Emerging immune biomarkers. Athanasios Kotsakis MD, PhD Ast. Professor of Medical Oncology School of Medicine, University of Crete

Emerging immune biomarkers Athanasios Kotsakis MD, PhD Ast. Professor of Medical Oncology School of Medicine, University of Crete

Disclosure: none

Cancer Immunotherapy Immunotherapy, mainly anti PD 1/PD L1 agents have been approved for the treatment of many tumor types such as melanoma, NSCLC, urothelial cancer, SCCHN and others BUT what about Toxicity and Cost?? Avoid toxic effects of the treatment to patients who will not benefit from such a therapy Spare money

A biomarker is defined as: European Medicines Agency: Biomarkers are tests that can be used to follow body processes and diseases in humans and animals. They can be used to predict how a patient will respond to a medicine or whether they have, or are likely to develop, a certain disease 1 European Medicines Agency. www.ema.europa.eu. Accessed March 2, 2015. Could be: - prognostic: are biological characteristics that are objectively measured and evaluated to predict the course of a disease or - predictive: is a clinical or biologic characteristic that provides information on the likely benefit from treatment

Distinct types of biomarkers Physical features 2,3 Molecular variations 4 Cellular features 5 Histology Molecular Variations image adapted from: http://www.bioonocology.com/research-education/braf/metastatic-melanoma/mutations. com/research education/braf/metastatic melanoma/mutations 1. OECD Policy Paper 2011. http://www.oecd.org/science/biotech/49023036.pdf. Accessed October 31, 2014. 2. Okonkwo OC et al. Neurology. 2014;83(19):1753-1760. 2. Rundle A. Cancer Epidemiol Biomarkers Prev. 2005;14(1):227-236. 3. NCCN. Clinical Practice Guidelines in Melanoma, V.3.2014. www.nccn.org. Accessed February 11, 2015. 4. NCCN. Clinical Practice Guidelines in Oncology-Breast Cancer, V.1.2015. www.nccn.org. Accessed February 11, 2015. 5. Spector N et al. Breast Cancer Res. 2007;9(2):205. 6. Menni C et al. Diabetes. 2013;62(12):4270-4276.

Immune-biomarkers are indicators of immune activity Immune-biomarkers are measures of activity within the tumor microenvironment, differing from established gene mutation biomarkers, such as BRAF and EGFR. As components and regulators of the immune response, immune-biomarkers include: Tumor-infiltrating immune cells Secreted peptides Cell surface proteins Immunosuppressive cells Evaluating multiple immune-biomarkers may provide a more realistic representation of the tumor microenvironment, as well as a more accurate and comprehensive assessment of clinical relevance. 15

Exploratory immune-biomarkers New immune-biomarkers are now being investigated across tumor types: 89-100 The field of immune-biomarkers aims to characterize the ongoing g interactions between the immune system and cancer. 16

Tumor infiltrating lymphocytes (TILs) TILs are evaluated in 1. Hematoxylin and Eosin stained tissue sections 2. Immunostained for CD3+ (T cells), CD4+ (Th cells/cd4+ Tregs), CD8+ (CTLs/CD8+ Tregs), CD20+ (B cells) or FoxP3+ (T regulatory cell marker). Intratumoral lymphocytes (itils) were defined as intraepithelial mononuclear cells within tumor cell nests or in direct contact with tumor cells and are reported as the percentage of the tumor epithelial nests that contain infiltrating lymphocytes. y Stromal lymphocytes y (stils) are defined as the percentage of tumor stroma area that contains a lymphocytic infiltrate without direct contact to tumor cells.

Mandal R., et al JCI Insight. 2016;1(17):e89829

Mandal R., et al JCI Insight. 016;1(17):e89829 We find that both HPV+ and HPV HNSCC tumors are among the most highly immune infiltrated cancer types.

