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From trials evaluating drugs to trials evaluating treatment algorithms – Focus on the SHIVA trial
- 1. From trials evaluating drugs to trials evaluating treatment algorithms – Focus on the SHIVA trial Christophe Le Tourneau, MD, PhD Institut Curie Head of the Phase I Program Department of Medical Oncology INSERM U900 EACR – Munich – July 7, 2014
- 2. Introduction Chemotherapy Targeted agent Chemotherapy Targeted agent Chemotherapy Targeted agent Chemotherapy Targeted agent Chemotherapy Targeted agent Chemotherapy Targeted agent Chemotherapy Targeted agent Chemotherapy Targeted agent
- 3. Introduction Targets EGFR HER-2 mTOR c-Kit SMO VEGF(R) HDAC NF-κB CTLA4 RAF ALK RET Drugs Erlotinib/Gefitinib Cetuximab/Panitumumab Cetuximab Trastuzumab/TDM-1 Lapatinib/Pertuzumab Trastuzumab Temsirolimus/Everolimus Everolimus Imatinib Vismodegib Bevacizumab Sunitinib Sorafenib Vorinostat Bortezomib Ipilumumab Vemurafenib Crizotinib Vandetanib/Cabozantinib Tumor types NSCLC CRC HNSCC Breast Breast Gastric adenocarcinoma Kidney Endocrine tumors GIST Basal cell carcinoma Breast, kidney, CRC, NSCLC Kidney, endocrine tumors Kidney, HCC Cutaneous lymphoma Myeloma Melanoma Melanoma NSCLC Medullary thyroid cancer Biomarkers EGFR mutation KRAS mutation - HER2 amplification HER2 amplification HER2 amplification - - c-Kit overexpression - - - - - - - V600E BRAF mutation ALK translocation -
- 4. Introduction Ciriello et al. Nature Genet 2013;45:1127-33
- 5. Introduction LUNG ADENOCARCINOMA – HER2 V659E MUTATION – LAPATINIB Serra et al. Cancer Discov 2013;3:1238-44
- 6. Introduction MELANOMA – KIT MUTATION – IMATINIB Hodi et al. JCO 2013;31:3182-90
- 7. Introduction Molecular profile Molecular alteration Targeted agentTargeted agentTargeted agentTargeted agent Targeted agent Targeted agent Targeted agent
- 8. Introduction Tsimberidou et al. CCR 2012;18:6373-83 Patients receiving matched targeted therapy Patients receiving no matched targeted therapy
- 9. Introduction Failure-free survival Patients receiving matched targeted Overall survival Patients receiving matched targeted therapy therapy Patients receiving no matched targeted therapy Tsimberidou et al. CCR 2012;18:6373-83
- 10. Outline • Stratified trials: - Molecular stratification - Histologic stratification • Algorithm-testing trials: - Non-randomized trials - Randomized trials Le Tourneau et al. Chin Clin Oncol 2014;3:13
- 11. Outline • Stratified trials: - Molecular stratification - Histologic stratification • Algorithm-testing trials: - Non-randomized trials - Randomized trials
- 12. Outline • Stratified trials: - Molecular stratification - Histologic stratification • Algorithm-testing trials: - Non-randomized trials - Randomized trials
- 13. • FOCUS4 trial Molecular stratification Kaplan et al. JCO 2013;31:4592-8
- 14. Stratified trials Algorithm-testing trials Molecularly- stratified Histology- stratified Non- randomized Randomized Tumor types Molecular Alterations Treatments Test Summary Personalized medicine trials
- 15. Stratified trials Algorithm-testing trials Histology- stratified Non- randomized Randomized Tumor types Molecularly- stratified 1 Molecular Alterations Treatments Test Summary Personalized medicine trials
- 16. Stratified trials Algorithm-testing trials Histology- stratified Non- randomized Randomized Tumor types Molecular Molecularly- stratified 1 N Alterations Treatments Test Summary Personalized medicine trials
- 17. Stratified trials Algorithm-testing trials Histology- stratified Non- randomized Randomized Tumor types Molecular Alterations Molecularly- stratified 1 N NTreatments Test Summary Personalized medicine trials
- 18. Outline • Stratified trials: - Molecular stratification - Histologic stratification • Algorithm-testing trials: - Non-randomized trials - Randomized trials
