SHIVA trial Prof. Christophe Le Tourneau Days of personalized medicine Vienne February 24, 2017

Transcription

1 SHIVA trial Prof. Christophe Le Tourneau Institut Curie Paris & Saint-Cloud France Department of Medical Oncology Head of Early Phase Clinical Trials INSERM U900 Research unit Days of personalized medicine Vienne February 24, 2017

2 Outline Background Patients and Methods Results Lessons Perspectives

3 Outline Background Patients and Methods Results Lessons Perspectives

4 Background Ciriello et al. Nature Genet 2013;45:

5 Background Trastuzumab (Herceptin ) HER-2 Amplification Lapatinib (Tykerb ) 20%

6 Background Trastuzumab (Herceptin ) HER-2 Amplification 20%

7 Background LUNG ADENOCARCINOMA HER2 V659E MUTATION LAPATINIB Serra et al. Cancer Discov 2013;3:

8 Background Molecular profile Molecular alteration Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent

9 Background HISTOLOGY-AGNOSTIC APPROACH Molecular profile Molecular alteration Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent

10 Background Molecular profile Molecular alteration = TREATMENT ALGORITHM Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent

11 Background Le Tourneau et al. JNCI [epub ahead of print on November 23, 2015]

12 Background Pilot study by von Hoff et al. von Hoff et al. JCO 2010;28:

13 Background 18/66 patients (27%): PFS ratio>1.3 von Hoff et al. JCO 2010;28:

14 Background Patients receiving matched targeted therapy Patients receiving no matched targeted therapy Tsimberidou et al. CCR 2012;18:

15 Background Failure-free survival Overall survival Patients receiving matched targeted therapy Patients receiving matched targeted therapy Patients receiving no matched targeted therapy Tsimberidou et al. CCR 2012;18:

16 Background

17 Background Question: >?

18 Outline Background Patients and Methods Results Lessons Perspectives

19 Patients with refractory cancer (all tumor types) Informed consent signed SHIVA Randomized proof-of-concept phase II trial comparing molecularly targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer Tumor biopsy NGS+ Cytoscan HD +IHC Targeted therapy based on molecular profiling Bioinformatics Informed consent signed R Non eligible patient Molecular biology board Eligible patient Conventional therapy at physicians' discretion Cross-over NO Specific therapy available YES Le Tourneau et al. Lancet Oncol 2015;16:

20 Treatment algorithm Targets Molecular alterations MTAs ER, PR HORMONE Protein expression >10% IHC Tamoxifen or Letrozole AR RECEPTOR Protein expression >10% IHC Abiraterone PATHWAY PI3KCA, AKT1 AKT2/3, mtor, RICTOR, RAPTOR PTEN STK11 INPP4B PI3K/AKT/mTOR PATHWAY Mutation/Amplification Amplification Homozygous deletion Heterozygous deletion + mutation or IHC Homozygous deletion Heterozygous deletion + mutation Homozygous deletion Everolimus BRAF Mutation/Amplification Vemurafenib KIT, ABL1/2, RET Mutation/Amplification Imatinib PDGFRA/B, FLT3 Mutation/Amplification Sorafenib EGFR RAF/MEK Mutation/Amplification Erlotinib HER-2 PATHWAY Mutation/Amplification Lapatinib + Trastuzumab SRC EPHA2, LCK, YES1 Mutation/Amplification Amplification Dasatinib

21 Treatment algorithm Variants of interest: - validated hotspots mutations * frequency: >4% for SNVs and >5% for indels * coverage: >30X for SNVs and >100X for indels - non targeted variants * outside a hotspot * frequency >10% * no synonymous mutations * no polymorphisms

22 Treatment algorithm Amplifications: - Gene copy number * diploid tumor: >6 * tetraploid tumor: >7 - Amplicon size * <1 Mb * <10 Mb if protein overexpression/or loss of expression is validated in IHC

23 Outline Background Patients and Methods Results Lessons Perspectives

24 Accrual Effective acrrual Expected acrrual Effective randomizations Expected randomizations

25 Feasibility Le Tourneau et al. BJC 2014;111:17-24

26 Flow chart 741 patients (pts) included 97% 716 pts underwent a biopsy 86% 70% 70% 67% ER/PR/AR: Ampliseq: Cytoscan HD: 638 pts 520 pts 522 pts Complete: 496 pts 40% 293 pts identified for randomization 26% 195 pts randomized Experimental arm: 99 pts Reference arm: 96 pts 99 pts treated 92 pts treated 91 PD or (76 PD/61 ) 90 PD or (79 PD/57 )