TILs in SCCHN: Prognostic value Increased levels of TILs (mononuclear cells) Associated with better DFS and DSS (Qiaoshi Xu et al., 2017, Translational Oncology Vol 10, no 1, pp 10-16)

TILs in SCCHN : Predictive value Tumor infiltrating lymphocytes predict response to definitive chemoradiotherapy in head and neck cancer High density of CD3 + and CD8 + TILs correlates with better OS, PFS, LFFS, DMFS (Balermpas P et al., 2014,BJC 110, 501 509)

HPV positive HNSCC had the highest levels of Treg infiltration, with HPV negative HNSCC having the second highest CD56 dim NK cell infiltration ti correlated ltdstrongly with overexpression of the KIR inhibitory receptor genes KIR2DL1 and KIR2DL3

higher levels of Treg infiltration were associated with superior OS Could be attributed to the ability of these cells to dampen inflammatory processes required for tumor survival/growth

intratumoral Treg are more immunosuppressive than circulating Treg

Mutational load

Haddad R, et al Journal of Clinical Oncology 35, no. 15_suppl (May 2017) 6009 6009 Background: Somatic mutational load (ML) is associated with response to anti CTLA 4 and PD 1/ L1 immunotherapies in select tumors, due to formation of neoepitopes not subject to central immune tolerance. Neoepitopes specific to HPV, EBV virus infection are also present in some HNSCC. An IFNγ gene expression profile (GEP) characteristic ti of tumor inflammation is also related ltdto response to anti PD 1/ L1 therapy. This study evaluated relationships between ML and clinical outcome and independent predictive values of ML and GEP in patients with HNSCC treated with pembrolizumab. Methods: Whole exome sequencing (WES) and GEP were assessed in FFPE tumor specimens of patients with HNSCC (KEYNOTE 012; subsets of B1 [PD L1 +, n = 34] and B2 [PD L1 + /, n = 73] cohorts). ML, neoantigen load (NL), HPV/EBV status and clonality were assessed by standard WES analytical methods. GEP score is a weighted sum of normalized expression values of 18 genes. Statistical testing of ML and response, and ML and GEP relationship by HPV/EBV status was prespecified. Results: There were 73 patients identified as HPV and EBV (n = 25 in B1; n = 48 in B2). In HPV and EBV patients in B1 and B2 cohorts, respectively, associations between ML and objective response (OR) (P = 0.029 and 0.055; AUROC 0.89 and 0.63), and GEP and OR (P = 0.064 and 0.01; AUROC 0.82 and 0.74) were statistically significant. In combined cohorts of HPV and EBV patients, ML and GEP were significantly associated with OR (P = 0.009 and 0.002; AUROC 0.70 and 0.76, respectively). ML and GEP were only weakly correlated (r = 0.173). In a joint model, ML was significantly associated with response (p = 0.020) after adjusting for GEP (also significant, p = 0.006). NL and clonality weighted ML were also significantly associated with response (P = 0.026 and 0.006, respectively). In HPV + or EBV + subjects, OR association was not significant for ML, possibly due to a dominance of viral vs somatic neoepitopes; GEP was significant, ifi likely l due to tumor inflammation. Conclusions: ML and GEP are independently d predictive of response to pembrolizumab in HPV /EBV patients with HNSCC; GEP was predictive regardless of viral status. ML and GEP may have utility in characterizing responses to anti PD 1 therapies and novel cancer regimens in HNSCC. Clinical trial information: NCT01848834.

Haddad R, et al Journal of Clinical Oncology 35, no. 15_suppl (May 2017) 6009 6009 Background: Somatic mutational load (ML) is associated with response to anti CTLA 4 and PD 1/ L1 immunotherapies in select tumors, due to formation of neoepitopes not subject to central immune tolerance. Neoepitopes specific to HPV, EBV virus infection are also present in some HNSCC. An IFNγ gene expression profile (GEP) characteristic ti of tumor inflammation is also related ltdto response to anti PD 1/ L1 therapy. This study evaluated relationships between ML and clinical outcome and independent predictive values of ML and GEP in patients with HNSCC treated with pembrolizumab. Methods: Whole exome sequencing (WES) and GEP were assessed in FFPE tumor specimens ML and objective of patients response with HNSCC (OR) (KEYNOTE (P = 0.029 012; subsets and 0.055; of B1 [PD L1 AUROC +, n 0.89 = 34] and B2 0.63), [PD L1 and + /, n GEP = 73] cohorts). ML, and neoantigen OR (P = load 0.064 (NL), and HPV/EBV 0.01; AUROC status and 0.82 clonality and were 0.74) assessed were statistically by standard WES significant. analytical methods. GEP score is a weighted sum of normalized expression values of 18 genes. Statistical testing of ML and response, and ML and GEP relationship by HPV/EBV status was prespecified. Results: There were 73 patients identified as HPV and EBV (n = 25 ML in B1; and n = GEP 48 in were B2). In only HPV weakly and EBV correlated patients in B1 and B2 cohorts, respectively, associations between ML and objective response (OR) (P = 0.029 and 0.055; AUROC 0.89 and 0.63), and GEP ML and OR GEP (P = may 0.064 have and 0.01; utility AUROC in characterizing 0.82 and 0.74) were responses statistically to anti significant. PD 1 therapies In combined and cohorts novel of HPV and EBV patients, ML and GEP were significantly cancer regimens associated in with HNSCC OR (P = 0.009 and 0.002; AUROC 0.70 and 0.76, respectively). ML and GEP were only weakly correlated (r = 0.173). In a joint model, ML was significantly associated with response (p = 0.020) after adjusting for GEP (also significant, p = 0.006). NL and clonality weighted ML were also significantly associated with response (P = 0.026 and 0.006, respectively). In HPV + or EBV + subjects, OR association was not significant for ML, possibly due to a dominance of viral vs somatic neoepitopes; GEP was significant, ifi likely l due to tumor inflammation. Conclusions: ML and GEP are independently d predictive of response to pembrolizumab in HPV /EBV patients with HNSCC; GEP was predictive regardless of viral status. ML and GEP may have utility in characterizing responses to anti PD 1 therapies and novel cancer regimens in HNSCC. Clinical trial information: NCT01848834.

