The inference of gene co-expressions from microarray and RNA-sequencing data has led to rich insights on biological processes and disease mechanisms. However, the bulk samples analyzed in most studies are a mixture of different cell types. As a result, the inferred co-expressions are confounded by varying cell type compositions across samples and only offer an aggregated view of gene regulations that may be distinct across different cell types. The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. However, the high sequencing depth variations and measurement errors in scRNA-seq data present significant challenges in inferring cell-type-specific gene co-expressions, and these issues have not been adequately addressed in the existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, built on a general expression-measurement model that explicitly accounts for sequencing depth variations and measurement errors in the observed single cell data. Systematic evaluations show that most existing methods suffer from inflated false positives and biased co-expression estimates and clustering analysis, whereas CS-CORE has appropriate false positive control, unbiased co-expression estimates, good statistical power and satisfactory performance in downstream co-expression analysis. When applied to analyze scRNA-seq data from postmortem brain samples from Alzheimer's disease patients and controls and blood samples from COVID-19 patients and controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from other methods.

The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, that explicitly models sequencing depth variations and measurement errors in scRNA-seq data. Systematic evaluations show that most existing methods suffered from inflated false positives as well as biased co-expression estimates and clustering analysis, whereas CS-CORE gave accurate estimates in these experiments. When applied to scRNA-seq data from postmortem brain samples from Alzheimer’s disease patients/controls and blood samples from COVID-19 patients/controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods.

Pancreatic ductal adenocarcinomas (PDACs) are highly lethal, mostly because they quickly develop resistance to current chemotherapies: Gemcitabine (GEM)/paclitaxel or cocktail FOLFIRINOX. Both chemotherapies rely on nucleoside analogues (GEM and 5-fluorouracil [5-FU], and resistance is developed without acquiring mutations, suggesting the role of non-mutational mechanisms for therapy resistance. Our recent findings demonstrated that PDACs depend on RNA splicing proteins to maintain tumors and are exquisitely susceptible to a range of therapies directed at RNA splicing, supporting the role of aberrant RNA splicing in PDAC therapy resistance. Here we evaluated the role of altered RNA splicing in PDAC therapy resistance. Three murine and human isogenic lines with either sensitivity or resistance to GEM were developed. We performed deep RNA sequencing (RNASeq, 80-100 million reads, traditional is 20-40 million reads), differential gene expression, and splicing analyses. GEM resistant PDAC cells demonstrated >40% of non-canonical RNA splice variants and altered expression of 30% of RNA-binding proteins encoded in the human/murine genome, compared to GEM sensitive ones. Specific alternatively spliced exons with common cis-elements were identified in therapy resistant cells, suggesting that these splicing changes are guided by a splicing factor or other RNA-binding protein(s) (RBPs). Top splicing changes were associated to mRNAs encoding ether lipid and pyruvate metabolism. This finding is important as previous studies have linked these metabolic pathways to therapy resistance, however, they have overlooked if protein isoforms, resulting from aberrant splicing, have different impacts in therapy resistance. In addition, GEM and H3B-8800, a novel spliceosome inhibitor showed higher synergistic activity in GEM resistant PDAC cells compared to GEM sensitive cells, suggesting that the development of resistance in PDAC is highly dependent on RNA splicing. Combinedly, these data strongly suggest that resistance to chemotherapeutic agents in PDAC cells requires altered RNA splicing and aberrant expression of associated proteins. Further investigation is ongoing to identify the splicing events and RBPs related to chemotherapy resistance, which will uncover novel mechanisms of therapy resistance and therapeutic modalities by targeting RNA splicing in pancreatic cancer. Citation Format: Md Afjalus Siraj, Xinning Shan, Robert Tseng, Luisa Escobar-Hoyos. Evaluating the role of altered RNA metabolism in pancreatic cancer therapy resistance [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B045.