27 Primary tumor location MTA arm Breast Ovary Lung CRC Cervix HNSCC Sarcoma Urothelial Pancreas ACUP Oesogastric ACC non-acc SGT HCC Anus Neuroenocrine Biliary tract UCNT melanoma Other TPC arm Other = CNS, prostate, uveal melanoma, germline, kidney (1 each) Other = mesothelioma, peritoneum (1 each)

28 Patient characteristics MTA arm (N=99) TPC arm (N=96) All (N=195) Median age yr [range] 61 [25-78] 62.5 [19-89] 62 [19-89] Sex Female no. (%) Male no. (%) 60 (61%) 39 (39%) 69 (72%) 27 (28%) 129 (66%) 66 (33%) Prior lines of treatment median [range] 3 [1-13] 3 [0-15] 3 [0-15] RMH score 0 or 1 no. (%) 2 or 3 no. (%) 51 (52%) 48 (48%) 48 (50%) 48 (50%) 99 (51%) 96 (49%) Molecular pathway altered no. (%) Hormone receptors PI3K/AKT/mTOR RAF/MEK 40 (40%) 46 (46%) 13 (13%) 42 (44%) 43 (45%) 11 (12%) 82 (42%) 89 (46%) 24 (12%)

29 Patient characteristics MTA arm (N=99) TPC arm (N=96) All (N=195) Median age yr [range] 61 [25-78] 62.5 [19-89] 62 [19-89] Sex Female no. (%) Male no. (%) 60 (61%) 39 (39%) 69 (72%) 27 (28%) 129 (66%) 66 (33%) Prior lines of treatment median [range] 3 [1-13] 3 [0-15] 3 [0-15] RMH score 0 or 1 no. (%) 2 or 3 no. (%) 51 (52%) 48 (48%) 48 (50%) 48 (50%) 99 (51%) 96 (49%) Molecular pathway altered no. (%) Hormone receptors PI3K/AKT/mTOR RAF/MEK 40 (40%) 46 (46%) 13 (13%) 42 (44%) 43 (45%) 11 (12%) 82 (42%) 89 (46%) 24 (12%)

30 Patient characteristics MTA arm (N=99) TPC arm (N=96) All (N=195) Median age yr [range] 61 [25-78] 62.5 [19-89] 62 [19-89] Sex Female no. (%) Male no. (%) 60 (61%) 39 (39%) 69 (72%) 27 (28%) 129 (66%) 66 (33%) Prior lines of treatment median [range] 3 [1-13] 3 [0-15] 3 [0-15] RMH score 0 or 1 no. (%) 2 or 3 no. (%) 51 (52%) 48 (48%) 48 (50%) 48 (50%) 99 (51%) 96 (49%) Molecular pathway altered no. (%) Hormone receptors PI3K/AKT/mTOR RAF/MEK 40 (40%) 46 (46%) 13 (13%) 42 (44%) 43 (45%) 11 (12%) 82 (42%) 89 (46%) 24 (12%)

31 Safety MTA arm TPC arm Grade 3 AEs % 36% 31% Grade 4 AEs % 7% 4% Grade 3/4 AEs % HT pathway PI3K pathway MAPK pathway RMH score of 0 or 1 RMH score of 2 or 3 43% 34% 50% 46% 45% 41% 35% 40% 33% 27% 27% 44%

32 Efficacy PFS whole population Le Tourneau et al. Lancet Oncol 2015;16:

33 Efficacy PFS Hormone receptor pathway Le Tourneau et al. Lancet Oncol 2015;16:

34 Efficacy 72 yo female AR+ breast cancer M0 Abiraterone M14

35 Efficacy PFS PI3K/AKT/mTOR pathway Le Tourneau et al. Lancet Oncol 2015;16:

36 Efficacy 41 yo female PI3KCA-mutated cervical ca M0 Everolimus M3

37 Efficacy

38 Efficacy PFS RAF/MEK pathway Le Tourneau et al. Lancet Oncol 2015;16:

39 Efficacy

40 Cross-over data 72% =70/97 25% =25/100 Le Tourneau et al. Ann Oncol (in press)

41 Cross-over data TPC MTA Hormone receptor pathway PI3K/AKT/mTOR pathway RAF/MEK pathway N PFS ratio >1.3 PFS ratio > % 35% 38% 54% 25% 20% 19% 18% MTA TPC 25 56% 28% Le Tourneau et al. Ann Oncol [epub ahead of print on Dec 19, 2016]

