In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 1922-1922
Abstract:
Rapid advancements in genomic sequencing technologies have catalyzed the molecular identification and treatment of previously elusive diseases. Despite these genomic advancements, disparities in precision medicine access and data have resulted in disparate impacts on different socioeconomic groups. Many of the gold standard datasets largely exclude minority populations, compounding their exclusion from medical research. This has resulted in a restricted understanding of cancer and other complex diseases. We demonstrate the effects of ancestral bias in gold standard genomics datasets through ancestral analysis of cancer-related genes in the Cancer Gene Census, spanning 17 ancestral populations. Additionally, we present a machine learning framework, PhyloFrame, that incorporates population genomics data to correct for ancestral bias by creating disease signatures representative of all ancestries. Our ancestral analysis results show that while a majority of the current cancer-related genes in the Cancer Gene Census have below average mutation frequency in non-European populations, there are peaks of ancestrally enriched mutations in ancestry-specific genes related to cancer, which can be targeted using PhyloFrame. PhyloFrame prioritizes gene expression from the cancer genes with high frequency mutations in a given human population in order to capture genes driving disease in each ancestry. It builds on existing disease gene signatures and big-data functional interaction networks to identify ancestry-relevant genes related to a disease, outputing an ancestry-agnostic disease signature. We test PhyloFrame on TCGA cancers with diverse patient populations, such as breast cancer, and compare PhyloFrame's disease signature output to the disease signature output of elastic net runs on cancer samples from a single ancestry. Our results demonstrate that the incorporation of ancestral information allows PhyloFrame to recapitulate disease signatures trained on only one ancestry in a dataset with individuals from many unquantified ancestries. With the incorporation of ancestral information, PhyloFrame is able to create disease signatures with genes pertinent to each ancestral population, even when individuals from those populations are not included in the training data. This work offers a quick and cheap alternative to the mass sequencing that would be required to capture disease-driving genes in minority populations in hopes to contribute to equitable representation in medical research. Citation Format: Leslie A. Smith, James A. Cahill, Kiley Graim. PhyloFrame: A machine learning framework for ancestry agnostic disease signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1922.
Type of Medium:
Online Resource
ISSN:
1538-7445
DOI:
10.1158/1538-7445.AM2023-1922
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2023
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
