Universit degli Studi di Bari Aldo Moro Dipartimento

Università degli Studi di Bari “Aldo Moro” Dipartimento di Scienze Biomediche e Oncologia Umana Unità di Oncologia Medica Direttore: Prof. Franco Silvestris TUMORI NEUROENDOCRINI: HIGHLIGHTS 2017 Mauro Cives Riunione nazionale COMU 2 -3 Febbraio 2018 Torino, Centro Congressi Torino Incontra

PRESENTATION OUTLINE 1. Epidemiology/classification of NETs a. Updated information on the incidence and prevalence of NETs b. New grading system for p. NETs (WHO 2017) c. New staging systems for p. NETs (m. ENETS) and SB NETs (AJCC 2017) 2. Biology of NETs a. Definition of the genomic landscape of p. NETs 3. Treatment of NETs a. Telotristat etiprate in the palliation of carcinoid syndrome b. PRRT for the treatment of SB NETs progressing on SSAs c. Immunotherapy?

THE INCIDENCE AND PREVALENCE OF NETs ARE STEADILY INCREASING THE INCIDENCE OF NETs IS RISING CARCINOID SYNDROME IS DIAGNOSED IN 19% OF PATIENTS WITH NEWLY DIAGNOSED NET Dasari A et al, JAMA Oncol 2017; Halperin DM et al, Lancet Oncol 2017

2017 WHO CLASSIFICATION OF p. NETs Grading Tumor Differentiation Mitotic index Ki-67 index NET G 1 Well differentiated <2/10 HPF <3% NET G 2 Well differentiated 2 -20/10 HPF 3 -20% NET G 3 Well differentiated >20/10 HPF >20% NEC G 3 Poorly differentiated >20/10 HPF >20% WELL DIFFERENTIATED TUMOR Genetic features Frequent mutations of: MEN 1, DAXX/ATRX, m. TOR pathway genes Frequent mutations of: RB 1, TP 53 POORLY DIFFERENTIATED TUMOR Lloyd RV et al. WHO/IARC Classification of tumours, 4 th Ed.

NEW STAGING SYSTEMS FOR p. NETs: THE m. ENETS CLASSIFICATION AJCC CLASSIFICATION ENETS CLASSIFICATION m. ENETS CLASSIFICATION Luo G et al, J Clin Oncol 2017

PRESENTATION OUTLINE 1. Epidemiology/classification of NETs a. Updated information on the incidence and prevalence of NETs b. New grading system for p. NETs (WHO 2017) c. New staging systems for p. NETs (m. ENETS) and SB NETs (AJCC 2017) 2. Biology of NETs a. Definition of the genomic landscape of p. NETs 3. Treatment of NETs a. Telotristat etiprate in the palliation of carcinoid syndrome b. PRRT for the treatment of SB NETs progressing on SSAs c. Immunotherapy?

SOMATIC DRIVER MUTATIONS IN PANCREATIC NETs Scarpa A et al, Nature 2017

THE GENOMIC LANDSCAPE OF PANCREATIC NETs Scarpa A et al, Nature 2017

PRESENTATION OUTLINE 1. Epidemiology/classification of NETs a. Updated information on the incidence and prevalence of NETs b. New grading system for p. NETs (WHO 2017) c. New staging systems for p. NETs (m. ENETS) and SB NETs (AJCC 2017) 2. Biology of NETs a. Definition of the genomic landscape of p. NETs 3. Treatment of NETs a. Telotristat etiprate for the palliation of carcinoid syndrome b. PRRT for the treatment of SB NETs progressing on SSAs c. Immunotherapy?

TELOTRISTAT FOR THE PALLIATION OF CARCINOID SYNDROME TELOTRISTAT ETIPRATE SIGNIFICANTLY REDUCES THE FREQUENCY OF BOWEL MOVEMENTS IN PATIENTS WITH CS TELOTRISTAT ETIPRATE INHIBITS THE PRODUCTION OF SEROTONIN Kulke M et al, J Clin Oncol 2017

PRRT IN PATIENTS WITH ADVANCED MIDGUT NETs PRRT SIGNIFICANTLY PROLONGS PFS AND OS IN PATIENTS WITH ADVANCED MIDGUT NETs THE SURVIVAL BENEFIT IS CONSISTENT ACROSS PRE-SPECIFIED SUBGROUPS Strosberg J et al, NEJM 2017

