Advances in Cancer Immunotherapy for Solid Tumors Expert Perspectives on The New Data Sunday, June 5, 2016

Advances in Cancer Immunotherapy for Solid Tumors Expert Perspectives on The New Data Sunday, June 5, 2016 Supported by an independent educational grant from AstraZeneca Not an official event of the 2016 ASCO Annual Meeting Not sponsored or endorsed by ASCO or the Conquer Cancer Foundation

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Learning Objectives 1. Evaluate the principles of tumor immunology and the mechanisms of action of current and emerging cancer immunotherapies used in solid tumors. 2. Appraise the latest clinical trial data regarding emerging cancer immunotherapies in SCCHN, NSCLC, mesothelioma, gastric cancer, melanoma, and other solid tumors, including use of both monotherapy and combination regimens. 3. Explore the role of biomarkers in patient selection to improve targeted use of immune checkpoint inhibitors. 4. Identify practical strategies for using current and emerging cancer immunotherapies, including prevention, early detection, and management of immune-related adverse effects.

Advances in Immunotherapy for Solid Tumors Expert Perspectives on Applying The Latest Data to Clinical Practice

Antoni Ribas, MD, PhD Professor of Medicine Professor of Surgery Professor of Molecular and Medical Pharmacology Director, Tumor Immunology Program, Jonsson Comprehensive Cancer Center University of California Los Angeles Chair, Melanoma Committee at SWOG Los Angeles, CA

Disclosures I consult for Advaxis, Compugen, CytomX, Five Prime, and FLX Bio I am a major stockholder in Kite Pharma I am not on any scientific advisory boards I am not a member of any speakers bureau

Immune Checkpoint Blockade Results in Melanoma

Anti-CTLA-4 and Anti-PD-1 Trials Meta-Analysis: PFS Study Drug Control Tx Risk ratio M-H, Random, 95% Cl Ribas 2013 Tremelimumab Temozolomide or dacarbazine Hodi 2010 Ipilimumab +/- gp100 gp100 Robert 2011 Weber 2015 Ipilimumab + dacarbazine Nivolumab Dacarbazine + placebo Dacarbazine or paclitaxel + carboplatin Ribas 2015 Pembrolizumab Chemotherapy Robert 2015 Nivolumab Dacarbazine Total (95% Cl) P=.0004 0.2 0.5 1 2 5 Favors immune Tx Favors control Tx Yun S, et al. Cancer Med. 2016.

Anti-CTLA-4 and Anti-PD-1 Trials Meta-Analysis: OS Study Drug Control Tx Risk ratio M-H, Random, 95% Cl Ribas 2013 Tremelimumab Temozolomide or dacarbazine Hodi 2010 Ipilimumab +/- gp100 gp100 Robert 2011 Ipilimumab + dacarbazine Dacarbazine + placebo Robert 2015 Nivolumab Dacarbazine Total (95% Cl) P=.001 0.2 0.5 1 2 5 Favors immune Tx Favors control Tx Yun S, et al. Cancer Med. 2016.

Nivolumab BRAFwt Untreated Melanoma Robert C, et al. N Engl J Med. 2015.

Pembrolizumab vs Ipilimumab Advanced Melanoma Pembrolizumab, Q3W Pembrolizumab, Q2W Overall Survival (%) Ipilimumab Month Robert C, et al. N Engl J Med. 2015.

Pembrolizumab KEYNOTE 001 Change from Baseline, % 100 80 60 40 20 0-20 -40-60 -80 Efficacy in 611 patients Ipilimumab treated Ipilimumab naive ORR: 33% ORR in previously untreated: 45% -100 Ribas A, et al. JAMA. 2016. As of October 18, 2014; median follow-up: 21 months Central radiology review by RECIST v1.1

KEYNOTE 001 Duration of Response Kaplan-Meier estimates of duration of response among responders in the total population (n=205) and the treatment-naive population (N=65), as assessed by RECIST v1.1 by independent central review for patients with confirmed response who had 1 dose of study treatment Ribas A, et al. JAMA. 2016.

Nivolumab Follow-up Overall Survival at 5 Years 1.0 0.9 0.8 All Patients (events: 69/107), median and 95% CI: 17.3 (12.5 37.8) NIVO 3 mg/kg (events: 11/17), median and 95% CI: 20.3 (7.2 NR) Probability of Survival 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Number of Patients at Risk All Patients NIVO 3 mg/kg 0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 Months Database lock Oct 2015 107 86 64 51 49 43 41 36 29 17 15 12 3 1 0 17 15 11 9 8 7 7 6 6 6 6 6 1 0 Hodi FS, et al. AACR. 2016. (Abstract CT001)

Immune Checkpoints Managing Toxicities

Pembrolizumab iraes with Incidence >5% Adverse Event, N (%) KEYNOTE-001 Total N=411 Adverse Event, N (%) Total N=411 Any Grade Grade 3/4 Any Grade Grade 3/4 Fatigue 36 2 Myalgia 9 0 Pruritus 24 <1 Headache 8 <1 Rash 20 <1 Hypothyroidism 8 <1 Diarrhea 16 <1 Decreased appetite 7 <1 Arthralgia 16 0 Dyspnea 7 <1 Nausea 12 <1 Chills 6 0 Vitiligo 11 0 Pyrexia 6 0 Asthenia 9 0 ALT increased 5 <1 Cough 9 0 Total 83 12 Similar safety profiles in IPI-N and IPI-T patients Ribas A, et al. ASCO. 2014.

