Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Di Maio Lucca 2013 Gli studi clinici nell’era delle terapie p personalizzate Highlights da Sidney Massimo Di Maio S.C. Sperimentazioni Cliniche Istituto Nazionale Tumori Fondazione G.Pascale – IRCCS Napoli [email protected] Di Maio Lucca 2013 Gli studi clinici nell’era delle terapie personalizzate Highlights da Sidney • Qualche progresso… ma anche fallimenti! • …siamo sicuri che il target sia sempre driver? • …aspettando i risultati delle nuove metodologie di studio • Bisogna fare i conti con l’eterogeneità intratumorale! Di Maio Lucca 2013 Mark Kris PL03.07 Sidney 2013 Di Maio Lucca 2013 Mark Kris, PL03.07 , Sidney 2013 Di Maio Lucca 2013 Mark Kris, PL03.07 , Sidney 2013 Di Maio Lucca 2013 D. Gandara, The Future of RCTs in the Molecular Era? Sidney 2013 Di Maio Lucca 2013 D. Gandara, The Future of RCTs in the Molecular Era? Sidney 2013 Di Maio Lucca 2013 Alcuni risultati negativi con la vecchia strategia “all comers” Drug Mechanism of action Selection Author ( (abstract) ) Necitumumab Anti-EGFR Nonsquamous Paz-Ares (O03 02) (O03.02) Phase III: Cis/Pem +/+/ Neci Cixutumumab Anti-IGF-1R Nonsquamous Scagliotti (P1.11-018 ) Phase II random: Cis/Pem +/+/ Cix Di Maio Lucca 2013 1° line treatment: Cis/Pem +/+/ cixutumumab (anti IGF-1R) Scagliotti, P1.11-018 , Sidney 2013 Di Maio Lucca 2013 Gli studi clinici nell’era delle terapie personalizzate Highlights da Sidney • Qualche Q l h progresso… ma anche h ffallimenti! lli ti! • …siamo sicuri che il target sia sempre driver? • …aspettando i risultati delle nuove metodologie di studio • Bisogna fare i conti con l’eterogeneità intratumorale! Di Maio Lucca 2013 Matthew Meyerson. What can we learn from lung cancer sequencing? Sidney 2013 Di Maio Lucca 2013 Matthew Meyerson. What can we learn from lung cancer sequencing? Sidney 2013 Di Maio Lucca 2013 Lawrence et al, Nature 489, 519–525 (27 September 2012) Matthew Meyerson. What can we learn from lung cancer sequencing? Sidney 2013 Di Maio Lucca 2013 Lawrence et al, Nature 489, 519–525 (27 September 2012) Matthew Meyerson. What can we learn from lung cancer sequencing? Sidney 2013 Di Maio Lucca 2013 Matthew Meyerson. What can we learn from lung cancer sequencing? Sidney 2013 Di Maio Lucca 2013 Gli studi clinici nell’era delle terapie personalizzate Highlights da Sidney • Qualche progresso… ma anche fallimenti! • …siamo sicuri che il target sia sempre driver? • …aspettando i risultati delle nuove metodologie di studio • Bisogna fare i conti con l’eterogeneità intratumorale! Di Maio Lucca 2013 Statistics of Personalised Medicine Clinical Cli i l Trial T i l Designs D i f Biomarker for Bi k Driven Di Th Therapies i in Early Disease (Adjuvant) Giorgio Scagliotti Clinical Trial Designs for Biomarker Driven Therapies in Advanced Disease Roy Herbst The Future of RCTs in the Molecular Era? David Gandara Di Maio Lucca 2013 G Scagliotti, Sidney 2013 Di Maio Lucca 2013 G Scagliotti, Sidney 2013 Di Maio Lucca 2013 G Scagliotti, Sidney 2013 Di Maio Lucca 2013 Statistics of Personalised Medicine Clinical Trial Designs for Biomarker Driven Therapies in Early Disease (Adjuvant) Giorgio Scagliotti Clinical Trial Designs for Biomarker Driven Therapies in Advanced Disease Roy Herbst The Future of