O DESAFIO DA INOVAÇÃO EM ONCOLOGIA EM PORTUGAL The Challenges of innovative oncology care in Portugal. Gabriela Sousa Oncologia Médica IPO Coimbra

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1 O DESAFIO DA INOVAÇÃO EM ONCOLOGIA EM PORTUGAL The Challenges of innovative oncology care in Portugal Gabriela Sousa Oncologia Médica IPO Coimbra

2 Incidência aumenta 3% ao ano Envelhecimento populacional Melhores tratamentos Estilos de vida

3 Aumento da mortalidade: 0,4% Dificuldades no acesso?

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7 O Cancro é a doença com maior impacto económico

8 INOVAÇÃO TERAPÊUTICA e Impacto na mortalidade

9 Era revolucionária no tratamento do cancro

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11 Bloqueio dos checkpoints imunitários no tratamento do cancro

12 Um novo pilar no tratamento do cancro Cancer therapy Surgery Chemotherapy Radiotherapy Targeted therapy Immunotherapy

13 Desenvolvimento acelerado da imunoterapia Nivolumab FDA approved for previously treated metastatic nonsquamous NSCLC Nivolumab EMA approved for previously treated metastatic non squamous NSCLC Ipilimumab FDA approved for metastatic melanoma Pembrolizumab FDA approved for metastatic melanoma Nivolumab FDA and EMA approved for previously treated metastatic squamous NSCLC Pembrolizumab EMA approved for previously treated PDL1 selected lung cancer Anti CTLA 4 Anti PD Anti PDL1 Nivolumab FDA approved for metastatic melanoma Pembrolizumab FDA approved for previously treated PDL1 selected lung cancer Atezolizumab FDA approved for metastatic urothelial cancer Atezolizumab demonstrated superior efficacy to docetaxel in previously treated metastatic NSCLC

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17 Biosimilars could save money and widen access to important treatments

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22 Tratamento do Cancro em Portugal Medicamentos Aumento da despesa (hospitais do SNS): 6 % Aumento em quantidade: 5,5 %

23 Indicações Dças Infeciosas Reumatologia Dças infeciosas Oncologia Reumatologia Reumatologia Dças Infeciosas Dças infeciosas Reumatologia Dças infeciosas

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26 Desafios da Imuno Oncologia Equidade no tratamento Seleção dos doentes (fatores preditores de resposta) Biologia (tumor vs hospedeiro)

27 Efficacy summary for anti PDL1 and anti PD1 therapies in previously treated NSCLC CheckMate ITT population (n=272) CheckMate ITT population (n=582) KEYNOTE ITT population (n=1033) OAK 3 ITT population (n=850) Histology Squamous Non squamous All comers All comers PD L1 selected No No Yes (TPS 1%) No HR 0.62 HR 0.75 HR 0.72 HR Nivo Doc Nivo Doc Pembro Doc Atezo Doc 2mg/kg ORR, % Nivo 20% vs doc 9% *Phase III dose: 2mg/kg q3w and 10mg/kg q3w; Tumour proportion score (TPS) Minimum is the proportion follow up of viable tumour cells Follow up showing partial or complete membrane PD-L1 expression 24.2 months Nivo 19% vs doc 12% Minimum follow up 24.2 months Pembro 2mg/kg 19% vs doc 10% Median follow up 19.2 months Atezo 14% vs doc 13% Minimum follow up 19 months Barlesi, et al. ESMO 2016 (Abs. 1215PD) Herbst, et al. ESMO 2016 (Abs. LBA48) Barlesi, et al. ESMO 2016 (Abs. LBA44)

28 Immunotherapy in previously treated patients: efficacy by PD L1 status CheckMate 017 (phase III) 1 2L nivo vs doc (n=272) CheckMate 057 (phase III) 2 2/3L nivo vs doc (n=582) KEYNOTE 010 (phase II/III) 3 2L pembro vs doc (n=1,033) OAK (phase III) 4 2L atezo vs doc (n=850) Histology Squamous Non squamous All comers All comers PD L1 selected No No Yes (TPS 1%) No Efficacy by PD L1 status Subgroup 10% (n=69) <10% (n=156) 5% (n=81) <5% (n=144) 1% (n=119) <1% (n=106) NQ (n=47) ITT (n=272) HR Subgroup 10% (n=165) HR 0.40 Subgroup (pooled doses) <10% (n=290) % (n=181) 0.43 <5% (n=274) % (n=442) 1% Different (n=246) consequences % (n=591) <1% (n=209) 0.87 <1% not available study design ITT (n=582) 0.72 ITT (n=1,033) HR Subgroup* HR TC3 or IC3 (n=137) 0.41 TC2/3 or IC2/3 (n=265) 0.67 TC1/2/3 or IC1/2/3 (n=463) 0.74 TC0 and IC0 (n=379) 0.75 ITT (n=850) HR HR HR HR 1 2 nivo doc nivo doc pembro doc atezo doc PD L1 assay 28 8 (Dako) on TCs 22C3 (Dako) on TCs SP142 (Ventana) on ICs and TCs *TC3 or IC3: 50% of TCs or 10% of ICs; TC2/3 or IC2/3: 5% of TCs or ICs; TC1/2/3 or IC1/2/3: 1% of TCs or ICs; TC0 and IC0: <1% of TCs and ICs 1. Brahmer, et al. N Engl J Med 2015; 2. Borghaei, et al. N Engl J Med Herbst, et al. Lancet 2015; 4. Barlesi, et al. ESMO 2016

