ics of lung cancer Utilization of disease registries in the precision world Ravi Salgia, MD, PhD
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1 ics of lung cancer Utilization of disease registries in the precision world Ravi Salgia, MD, PhD Professor and Chair, Medical Oncology and Therapeutics Research City of Hope 04/04/2018
2 Objectives Lung Cancer Overview Heterogeneity Macro- and Micro-Heterogeneity Spatial- and Temporal-Heterogeneity Registry of Hope City of Hope Thoracic Oncology Registry (THOR) THOR and Data Analysis
3 Siegel et. al. Cancer Statistics, 2018 Lung Cancer Severity and Impact
4 NSCLC 85% of all cases of lung cancer Adenocarcinoma ~40% Squamous cell carcinoma ~25-30% Large cell carcinoma ~10-15% 5-year relative survival rates Stage I: ~66-82% Stage II: ~47-52% Stage III: ~19-36% Stage IV: ~6% Markers of NSCLC subtypes TTF-1 Napsin A CK7 p63 CK5/6 Grows more slowly Surgery possible in 35% of patients <40% chemotherapy response rate Chemotherapy indicated in Select Patients Targeted and immunotherapy available MORE AND MORE THERAPEUTIC OPTIONS TO BECOME AVAILABLE SCLC 15% of all cases of lung cancer Small cell carcinoma >90% Combined small cell carcinoma <10% Variant <5% 5-year relative survival rates Stage I: ~31% Stage II: ~19% Stage III: ~8% Stage IV: ~2% Markers of Neuroendocrine Differentiation Chromogrannin A Synaptophysin Leu-7 Bombesin or Gastrin Releasing Peptide Fast growing and aggresive Surgery possible in <10% of patients >80% chemotherapy response rate Chemotherapy indicated in All Patients Treatment limited to Platinum chemo and radiation MORE WORK NEEDS TO BE DONE IN TERMS OF BIOLOGY AND THERAPEUTICS
5 Case Example of Various Heterogeneities Temporal Spatial Macro Micro Mambetsariev et. int. Salgia, BMC Cancer, 2018
6 Salgia Expert Rev Mol Diagn Macro-Heterogeneity
7 Micro-Heterogeneity in lung cancer Hensing, Mambetsariev, Salgia; 2017
8 Micro-Heterogeneity in lung cancer Hensing, Mambetsariev, Salgia; 2017
9 City of Hope: Registry Conception Identified a need for clinical data collection and translation research integration Assessed the importance of inter- and intra-disease team communication and collaboration (both clinical and research) Incorporated the previous lessons with databases to build the Thoracic Oncology Registry (THOR) Observed innumerable examples of macro- and micro-heterogeneity; spatial- and temporalheterogeneity Addressed the need for disease team focused registries by implementing the Registry of Hope platform Mambetsariev et int. Salgia, 2018
10 City of Hope Experience: THOR SOP 300 page-document detailing the procedures for the consenting process, enrollment of patients, and data abstraction of clinical information into THOR. Mambetsariev et int. Salgia, 2018
11 THOR Infrastructure Creation and Development Process Pa#ent Registry Template: -Curated various literature and publicly available databases for data dicaonary templates Personnel: Isa Mambetsariev Tarrah Kirkpatrick THOR Data Dic#onary Crea#on: -Combined various databases, guidelines, and medical terminology in one data dicaonary Personnel: Isa Mambetsariev Rebecca Pharaon Blake Hewelt Vijay Nair Tarrah Kirkpatrick Data Dic#onary Upload: IniAal REDcap upload for immediate data abstracaon THOR Data Dic#onary Modifica#on: -Modified the Data DicAonary based on individual City of Hope modificaaons and preferences Personnel: Isa Mambetsariev Rebecca Pharaon Blake Hewelt Tarrah Kirkpatrick Data Dic#onary Update: THOR data dicaonary updated and perfected alongside REDcap database Medical Oncology: Ravi Salgia Karen Reckamp Marianna Koczywas Erminia Massarelli Radiology: Lalit Vora Radia#on Oncology: Sagus Sampath Pathology: Peiguo Chu Surgery: Jae Kim Dan Raz LoreSa Erhunmwunsee Data Transfer: Transfer data abstracted in REDcap to the Registry of Hope plavorm Data Dic#onary Upload: THOR data dicaonary uploaded to the Registry of Hope plavorm Omics: Salgia TranslaAonal Laboratory Mambetsariev et int. Salgia, 2018 Registry of Hope PlaAorm Developed: Website and registry database created with Center for InformaAcs. Personnel: Sorena Nadaf Vijay Nair Registry of Hope beyond THOR: UAlize lessons from THOR to help establish other disease teams Women s Cancer: Susan Yost Head and Neck: Rebecca Pharaon Genomics script: Yingyu Wang
12 Registry of Hope Multi-Disciplinary integration 10 Disease Teams Disease Team focused and owned Data abstraction: Automatically from AllScripts and EPIC EMRs Genomic data abstracted using python scripts from Foundation Medicine, Guardant 360, CARIS, etc. Automatic Abstraction and Manual Validation from CoPath SQL database Manual Abstraction of nondiscreet data Mambetsariev et int. Nadaf, Salgia, 2018
