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許駿 醫師 台大醫學院 腫瘤醫學研究所 台大醫院 腫瘤醫學部 台灣大學 癌症中心醫院
TJCC Mini-symposium for cancer immunotherapy 06 May 2017 Biomarker research: what we have learned from trials of immune checkpoint inhibitors 許駿 醫師 台大醫學院 腫瘤醫學研究所 台大醫院 腫瘤醫學部 台灣大學 癌症中心醫院
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Conflict of interest disclosure
Research grant Ministry of Science and Technology (Taiwan), Celgene Honorarium (speaker/advisor/travel) AstraZeneca, Bayer, Bristol-Myers Squibb, Eli Lilly, MSD, Novartis, Roche, TTY Biopharm
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Landmark immunotherapy approval by FDA
Merkel cell RCC Melanoma NSCLC Bladder ca. Hodgkin’s HNSCC Prostate ALL * Accelerated approval Avelumab* IL-2 Nivo. Nivo.* Ipi. + Nivo.* IFN Peg-IFN IL-2 Ipi. Ipi. Pembro.* Pembro. T-Vec Faster drug development/ approval Extended indication Nivo. Atezo. Pembro. Pembro. Nivo.* Atezo.* BCG Atezo.* Durva.* Nivo.* Pembro.* Pembro.* Nivo. Sipuleucel-T Blinatumomab* 1990 1992 1998 2010 2011 2014 2015 2016 2017 Chemokines/ cytokines Cell therapy/ vaccines Immune agonists Non-specific Checkpoint inhibitors Ipilimumab (anti-CTLA4) nivolumab, pembrolizumab (anti-PD1) Atezolizumab, avelumab, durvalumab (anti-PD-L1) (accessed 02 May 2017; Modified from: Craft JE, ASCO 2016 and Hoos Nat Rev Drug Discov 2016)
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Biomarkers research: what we have learned from trials of immune checkpoint inhibitors?
Types of biomarkers (arbitrary) Examples Static Mutation loads (carcinogen exposure, mismatch repair deficiency, etc.) Specific genetic defects: β-catenin, EGFR, JAK1/2, PTEN loss, MDM2/4 , etc. Dynamic PD-L1 expression (tumor cells, immune cells) Immune-response gene expression Chaotic Gut microbiome Predictors for (1) efficacy; (2) resistance; (3) ‘hyper-progression’
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What we have learned from trials of immune checkpoint inhibitors
What we have learned from trials of immune checkpoint inhibitors? Anti-PD-1/ anti-PD-L1 as examples Objective response rate >50% (e.g., refractory Hodgkin’s disease) Objective response ca. 20% (e.g., NSCLC, HNSCC, urothelial ca.) Objective response rate < 10% (e.g., pancreatic adenocarcinoma, colon cancer)
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New hope for refractory cancers (1) Hodgkin’s disease
23 patients Nivolumab 3 mg/kg q 2 weeks 210 patients Pembrolizumab 200 mg q 3 weeks Cohort A Cohort B Cohort C Ansell SM, et al. N Engl J Med 2015; 372: Chen R, et al. J Clin Oncol (E-pub Apr. 2017)
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What we have learned from trials of immune checkpoint inhibitors
What we have learned from trials of immune checkpoint inhibitors? Anti-PD-1/ anti-PD-L1 as examples Objective response rate >50% (e.g., refractory Hodgkin’s disease) Objective response ca. 20% (e.g., NSCLC, HNSCC, urothelial ca.) Objective response rate < 10% (e.g., pancreatic adenocarcinoma, colon cancer)
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New hope for refractory cancers (2) Cancers with mismatch-repair (MMR) deficiency
Immune effector cells Mutation loads Le DT et al. N Engl J Med 2015; 372:
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The mutation load theory:
High mutational loads → neoantigens → host antitumor immune response → biomarkers Melanoma, lung cancers, MMR deficient cancer 0.01 0.1 1 10 100 1000 Frequently Somatic mutation prevalence per megabase Regularly Occasionally Schumacher TN et al. Science 2015; 348: 69-74
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Neoantigens → host immune response in melanoma
由同一病患的T 細胞中辨識可誘發免疫反應的neoantigens Carreno BM et al. Science 2015; 348: 803-9
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Mutation/neoantigen loads and heterogeneity
‘All mutations are not created equal’ Phylogenetic analysis based on multi-regional exome sequencing Hiley C, et al. Genome Biol 2014; 15: 453 McGranahan N, et al. Science 2016; 351:
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Mutation/neoantigen loads, heterogeneity, and response to checkpoint inhibitor therapy
