Hollier, B. G. | Search bioRxiv

bioRxiv2022

The landscape of alternative polyadenylation during EMT and its regulation by the RNA-binding protein Quaking

Epithelial-mesenchymal transition (EMT) plays important roles in tumour progression and is orchestrated by dynamic changes in gene expression. While it is well established that post-transcriptional regulation plays a significant role in EMT, the extent of alternative polyadenylation (APA) during EMT has not yet been explored. Using 3 end anchored RNA sequencing, we mapped the alternative polyadenylation landscape (APA) following TGF-{beta}-mediated induction of EMT in human mammary epithelial cells and found APA generally causes 3UTR lengthening during this cell state transition. Analysis of the RNA-binding protein Quaking (QKI), a splicing factor induced during EMT, revealed enrichment of its binding adjacent to cleavage and polyadenylation sites within 3UTRs. Following QKI knockdown, APA of many transcripts are altered to produce predominantly shorter 3UTRs associated with reduced gene expression. Among these, QKI binds to its own cleavage site to produce a transcript with a longer 3UTR. These findings reveal extensive changes in APA occur during EMT and identify a novel function for QKI in this process.

molecular biology↗

bioRxiv2021

Gene expression based inference of drug resistance in cancer

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

cancer biology↗