Higher levels of tobacco mutational signature is associated with higher tumor mutational g g g burden, consistent with results reported in NSCLC However, tobacco mutational signature correlated inversely with the degree of T cell infiltrate, immune cell infiltrate, and IFN γ signaling Tumors with high levels of immune cell (HR = 0.66, P = 0.023), T cell (HR = 0.53, P = 6 10 4) and CD8+ T cell (HR = 0.67, P = 0.029) infiltration were associated with better OS. Increasing level of tobacco mutational signature was associated with poorer survival (P = 0.005)

PD-L1 as a predictive biomarker

Nivolumab for SCCHN CheckMate 141: Overall Survival OS (%) 100 90 80 70 60 50 40 30 20 10 0 No. at risk Median OS, mo (95% CI) HR (97.73% CI) P value Nivolumab (n = 240) 7.5 (5.5, 9.1) 0.70 Standard Therapy (n = 121) 5.1 (4.0, 6.0) (0.51, 0.96) 1-year OS rate (95% CI) 36.0% (28.5, 43.4) 16.6% 6% (8.6, Standard Therapy Nivolumab 0 3 6 9 12 26.8) 15 18 Months Nivolumab 240 167 109 52 24 7 0 121 87 42 17 5 1 0 Standard Therapy 0.01 Adapted from Ferris et al. NEJM 2016; doi: 10.1056/NEJM0a1602252. Abbreviations and references can be found in the speaker notes.

Nivolumab for SCCHN CheckMate 141: Overall Survival by PD-L1 Expression PD-L1 Expression 1% No. of Median OS No. of Patient mo (95% Deaths s CI) Nivolumab 88 49 8.7 (5.7 9.1) Standard Therapy 61 45 4.6 (3.8 5.8) PD-L1 Expression <1% No. of No. of Median OS Patients Deaths mo (95% CI) Nivolumab 73 45 5.7 (4.4 12.7) Standard Therapy 38 25 5.8 (4.0 9.8) Overall Surviv val (% of patien nts) 100 90 80 70 60 50 40 30 20 10 No. at Risk Hazard ratio for death, 0.55 (95% CI, 0.36 0.83) Standard Therapy Nivolumab 0 0 3 6 9 12 15 18 Months 100 90 80 70 60 50 40 20 10 Hazard ratio for death, 0.89 (95% CI, 0.54 1.45) 30 Nivolumab Standard Therapy 0 0 3 6 9 12 15 18 Months Nivolumab88 67 44 18 6 0 73 52 33 17 8 3 0 Standard 61 Therapy 42 20 6 2 0 38 29 14 6 2 0 0 Estimates for OS HR for PD-L1 expression levels of 5% and 10% are similar to those for 1%. Adapted from Ferris et al. NEJM 2016; doi: 10.1056/NEJM0a1602252. Abbreviations and references can be found in the speaker notes.