29 Do we have the right assay? Nivolumab Pembrolizumab Atezolizumab Durvalumab Detection antibody C3 1 SP142 3 SP263 4 IHC platform Dako 1 Dako 1 Ventana 1 Ventana 4 Cell types scored for NSCLC TC 1 TC 1 IC and TC 1,3 TC 1 Cut-offs in NSCLC PDL1-selected as 5% of TCs exhibiting positive membrane PD-L1 staining at any intensity PDL1-selected as 50% (treatment-naïve) or 1% (previously treated) of viable TCs showing partial or complete membrane PD-L1 expression* Different antibodies Different assays TC3 or IC3: 50% of TCs or 10% of ICs TC2/3 or IC2/3: 5% of TCs or ICs TC1/2/3 or IC1/2/3: 1% of TCs or ICs TC0 and IC0: <1% of TCs and ICs (proportion of cells stained at any intensity) PDL1-selected as 25% of TCs with membrane PD-L1 staining Estimated PD-L1 prevalence in NSCLC TC 5% TC <5% 2L 1 46% 54% TPS 50% TPS 1 49% TPS <1% 1L 2 2L 2 Different cut offs 30% 27% 40% 38% 31% 35% 16% 37% 68% TC 25% TC <25% 2L 5 46% 54% 1. Kerr, et al. J Thorac Oncol 2015; 2. Aggarwal, et al. ESMO Vansteenkiste, et al. ECC 2015; 4. Rebelatto, et al. ASCO 2015; 5. Rizvi, et al. ASCO 2015 *For the 22C3 assay, the proportion of viable tumour cells showing partial or complete membrane PD L1 staining is termed the tumour proportion score (TPS)

30 Cancer R&D investment sources Few relevant clinical Questions answered Key Clinical Questions answered Industry R&D by Academia

31 Tumor and Immune Biomarkers Being Evaluated to Predict Better Outcomes to Immuno Oncology Therapy Tumor Antigens Inflamed Tumor Microenvironment Biomarkers indicative of hypermutation & neoantigens may predict response to IO treatment Biomarkers (intra or peri tumoral) indicative of an inflamed phenotype may predict response to IO treatment Examples: TMB, MSI high, neoantigens Tumor Antigens Inflamed Tumor Examples: PD L1, inflammatory signatures Tumor Immune Suppression Biomarkers that identify tumor immune system evasion beyond PD 1/CTLA 4 to inform new IO targets and rational combinations Tumor Immune Suppression Host Environment Biomarkers which characterize the host environment, beyond tumor microenvironment, may predict response to IO treatment Examples: Tregs, MDSCs, IDO, LAG 3 IDO = indoleamine 2,3 dioxygenase; LAG 3 = lymphocyte activation gene 3; MDSCs = myeloid derived suppressor cells; MSI high = microsatellite instability high; TMB = tumor mutational burden. Adapted from Blank C.U. et al. Science 2016;352: Examples: Microbiome, germline genetics

32 Tumor Mutation Burden as a Predictive Biomarker for Immuno Oncology Therapies 1. Snyder A, et al. N Engl J Med 2014;371: ; 2. Rizvi NA, et al. Science 2015;348: ; 3. Van Allen EM, et al. Science 2015;350: ; 4. Rosenberg JE, et al. Lancet 2016;387: ; 5. Hugo W, et al. Cell 2016;165:35 44; 6. Hellmann M. Presented at the 14th International Congress on Targeted Anticancer Therapies; March 21 23, 2016; Washington, DC, USA. Oral O2.2; 7. Kowanetz M, et al. Presented at the 2016 IASLC 17th World Conference; December 4 7, 2016; Vienna, Austria. Oral OA20.01

33 PFS by Tumor Mutation Burden Subgroup CheckMate 026 TMB Analysis: Nivolumab in First Line NSCLC PFS (%) High TMB Median PFS, months (95% CI) Nivolumab Chemotherapy n = 47 n = (5.1, NR) 5.8 (4.2, 8.5) HR = 0.62 (95% CI: 0.38, 1.00) No. at Risk Months Nivolumab Chemotherapy Nivolumab Chemotherapy Low/medium TMB Median PFS, months 4.1 (95% CI) (2.8, 5.4) Nivolumab Chemotherapy n = 111 n = (5.5, 8.6) HR = 1.82 (95% CI: 1.30, 2.55) Chemotherapy Nivolumab Months

34 Blank CU et al. Science 2016; 352:658

35 Precision Medicine: Are we ready?

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37 NGS heterogeneity

38 The French project

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43 2781 euros 1967 euros

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