Genetic ancestry inference from cancer-derived molecular data across genomic and transcriptomic platforms

Belleau, Pascal, Deschênes, Astrid, Chambwe, Nyasha, Tuveson, David A, Krasnitz, Alexander (December 2022) Genetic ancestry inference from cancer-derived molecular data across genomic and transcriptomic platforms. Cancer Research. CAN-22. ISSN 0008-5472

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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.

Item Type:	Paper
Subjects:	bioinformatics diseases & disorders > cancer diseases & disorders > cancer > drugs and therapies > patient outcomes
CSHL Authors:	Belleau, Pascal Tuveson, David A. Krasnitz, Alexander Deschenes, Astrid
Communities:	CSHL labs > Krasnitz lab CSHL labs > Tuveson lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	13 December 2022
Date Deposited:	14 Nov 2022 18:05
Last Modified:	16 Dec 2022 20:11
URI:	https://repository.cshl.edu/id/eprint/40756

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