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Background: There are known disparities in incidence and outcomes of colorectal cancer (CRC) by race and ethnicity. Some of these disparities may be mediated by molecular changes in tumors that occur at different rates across populations. Genetic ancestry is a measure complementary to race and ethnicity that can overcome missing data issues and better capture genetic similarity in admixed populations. We aimed to identify somatic mutations and tumor gene expression differences associated with both genetic ancestry and imputed race and ethnicity. Methods: Sequencing was performed with the Tempus xT NGS 648-gene panel and whole exome capture RNA-Seq for 8454 primarily late-stage CRC patients. Genetic ancestry proportions for five continental groups—Africa (AFR), American indigenous (AMR), East Asia (EAS), Europe (EUR), and South Asia (SAS)—were estimated using ancestry informative markers. To address data gaps, race and ethnicity categories were imputed, resulting in assignments for 952 Hispanic/Latino, 420 non-Hispanic (NH) Asian, 1061 NH Black, and 5763 NH White individuals. We assessed association of genetic ancestry proportions and imputed race and ethnicity categories with somatic mutations in relevant CRC genes and in 2608 expression profiles, as well as 1957 consensus molecular subtypes (CMS). Results: Increased AFR ancestry was associated with higher odds of somatic mutations in APC, KRAS, and PIK3CA and lower odds of BRAF mutations. Additionally, increased EAS ancestry was associated with lower odds of mutations in KRAS, EUR with higher odds in BRAF, and the Hispanic/Latino category with lower odds in BRAF. Greater AFR ancestry and the NH Black category were associated with higher rates of CMS3, while a higher proportion of Hispanic/Latino patients exhibited indeterminate CMS classifications. Conclusions: Molecular differences in CRC tumor mutation frequencies and gene expression that may underlie observed differences by race and ethnicity were identified. The association of AFR ancestry with increased KRAS mutations aligns with higher CMS3 subtype rates in NH Black patients. The increase of indeterminate CMS in Hispanic/Latino patients suggests that subtype classification methods could benefit from enhanced patient diversity. [ABSTRACT FROM AUTHOR]

Summary RNA sequencing (RNA-seq) can be applied to diverse tasks including quantifying gene expression, discovering quantitative trait loci and identifying gene fusion events. Although RNA-seq can detect germline variants, the complexities of variable transcript abundance, target capture and amplification introduce challenging sources of error. Here, we extend DeepVariant, a deep-learning-based variant caller, to learn and account for the unique challenges presented by RNA-seq data. Our DeepVariant RNA-seq model produces highly accurate variant calls from RNA-sequencing data, and outperforms existing approaches such as Platypus and GATK. We examine factors that influence accuracy, how our model addresses RNA editing events and how additional thresholding can be used to facilitate our models' use in a production pipeline. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

The incompleteness of race and ethnicity information in real-world data (RWD) hampers its utility in promoting healthcare equity. This study introduces two methods-one heuristic and the other machine learning-based-to impute race and ethnicity from genetic ancestry using tumor profiling data. Analyzing de-identified data from over 100,000 cancer patients sequenced with the Tempus xT panel, we demonstrate that both methods outperform existing geolocation and surname-based methods, with the machine learning approach achieving high recall (range: 0.859-0.993) and precision (range: 0.932-0.981) across four mutually exclusive race and ethnicity categories. This work presents a novel pathway to enhance RWD utility in studying racial disparities in healthcare.