HNSCC Cohorts of Nonrandomized, Phase 1b KEYNOTE-012 Trial Biomarker analysis A Phase 1b Study of Pembrolizumab in Patients With HPV-Positive and HPV-Negative Head and Neck Cancer Patient population R/M HNSCC Measurable disease (RECIST v1.1) ECOG PS 0-1 PD-L1 positive (initial cohort) PD-L1 positive or PD-L1 negative (expansion cohort) Initial Cohort B N=60 Pembrolizumab 10 mg/kg q2w Expansion Cohort B2 N=132 Pembrolizumab 200 mg q3w Treat for 24 months or until progression or intolerable toxicity Pre-treatment samples collected for biomarker analyses Pre-treatment biomarker levels were correlated with efficacy outcomes (ORR, PFS, OS; central imaging vendor review) HNSCC = recurrent or metastatic squamous cell carcinoma; ORR = overall response rate; OS = overall survival; PD-L1 = programmed death ligand 1; PFS = progression-free survival; q2w = every 2 weeks; q3w = every 3 weeks; RECIST v1.1 1 = Response Evaluation Criteria In Solid Tumors version 1.1. 1 1. Chow LQ et al. J Clin Oncol. 2016;34(15;suppl): abstract 6010. 23 For Internal Use Only

KEYNOTE-012: Overall Response by PD-L1 Status 1 PD-L1 Status Nonresponders, n Responders, n ORR % (95% CI) P TPS (tumor cells) PD-L1 positive PD-L1 negative 101 22 18 (12 26) 26) 53 12 19 (10 30) 0.461 P values based on logistic regression one-sided testing. CI = confidence interval; CPS = combined positive score; ORR = overall response rate; PD-L1 = programmed death ligand 1; TPS = tumor proportion score. 1. Chow LQ et al. J Clin Oncol. 2016;34(15;suppl): abstract 6010. 24 For Internal Use Only

KEYNOTE-012: Overall Response by PD-L1 Status 1 Incorporation of inflammatory cells improves ability to detect responders PD-L1 Status Nonresponders, n Responders, n ORR % (95% CI) P TPS (tumor cells) PD-L1 positive PD-L1 negative 101 22 18 (12 26) 26) 53 12 19 (10 30) 0.461 CPS (either tumor and/or inflammatory cells) PD-L1 positive PD-L1 negative 120 32 21 (15 28) 34 2 6 (1 19) 0.023 P values based on logistic regression one-sided testing. CI = confidence interval; CPS = combined positive score; ORR = overall response rate; PD-L1 = programmed death ligand 1; TPS = tumor proportion score. 1. Chow LQ et al. J Clin Oncol. 2016;34(15;suppl): abstract 6010. 25 For Internal Use Only

KEYNOTE-012: Progression-free Survival by PD-L1 Status 1 og ess o ee Su a by Saus TPS (tumor cells) CPS (tumor and inflammatory cells) 1.00 P = 0.378 1.00 P = 0.026 Sur rvival Probab bility 0.75 0.50 0.25 PD-L1+ PD-L1 Surv vival Probab bility 0.75 0.50 0.25 PD-L1+ PD-L1 0.00 0.00 0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700 Time, days Time, days TPS<1 65 22 14 8 5 5 5 2 TPS 1 123 48 30 21 3 3 3 0 CPS<1 36 9 4 3 2 2 2 1 CPS 1 152 61 40 26 6 6 6 1 Median (95% CI) PD-L1 positive, 63 days (58 98) PD-L1 negative, 62 days (59 67) P values based on logistic regression one-sided testing. CI = confidence interval; CPS = combined positive score; PD-L1 = programmed death ligand 1; TPS = tumor proportion score. 1. Chow LQ et al. J Clin Oncol. 2016;34(15;suppl): abstract 6010. 26 For Internal Use Only Median (95% CI) PD-L1 positive, 64 days (59 98) PD-L1 negative, 60 days (51 66)

KEYNOTE-012: Overall Survival by PD-L1 Status 1 TPS (tumor cells) CPS (tumor and inflammatory cells) 1.00 P = 0.478 1.00 P = 0.008 Survival Prob bability 0.75 0.50 0.25 PD-L1+ PD-L1 Survival Prob bability 0.75 0.50 0.25 PD-L1+ PD-L1 0.00 0 100 200 300 400 500 600 700 0.00 0 100 200 300 400 500 600 700 Time, days Time, days TPS<1 65 46 31 21 9 9 8 2 TPS 1 123 88 69 53 16 8 6 0 CPS<1 36 21 11 7 3 3 2 1 CPS 1 152 113 89 67 22 14 12 1 Median (95% CI) PD-L1 positive, 290 days (241 377) PD-L1 negative, 246 days (174 626) Median (95% CI) PD-L1 positive, 303 days (259 385) PD-L1 negative, 151 days (84 247) P values based on logistic regression one-sided testing. CI = confidence interval; CPS = combined positive score; PD-L1 = programmed death ligand 1; TPS = tumor proportion score. 1. Chow LQ et al. J Clin Oncol. 2016;34(15;suppl): abstract 6010. 27 For Internal Use Only

