A consensus-based classification workflow to determine genetically inferred ancestry from comprehensive genomic profiling of patients with solid tumors.

Autor:	Wallen ZD; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Nesline MK; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Pabla S; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Gao S; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Vanroey E; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Hastings SB; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Ko H; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Strickland KC; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States.; Department of Pathology, Duke University Medical Center, Duke Cancer Institute, 40 Duke Medicine Cir, Durham, NC 27710, United States., Previs RA; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States.; Department of Obstetrics & Gynecology, Duke University Medical Center, Duke Cancer Institute, 40 Duke Medicine Cir, Durham, NC 27710, United States., Zhang S; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Conroy JM; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., Jensen TJ; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States., George E; Labcorp, 531 South Spring Street, Burlington, NC 27215, United States., Eisenberg M; Labcorp, 531 South Spring Street, Burlington, NC 27215, United States., Caveney B; Labcorp, 531 South Spring Street, Burlington, NC 27215, United States., Sathyan P; Oncology Medical Affairs, Illumina Inc, 5200 Illumina Way, San Diego, CA 92122, United States., Ramkissoon S; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States.; Department of Pathology, Wake Forest Comprehensive Cancer Center, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27109, United States., Severson EA; Medical Oncology, Labcorp Oncology, 6 Moore Dr., Durham, NC 27560, United States.
Jazyk:	angličtina
Zdroj:	Briefings in bioinformatics [Brief Bioinform] 2024 Sep 23; Vol. 25 (6).
DOI:	10.1093/bib/bbae557
Abstrakt:	Disparities in cancer diagnosis, treatment, and outcomes based on self-identified race and ethnicity (SIRE) are well documented, yet these variables have historically been excluded from clinical research. Without SIRE, genetic ancestry can be inferred using single-nucleotide polymorphisms (SNPs) detected from tumor DNA using comprehensive genomic profiling (CGP). However, factors inherent to CGP of tumor DNA increase the difficulty of identifying ancestry-informative SNPs, and current workflows for inferring genetic ancestry from CGP need improvements in key areas of the ancestry inference process. This study used genomic data from 4274 diverse reference subjects and CGP data from 491 patients with solid tumors and SIRE to develop and validate a workflow to obtain accurate genetically inferred ancestry (GIA) from CGP sequencing results. We use consensus-based classification to derive confident ancestral inferences from an expanded reference dataset covering eight world populations (African, Admixed American, Central Asian/Siberian, European, East Asian, Middle Eastern, Oceania, South Asian). Our GIA calls were highly concordant with SIRE (95%) and aligned well with reference populations of inferred ancestries. Further, our workflow could expand on SIRE by (i) detecting the ancestry of patients that usually lack appropriate racial categories, (ii) determining what patients have mixed ancestry, and (iii) resolving ancestries of patients in heterogeneous racial categories and who had missing SIRE. Accurate GIA provides needed information to enable ancestry-aware biomarker research, ensure the inclusion of underrepresented groups in clinical research, and increase the diverse representation of patient populations eligible for precision medicine therapies and trials. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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