13 Registry of Hope Current Build Registry of Hope Advantages: Greater accessibility and customization Integration directly with EPIC EMR genetic tissue and blood markers Future integration with Translational Laboratory and Biorepository
14 Registry of Hope Data Collection Mambetsariev et int. Salgia, 2018
15 THOR Patients Pilot Project Evaluated 350 lung adenocarcinoma patients from the Thoracic Oncology Registry Analyzed the detailed clinical information as well as the comprehensive genomic data available from commercial tests Showcase the insight provided into realworld data by a comprehensive disease team registry
16 Thoracic Oncology Registry (THOR) Profile of Adenocarcinomas Total EGFR KRAS ALK MET BRAF ROS1 RET NTRK % (#) % (#) % (#) % (#) % (#) % (#) % (#) % (#) % (#) N = Sex Male 41% (142) 36% (64) 42% (23) 27% (8) 61% (11) 41% (7) 50% (4) 33% (4) 25% (3) Female 59% (208) 64% (114) 58% (32) 73% (22) 39% (7) 59% (10) 50% (4) 67% (8) 75% (9) Age Mean Race Caucasian 57% (198) 44% (78) 78% (43) 47% (14) 61% (11) 76% (13) 62% (5) 67% (8) 67% (8) African American 2% (8) 2% (4) 5% (3) 3% (1) 0% 6% (1) 0% 0% 0% Asian 34% (119) 48% (86) 9% (5) 30% (9) 39% (7) 12% (2) 38% (3) 16.5% (2) 16.5% (2) Native Hawaiian 0.5% (1) 0% 2% (1) 0% 0% 0% 0% 0% 0% American Indian 0.5% (1) 0% 2% (1) 0% 0% 0% 0% 0% 0% Other/Unknown 7% (23) 6% (10) 4% (2) 20% (6) 0% 6% (1) 0% 16.5% (2) 16.5% (2) Smoking Status Smoker 47% (166) 31% (56) 84% (46) 40% (12) 61% (11) 59% (10) 12% (1) 42% (5) 67% (8) Non-Smoker 52% (182) 69% (122) 16% (9) 60% (18) 39% (7) 41% (7) 88% (7) 58% (7) 33% (4) Unknown 1% (2) 0% 0% 0% 0% 0% 0% 0% 0% Stage of Disease Stage I 5% (18) 4% (8) 11% (6) 3% (1) 11% (2) 12% (2) 12% (1) 0% 0% Stage II 3% (11) 2% (3) 5.5% (3) 0% 6% (1) 6% (1) 0% 0% 8% (1) Stage III 6% (20) 3% (5) 5.5% (3) 3% (1) 6% (1) 12% (2) 0% 0% 8% (1) Stage IV 86% (300) 91% (162) 78% (43) 94% (28) 77% (14) 70% (12) 88% (7) 100% (12) 84% (10) Unknown 0.5% (1) 0% 0% 0% 0% 0% 0% 0% 0% Mambetsariev et int. Salgia. 2018
17 THOR Genomic Characteristics Mambetsariev et int. Salgia. 2018
18 THOR Genomic Characteristics Mambetsariev et int. Salgia. 2018
19 Mambetsariev et int. Salgia. 2018
20 THOR Age Distribution Mambetsariev et int. Salgia. 2018
21 THOR Metastatic Sites Mambetsariev et int. Salgia. 2018
22 THOR Survival by Actionability Mambetsariev et int. Salgia. 2018
23 THOR Survival by Driver Mambetsariev et int. Salgia. 2018
24 Understanding ALK Heterogeneity within THOR Extracted ALK mutated lung adenocarcinoma patients from the Thoracic Oncology Registry Performed comprehensive radiogenomic analysis of patients tumor profile and radiological signature of primary tumor as well as metastatic sites Performed innovative 3D volumetric analysis of metastatic sites
25 Receptor Tyrosine Kinases Blume-Jensen, Nature 2001
26 ALK NSCLC Patients Metastatic Sites Gupta et int. Salgia. 2018
27 ALK NSCLC Patients Metastatic Sites Physiological landscape of unusual metastaac sites Peritoneal Ovarian Omental Spleen PancreaAc Thyroid Epidural Kidney Chest wall Skin Abdominal wall Tracheal Mesenteric Leptomeningeal Pericardial So_ Assue Unusual Sites of Metastasis Number of paaents (total n=24) Gupta et int. Salgia. 2018
28 Mutations associated with ALK+ NSCLC based on NGS sequencing Gupta et int. Salgia. 2018
29 Mutations associated with uncommon metastatic sites Gupta et int. Salgia. 2018
30 Examples of 3D volumetric images of unusual sites of metastasis PaAent with adnexal metastasis PaAent with abdominal metastasis Rahmanuddin/Gupta et int. Salgia PaAent with renal and abdominal metastasis
31 ALK Survival by Metastatic status Gupta et int. Salgia. 2018
32 ALK rearranged NSCLC SeIng Drug Genera#on FDA approval EMA approval Key trials First line AlecAnib Second ü awaited J-ALEX/ALEX First line CrizoAnib First ü ü PROFILE 1014 First line CeriAnib Second ü awaited ASCEND 1,3,4 Post crizoanib CeriAnib Second ü ü ASCEND 1,2,5 Post crizoanib BrigaAnib Second ü awaited ALTA Post crizoanib AlecAnib Second ü awaited Phase 2 NA, Intl Post chemo CrizoAnib First ü ü PROFILE 1005,1007
33 FDA-approved and promising targeted therapies Mayekar et al. Clinical Pharmacology & Therapeutics, 2017
34 Conclusion Registries allow for real-world insight into macroand micro-heterogeneity Registries can accelerate translational research and multiple biopsies could help track tumor evolution over time (spatial and temporal) Registry of Hope will continue to grow for individual disease teams Lung Cancer is an exciting area to pursue understanding diagnostics, prognostics/ predictive biomarkers, therapeutic value, resistance mechanisms ultimately heterogeneity of the disease
35 Steve Rosen, MD
Case Studies. Ravi Salgia, MD, PhD
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