Mejia-Garcia, A. | Search bioRxiv

bioRxiv2025

Breast Cancer Clustering Integrating Complete Gene Expression Profiles and Genetic Ancestry

Breast cancer (BC) remains the leading cause of cancer-related mortality among women globally. Precise subtyping of BC is critical for optimizing treatment strategies. This study explored the capacity of bulk RNA- seq data to improve breast cancer characterization by analysis of complete expression profiles. We analyzed RNA-seq 274 tumor samples and six healthy tissue samples from diverse geographical origins. Using over 9,800 SNPs directly genotyped from RNA-seq data, we successfully predicted broad genetic ancestry, identifying European, African, Asian, South Asian, and Admixed American origins. Molecular subtyping through PAM50 showed ambiguous classifications for about half of the samples, underscoring the limitations of current molecular diagnostic tools. Unsupervised clustering separated tumors in three main clusters. Cluster C1 demonstrated immune activation and inflammatory response pathways, while C2 highlighted metabolic and immune interaction processes. Cluster C3 exhibited enriched metabolic regulation and adipokine signaling pathways. In silico drug sensitivity analysis identified potential therapeutic strategies, including vinorelbine and AZD6482, with cluster-specific efficacy. Our findings emphasize the integration of ancestry-informed data and complete transcriptomic profiles to redefine BC subtyping. These insights offer a foundation for more equitable, ancestry-informed therapeutic strategies and highlight the importance of diversity in cancer research.

cancer biology↗

bioRxiv2025

Using the ancestral recombination graph to study the history of rare variants in founder populations

Gene genealogies represent the ancestry of a sample and are often encoded as ancestral recombination graphs (ARG). It has recently become possible to infer these gene genealogies from sequencing or genotyping data and use them for evolutionary and statistical genetics. Unfortunately, inferred gene genealogies can be noisy and subject to biases, making their applications more challenging. This project aims to study the application of ARG methods to systematically impute and trace the transmission of all disease variants in founder populations where long-shared haplotypes allow for accurate timing of relatedness. We applied these methods to the population of Quebec, where multiple founder events led to an uneven distribution of pathogenic variants across regions and where extensive population pedigrees are available. We validated our approach with nine founder mutations for the SLSJ region, demonstrating high accuracy for mutation age, imputation, and regional frequency estimation. Moreover, we showed that this subset of high-quality carriers is sufficient to capture previously described associations with pathogenic variants in the LPL gene. This method systematically characterizes rare variants in founder populations, establishing a fast and accurate approach to inform genetic screening programs.

genetics↗