Immune Checkpoints Toxicity Management Guidelines CBCs, metabolic panels, LFTs and thyroid function tests should be obtained at each treatment and q6-12 wks for 6 mos post-treatment in all pts receiving checkpoint inhibiting antibodies. ACTH and cortisol should also be checked in pts with fatigue and nonspecific symptoms, plus testosterone in men. Frequency of follow-up testing should be adjusted to individual response and AEs that occur. Corticosteroids can reverse nearly all toxicities associated with these agents, but should be reserved for grade 3/4, or prolonged grade 2, iraes. Weber JS, et al. J Clin Oncol. 2015.

Immune Checkpoints Toxicity Management iraes Requiring Greater Vigilance And Early Intervention Pulmonary Hepatic Renal GI Endocrine Neurological

Checkpoint Inhibition Managing Gr 3/4 Treatment-Related iraes Grade 3/4 pneumonitis, nephritis, enterocolitis, hepatitis, or infusionrelated reaction New or worsening neuropathy Any life-threatening or Grade 4 AE Any severe or Grade 3 recurrent AE Hepatitis associated with: AST/ALT > 5 x ULN AST/ALT 50% from baseline lasting 1 wk* Total bilirubin > 3 x ULN Grade 4 elevation of pancreatic enzymes Initiate steroid therapy Permanently discontinue a PD-1 tx Usually resolves with tx interruption If no improvement in colitis or pneumonitis, infliximab or mycophenolate If no improvement in hepatitis, consider mycophenolate; infliximab contraindicated *In pts with liver metastasis who begin treatment with Grade 2 elevation of AST/ALT. Pts receiving ipilimumab may tolerate treatment with PD-1/PD-L1 inhibitor alone. Steroids do not appear to accelerate the rate of improvement. FDA Pembrolizumab, Nivolumab, Ipilimumab Prescribing Information.

Immune Checkpoint Blockade iraes and Response to Therapy Frequent Development Of Vitiligo (Skin Depigmentation) In Responding Patients

PD-1 Blockade iraes and Response to Therapy Disappearance of a Pigmented Birth Mark Before After

PD-1 Blockade Biomarkers of Response

PD-1 Blockade Prerequisites for Killing Cancer A T-cell specific for cancer Have the appropriate TCR to specifically recognize cancer cells Have been licensed to kill that cancer Be turned off by PD-1:PD-L1 interaction A cancer cell that is recognized by the T-cells The cancer cell needs to have antigens that differentiate it from normal cell (eg, neoantigens, viral antigens, shared tumor antigens) The cancer cell needs to be sensitive to T-cell attack The cancer needs to be limiting T-cells through the PD- 1:PD-L1 interaction

Signal 1 Signal 2 Dendritic cell MHC B7 TCR CD28 T cell Inhibitor of signal 2 Lymph node TCR MHC Cancer PD-1 PD-L1

PD-1 blockade Inhibiting Adaptive Immune Resistance Anti-PD-1 Anti-PD-L1 Melanoma cell or tumor macrophage Interferons Tumeh et al. Nature 2014

PD-1 blockade Inhibiting Adaptive Immune Resistance Anti-PD-1 Anti-PD-L1 Melanoma cell or tumor macrophage Interferons Tumeh et al. Nature 2014

Inhibiting Adaptive Immune Resistance PD-1 Blockade-Induced Responses Response Progression Melanoma cell or tumor macrophage Interferons Hypothesis formulated based on quantitative IHC analyses of 46 cases from UCLA PD-L1 PD-1 CD8 Melanoma cell or tumor macrophage Tumeh PC, et al. Nature. 2014.

Predicting Responses Gustave Roussy Validation Set Pt CD8+ Density, Invasive Margin Before Tx Predicted Prob. of Response Blinded Prediction 1 58 0.35 Progression 2 159 0.37 Progression 3 329 0.4 Progression 4 341 0.41 Progression 5 2120 0.75 Response 6 5466 0.98 Response 7 2211 0.76 Response 8 3810 0.92 Response 9 4294 0.95 Response 10 4948 0.97 Response 11 5565 0.98 Response 12 6004 0.99 Response 13 5951 0.99 Response 14 7230 0.99 Response 15 6320 0.99 Response Paul C. Tumeh, UCLA; Christine Mateus, Caroline Robert, Gustave Roussy

