Pursuing Precision in Immuno-Oncology Biology, Big data and Biomarkers

Transcription

1 Pursuing Precision in Immuno-Oncology Biology, Big data and Biomarkers Tom Lillie, MD, PhD Vice President, EU clinical development MSD

2 Change From Baseline in Tumor Size, % Pembrolizumab monotherapy has shown activity in >2 tumors Melanoma NSCLC 2 1 H&N 3 Urothelial 4 TNBC Gastric 6 1 chl 7 NHL PMBCL Mesothelioma Ovarian 1 1 SCLC 11 1 Esophageal 12 1 NPC 13 1 Anal 14 Biliary Tract 15 1 Colorectal ER + / HER2 BC Cervical 18 Thyroid 19 Salivary 2 Endometrial 21 Prostate 22 GBM Daud A et al. ASCO 215; 2. Garon EB et al. ESMO 214; 3. Seiwert T et al. ASCO 215; 4. Plimack E et al. ASCO 215; 5. Nanda R et al. SABCS 214; 6. Bang YJ et al. ASCO 215 ; 7. Moskowitz C et al. ASH 214; 8. Zinzani PL et al. ASH 215; 9. Alley EA et al. AACR 215; 1. Varga A et al. ASCO 215; 11. Ott PA et al. 215 ASCO; 12. Doi T et al. ASCO 215; 13. Hsu C et al. ECC 215; 14. Ott PA et al. ECC 215; 15. Bang Y-J et al. ECC 215; 16. O Neil B et al. ECC 215; 17. Rugo HS et al. SABCS 215; 18. Frenel JS et al. ASCO 216; 19. Mehnert JM et al. ASCO 216; 2. Cohen R et al. ASCO 216; 21. Ott PA et al. ASCO 216; 22. Hansen AR et al. ESMO 216; 23. Reardeon D et al. SNO

3 Overall Survival % Pembrolizumab has also demonstrated improvements in Overall Survival... 1L NSCLC 1 2L NSCLC 2 1L Melanoma 3 2L Bladder 4 Time in Months 1. Section 14.2, Figure 3, KEYTRUDA prescribing information; 2. Section 14.2, Figure 4, KEYTRUDA prescribing information; 3. Section 14.1, Figure 1, KEYTRUDA prescribing information; 4. Section 14.5, Figure 5, KEYTRUDA prescribing information

4 Sep-14 Nov-14 Jan-15 Mar-15 May-15 Jul-15 Sep-15 Nov-15 Jan-16 Mar-16 May-16 Jul-16 Sep-16 Nov-16 Jan-17 Mar-17 Pembrolizumab development program rapid progress and growth NPC GASTRIC H& N TNBC MELANOMA Bladder NSCLC ANAL SCLC PMBCL COLORECTAL UROTHELIAL OVARIAN ESOPHAGEAL RENAL MESOTHELIOMA chl Number of Clinical Trials 6 5 Broad global clinical program with more than 48 studies (largest PD-1 / PD-L1 program) BMS MRK ROG AZ Updated 1-May-217; source: clinicaltrials.gov

5 Pembrolizumab: warnings & precautions % of 2799 Patients who received KEYTRUDA Immune-Mediated Pneumonitis 3.4%* Immune-Mediated Colitis 1.7% Immune-Mediated Hepatitis.7% Immune-Mediated Endocrinopathies: -- -Hypophysitis.6% -Thyroid disorders: -- Hyperthyroidism 3.4% Hypothyroidism 8.5%** Thyroiditis.6% -Type 1 diabetes mellitus.2% Immune-Mediated Nephritis.3% Infusion-related reactions (severe or life-threatening).2% *Pneumonitis occurred more frequently in patients with a history of prior thoracic radiation (6.9%) than in patients who did not receive prior thoracic radiation (2.9%). **Incidence of new or worsening hypothyroidism was higher in patients with HNSCC occurring in 28 (15%) of 192 patients. Refer to pembrolizumab SmPC for additional prescribing information