- 19. Histologic stratification • 1 drug • Multiple tumor types harbouring specific molecular alterations • V-BASKET: vemurafenib (V600E BRAF mutation) • CREATE: crizotinib (ALK/MET alterations)
- 20. Stratified trials Algorithm-testing trials Non- randomized Randomized Tumor types Molecular Alterations Molecularly- stratified 1 N Histology- stratified N 1 or N N 1Treatments Test Summary Personalized medicine trials
- 21. Stratified trials Algorithm-testing trials Non- randomized Randomized Tumor types Molecular Alterations Molecularly- stratified 1 N Histology- stratified N 1 or N Treatments N 1 Test Test drugs efficacy Summary Personalized medicine trials
- 22. Outline • Stratified trials: - Molecular stratification - Histologic stratification • Algorithm-testing trials: - Non-randomized trials - Randomized trials
- 23. Outline • Stratified trials: - Molecular stratification - Histologic stratification • Algorithm-testing trials: - Non-randomized trials - Randomized trials
- 24. • Pilot study by Von Hoff et al. Non-randomized trials von Hoff et al. JCO 2010;28:4877-83
- 25. Non-randomized trials von Hoff et al. JCO 2010;28:4877-83 • 18/66 patients (27%): ratio>1.3
- 26. Outline • Stratified trials: - Molecular stratification - Histologic stratification • Algorithm-testing trials: - Non-randomized trials - Randomized trials
- 28. SHIVA • Promotion: Institut Curie (Paris & Saint-Cloud) • Other participating centers: - Centre Léon Bérard (Lyon) - Centre René Gauducheau (Nantes) - Institut Claudius Régaud (Toulouse) - Institut Paoli-Calmettes (Marseille) - Centre Georges-François Leclerc (Dijon) - Centre Alexis Vautrin (Nancy)
- 29. SHIVA • Primary objective: - To compare the efficacy of targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer • Primary end point: - Progression-free survival Le Tourneau et al. Target Oncol 2012;7:253-65
- 30. SHIVA • Secondary objectives: - To evaluate overall response rate - To compare overall survival - To evaluate tumor growth kinetics* - To evaluate safety - To evaluate the ability of circulating tumor DNA to early predict treatment efficacy/resistance - To evaluate the medico-economic impact of the experimental strategy *Le Tourneau et al. BJC 2012;106:854-7
- 31. SHIVA • Inclusion criteria: - >18 years old - patients with any type of cancer that is refractory to standard of care - biopsiable & measurable disease - ECOG PS 0 or 1 - adequate blood counts and organ functions
- 32. Informed consent signed Tumor biopsy Molecular profile Patients with refractory cancer (all tumor types)
- 33. Molecular profile • Mutations: - Ampliseq Cancer Panel - Ion Torrent / PGM (Life Technologies®) • Gene copy number alterations: - Cytoscan HD (Affymetrix®) • Protein expression: - IHC (ER, PR, AR)
- 34. Molecular profile • 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 an hotspot * frequency >10% * no synonymous mutations * no polymorphisms
- 35. Molecular profile • Amplifications: - Gene copy number * diploid tumor: >6 * tetraploid tumor: >7 - Amplicon size * <1 Mb * <10 Mb if protein expression is validated in IHC Servant et al. Frontiers in Genetics 2014;5:e152
- 36. Informed consent signed Tumor biopsy NGS+ Cytoscan HD +IHC Bioinformatics Patients with refractory cancer (all tumor types)
- 37. Informed consent signed Molecular biology board Patients with refractory cancer (all tumor types) Tumor biopsy NGS+ Cytoscan HD +IHC Bioinformatics Pathologists Biologists Plateforms managers Bioinformaticians Physicians
- 38. Molecular profile Le Tourneau et al. BJC [Epub ahead of print April 24, 2014]
- 39. Informed consent signed Tumor biopsy NGS+ Cytoscan HD +IHC Bioinformatics Molecular biology board Specific therapy available Non eligible patient NO Eligible patient YES Patients with refractory cancer (all tumor types)
