Ravi Salgia, MD, PhD Professor and Chair, Medical Oncology and - - PowerPoint PPT Presentation

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Ravi Salgia, MD, PhD Professor and Chair, Medical Oncology and - - PowerPoint PPT Presentation

Oct 09, 2022 243 likes •617 views

ics of lung cancerUtilization 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 Objectives Lung Cancer Overview Heterogeneity

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Professor and Chair, Medical Oncology and Therapeutics Research City of Hope 04/04/2018

ics of lung cancer—Utilization

  • f disease registries in the precision

world

Ravi Salgia, MD, PhD

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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
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Lung Cancer Severity and Impact

Siegel et. al. Cancer Statistics, 2018

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NSCLC

MORE AND MORE THERAPEUTIC OPTIONS TO BECOME AVAILABLE

5-year relative survival rates Stage I: ~66-82% Stage II: ~47-52% Stage III: ~19-36% Stage IV: ~6% 85% of all cases of lung cancer Adenocarcinoma ~40% Squamous cell carcinoma ~25-30% Large cell carcinoma ~10-15% Grows more slowly Markers of NSCLC subtypes TTF-1 Napsin A CK7 p63 CK5/6 Surgery possible in 35% of patients <40% chemotherapy response rate Chemotherapy indicated in Select Patients Targeted and immunotherapy available

SCLC

MORE WORK NEEDS TO BE DONE IN TERMS OF BIOLOGY AND THERAPEUTICS

5-year relative survival rates Stage I: ~31% Stage II: ~19% Stage III: ~8% Stage IV: ~2% 15% of all cases of lung cancer Small cell carcinoma >90% Combined small cell carcinoma <10% Variant <5% 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

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Case Example of Various Heterogeneities

Mambetsariev et. int. Salgia, BMC Cancer, 2018

Spatial Temporal Macro Micro

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Macro-Heterogeneity

Salgia Expert Rev Mol Diagn. 2016

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Micro-Heterogeneity in lung cancer

Hensing, Mambetsariev, Salgia; 2017

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Micro-Heterogeneity in lung cancer

Hensing, Mambetsariev, Salgia; 2017

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City of Hope: Registry Conception

Mambetsariev et int. Salgia, 2018

  • 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 temporal- heterogeneity

  • Addressed the need for disease team focused

registries by implementing the Registry of Hope platform

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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

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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 THOR Data Dic#onary Modifica#on:

  • Modified the Data DicAonary based on individual City
  • f Hope modificaAons and preferences

Personnel: Isa Mambetsariev Rebecca Pharaon Blake Hewelt Tarrah Kirkpatrick Radiology: Lalit Vora Radia#on Oncology: Sagus Sampath Pathology: Peiguo Chu Surgery: Jae Kim Dan Raz LoreSa Erhunmwunsee Medical Oncology: Ravi Salgia Karen Reckamp Marianna Koczywas Erminia Massarelli Data Dic#onary Upload: IniAal REDcap upload for immediate data abstracAon Registry of Hope PlaAorm Developed: Website and registry database created with Center for InformaAcs. Personnel: Sorena Nadaf Vijay Nair Data Transfer: Transfer data abstracted in REDcap to the Registry of Hope plaVorm Data Dic#onary Update: THOR data dicAonary updated and perfected alongside REDcap database Data Dic#onary Upload: THOR data dicAonary uploaded to the Registry of Hope plaVorm Omics: Salgia TranslaAonal Laboratory 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

Mambetsariev et int. Salgia, 2018

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Registry of Hope

  • Multi-Disciplinary integration
  • 10 Disease Teams
  • Disease Team focused and
  • wned
  • 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 non-

discreet data

Mambetsariev et int. Nadaf, Salgia, 2018

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Registry of Hope Current Build

Registry of Hope Advantages:

  • Greater accessibility and customization
  • Integration directly with EPIC EMR
  • 1700+ genetic tissue and blood markers
  • Future integration with Translational Laboratory and

Biorepository

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Registry of Hope Data Collection

Mambetsariev et int. Salgia, 2018

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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 real-

world data by a comprehensive disease team registry

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Thoracic Oncology Registry (THOR) Profile of Adenocarcinomas

Total EGFR KRAS ALK MET BRAF ROS1 RET NTRK % (#) % (#) % (#) % (#) % (#) % (#) % (#) % (#) % (#)

N =

350 178 55 30 18 17 8 12 12

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 61.8 61.1 66.2 49.8 65.9 64.6 57.9 58.0 52.7 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

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THOR Genomic Characteristics

Mambetsariev et int. Salgia. 2018

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THOR Genomic Characteristics

Mambetsariev et int. Salgia. 2018

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Mambetsariev et int. Salgia. 2018

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THOR Age Distribution

Mambetsariev et int. Salgia. 2018

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THOR Metastatic Sites

Mambetsariev et int. Salgia. 2018

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THOR Survival by Actionability

Mambetsariev et int. Salgia. 2018

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THOR Survival by Driver

Mambetsariev et int. Salgia. 2018

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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

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Receptor Tyrosine Kinases

Blume-Jensen, Nature 2001

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ALK NSCLC Patients Metastatic Sites

Gupta et int. Salgia. 2018

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ALK NSCLC Patients Metastatic Sites

1 2 3 4 5 6 7 8 So_ Assue Pericardial Leptomeningeal Mesenteric Tracheal Abdominal wall Skin Chest wall Kidney Epidural Thyroid PancreaAc Spleen Omental Ovarian Peritoneal

Unusual Sites of Metastasis

Physiological landscape of unusual metastaAc sites Number of paAents (total n=24)

Gupta et int. Salgia. 2018

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Mutations associated with ALK+ NSCLC based on NGS sequencing

Gupta et int. Salgia. 2018

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Mutations associated with uncommon metastatic sites

Gupta et int. Salgia. 2018

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Examples of 3D volumetric images of unusual sites of metastasis

PaAent with adnexal metastasis PaAent with abdominal metastasis PaAent with renal and abdominal metastasis Rahmanuddin/Gupta et int. Salgia. 2018

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ALK Survival by Metastatic status

Gupta et int. Salgia. 2018

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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

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FDA-approved and promising targeted therapies

Mayekar et al. Clinical Pharmacology & Therapeutics, 2017

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Conclusion

  • Registries allow for real-world insight into macro-

and 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

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Steve Rosen, MD