6 Multiple strategies may work to overcome primary resistance to PD1 blockade Atkins ESMO 216; 2. Lancet Oncology 216; 3. Taylor ESMO 216; 4. Gangadhar T et al. SITC 215; 5. San Miguel L et al. ASH 215; 6. Badros A et al. ASH 215; 7. Papadimitrakopoulou V et al. ASCO 215; 8. Gangadhar T et al. ESMO 216; 9. Herbst ESMO 216; 1. Patnaik A et al. ASCO 215; 11. Long ASCO 216; 12. Tolcher ASCO 216

7 Multiple strategies may work to overcome primary resistance to PD1 blockade Presenter Data Presented Combination agent Indication n ORR Notes Ribas SMR 216 SD-11 Melanoma 5 8% Kaufman SITC 216 Cavatak Melanoma 1 7% Taylor ESMO 216 Lenvatinib All comers 13 69% Atkins ESMO 216 Axitinib RCC 52 67% Ribas ASCO 216 Dabraf-tramet melanoma 15 6% Bedros ASH 215 Pomalidomide-dex RRMM 27 6% Gangadhar ESMO 216 Epacadostat melanoma 19 58% Treatment naive melanoma Long ASCO 216 T vec melanoma 21 57% Long (KN29 EC) ASCO 216 low dose ipilimumab melanoma 17 57% Langer 216 Lancet Oncology Pemetrexed-carbo NSCLC 6 55% Chemo alone 29%; PFS 8 m vs4.9 m Gadgeel ASCO 216 Paclitaxel-carbo NSCLC 25 52% Mateos ASCO 216 Lenalidomide-dex RRMM 4 5% Gadgeel ASCO 216 Paclitaxel-carbo-bev NSCLC 25 48% Davar (Pitt OCSP) ASCO 216 PEG-IFN melanoma 24 43% Algazi SITC 216 IT-pIL12 EP Melanoma 15 4% High risk biomarker group McDermott ESMO 215 Pazopanib RCC 2 4% Tolaney SABCS 216 Eribulin TNBC 39 33% Atkins (KN-29) ASCO 216 low dose ipilimumab RCC 1 3% Herbst ESMO 216 Ramucirumab NSCLC 27 3% Besse World Lung 216 Necitumumab NSCLC 34 29% Stenehjem ESMO 216 FOLFOX GI 7 29% Immunotherapy Chemotherapy Targeted Therapy

8 Approaches to combinations targeting cancer immune biology Potential Targets: GITR, STING, CTLA4, TLR, Oncolytic viruses, OX-4, NY- ESO1 TCR, vaccines, anti-cd19/cd3 Potential Targets: Chemo, CDK, ERK, HDAC, PARP, TKi s, etc. Potential Targets: PD-1 / L1, CTLA-4, IL-1, LAG3, TIGIT, IDO/TDO, IDO, PI3Kd, IL-12, VEGF, etc

9 KEYNOTE-21G: pembrolizumab and chemo in 1 st line NSCLC KN-21G approved in US based on significant improvement in ORR and PFS OS data is maturing High rate of crossover (75%) 9

10 KEYNOTE-173 & I-SPY 2 TRIAL: Early promise of chemo-combo in breast cancer KEYNOTE-173: Promising preliminary data add to the growing body of evidence for pembrolizumab activity in breast cancer I-SPY 2 TRIAL: Nearly tripled pcr rate for HR+/HER2- patients and tripled pcr rate for TNBC patients The Bayesian model estimated pcr rates appropriately adjust to characteristics of the I-SPY 2 population. The raw pcr rates (not shown) are higher than the model estimate of.64 in TNBC.

11 Immuno-modulators combination: Signal finding - pembro with IDO Inhibitor (epacadostat) in melanoma ORR 58% Strong efficacy signal supports registration trial Minimal additional toxicity (rash) Gangadhar et al ESMO 216

12 Targeted therapies: pembrolizumab + VEGFR- MTKi (axitinib) signal finding data in RCC Atkins et al, ESMO 216 Patients with a response, n 52 Objective Response rate, % 71% Best overall response, n (%) Complete response Partial response Stable disease Progressive disease 6% 65% 19% 4% Combo mpfs of 15 months vs. Axitinib mono mpfs of 1 months Results indicate high level of antitumour activity and acceptable toxicity Combo ORR of 71% vs. Axitinib mono ORR of 32%