RCTs in the Molecular Era? David Gandara Di Maio Lucca 2013 Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Clinical Trial Design for Drug Development Adaptive Designs: For J Jack Lee J. Adaptive Designs: Against Marc Buyse Pro/Con session, Sidney 2013 Di Maio Lucca 2013 Adaptive randomization: pro Jack Lee, ASCO 2012 Di Maio Lucca 2013 Adaptive randomization: contra • Adaptive randomization, which consists of allocating more patients to the treatment that appears to have more efficacy ("play-the-winner"), is justified neither statistically t ti ti ll nor ethically. thi ll • This strategy may produce slight reductions in the number b off patients ti t exposed d to t the th inferior i f i treatment, t t t but b t it may increase the total sample size of the trial as compared to using a fixed allocation ratio ratio. • More importantly, this adaptive strategy conveys the misleading impression that one treatment is known to be better than the other, a situation in which equipoise is not maintained and randomization no longer ethical ethical. Marc Buyse, Sidney 2013 Di Maio Lucca 2013 Rationale for Master Protocol Design • Multi-arm Master Protocol • Homogeneous patient populations & consistent eligibility from arm to arm • Each arm independent of the others • Infrastructure facilitates opening new arms faster • Phase II-III II III design allows rapid drug/biomarker testing for detection of “large effects” • Screening large numbers of patients for multiple targets by a broad-based NGS platform reduces the screen failure rate • Provides a sufficient “hit hit rate rate” to engage patients & physicians • Bring safe & effective drugs to patients faster • Designed to faciliate FDA approval of new drugs Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Master Protocol: Potential targets and drugs Target Drug Biomarker Prevalence IGFR LDK378 IGFR expression 60% PI3K BKM120 (PI3Ka) MLN1117 (AKT)GSK2110183 PI3K expr/ amplif, p/ p , PIK3CA mut PTEN loss AKT , PIK3CA fus. 25%. 16% 15% FGFR LY2874455 JNJ42756493 FGF Trap Trap‐ GSK3052230 FRGFR expr FGFR1, 2 amplif, FGFR 1, 2 mut FGFR 1, 2 mut 15%. 10% p53 MK‐1775 (+gem) TP53 mut 81% MET AMG337 LY2801653 JNJ38877605 F ti ib Foretinib (GSK1363089) MET expression 50% HGF AMG102 HGF expression PD‐1 MEDI4736 (PD‐L1) PDL‐1 expression Roy Herbst, Sidney 2013 50% Di Maio Lucca 2013 Target Drug Biomarker HDM2 Anti‐HDM2 HDM2 amplif RANKL Denosumab RANK/RANKL expr Notch LY2835219 Notch1 mut 8% EGFR CO1686 L858R, Del(19), T790M 1‐3% RAS MEKi+panPI3K RAS CKN2A LY2835219 (CDK4/6) CDKN2A mut, deletion deletion, methylation CCND1 amplif HER3 HER3mAb HER3 expression HER3 expression mTOR1/TORC2 MLN0128 STK11,TSC1, TSC2 mut Raf MLN2480 TBD Roy Herbst, Sidney 2013 Prevalence 15%, 30% 21% 13% 2%, 3%, 3% Di Maio Lucca 2013 S1400: MASTER LUNGLUNG-1: Squamous Lung Cancer Cancer-- 2nd Line Therapy CT* Biomarker Profiling (NGS/CLIA) Biomarker Non‐‐Match Non Non‐ Non‐ Match Drug Multiple Phase II‐ III Arms with “rolling Opening & Closure Biomarker A Biomarker A Biomarker TT A TT A CT* Endpoint (Interim PFS) OS Biomarker Β Biomarker Β Biomarker TT B TT B CT* Endpoint (Interim PFS) OS Biomarker C Biomarker C Biomarker TT C TT C+CT CT* Endpoint (Interim PFS) OS Biomarker D Biomarker D Biomarker TT D TT