CA, NDST1, ZNF395, RNASE4, SMURF1, EDN1, SSBP3, SKIL, TBPL1, ALOX5AP, JUN, RARA, LMCD1, SERTAD2, ETS2, ABTB2, BET1L, MYC, CDK17, DOPEY1, SERPINE1, PFKFB3, TBC1D2B, PKIA, BMPR2, and NCOR2. 9. The method of claim 8 wherein the oncological breast cancer therapy regimen further includes at least one chemotherapy agent in addition to bevacizumab. 10. The method of claim 9, wherein the initiating comprises: initiating the breast cancer therapy regimen by administering the first dose of bevacizumab to the patient without administering the at least one additional chemotherapy agent to the patient. 11. The method of claim 7 wherein the baseline and response gene expression level information are messenger ribonucleic acid (mRNA) level information or protein level information, and the differential gene expression level information is one of differential mRNA level information and differential protein level information. 12. A method comprising: for each study subject of a population of study subjects: initiating an oncological therapy regimen including at least a neoadjuvant therapeutic agent by administering a first dose of the neoadjuvant therapeutic agent to the study subject; before the initiating, acquiring a baseline sample of a malignant tumor in the study subject; after the initiating, acquiring a response sample of the malignant tumor in the study subject; after acquiring the response sample, completing the oncological therapy regimen for the study subject; after completing the oncological therapy regimen, determining pathological complete response (pCR) status of the study subject; processing the baseline and response samples to generate baseline gene expression level information and response gene expression level information respectively for a plurality of genes; and calculating differential gene expression level information for the study subject comparing the baseline gene expression level information and the response gene expression level information; training a classifier using the differential gene expression level information calculated for the study subjects of the population as training data to generate a trained classifier that outputs a pCR prediction computed based on received differential gene expression level information for an input set of genes; determining, by the trained classifier, a pCR prediction for an oncological patient based on differential gene expression level information from the patient; determining, by a physician treating the oncological patient, based on the computed pCR prediction for the patient, whether to: (i) continue the oncological therapy regimen; or (ii) modify the oncological therapy regimen; and treating the patient by: (i) continuing the oncological therapy regimen; or (ii) modifying the oncological therapy regimen; wherein the plurality of genes comprises at least CDKN2B, ATL2, CTGF, INHBA, ID4, BMPR1A, CD1 E, TFDP1, AMIGO2, DDIT4, TGFB2, SPP1, CD28, PMEPA1, FAT4, KDM6B, MAP3K4, FAM162A, MYH11, PPP2R1B, LTBP1, COL1A1, YIPF5, VEGFA, C18orf25, FNDC3B, MYBL1, CDKN1A, ARHGEF40, LARP6, PAIP2B, RBMS1, NR2F2, ANGEL2, LEMD3, PPP2CA, NDST1, ZNF395, RNASE4, SMURF1, EDN1, SSBP3, SKIL, TBPL1, ALOX5AP, JUN, RARA, LMCD1, SERTAD2, ETS2, ABTB2, BET1L, MYC, CDK17, DOPEY1, SERPINE1, PFKFB3, TBC1D2B, PKIA, BMPR2, and NCOR2.

Details for Patent 10,460,831

Applicant	Tradename	Biologic Ingredient	Dosage Form	BLA	Approval Date	Patent No.	Expiredate
Genentech, Inc.	AVASTIN	bevacizumab	Injection	125085	02/26/2004		2032-12-03