PD-L 1 EXPRESSION IN SB NETs BASELINE CHARACTERISTICS Age at diagnosis (years) Median Range Sex Male Female Carcinoid syndrome Yes No Tumor location Duodenum Jejunum Ileum Right colon Unknown Tumor size (cm) Median Range TNM stage (AJCC classification) I IIA IIB IIIA IIIB IV Unknown Tumor grade (WHO 2010) G 1 G 2 Follow-up (months) Median Range n of patients (n=102) IHC III % 60 27 -95 52 50 51 49 23 77 10 2 86 1 3 10 2 84 1 3 1. 9 0. 3 -8 2 5 4 0 33 55 3 2 5 4 0 32 54 3 94 8 92 8 61 1 -182 PD-L 1 cut-off positivity (clone 28 -8 Abcam): ≥ 50% of tumor cells ≥ 1% or ≥ 5% of tumor cells 40/102, 39% (95% CI, 30 -49%) 14/102, 14% (95% CI, 8 -22%) PD-L 1 positive Percentage of patients (%) Characteristics IHC II IHC I PD-L 1 negative 100% 80% 60% p<0. 0001 40% 20% 0% Duodenum Midgut

PROGNOSTIC ROLE OF PD-L 1 IN STAGE IV SB NETs p=0. 87 p=0. 82 p=0. 38 p=0. 96 PD-L 1≥ 50% cut-off CANCER-SPECIFIC SURVIVAL OVERALL SURVIVAL PD-L 1≥ 1% cut-off

IMMUNE INFILTRATION IN SB NETs Tumor Li= 0 Li= >50 Li= >100 Li=500 Significant association between PD-L 1 expression by tumor cells and immune infiltration density (p=0. 001) Percentage of patients (%) PD-L 1 positive PD-L 1 negative 100% 80% 60% 40% 20% 0% LLS-positive LLS-negative

PD-L 1 EXPRESSION AND LLS PRESENCE: ANY BIOLOGICAL SIGNIFICANCE? mi. RNA PROFILING UNSUPERVISED HIERARCHICAL ANALYSIS LYMPH NODE-LIKE STRUCTURES IN SI-NETs CD 27 Immune infiltration very low Immune infiltration very high

IMMUNE-RELATED mi. RNAs ARE OVEREXPRESSD IN LLS+ SI-NETs DIFFERENTIALLY EXPRESSED mi. RNA IN SI-NETs BASED ON LLS PRESENCE mi. R 181 a-3 p mi. R 376 c-5 p mi. R 499 a-5 p mi. R-577

IMMUNE MICROENVIRONMENT AND RESPONSE TO CANCER IMMUNOTHERAPY TYPE I TUMOR MICROENVIRONMENT IS THOUGHT TO BE THE GROUP RESPONDING BETTER TO IMMUNOTHERAPY THE IMMUNE MICROENVIRONMENT IN SB NETs PD-L 1 TIL+ PD-L 1+ TIL+ Tolerance 33% Intrinsic induction 7% PD-L 1+ TIL- Adaptive immune resistance 33% Immunological ignorance 27% PD-L 1 TIL- Teng MWL et al. Cancer Res 2015; Cives M et al. submitted

KEYNOTE-028: PEMBROLIZUMAB FOR PD-L 1+ ADVANCED NETs Courtesy of Mehnert JM et al, presented at ESMO 2017

ENGINEERING T CELLS TO TREAT CANCER Ectodomain v Antigen recognition v Usually an Ab single-chain variable fragment Endodomain v Intracellular signaling v Costimulatory domains (usually CD 28 and 4 -1 BB) v Stimulatory domain (usually the CD 3 zeta chain of the T-cell receptor) Output v T cell proliferation v Cytokine production v Tumor cell killing Maude SL et al. Blood 2015

ACKNOWLEDGEMENTS Franco Silvestris, MD Paola Cafforio, Ph. D Anna Passarelli, MD Ester D’Oronzo, MD Eleonora Pellè, MD Claudia Felici, Ph. D Davide Quaresmini, MD Dominga Lovero, Ph. D Stefania Stucci, MD Francesco Mannavola, MD Marco Tucci, MD, Ph. D Raffaele Palmirotta, MD