Genetic differences in colorectal cancer across race and ethnicity.

Abstract

28 Background: Colorectal cancer (CRC) is the fourth most common cause of cancer death and cancer-related mortality is expected to increase exponentially. Ethnic minorities, in particular African Americans, have increased mortality from CRC. Although multifactorial, differences in somatic gene mutations could contribute to these racial discrepancies. Mutations in the critical oncogene KRAS are associated with worse prognosis in CRC. Recent literature suggests that in CRC patients, KRAS mutations are more frequently found in African Americans compared to Caucasians. This finding led to our hypothesis that molecular alterations across multiple races/ethnicities could explain differences in patient outcomes. This project will also inform a larger analysis using artificial intelligence learning models to predict race from tumor-specific genetic variants, as a tool to identify genetic drivers of race-specific patient outcome. Methods: We examined the molecular alterations in 304 patients diagnosed with CRC who had genetic testing between June 2021 and April 2023 at NYP Queens, NYP Brooklyn, and NYP Cornell. Race and ethnicity, genetic mutations, mortality, tumor location, and age, stage, and the presence of metastasis at diagnosis was collected. Patients self-identified as Non-Hispanic Black (NHB), White (NHW), Asian (NHA), American Indian (NHAI), or Hispanic, Other, or Declined. Median and interquartile range were used to summarize continuous variables, and frequency and proportion were used to summarize categorical variables. The significance of difference across ethnicity groups was tested using Kruskal-Wallis rank sum test for continuous variables and Fisher’s exact test for categorical variables. Mutational correlates of race using a multiple-instance learning artificial intelligence approach (i.e. Anaya et al., Nature Biomedical Engineering, 2023), will be presented. This method will predict race from patients' tumor-specific genetic variants, allowing us to identify specific variants and patterns predictive of race, and potential drivers of patient outcome. Results: Amongst the 304 CRC patients, we identified 116 NHW, 51 NHA, 40 NHB, 35 Hispanic and 1 NHAI, for a total population of 243. BRAF mutations were almost absent in NHB (2.5%) and NHA (3.9%), compared to NHW (13.8%; p=0.02). KRAS mutations were also more prevalent in underserved populations; 65.7% Hispanic, 57.5% NHB, compared with 44.8% NHW (p=0.14). We further identified substantial differences in KRAS mutations when dividing the Asian population into East Asian (62.9%) and South Asian (37.5%). Conclusions: We identified significant differences in key molecular drivers between ethnicities. Our findings suggest that genomic and tumor specific differences across ethnicities could in part explain differences in patient survival across these groups. Mutational correlates of race and survival using natural language processing will be presented.