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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URL: https://www.ncbi.nlm.nih.gov/pubmed/36351074
DOI: 10.1158/0008-5472.CAN-22-0682

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 > genomics and proteomics > annotation
bioinformatics
diseases & disorders > cancer
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification
diseases & disorders
bioinformatics > genomics and proteomics > annotation > gene expression profiling annotation
bioinformatics > genomics and proteomics > genetics & nucleic acid processing
bioinformatics > genomics and proteomics
diseases & disorders > neoplasms
diseases & disorders > cancer > drugs and therapies
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function
diseases & disorders > cancer > drugs and therapies > patient outcomes
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > single nucleotide polymorphism
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > transcriptomes
CSHL Authors:
Communities: CSHL Cancer Center Program
CSHL Cancer Center Shared Resources > Animal Services
CSHL labs > Krasnitz lab
CSHL labs > Tuveson lab
CSHL Cancer Center Program > Cancer Genetics
CSHL Cancer Center Program > Cancer Genetics and Genomics Program
CSHL Cancer Center Shared Resources > Sequencing Technology & Analysis Service
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 13 December 2022
Date Deposited: 14 Nov 2022 18:05
Last Modified: 09 Feb 2024 13:57
URI: https://repository.cshl.edu/id/eprint/40756

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