Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms

Author:

Belleau Pascal12ORCID,Deschênes Astrid23ORCID,Chambwe Nyasha4ORCID,Tuveson David A.23ORCID,Krasnitz Alexander12ORCID

Affiliation:

1. 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York.

2. 2Cancer Center, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York.

3. 3Lustgarten Foundation Pancreatic Cancer Research Laboratory, Cold Spring Harbor, New York.

4. 4Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York.

Abstract

Abstract Genetic ancestry–oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole-exome sequencing, transcriptome sequencing, and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examined the feasibility and accuracy of computational inference of genetic ancestry relying exclusively on cancer-derived data. A data synthesis framework was developed to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. The data synthesis framework is applicable to multiple profiling platforms, making it possible to assess the performance of inference specifically for a given molecular profile and separately for each continental-level ancestry; this ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. The inference procedure was demonstrated to be accurate and robust in a wide range of sequencing depths. Testing of the approach in four representative cancer types and across three molecular profiling modalities showed that continental-level ancestry of patients can be inferred with high accuracy, as quantified by its agreement with the gold standard of deriving ancestry from matching cancer-free molecular data. This study demonstrates that vast amounts of existing cancer-derived molecular data are potentially amenable to ancestry-oriented studies of the disease without requiring matching cancer-free genomes or patient self-reported ancestry. Significance: The development of a computational approach that enables accurate and robust ancestry inference from cancer-derived molecular profiles without matching cancer-free data provides a valuable methodology for genetic ancestry–oriented cancer research.

Funder

New York Genome Center

Simons Foundation

Lustgarten Foundation

National Institutes of Health

Pershing Square Foundation

William Ackman

Neri Oxman

Publisher

American Association for Cancer Research (AACR)

Subject

Cancer Research,Oncology

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