42 Outline Background Patients and Methods Results Lessons Perspectives

43 Lessons Proof-of-concept not done

44 Lessons Proof-of-concept not done Results from non-randomized studies not confirmed

45 Lessons Proof-of-concept not done Results from non-randomized studies not confirmed Encouraging results in the RAF/MEK pathway

46 Lessons Proof-of-concept not done Results from non-randomized studies not confirmed Encouraging results in the RAF/MEK pathway Results of the cross-over suggest that taking each patient as his/her own control is a relevent strategy for precision medicine trials

47 Lessons The treatment algorithm needs to be refined

48 Lessons The treatment algorithm needs to be refined Better drugs

49 Lessons The treatment algorithm needs to be refined Better drugs Coexisting molecular alterations

50 Lessons The treatment algorithm needs to be refined Better drugs Coexisting molecular alterations Drug combinations

51 Lessons The treatment algorithm needs to be refined Better drugs Coexisting molecular alterations Drug combinations Less heavily pretreated patients

52 Outline Background Patients and Methods Results Lessons Perspectives

53 Perspectives Retrospective analyses from SHIVA:

54 Perspectives Retrospective analyses from SHIVA: - refined predictors of response

55 Perspectives Retrospective analyses from SHIVA: - refined predictors of response - identification of resistance mutations using ctdna

56 Perspectives Retrospective analyses from SHIVA: - refined predictors of response - identification of resistance mutations using ctdna - use of additionnal technologies

57 Perspectives Retrospective analyses from SHIVA SHIVA02 & SHIVAimmuno

58 SHIVA02 SHIVA02 & SHIVAimmuno: - RAF/MEK pathway - immunotherapy / inflamation signature - PFS ratio as a primary end point - both PFS assessed during the trial

59 Conclusions It remains to be determined whether the use of treatment algorithms will improve patients outcome Treatment algorithms will need to be refined to bring the proof-of-principle Sharing data will be key in this aim

60 Acknowledgments Direction Thierry Philip Claude Huriet Pierre Teillac Daniel Louvard ICGEX Olivier Delattre Thomas Rio Frio Virginie Bernard UGEC Patricia Tresca Sebastien Armanet Fabrice Mulot Biostatistics Xavier Paoletti Lisa Belin Corine Plancher Cécile Mauborgne Pathology Anne Salomon Odette Mariani Frédérique Hammel Xavier Sastre Didier Meseure Translational research Maud Kamal David Gentien Sergio Roman-Roman Radiology Vincent Servois Daniel Szwarc Bioinformatics Philippe Huppé Nicolas Servant Julien Romejon Emmanuel Barillot Philippe La Rosa Alexandre Hamburger Pierre Gestraud Fanny Coffin Séverine Lair Bruno Zeitouni Alban Lermine Camille Barette Comunication Céline Giustranti Catherine Goupillon-Senghor Cécile Charre Genetics Ivan Bièche Gaëlle Pierron Etienne Rouleau Céline Callens Marc-Henri Stern Surgery Thomas Jouffroy José Rodriguez Angélique Girod Pascale Mariani Virginie Fourchotte Fabien Reyal Foundation Hélène Bongrain- Meng Ifrah El-Alia Véronique Masson Agnès Hubert Clinical research Malika Medjbahri Sampling Solène Padiglione Pharmacy Laurence Escalup Oncology Alain Livartowski Suzy Scholl Laurent Mignot Philippe Beuzeboc Paul Cottu Jean-Yves Pierga Véronique Diéras Valérie Laurence Sophie Piperno-Neumann Catherine Daniel Wulfran Cacheux Bruno Buecher Emmanuel Mitry Astrid Lièvre Coraline Dubot Etienne Brain Barbara Dieumegard Frédérique Cvitkovic