Grant Details
Grant Number: |
1P01CA275746-01A1 Interpret this number |
Primary Investigator: |
Sawyers, Charles |
Organization: |
Sloan-Kettering Inst Can Research |
Project Title: |
Leveraging Observational (Real World) Data to Advance Precision Oncology |
Fiscal Year: |
2024 |
Abstract
OVERALL ABSTRACT
Leveraging Observational (Real World) Data to Advance Precision Oncology
Principal Investigator: Charles Sawyers, Memorial Sloan Kettering Cancer Center
Precision oncology is a firmly established pillar in the practice of cancer medicine, but we now recognize new
challenges to its broad implementation. These include: (i) heterogeneity in response to precision oncology
drugs in patients with identical driver mutations, (ii) differences in driver mutation frequencies in patients from
different ethnicities, with implications for ensuring optimal treatment, (iii) increased subtyping of cancer into
hundreds of “rare” diseases, and the resultant operational challenges in clinical trial design and execution, and
(iv) a limited understanding of how to effectively leverage observational (real world) data to address these
challenges.
The investigators in this P01 Program have had a longstanding collaboration over the past 8 years, helping to
build a large (>148,000 patients), international clinico-genomic cancer registry known as AACR Project GENIE.
We have come together to investigate these issues and propose four highly integrated projects related to these
themes. Project 1 seeks to overcome methodologic barriers in the analysis of observational clinico-genomic
data. Project 2 will address the role of genetic ancestry in precision oncology outcomes and potential inequities
in how precision oncology diagnostic tests are developed. Project 3 will use real world evidence to inform clinical
decisions for the treatment of cancer patients that cannot be addressed using conventional clinical trial datasets
and will optimize the reporting of these findings using the OncoKB knowledge base. The projects will be
supported by the Curation and Statistical Analysis Core (data abstraction and biostatistics support); the
Molecular Pathology and Bioinformatics Core (molecular profiling and data capture); and the Administrative
Core (incorporating existing GENIE infrastructure for data sharing, communications, and administrative support).
Our proposal is highly synergistic as it brings together a multi-institutional team of distinguished investigators in
population science, population genetics, cancer genomics and experimental therapeutics, with a substantial
track record of collaborative interactions, who will work together to address these important topics in precision
oncology.
Publications
None