Pt Predicting Responses Gustave Roussy Validation Set CD8+ Density, Invasive Margin Before Tx Predicted Prob. of Response Blinded Prediction Clinical Response (RECIST 1.1) 1 58 0.35 Progression Progression 2 159 0.37 Progression Progression 3 329 0.4 Progression Progression 4 341 0.41 Progression Progression 5 2120 0.75 Response Response 6 5466 0.98 Response Progression 7 2211 0.76 Response Response 8 3810 0.92 Response Response 9 4294 0.95 Response Response 10 4948 0.97 Response Response 11 5565 0.98 Response Response 12 6004 0.99 Response Response 13 5951 0.99 Response Response 14 7230 0.99 Response Response 15 6320 0.99 Response Response Paul C. Tumeh, UCLA; Christine Mateus, Caroline Robert, Gustave Roussy

TCR Clonality Pembrolizumab TIL infiltrate (rearrangements/genome) 0.35 0.3 0.25 0.2 0.15 Only progressors in bottom left quadrant (below median Clonality and % T cell) P=0.005 by Fisher s Exact 0 0.1 0.1 0.2 0.3 0.4 0.5 0.05 0 Clonality Progressors (N=13) Responders (N=12) Clonality 0.4 0.3 0.2 0.1 Response ** Progression Tumeh PC, et al. Nature. 2014.

IFN Signature Pembrolizumab 2.4 Best Overall Response, RECISTv1.1 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 Ribas A, et al. ASCO. 2015. Nonresponder Responder

PDL Amplicon Nivolumab 100 kb chr9:5,270,000 RP11-599h20 PDL1 PDL2 RP11-635N21 chr9:5,700,000 PDL1/2 Gain PDL1/2 Amplification Ansell SM, et al. N Engl J Med. 2015.

Mutational Load Pembrolizumab # Nonsynonymous mutations/tumor 1200 800 400 200 0 All Tumors DCB NDB Somatic mutations per tumor 5000 4000 3000 2000 1000 0 P=.02 Objective Response Stable Disease Progressive Disease Rizvi NA, et al. Science. 2015; Le DT, et al. N Engl J Med. 2015.

PD-1 Blockade Mesenchymal Transcriptome EMT/ Metastasis Hypoxia Angiogenesis TGFβ Wound healing Interferon induced Hugo W, et al. Cell. 2016. Non-responding (n=13) Responding (n=15) JAEGER_METASTASIS_UP MAPKi_INDUCED_EMT (*FDR=0.9) LEF1_UP.V1_UP (*FDR=0.11) POOLA_INVASIVE_BREAST_CANCER_UP YE_METASTATIC_LIVER_CANCER ANASTASSIOY_CANCER_MESENCHYMAL_TRANSTITIONAL CHARAFE_BREAST_CANCER_BASAL_VS_MESEN(*FDR=0.24) MAHADEVAN_GIST_MORPHOLOGICAL_SWITCH VECCHI_GASTRIC_CANCER_ADVANCED_VS_EARLY_UP LIEN_BREAST_CARCINOMA_METAPLASTIC LU_TUMOR_ENDOTHELIAL_MARKERS_UP LU_TUMOR_VASCULATURE_UP LU_TUMOR_ANGIOGENESIS_UP ROY_WOUND_BLOOD_VESSEL_UP (*FDR=0.05) MAPKi_INDUCED_ANGIOGENESIS (*FDR=0.11) EP_BLOOD_VESS_DEVIL_DN_IN_R WESTON_VEGFA_TARGETS_12HR (*FDR=0.11) WESTON_VEGFA_TARGETS_6HR MAINA_VHL_TARGETS_DN MS_RESP_TO_HYPOXIA_UP_IN_MAPKi_aPDL1_NR HARRIS_HYPOXIA KARAKAS_TGFB1_SIGNALING JEON_SMAD6_TARGETS_DN POST_OP_WOUNDHEALING MISHRA_CARCINOMA_ASSOCIATED_FIBROBLAST_UP MS_RESP_TO_WOUNDHEALING_UP_IN_MAPKi_aPDL1_NR DER_IFN_GAMMA_RESPONSE_UP DER_IFN_ALPHA_RESPONSE_UP DER_IFN_BETA_RESPONSE_UP GRANDVAUX_IFN_RESPONSE_NOT_VIA_IRF3 ZHANG_INTERFERON_RESPONSE NATSUME_RESPONSE_TO_INTERFERON_BETA_UP RADAEVA_RESPONSE_TO_IFNA1_UP MOSERLE_IFNA_RESPONSE SANA_RESPONSE_TO_IFNG_UP HECKER_IFNB1_TARGETS Row Z-score -2-1 0 1 2

Future Cancer Management Post-Anti-PD-1/PD-L1 Era Anti-PD-1/anti-PD-L1 Bring T cells into tumors: + Anti-CTLA4 + Immune activating antibodies or cytokines + TLR agonists or oncolytic viruses + IDO or macrophage inhibitors + Targeted therapies Generate T cells: + Vaccines + TCR engineered ACT + CAR engineered ACT

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