High clonal neoantigen loads and low heterogeneity (< 5% sub-clonal antigens) predicted durable response. Sub-clonal neoantigens (spontaneous or induced) were not associated with treatment efficacy. Multiple immune evasive mechanisms, e.g., MHC loss. McGranahan N, et al. Science 2016; 351:
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What we have learned from trials of immune checkpoint inhibitors
What we have learned from trials of immune checkpoint inhibitors? Anti-PD-1/ anti-PD-L1 as examples Objective response rate >50% (e.g., refractory Hodgkin’s disease) Objective response ca. 20% (e.g., NSCLC, HNSCC, urothelial ca., and many others) Objective response rate < 10% (e.g., pancreatic adenocarcinoma, colon cancer) Broad-spectrum anti-tumor activity ‘Universal’ mechanism of action? ‘Universal’ biomarker?
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Kerr K, et al. J Thor Oncol 2015; 10: 985- 9
PD-L1 expression as biomarkers: Anti-PD-1/ anti-PD-L1 in non-small cell lung cancer Kerr K, et al. J Thor Oncol 2015; 10:
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感謝 台大醫院腫瘤醫學部 高祥豐醫師 提供
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Teng MW, et al. Cancer Res 2015; 75: 2139-45
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PD-L1 expression: tumor cells vs. immune cells (1)
PD-L1 expression: tumor cells vs. immune cells (1)? Atezolizumab in NSCLC Fehrenbacher L, et al. Lancet 2016; 387:
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PD-L1 expression: tumor cells vs. immune cells (2)
PD-L1 expression: tumor cells vs. immune cells (2)? Atezolizumab in NSCLC Rosenberg JE, et al. Lancet 2016; 387:
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PD-L1 expression: tumor cells vs. immune cells (3)
PD-L1 expression: tumor cells vs. immune cells (3)? Pembrolizumab in HNSCC IFN- 6-gene signature CXCL9, CXCL10, IDO1, IFNG, HLA-DRA, STAT1 Composite score calculated by averaging normalized† values for each gene
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Challenges of interpreting PD-L1 expression (1)
Constitutive Genetic and epigenetic regulation Induced ‘immunogenic’ tumors or therapy Tumor-specific T cell response and interferon-γ production Value of baseline (pre-treatment) samples questionable Serial tumor sampling seldom feasible Modified from Ribas A, et al. J Exp Med 2016; 213:
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Challenges of interpreting PD-L1 expression (2)
Tissue markers: dynamic and heterogeneous Functional imaging: Non-invasive PD-1/PD-L1 labeling Immune cell labeling Sensitivity/ specificity McLaughlin J, et al. JAMA Oncol 2016; 2: 46-54
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EGFR activating mutations associated with poor efficacy of anti-PD1/antiPD-L1 therapy
Treatment Patient no. Overall survival (mon) EGFR mutation (%) EGFR wild-type (%) Nivolumab vs. docetaxel 582 12.2 vs. 9.4 14 58 Pembrolizumab vs. docetaxel 1034 10.4 vs vs. 8.5 8 85 Atezolizumab vs. docetaxel 287 12.6 vs. 9.7 6 51 Lee CK et al. J Thor Oncol 2017; 12: 403-7
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Genetic aberrations of lung cancer and the immune microenvironment
EGFR-mutant (N = 62) KRAS-mutant (N = 65) P value PD-L1 positivity PD-L1+ ≥ 50% 7 (11%) 11 (17%) 0.449 PD-L1+ ≥ 5% 10 (16%) 20 (31%) 0.062 CD8+ TILs (image-based) per mm2 Median 185.1 330.1 0.011 (Range) (6.1–1,161.9) (8.5–2,567.3) Concurrent PD-L1 expression & CD8+ TILs PD-L1+ ≥ 50% & high CD8+ TILs 2/46 (4.3%) 10/56 (18%) 0.061 PD-L1+ ≥ 5% & high CD8+ TILs 15/56 (27%) 0.003 Gainor JF, et al. Clin Cancer Res 2016; 22:
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-catenin pathway activation correlates with T-cell exclusion in melanoma
T-cell exclusion is caused by impaired priming of anti-tumor T cells and reduced numbers of CD103+ dermal dendritic cells. Wnt/-catenin signaling induces expression of ATF3, which suppresses CCL4 and thereby interferes recruitment and activation of CD103+ dendritic cells. Spranger S. et al Nature 2015;523:
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Does ‘hyper-progression’ after immunotherapy really happen?