- 40. R Conventional therapy at physicians' discretion Bioinformatics Informed consent signed Tumor biopsy NGS+ Cytoscan HD +IHC therapy available Molecular biology board YESNO Non eligible patient Eligible patient Informed consent signed Patients with refractory cancer (all tumor types) Imatinib Everolimus Sorafenib Erlotinib Dasatinib Lapatinib Trastuzumab Vemurafenib Tamoxifen Letrozole Abiraterone Targeted therapy based on molecular profiling Cross-over Specific
- 41. Treatment algorithm Targets KIT, ABL1/2, RET PI3KCA, AKT1 AKT2/3, mTOR, RICTOR, RAPTOR PTEN STK11 INPP4B BRAF PDGFRA/B, FLT3 EGFR HER-2 SRC EPHA2, LCK, YES1 ER, PR AR Molecular alterations Mutation/Amplification Mutation/Amplification Amplification Homozygous deletion Heterozygous deletion + mutation or IHC Homozygous deletion Heterozygous deletion + mutation Homozygous deletion Mutation/Amplification Mutation/Amplification Mutation/Amplification Mutation/Amplification Mutation/Amplification Amplification Protein expression >10% IHC Protein expression >10% IHC Targeted therapies Imatinib Everolimus Vemurafenib Sorafenib Erlotinib Lapatinib + Trastuzumab Dasatinib Tamoxifen or Letrozole Abiraterone Le Tourneau et al. BJC [Epub ahead of print April 24, 2014]
- 42. Treatment algorithm • Multiple molecular alterations: - DNA alterations are considered of a higher impact than hormone receptors expression - If AR and ER/PR are both overexpressed, the hormone receptor with the highest expression level is taken into account - If >2 DNA alterations are identified, clinically validated alterations prevail (i.e. HER-2 amplification) - Erlotinib is not given in case of KRAS mutation
- 43. SHIVA • Randomization: - 1:1 - stratification on the Royal Marsden Prognostic score for oncology phase I cancer patients - stratification on the signalling pathway (PI3K/AKT/mTOR, Hormone receptors, MAPK pathway) Arkenau et al. JCO 2009;27:2692-6
- 44. SHIVA • Sample size: - 6 months PFS = 15% in phase I cancer patients treated with cytotoxic agents - Hypothesis: 6 months PFS = 30% in the experimental arm (HR = 0.625) 142 events with a type 1 error of 5% and a power of 80% in the bilateral setting 200 patients should be randomized up to 1,000 patients might have to be included Hortsmann et al. NEJM 2005;352:895-904
- 47. MPACT (NCI) Kummar et al. NCI-EORTC-ASCO 2013
- 48. Stratified trials Algorithm-testing trials Molecularly- stratified Histology- stratified Non- randomized Randomized 1 or NTumor types Molecular Alterations 1 N N 1 or N Treatments N 1 Test Test drugs efficacy Summary Personalized medicine trials
- 49. Stratified trials Algorithm-testing trials Molecularly- stratified Histology- stratified Non- randomized Randomized Tumor types Molecular Alterations 1 N N 1 or N 1 or N N Treatments N 1 Test Test drugs efficacy Summary Personalized medicine trials
- 50. Stratified trials Algorithm-testing trials Molecularly- stratified Histology- stratified Non- randomized Randomized Tumor types Molecular Alterations 1 N N 1 or N 1 or N N Treatments N 1 N Test Test drugs efficacy Summary Personalized medicine trials
- 51. Stratified trials Algorithm-testing trials Molecularly- stratified Histology- stratified Non- randomized Randomized Tumor types Molecular Alterations 1 N N 1 or N 1 or N N Treatments N 1 N Test Test drugs efficacy Test algorithm efficiency Summary Personalized medicine trials
- 52. Conclusions • High-throughput technologies have entered the clinic • Emergence of personalized medicine clinical trials • Multiples challenges • It remains to be demonstrated that the use of high throughput technologies improves patients outcome
- 53. Acknowledgments • Direction Thierry Philip Claude Huriet Pierre Teillac Daniel Louvard • ICGEX Olivier Delattre Thomas Rio Frio Quentin Leroy Virginie Bernard • UGEC Patricia Tresca Sebastien Armanet Fabrice Mulot • Biostatistics Xavier Paoletti 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