Tumor Mutations Across Racial Groups in a Real-World Data Registry

Abstract

The use of race in medicine has recently come to a reckoning, 1,2 and the field of cancer genomics is not exempt from this critical introspection. Race must be understood as a social construct in scientific pursuits, yet equitable genomics research requires adequate racial representation and diversity. 3 To overcome this challenge, large registries of real-world genetic data have been assembled to accelerate progress and discovery. These registries have been used previously to describe novel associations of genetic mutations with race in various tumors. 4,5 However, caution must be exercised when interpreting discoveries on the basis of retrospective tumor molecular genotyping given the multiple caveats surrounding sample acquisition and the definition of race itself in these registries. Significant differences found between races may represent confounded associations that are ultimately spurious. In addition, most registries largely over-represent White patients. 6 Thus, we sought to update the aforementioned analyses 4,5 with a larger set of tumor types by race to assess validity and robustness of previous findings and enable further biomarker discovery. Using data extracted from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE), version 8.1, 7 we analyzed tumor mutational profiles from the top five leading causes of new cancer cases or deaths in the United States for 2021 (breast, colorectal, lung, pancreas, and prostate) 8 across three reported races (Asian, White, and Black) to detect differences in mutational frequencies or distributions between racial groups. Analysis was limited to tumors sequenced by Memorial Sloan Kettering (MSK) and Dana-Farber Cancer Institute (DFCI), as most evaluated tumor types in the GENIEv8.1 registry (66%) are captured by MSK/DFCI sequencing initiatives (Data Supplement).

Analysis of evolutionary dynamics and clonal architecture in prostate cancer

“…These disparities are informed by social and structural determinants of equity and health, which impact a variety of socioeconomic, environmental, and health-related factors that influence prostate cancer biology and outcomes (15,16) . Due to the paucity of tumors from patients who self-report as Black in the majority of published genomic studies of PC, as well as limited information regarding the myriad of social factors that confound associations between race and outcome, the relationship between tumor genetics and inequities in clinical outcomes for Black patients is poorly understood (17,18) . To determine if the previously reported association between prostate cancer tumor polyclonality and clinical outcome extends to patients who self-report as Black, we performed clonal architecture analysis on the 560 primary PCs in our cohort, including 112 tumors from Black men (Supp.…”

Racial Disparity in Utilizing Genetic Testing for Personalized Care of Prostate Cancer

“…Analyzing and comparing the percentage of patients in each study, we noticed that the Ledet et al 64 67 Kamran et al 69 Table 3. The prevalence of TP53, PTEN and TMPRSS2-ERG mutations in AAM and EAM with metastatic PCa.…”

“…The most strikingly consistent dissimilarities may be the lower frequencies of rearrangements and PTEN mutations 67 67 study. Even though various studies analyzing these genetic aberrations in metastatic PCa have yielded agreeable findings where AAM has lower mutational frequencies in these genes compared to their EAM counterparts [64][65][66][67][68][69] , more studies are needed to confirm the racial differences in mutational frequencies in these genes.…”

An Approach to Solving the Complex Clinicogenomic Data Landscape in Precision Oncology: Learnings From the Design of WAYFIND-R, a Global Precision Oncology Registry

“…Furthermore, GENIE has an overwhelming majority of White participants, thus limiting interracial comparisons of genomic differences and, therefore, efforts to reduce disparities in cancer health outcomes between these populations. [35][36][37] However, overall, data in registries and other sources of RWD, although plentiful, are frequently nonstandardized, incomplete, nonaccessible, and siloed, which limits linkage and pooling between data sets and their consequent usefulness in answering particular scientific questions. 8 Measures and processes to ensure data quality or relevancy are often missing.…”