Review of tumor growth kinetic ratio (TGKR) in 34 HNSCC patients treated with anti-PD1/anti-PD-L1 TGKR > 2 more common in patients with cervical LN metastasis and regional recurrence Saâda-Bouzid E. et al, 2017 NGS data from 155 stage IV cancer patients who received immunotherapies TTF <2 months associated with MDM2/4, EGFR, or DNMT3A alterations MDM2/MDM4 amplification (4 of 6) and EGFR alterations (3 of 10) associated with ‘hyper-progression’ (TTF <2 months, >50% increase in tumor burden,>2-fold increase in progression pace) Kato S, et al. 2017 Saâda-Bouzid E. et al Ann Oncol 2017 (E pub) Kato S, et al. Clin Cancer Res 2017 (E pub)
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Chen DS, Mellman I. Nature 2017; 541: 321-30
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Anti-PD-1-based combination therapy for NSCLC
KEYNOTE 021G CHECKMATE 012 Tx Chemo* Chemo* + Pembro Chemo* + Nivo n 63 60 15 ORR 29% 55% 47% PFS 8.9 months 13.0 months (HR: 0.53, p=0.10) 6.8 months OS HR: 0.90, p=0.39 19.2 months PD-L1 status and ORR PD-L1>1%: ORR: 54% PD-L1<1%: ORR: 57% PD-L1>1%: ORR: 48% PD-L1<1%: ORR: 43% Langer C, Lancet Oncol 2016;17: 1497–508 Rizvi NA, J Clin Oncol 2016;34: * Carboplatin + pemetrexed 感謝 台大醫院腫瘤醫學部 高祥豐醫師 提供
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Atezolizumab (anti-PD-L1) + bevacizumab (antiangiogenic) vs
Atezolizumab (anti-PD-L1) + bevacizumab (antiangiogenic) vs. sunitinib for renal cell carcinoma (1) McDermott D, et al. AACR 2017
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Atezolizumab (anti-PD-L1) + bevacizumab (antiangiogenic) vs
Atezolizumab (anti-PD-L1) + bevacizumab (antiangiogenic) vs. sunitinib for renal cell carcinoma (2) McDermott D, et al. AACR 2017
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Atezolizumab (anti-PD-L1) + bevacizumab (antiangiogenic) vs
Atezolizumab (anti-PD-L1) + bevacizumab (antiangiogenic) vs. sunitinib for renal cell carcinoma (3) McDermott D, et al. AACR 2017
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T-cell complexities = more drug targets
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Challenges of future immunotherapy trial design and biomarker development
Selection of the ‘optimal’ combination modalities Identification of (immune) therapeutic targets Comprehensive vs. specific biomarkers Development of preclinical models Collaboration beyond the biomedical community
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‘I do not fancy I know what I do not.’
You can't connect the dots looking forward; you can only connect them looking backward. You have to trust that the dots will somehow connect in your future. Steve Jobs, Stanford commencement address, 2005 ‘I do not fancy I know what I do not.’ Socrates, in Plato’s Apology
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謝謝大家! 感謝 盧建宏醫師提供
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