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• W4286295540 abstract "e13588 Background: There is ample epidemiological evidence that, for multiple cancer types, incidence, stage at presentation and outcome correlate strongly with the patient’s race or ethnicity. Recent examination of cancer data repositories such as TCGA has revealed has revealed, for a number of cancers, ancestry dependence of somatic mutation spectrum and transcriptional phenotype. Further progress in genetic ancestry-oriented cancer research requires the ability to perform accurate and robust ancestry inference from existing cancer-derived data, including whole exomes, transcriptomes and targeted gene panels, very often in the absence of matching cancer-free genomic data. Such inference is potentially challenging, given widespread somatic alterations in tumor genomes, observed in multiple types of cancer. Methods: In order to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile, we develop a data synthesis framework. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. We then infer the substitute individual’s ancestry, using well-established methods of population genetics and relying on the 1000 Genomes collection as a population reference data set. Data synthesis is applicable to multiple profiling platforms and makes 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. Results: We test our approach for three representative cancer types: pancreatic and ovarian cancers as representative of epithelial tumor types, and acute myeloid leukemia as an example of hematopoietic malignancy. We consider tumor-derived data acquired by the whole-exome sequencing, by exome sequencing restricted to a panel of cancer-associated genes, and by RNA sequencing. For all three cancer types, and across the three sequencing platforms, we demonstrate that global, continental-level ancestry of the patient can be inferred accurately and robustly. Specifically, we find the inferred ancestries to be in at least 97% agreement with the golden standard of the ancestry derived from the matching cancer-free genomes. We further show that our inference procedure is accurate and robust in a wide range of sequencing depths. Conclusions: Our study demonstrates that vast amounts of existing cancer-derived molecular data are amenable to ancestry-oriented studies of the disease, without recourse to matching cancer-free genomes or patients' self-identification by ancestry. Furthermore, the procedure we developed facilitates ancestry-oriented research using archived tumor tissue specimens, for which matching cancer-free specimens may not be available." @default.
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• W4286295540 date "2022-06-01" @default.
• W4286295540 modified "2023-09-23" @default.
• W4286295540 title "Reliable determination of genetic ancestry from cancer-derived molecular data." @default.
• W4286295540 doi "https://doi.org/10.1200/jco.2022.40.16_suppl.e13588" @default.
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