Brorson, I. S. | bioRxiv Science

bioRxiv2025

CLONAL SELECTION SUPPORTED BY SINGLE CELL DNA SEQUENCING REVEALS HORMONAL ADAPTATION AND RESISTANCE IN LOCALLY ADVANCED BREAST CANCER DURING NEOADJUVANT AROMATASE INHIBITION

BackgroundThe aromatase inhibitors (AI) letrozole and exemestane, are often used in sequence in targeting ER+ breast cancers. However resistance to AI poses a major barrier to sustained clinical benefit, while the biological mechanisms underlying the phenomenon remain largely unknown. In this study, we build on our clinical NeoLetExe trial, with the aim to investigate the molecular basis of resistance to AI, by analysing subclonal evolutionary dynamics during sequential treatment. MethodsWe use whole-exome sequencing (WES) data from 11 ER+ breast cancer patients and 3 timepoints of the Neoletexe trial to reconstruct cancer cell fraction-based subclonal composition. Single-cell DNA sequencing from matched tumour samples is used for validating identified clones and variants. Subclonal variants were annotated to genes by integrating evidence from public data and ExpectoSc. Pathway enrichment analysis using Human Base was conducted. ResultsHigher cancer cell fraction clone trajectories were significantly associated with reduced treatment response (p = 0.023). Clones reconstructed by WES were validated at 81% using single-cell DNA sequencing. Clones resistant to both letrozole and exemestane demonstrated PIK3CA/AKT/mTOR signaling activation, KRAS pathway dysregulation, hedgehog signaling, and androgen receptor pathways, alongside extensive immune activation and metabolic reprogramming. Drug-specific resistance patterns showed exemestane-resistant clones enriched for epigenetic control and miRNA-mediated silencing, while letrozole-resistant clones displayed metabolic dysregulation but notably lacked immune pathway activation. In contrast, treatment-sensitive clones maintained coordinated cell cycle control, preserved DNA damage responses, and retained immune signaling capacity. Analysis of FDA-approved breast cancer targets identified actionable alterations in PIK3CA (4 patients) and AKT1 (1 patient) that persisted through AI treatment, with RNA expression analysis revealing 48 additional therapeutic targets spanning PI3K/AKT/mTOR, CDK4/6, DNA repair (BRCA1/2, ATM), and immune checkpoint pathways. ConclusionWES-based cancer cell fraction analysis successfully captured subclonal evolutionary trajectories during AI treatment, revealing drug-specific mechanisms and identifying key molecular players in endocrine therapy resistance. This work establishes a framework for precision oncology approaches by providing actionable therapeutic targets and advancing our understanding of resistance mechanisms to improve clinical outcomes in sequential AI therapy.

cancer biology↗

bioRxiv2025

HiFIseek: gene-specific enrichment of high-impact mutations in associated genomic regions

Transcription is regulated through the sequence-specific binding of transcription factors (TFs) to cis-regulatory regions (CRRs). Although scattered along the genome, multiple CRRs are brought in close spatial proximity to the genes they regulate through the formation of DNA loops. The sequence component of transcriptional regulation suggests that DNA variants in CRRs could disrupt TF-DNA interactions and gene regulatory networks through a cascading effect. To date, only a few cases of recurrent cis-regulatory variants have been described. As an alternative to variant recurrence, some methods utilize genomic annotations that indicate the functional impact (FI) of a variant and the CRRs of each gene to detect the enrichment of high-impact cis-regulatory variants (CRVs). However, the agreement across the different associations and FI scoring methods remains unexplored. This work demonstrates that gene-CRR and FI scoring methods exhibit little consensus, highlighting the impact of the choice of a specific CRR-score combination in a given analysis. In addition, we demonstrate that cancer genes exhibit a higher frequency of FI variants compared to non-cancer genes. Based on this, we developed HiFIseek, a Snakemake pipeline exploring the enrichment of FI variants in associated genomic regions using all region-score combinations and two enrichment detection methods. We apply HiFIseek to detect genes exhibiting high FI CRVs in eight cancer cohorts and a set of breast cancer risk-associated single-nucleotide polymorphisms (SNPs). In the cancer cohorts, we observe a small degree of agreement across CRR-score combinations. Although HiFIseek detected known cancer-related genes with FI CRVs, most of them did not show significant changes in their expression. In the SNPs cohort, HiFIseek found small consensus across CRR-score combinations. However, one of the combinations returned 18 known cancer-related genes, including BRCA1, showing an enrichment of high-impact CRVs.

bioinformatics↗