SHIVA01 Prof. Christophe Le Tourneau, MD, PhD GFCO Paris October 11, 2017
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1 SHIVA01 Prof. Christophe Le Tourneau, MD, PhD Institut Curie Paris & Saint-Cloud France Department of Medical Oncology Head of Clinical Research INSERM U900 Research unit GFCO Paris October 11, 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 Pilot study by von Hoff et al. von Hoff et al. JCO 2010;28:
10 Background 18/66 patients (27%): PFS ratio>1.3 von Hoff et al. JCO 2010;28:
11 Background Patients receiving matched targeted therapy Patients receiving no matched targeted therapy Tsimberidou et al. CCR 2012;18:
12 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:
13 Background Question: >?
14 Outline Background Patients and Methods Results Lessons Perspectives
15 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:
16 Treatment algorithm Targets Molecular alterations MTAs ER, PR Protein expression >10% IHC Tamoxifen or Letrozole AR Protein expression >10% IHC Abiraterone PI3KCA, AKT1 AKT2/3, mtor, RICTOR, RAPTOR PTEN STK11 INPP4B HORMONE RECEPTOR PATHWAY 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 Mutation/Amplification Erlotinib RAF/MEK PATHWAY HER-2 Mutation/Amplification Lapatinib + Trastuzumab SRC EPHA2, LCK, YES1 Mutation/Amplification Amplification Dasatinib
17 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
18 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
19 Outline Background Patients and Methods Results Lessons Perspectives
20 Accrual Effective acrrual Expected acrrual Effective randomizations Expected randomizations
21 Feasibility Le Tourneau et al. BJC 2014;111:17-24
22 Flow chart 741 patients (pts) included 97% 716 pts underwent a biopsy 86% 70% 70% 67% ER/PR/AR: 638 pts Ampliseq: 520 pts Cytoscan HD: 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 )
23 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)
24 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%)
25 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%)
26 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%)
27 Efficacy PFS whole population Le Tourneau et al. Lancet Oncol 2015;16:
28 Efficacy PFS Hormone receptor pathway Le Tourneau et al. Lancet Oncol 2015;16:
29 Efficacy 72 yo female AR+ breast cancer M0 Abiraterone M14
30 Efficacy PFS PI3K/AKT/mTOR pathway Le Tourneau et al. Lancet Oncol 2015;16:
31 Efficacy 41 yo female PI3KCA-mutated cervical ca M0 Everolimus M3
32 Efficacy
33 Efficacy PFS RAF/MEK pathway Le Tourneau et al. Lancet Oncol 2015;16:
34 Efficacy
35 Cross-over data 72% =70/97 25% =25/100 Le Tourneau et al. Ann Oncol [epub ahead of print on Dec 19, 2016]
36 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]
37 Outline Background Patients and Methods Results Lessons Perspectives
38 Lessons Proof-of-concept not done
39 Lessons Proof-of-concept not done Results from non-randomized studies not confirmed
40 Lessons Proof-of-concept not done Results from non-randomized studies not confirmed Encouraging results in the RAF/MEK pathway
41 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
42 Lessons The treatment algorithm needs to be refined
43 Lessons Molecular profile Molecular alteration = TREATMENT ALGORITHM Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent Targeted agent
44 Perspectives Treatment algorithm: Le Tourneau et al. JNCI 2015;108:4
45 Perspectives Treatment algorithm: - technology used to identify molecular alterations Le Tourneau et al. JNCI 2015;108:4
46 Perspectives Ratan et al. Plos One 2013;8
47 Perspectives Treatment algorithm: - technology used to identify molecular alterations - thresholds used Le Tourneau et al. JNCI 2015;108:4
48 Perspectives O Rawe et al. Genomic Medicine 2013;5:28
49 Perspectives Treatment algorithm: - technology used to identify molecular alterations - thresholds used - molecular alterations/drugs matching Le Tourneau et al. JNCI 2015;108:4
50 Perspectives Treatment algorithm: - technology used to identify molecular alterations - thresholds used - molecular alterations/drugs matching - molecular alterations priorization Le Tourneau et al. JNCI 2015;108:4
51 Lessons The treatment algorithm needs to be refined Better drugs
52 Lessons The treatment algorithm needs to be refined Better drugs Coexisting molecular alterations
53 Lessons The treatment algorithm needs to be refined Better drugs Coexisting molecular alterations Drug combinations
54 Lessons The treatment algorithm needs to be refined Better drugs Coexisting molecular alterations Drug combinations Less heavily pretreated patients
55 Outline Background Patients and Methods Results Lessons Perspectives
56 Perspectives Retrospective analyses from SHIVA01:
57 Perspectives Retrospective analyses from SHIVA01: - refined predictors of response
58 Perspectives Retrospective analyses from SHIVA01: - refined predictors of response - identification of resistance mutations using ctdna
59 Perspectives Retrospective analyses from SHIVA01: - refined predictors of response - identification of resistance mutations using ctdna - use of additionnal technologies
60 Perspectives Retrospective analyses from SHIVA01 SHIVA02
61 SHIVA02 SHIVA02: - RAF/MEK pathway - PFS ratio as a primary end point - both PFS assessed during the trial
62 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
63 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
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