13 Progression-Free Survival, % Novel vaccines: pembrolizumab + TVEC Signal finding data in melanoma Patients with a response, n 12 Response rate, % (95% CI) 57.1% Best overall response, n (%) Complete response Partial response Stable disease Progressive disease 24% 33% 14% 29% Median PFS not reached Strong early efficacy signal supported registration trial No additional toxicity Time From Baseline, months Long et al ASCO 216

14 Multiple biomarkers predict response to pembrolizumab Ligand Expression on Tumor Immunogenic Microenvironment Increased Antigen Presentation Due to High DNA Mutation Load PD-L1 and PD-L2 Expression Immune-Related Gene Expression (GEP) Signatures DNA Mismatch Repair Deficiency, DNA Polymerase Mutation, Mutational Load

15 NSCLC: superior Overall Survival vs. chemotherapy in PD-L1 defined subgroups KEYNOTE 1: Advanced NSCLC PD-L1 1% TPS KEYNOTE 24: Previously Untreated NSCLC PD-L1 5% TPS R Herbst et al. Lancet 216 M Reck et al. NEJM 216

16 Multiple PD-L1 IHC assays and cutoffs % Immune Cell Staining Agent Pembrolizumab Nivolumab Durvalumab Atezolizumab Diagnostic Platform Dako Ventana Antibody 22-C SP 263 SP 142 Cut-off(s) being tested TC 1 1%, 5% 1%, 5% or 1% (TC 1 ) TC 1 25% TC 1 or IC 2 1%, 5%,1% 1) TC = tumor cell staining. 2) IC = infiltrating immune cell staining AACR-sponsored Blueprint project designed to compare the 4 assays % Tumor Staining % Tumor Staining Cases Cases 22C SP142 SP263 22C SP142 SP263

17 GEP - the biology of T-Cell inflamed tumours

18 T-Cell inflamed Gene Expression Profile (GEP) Signatures defined and validated in melanoma (N=19 training, N=62 validation) E x p r e s s i o n S c o r e Melanoma validation set (N=62) Signatures validated and refined in SCCHN (N=43) and gastric CA (n=33) 1..8 Nonresponder Responder SCCHN (N=43) Best Overall Response, RECISTv1.1 Gastric CA (N=33) Final GEP generated using penalized regression model in 9 solid tumours (n=22) R = responder GEP further validated vs. PD-L1 IHC in PD-L1-unselected population (n=96) N = 22 (gastric, TNBC, SCCHN, urothelial, anal, biliary, colorectal, esophageal and ovarian cancers)

19 T-Cell inflamed GEP and PD-L1 IHC in an unselected population n=96, PD-L1-unselected patients from KN2 SSCHN B2 cohort

20 PFS (days) GEP delineates categories of non-responders C Group C: almost all responders have baseline T- cell inflammation Group A: T-cell non-inflamed tumors rarely respond to pembrolizumab PFS A B Nanostring 57 gene signature Predict pembro monotherapy response New target identification/validation Inform drug combinations Combined data from KEYNOTE 1 melanoma and KEYNOTE 12 (SCCHN, bladder and gastric) cohorts T-Cell Inflamed GEP Group B: some non-responders have T-cell inflamed tumors 2

21 Mutational burden, neo-antigens, and response to checkpoint blockade ** * * MSI subset **Triple-negative subset Schumacher and Screiber, Science 215

22 P e rc e n t p ro g re s s io n -fre e P ercent p rogressio n -free P e rc e n t p ro g re s s io n -fre e 1 Mutational load and neo-antigen burden predict response to pembrolizumab in NSCLC C o h o r t 1 - D is c o v e r y High non-synonymous burden H ig h n o n s y n o n y m o u s b u rd e n (n = 8 ) L o w n o n s y n o n y m o u s b u rd e n (n = 8 ) Low non-synonymous burden 1 N e o a n tig e n b u r d e n H h n e o a n e n b u e n (n = 1 7 ) High neoantigen burden L o w n e o a n tig e n b u rd e n (n = 1 7 ) Low neoantigen burden 5 5 A ll s e q u e n c e d tu m o r s M o n th s T ra n s v e rs io n h ig h (n = 1 6 ) M o n th s T ra n s v e rs io n lo w (n = 1 8 ) Smoking signature (high transversion / transition) No smoking signature (low transversion / transition) 5 Rizvi et al. Science M o n th s