Challenges to use PD L1 as biomarker: Fluctuation in expression at different time points Variation within tumor tissue Lack of uniformity in cutoff points employed in different trials, in kit and antibodies used for the detection of PD L1 expression Where to look for them: TC, IC or both? Responses in PD L1 negative tumors OR No response even in highly PD L1 positive tumors While there are many doubts about how perfect PD L1 testing is, there is a belief it plays a role for enrichment!!

PD-L2 as a predictive biomarker

Jennifer H. Yearley et al, Clin Cancer Res; 23(12) June 15, 2017

KEYNOTE-012: Overall Response by PD-L2 Status 1 PD-L2 expression on tumor and inflammatory cells is predictive of response to pembrolizumab Samples from172 patients were evaluated for PD-L2 expression CPS (tumor and/or inflammatory cells) Nonresponders, Responders, ORR n n %(95% CI) PD-L2+ 86 25 23 (15 31) PD-L2 55 6 10 (4 20) P 0022 0.022 P values based on logistic regression one-sided testing. CI = confidence interval; CPS = combined positive score; ORR = overall response rate; PD-L2 = programmed death ligand 2. 32 For Internal Use Only

KEYNOTE-012: Correlation of PD-L1, PD-L2, and Response 1 Data suggest that PD-L2 predicts clinical outcome in pembrolizumab treated pts n=172 n = 39 PD-L1+/ PD-L2 n = 22 PD-L1 / PD-L2 n = 108 PD-L1+/ PD-L2+ PD-L1 Positive n ORR % (95% CI) PD-L1 Negative n ORR % (95% CI) n = 3 PD-L1 / PD-L2+ Significant association (P<0.001) between PD-L1 and PD-L2 expression 33 For Internal Use Only 1. Chow LQ et al. J Clin Oncol. 2016;34(15;suppl): abstract 6010. PD-L2+ 108 23 (16 32) 3 0 (0 71) PD-L2 39 10 (3 24) 22 9 (1 29) High response in PD-L1 positive/ PD-L2 positive tumors

HPV status t as a predictive biomarker

Nivolumab for SCCHN CheckMate 141: Overall Survival OS (%) 100 90 80 70 60 50 40 30 20 10 0 No. at risk Median OS, mo (95% CI) HR (97.73% CI) P value Nivolumab (n = 240) 7.5 (5.5, 9.1) 0.70 Standard Therapy (n = 121) 5.1 (4.0, 6.0) (0.51, 0.96) 1-year OS rate (95% CI) 36.0% (28.5, 43.4) 16.6% 6% (8.6, Standard Therapy Nivolumab 0 3 6 9 12 26.8) 15 18 Months Nivolumab 240 167 109 52 24 7 0 121 87 42 17 5 1 0 Standard Therapy 0.01 Adapted from Ferris et al. NEJM 2016; doi: 10.1056/NEJM0a1602252. Abbreviations and references can be found in the speaker notes.

Nivolumab for SCCHN CheckMate 141: Overall Survival by p16 Status t p16-positive p16-negative No. of No. of Median OS No. of No. of Median OS Patients Deaths mo (95% CI) Patients Deaths mo (95% CI) Nivolumab 63 33 9.1 (7.2 10.0) Nivolumab 50 28 7.5 (3.0 NA) Standard Therapy 29 20 4.4 (3.0 9.8) Standard Therapy 36 25 5.8 (3.8 9.5) 100 100 Hazard ratio for death, 0.56 (95% CI, 0.32 Hazard ratio for death, 0.73 (95% CI, 0.42 90 0.99) 90 1.25) patients) Overall Survival (% of 80 70 60 50 40 30 20 10 0 Standard Therapy Nivolumab 0 3 6 9 12 15 18 No. at Risk Months Nivolumab63 49 35 18 10 3 0 Standard Therapy 29 20 11 4 1 0 0 80 70 60 50 40 30 20 10 0 Nivolumab Standard Therapy 0 3 6 9 12 15 18 Months 50 32 25 12 6 1 0 36 26 13 7 3 1 0 mos appeared longer for patients treated with nivolumab than standard therapy regardless of p16 status Adapted from Ferris et al. NEJM 2016; doi: 10.1056/NEJM0a1602252. Abbreviations and references can be found in the speaker notes.

