SAMBAI-Columbia

SAMBAI: Societal, Ancestry, Molecular and Biological Analyses of Inequalities Research Abstract Background Prostate, breast, and pancreatic cancers all have a disproportionately higher rate of aggressive tumor grade and early onset in Black patients, with recent spikes of high incidence in West African nations compared to other African regions. The genetic background correlations implicate predispositions. Members of our SAMBAI team of investigators have pioneered genomics in cancer disparities research, and over the past two decades, we have uncovered compelling evidence of distinct immunological mechanisms associated with genetic ancestry. Our SAMBAI team members have developed methods to quantify environmental exposures and interrogate the lived experiences of marginalized populations, including epigenetic responses to racism. Aims We will partner with scientists across the US, Africa, and the UK to build an unprecedented resource, the SAMBAI Biobank and Data Repository for Cancer Equity Research. We will generate a comprehensive, accurate, and relevant measurement of social, environmental, genetic, and immunological factors to complete an integrated set of analyses to define the causal vs. modifier relationships of disparate outcomes in diverse underserved populations. We will establish a sustainable framework for team science approaches with under-represented partners and establish best practices for coordinating cancer equity research on a global scale. Methods We propose to utilize multiple methods across our different work packages. Social Determinants include self-reporting surveys and database abstractions. Exposomes utilize mass spectrometry of plasma. Genomics will utilize three sequencing methods on germline and tumor tissue, including long read, short/deep, and ultra-low pass whole genome sequencing. Lastly, immunological profiles will be measured with spatial transcriptomics and circulating multiplex immunoassays. These data require novel computational frameworks, including cloud-based virtualization and the use of machine learning technologies to identify novel associations across the strata of social to spatial data elements and across our diverse geographic and ancestral SAMBAI cohorts. Utility and Impact We will improve research capacity in under-resourced environments for large-scale cancer research and equitable access to data with equitable feasibility to improve treatment and outcomes. We will define interactions of environmental exposures, social determinants, and genetic ancestry that determine immunological landscapes of primary tumors and/or circulating immunological profiles in patients of African descent. Our project will contribute a data repository with 100K features/patient, for 40,000 patients. The impact on this population includes a novel trial design, in collaboration with our patient advocacy partners, to ensure that the specific genomic and immunological features we uncover become part of targeted precision oncology theragnostic options.