Abstract

Abstract In the era of precision medicine, performing comparative analysis over diverse patient populations is a fundamental step toward tailoring healthcare interventions. However, the critical aspect of equitably selecting molecular features across multiple patients is often overlooked. To address this challenge, we introduce FALAFL (FAir muLti-sAmple Feature seLection), a novel algorithmic approach based on integer linear programming. FALAFL is designed to bridge the gap between molecular feature selection and algorithmic fairness, ensuring a balanced selection of molecular features across different patient samples. We have applied FALAFL to the problem of selecting lineage-informative CpG sites within a cohort of 9 colorectal cancer patients subjected to low-coverage single-cell methylation sequencing. Our results demonstrate that FALAFL excels at rapidly determining the optimal set of CpG sites, which are well covered by cells across the vast majority of the patients, while ensuring that each patient contributes a similar number of sites to the final selection. An analysis of the FALAFL-selected CpG sites reveals that their lineage informativeness exhibits a strong association across diverse patient profiles, suggesting the non-stochasticity and universality of methylation changes in tumor evolution. Furthermore, these universally lineage-informative sites exhibit subclonal level alterations in the colorectal cancer primary tumors and are strongly associated with lineage-specific gene expression patterns. By integrating equity considerations into the molecular feature selection process, FALAFL is poised to propel equitable healthcare data science practices and advance our understanding of cancer and other complex diseases. Citation Format: Xuan Cindy Li, Yuelin Liu, Alejandro A. Schäffer, Stephen M. Mount, Süleyman C. Sahinalp. Balanced molecular feature selection unveils universally tumor-lineage informative methylation sites in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1615.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call