IFN γ signature

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T-Cell Inflamed Phenotype Gene Expression Signatures Predict Benefit from Pembro across Multiple Tumor Types Patients enrolled FFPE tumor in clinical study tissue NanoString Gene expression data RNA Evaluate genes and signatures associated with anti-pd-1 response Collected at baseline prior to receiving anti-pd-1 therapy Transcripts for genes of interest are counted 680 genes on platform immune focused (custom design) six IFN-γ regulated genes with a gene signature panel IFN-γ (6 gene) significantly associated with ORR, PFS (IFN CXCL9, CXCL10, IDO1, HLA-DRA, STAT1 - Nanostring) Consistent data across CRC, Esophageal, Biliary, Anal and Ovarian 40 For Internal Use Only

Inflamed-phenotype gene expression signatures to predict benefit from the anti-pd-1 antibody pembrolizumab in PD-L1+ head and neck cancer patients Tanguy Y. Seiwert et al abstr 6017 They investigated 4 multi-gene expression signatures previously described in melanoma and gastric patients (pts). FFPE-extracted RNA was analyzed on the NanoString ncounter system which is being developed as a companion diagnostic Interferon γ γ signature correlated strongly with the previously independently discovered inflamed/mesenchymal HNSCC intrinsic subtype

IMS subtype is characterized among others by CD8+ T-cell infiltration

Seiwert TY et al, Lancet Oncol. 2016 Jul;17(7):956 965. The gene expression composite score provided a positive predictive value of 40% and a negative predictive value of 95% th ti f ti t ith ll i th bi k l t d the proportion of patients with an overall response in the biomarker selected population was nearly double that seen in the trial overall

KEYNOTE-012: Progression-free Survival and Overall Survival by IFN- 6-gene esg Signature auescoe Score 1 Progression-free Survival Overall Survival 1.00 IFN- Score < Q1 IFN- Score Q1 1.00 IFN- Score < Q1 IFN- Score Q1 Su urvival Proba ability 0.75 0.50 0.25 Su urvival Proba ability 0.75 0.50 0.25 0.00 0.00 0 100 200 300 400 500 600 700 Time, days IFN- Score < Q1 32 7 3 1 0 0 0 0 IFN- Score Q1 118 55 36 24 6 6 6 2 0 100 200 300 400 500 600 700 Time, days IFN- Score < Q1 32 19 10 7 1 0 0 0 IFN- Score Q1 118 91 74 56 20 13 12 2 Signature score was associated with improved PFS and OS Nonresponder Q1 defined as -0.2365. IFN = interferon; OS = overall survival; PFS = progression-free survival. 1. Chow LQ et al. J Clin Oncol. 2016;34(15;suppl): abstract 6010. 44 For Internal Use Only

Slide 20 Presented By Drew Pardoll at 2015 ASCO Annual Meeting

T cell inflamed phenotype (TCIP) defined by a 12 gene chemokine signature (CCL2, CLL3, CLL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11 and CXCL13) was evaluated in a cohort of 134 HNSCC from the University of Chicago and 424 HNSCC samples from TCGA The presence of the TCIP was associated with infiltration of CD8+ cells in a subset of HNSCCs; 21% of HPV negative tumors were TCIP high and 51% of HPV positive tumors were TCIP high. The TCIP high phenotype was associated with mesenchymal subtype and higher prevalence of PD L1 expression, suggesting that this phenotype could represent the sensitivity to anti PD1/PD L1 therapies.

Inflamed phenotype correlates with Immune escape mechanisms Presented by T. Seiwert ESMO 2016

Conclusion Immunotherapy is active in SCCHN Identification of pts who will more likely benefit from ICI Most research is focused in TME Intensive research in this field; PD L1, PD L2, IFN γ gene signature and others have been proposed p The role of peripheral blood?? Lot of suppressive Treg, MDSC subpopulations have been suggested as potential biomarkers for response

Thank you