Biology subjects

Vaske, C.

Publications and source records attributed to Vaske, C..

3 recordsLinked to original sources

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↗

bioRxiv2021

An international report on bacterial communities in esophageal squamous cell carcinoma

The incidence of esophageal squamous cell carcinoma (ESCC) is disproportionately high in the eastern corridor of Africa and parts of Asia. Emerging research has identified a potential association between poor oral health and ESCC. One proposed biological pathway linking poor oral health and ESCC involves the alteration of the microbiome. Thus, we performed an integrated analysis of four independent sequencing efforts of ESCC tumors from patients from high- and low-incidence regions of the world. Using whole genome sequencing (WGS) and RNA sequencing (RNAseq) of ESCC tumors and WGS of synchronous collections of saliva specimens from 61 patients in Tanzania, we identified a community of bacteria, including members of the genera Fusobacterium, Selenomonas, Prevotella, Streptococcus, Porphyromonas, Veillonella, and Campylobacter, present at high abundance in ESCC tumors. We then characterized the microbiome of 238 ESCC tumor specimens collected in two additional independent sequencing efforts consisting of patients from other high-ESCC incidence regions (Tanzania, Malawi, Kenya, Iran, China). This analysis revealed a similar tumor enrichment of the ESCC-associated bacterial community in these cancers. Because these genera are traditionally considered members of the oral microbiota, we explored if there is a relationship between the synchronous saliva and tumor microbiomes of ESCC patients in Tanzania. Comparative analyses revealed that paired saliva and tumor microbiomes are significantly similar with a specific enrichment of Fusobacterium and Prevotella in the tumor microbiome. Together, these data indicate that cancer-associated oral bacteria are associated with ESCC tumors at the time of diagnosis and support a model in which oral bacteria are present in high abundance in both saliva and tumors of ESCC patients. Longitudinal studies of the pre-diagnostic oral microbiome are needed to investigate whether these cross-sectional similarities reflect temporal associations.

microbiology↗

bioRxiv2017

Predicting DNA accessibility in the pan-cancer tumor genome using RNA-seq, WGS, and deep learning

DNA accessibility, chromatin regulation, and genome methylation are key drivers of transcriptional events promoting tumor growth. However, understanding the impact of DNA sequence data on transcriptional regulation of gene expression is a challenge, particularly in noncoding regions of the genome. Recently, neural networks have been used to effectively predict DNA accessibility in multiple specific cell types [14]. These models make it possible to explore the impact of mutations on DNA accessibility and transcriptional regulation.\n\nOur work first improved on prior cell-specific accessibility prediction, obtaining a mean receiver operating characteristic (ROC) area under the curve (AUC) = 0.910 and mean precision-recall (PR) AUC = 0.605, compared to the previous mean ROC AUC = 0.895 and mean PR AUC = 0.561 [14].\n\nOur key contribution extended the model to enable accessibility predictions on any new sample for which RNA-seq data is available, without requiring cell-type-specific DNase-seq data for re-training. This new model obtained overall PR AUC = 0.621 and ROC AUC = 0.897 when applied across whole genomes of new samples whose biotypes were held out from training, and PR AUC = 0.725 and ROC AUC = 0.913 on randomly held out new samples whose biotypes were allowed to overlap with training.\n\nMore significantly, we showed that for promoter and promoter flank regions of the genome our model predicts accessibility to high reliability, achieving PR AUC = 0.839 in held out biotypes and PR AUC = 0.911 in randomly held out samples.\n\nThis performance is not sensitive to whether the promoter and flank regions fall within genes used in the input RNA-seq expression vector.\n\nFinally, we utilize this tool to investigate, for the first time, promoter accessibility patterns across several cohorts from The Cancer Genome Atlas (TCGA) [27].

bioinformatics↗