D+E E* Endpoint (Interim PFS) OS TT=Targeted therapy, CT=chemotherapy (docetaxel TT=Targeted therapy, CT=chemotherapy ( docetaxel or gemcitabine), E=erlotinib or gemcitabine), E=erlotinib p p ( ) PI: V. Papadimitrakopoulou (SWOG) Steering Committee Chair: R. Herbst (YALE, SWOG) Roy Herbst, Sidney 2013 Di Maio Lucca 2013 S1400: MASTER LUNGLUNG-1: Squamous Lung Cancer Cancer-- 2nd Line Therapy CT* Biomarker Profiling (NGS/CLIA) Biomarker Non‐‐Match Non PD‐‐L1i PD Multiple Phase II‐ III Arms with “rolling Opening & Closure PiK3CA Mut PiK3CA Mut PiK3CA CCND1 ampl or CCND1 CCND1 ampl CDKN2 loss + RB WT CT* CDK 4/6i CT* Endpoint (Interim PFS) OS Endpoint (Interim PFS) OS PI3Ki FGFR ampl, FGFR FGFR ampl ampl,, , ampl Mut, Fusion Mut , Fusion FGFRi+CT CT* Endpoint (Interim PFS) OS MET Expr MET Expr MET HGFi+E E* Endpoint (Interim PFS) OS TT=Targeted therapy, CT=chemotherapy (docetaxel TT=Targeted therapy, CT=chemotherapy ( docetaxel or gemcitabine), E=erlotinib or gemcitabine), E=erlotinib Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Gli studi clinici nell’era delle terapie personalizzate Highlights da Sidney • Qualche progresso… ma anche fallimenti! • …siamo sicuri che il target sia sempre driver? • …aspettando i risultati delle nuove metodologie di studio • Bisogna fare i conti con l’eterogeneità intratumorale! Di Maio Lucca 2013 Unmet Needs in Future NSCLC Clinical Trials when viewed as a Multitude of Genomic Subsets • H How tto develop d l d drugs ffor uncommon-rare genotypes? • How to apply broad-based genomic screening ( (NGS)? ) • How to initiate therapy with an acceptable turnaround d time i ffor molecular l l testing? i ? ((<2 2 weeks) k ) • H How d do you accountt ffor b both th iintert and d iintra-tumor t t heterogeneity? D. Gandara, The Future of RCTs in the Molecular Era? Sidney 2013 Di Maio Lucca 2013 Charlie Swanton, Sidney 2013 Di Maio Lucca 2013 Implications for Therapy and Outcome p py Intertumour Heterogeneity Intratumour Heterogeneity Intercellular Heterogeneity Review principles of intratumour p p heterogeneity learned from Renal Cancer g y Apply methods to study cancer evolution in Non‐Small Cell Lung Cancer Charlie Swanton, Sidney 2013 Burrell, Mcgranahan, Bartek and Swanton Nature 2013 Di Maio Lucca 2013 Charlie Swanton, Sidney 2013 Di Maio Lucca 2013 Di Maio Lucca 2013 Charlie Swanton, Sidney 2013 Yap, Gerlinger, Pusztai, Futreal and Swanton Sci Trans Med 2012 Di Maio Lucca 2013 Target Tumour Phylogenetic Trunks and Resolve Branches Branched Genetic Events Present in Some Cancer Cells not others Dynamic during disease course Monitor subclonal events to define drug resistance mechanisms Trunk Genetic Events Present in Every y Cancer Cell DEFINE TRUNK DRIVERS Charlie Swanton, Sidney 2013 Di Maio Lucca 2013 Roy Herbst, Sidney 2013 Di Maio Lucca 2013 Gli studi clinici nell’era delle terapie personalizzate Highlights da Sidney • Qualche progresso… ma anche fallimenti! • …siamo sicuri che il target sia sempre driver? • …aspettando i risultati delle nuove metodologie di studio • Bisogna fare i conti con l’eterogeneità intratumorale! Di Maio Lucca 2013 Grazie per l’attenzione! Massimo Di Maio S.C. Sperimentazioni Cliniche Istituto Nazionale Tumori Fondazione G.Pascale – IRCCS Napoli [email protected]