23 NS Mutational Load (log scale) Mutational load predicts response to pembrolizumab across tumour types Non-responder [PD/SD] Responder [CR/PR] n = 85 n = P =.36 not PR or CR PR or CR BOR, Central Review Subgroup of patients from KEYNOTE N12 and KEYNOTE 28 (n=119, representing 2 tumour types)

24 KEYNOTE 16: pembrolizumab in MSS colorectal and MSI colorectal and non-colorectal tumours Durability of disease control in MMR-deficient non-colorectal tumours 1 Presented by L. Diaz, ASCO 216 Presented by Dung Le, ASCO 215

25 WES NS ML (log scale) GEP and mutational load: independent measures with comparable predictive value Low correlation between mutational load and T-cell inflamed gene signature (NanoString) Comparable high predictive value of gene signature and ML 3 1 not PR or CR PR or CR Overall r =.281 (p =.6) r in responders =.321 (p =.242) r in non-responders =.192 (p =.91) gene inflammation signature Pre-specified hypothesis testing using KN28 / 12 as validation set When jointly modeled, ML showed significant association with response (p=.78 ) after adjusting for GEP (p=.251)

26 MSD vision for precision Immuno-Oncology One Sample One Multiplexed Diagnostic Problem: As the number of immunotherapybased combination treatment regimens expands, patients physicians, and payers will need a rational way to choose among these options Treatment 1 Treatment 2 Treatment 3 Treatment 4 Solution: A single diagnostic signature, using a single sample, which identifies the option most likely to benefit the patient 26

27 Immunogenicity-inflammation pathways Environmental Mutagens POL-D/E Mutation High Mutational Load Immunogenicity High Neoantigen Formation Inflammation T-Cell Inflamed Tumour Immune Modulation Defective Mismatch Repair Protein Micro- Satellite Instability PD-L1 Expression immunogenicity and inflammation have low but significant correlation and independent predictive value

28 Immunogenicity-inflammation pathways Environmental Mutagens POL-D/E Mutation High Mutational Load Immunogenicity Next-Generation Biomarkers High Neoantigen Formation Inflammation T-Cell Inflamed Tumour Immune Modulation Defective Mismatch Repair Protein Micro- Satellite Instability Clinically Validated Biomarkers PD-L1 Expression

29 IFNγ GEP Categorisation of response to I-O agents or combos Combo Response PFS Monotherapy Response Confirmation of biological hypotheses Indication selection Enrichment studies Complementary diagnostic tools to navigate combo landscape

30 Future State: Decision tree for precision Immuno-Oncology? T-cell poor tumors Biomarker 1: T-Cell Inflamed Signature T-cell rich tumors Tumor cell-intrinsic pathway activation (e.g. Wnt/β-Catenin) Targeted Therapies + αpd-1 Biomarker 2 Failed T-cell priming Extrinsic factors (vasculature, CAF) VEGF, Ang2 inhibition CAF normalization + αpd-1 Additional resistance mechanisms Biomarker 5 Biomarker 4 No additional resistance mechanisms αpd-1 Monotherapy Low neoepitope burden Biomarker 3: Mutational Load Poor tumor Ag presentation in LN Extrinsic: MDSC or Treg Treg or myeloid targeting + αpd-1 Intrinsic: additional inhibitory receptors Lag3, Tim3 blockade, etc. + αpd-1 Mutagenic chemo Epigenetic modifiers + αpd-1 Tumor vaccines Oncolytics Immune agonists + αpd-1 3

31 Progress in precision Immuno-Oncology Pembrolizumab is shifting the development, clinical, regulatory and reimbursement landscapes rapidly Multi-indications, mono + combo - volume and rate of data are issues The biology driving clinical data is becoming apparent Current approved PD-L1 biomarkers are a starting point Complex biomarker signatures emerging, first step to define Precision I-O Tumour inflammation, mutational load and microenvironment / resistance Challenges Integration of translational research into more (all?) trials, novel designs Need for large clinico-genomic databases, implementation into clinical practice Are current regulatory / payor paradigms optimal for these approaches? Can more complex signatures be regulated like single test / drug diagnostics? 31