Biology subjects

Rashid, N. U.

Publications and source records attributed to Rashid, N. U..

8 recordsLinked to original sources

bioRxiv2024

Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes

Cancer-associated fibroblast (CAF) subpopulations in pancreatic ductal adenocarcinoma (PDAC) have been identified using single-cell RNA sequencing (scRNAseq) with divergent characteristics, but their clinical relevance remains unclear. We translate scRNAseq-derived CAF cell-subpopulation-specific marker genes to bulk RNAseq data, and develop a single- sample classifier, DeCAF, for the classification of clinically restraining and permissive CAF subtypes. We validate DeCAF in 19 independent bulk transcriptomic datasets across four tumor types (PDAC, mesothelioma, bladder and renal cell carcinoma). DeCAF subtypes have distinct histology features, immune landscapes, and are prognostic and predict response to therapy across cancer types. We demonstrate that DeCAF is clinically replicable and robust for the classification of CAF subtypes in patients for multiple tumor types, providing a better framework for the future development and translation of therapies against permissive CAF subtypes and preservation of restraining CAF subtypes. SignificanceWe introduce a replicable and robust classifier, DeCAF, that delineates the significance of the role of permissive and restraining CAF subtypes in cancer patients. DeCAF is clinically tractable, prognostic and predictive of treatment response in multiple cancer types and lays the translational groundwork for the preclinical and clinical development of CAF subtype specific therapies.

genomics↗

bioRxiv2021

Sub-Cluster Identification through Semi-SupervisedOptimization of Rare-cell Silhouettes (SCISSORS) in Single-Cell Sequencing

Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choice can greatly alter clustering solutions and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which is not favorable for identifying cells of extremely low abundance due to their subtle contributions towards overall patterns of gene expression. Here we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within major cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by implementing a multi-step, semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of rare cells, which may be used for further study. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. SCISSORS, including source code and vignettes for two example datasets, is freely available at https://github.com/jrleary/SCISSORS.

bioinformatics↗

bioRxiv2021

Kinome state is predictive of cell viability in pancreatic cancer tumor and stroma cell lines

Numerous aspects of cellular signaling are regulated by the kinome - the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation being a key driver of many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tumor microenvironment. Fundamentally, it is an open question as to the degree to which knowledge of the state of the kinome at the protein level is able to provide insight into the downstream phenotype of the cell. In this work, we attempt to link the state of the kinome, or kinotype, with cell viability in representative PDAC tumor and stroma cell lines. Through the application of both regression and classification models to independent kinome perturbation and kinase inhibitor cell screen data, we find that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. While regression models perform poorly, we find that classification approaches are able to predict drug viability effects. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines. Using the models to predict new compounds with cell viability effects and not in the initial data set, we conducted a validation screen that confirmed the accuracy of the models. These results suggest that characterizing the state of the protein kinome provides significant opportunity for better understanding signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapy design for PDAC.

bioinformatics↗

bioRxiv2021

Defining the KRAS-regulated kinome in KRAS-mutant pancreatic cancer

Oncogenic KRAS drives cancer growth by activating diverse signaling networks, not all of which have been fully delineated. We set out to establish a system-wide profile of the KRAS-regulated kinase signaling network (kinome) in KRAS-mutant pancreatic ductal adenocarcinoma (PDAC). We knocked down KRAS expression in a panel of six cell lines, and then applied Multiplexed Inhibitor Bead/Mass Spectrometry (MIB/MS) chemical proteomics to monitor changes in kinase activity and/or expression. We hypothesized that depletion of KRAS would result in downregulation of kinases required for KRAS-mediated transforming activities, and in upregulation of other kinases that could potentially compensate for the deleterious consequences of the loss of KRAS. We identified 15 upregulated and 13 downregulated kinases in common across the panel. In agreement with our hypothesis, all 15 of the upregulated kinases have established roles as cancer drivers (e.g., SRC, TGFBR1, ILK), and pharmacologic inhibition of the upregulated kinase, DDR1, suppressed PDAC growth. Interestingly, 11 of the 13 downregulated kinases have established driver roles in cell cycle progression, particularly in mitosis (e.g., WEE1, Aurora A, PLK1). Consistent with a crucial role for the downregulated kinases in promoting KRAS-driven proliferation, we found that pharmacologic inhibition of WEE1 also suppressed PDAC growth. The unexpected paradoxical activation of ERK upon WEE1 inhibition led us to inhibit both WEE1 and ERK concurrently, which caused further potent growth suppression and enhanced apoptotic death than WEE1 inhibition alone. We conclude that system-wide delineation of the KRAS-regulated kinome can identify potential therapeutic targets for KRAS-mutant pancreatic cancer.

cancer biology↗

bioRxiv2020

Compression of quantification uncertainty for scRNA-seq counts

Motivation Quantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of “inferential replicates”, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements.Results We demonstrate that storing only the mean and variance from a set of inferential replicates (“compression”) is sufficient to capture gene-level quantification uncertainty. Using these values, we generate “pseudo-inferential” replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. We show reduced false positives when applying this procedure to trajectory-based differential expression analyses. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory consumption without any loss in performance. Lastly, we show that the removal of multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset.Availability and implementation makeInfReps and splitSwish are implemented in the development branch of the R/Bioconductor fishpond package available at http://bioconductor.org/packages/devel/bioc/html/fishpond.html. Sample code to calculate the uncertainty-aware p-values can be found on GitHub at https://github.com/skvanburen/scUncertaintyPaperCode.Contact michaelisaiahlove{at}gmail.comCompeting Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2020

Differential Transcript Usage Analysis Incorporating Quantification Uncertainty Via Compositional Measurement Error Regression Modeling

Differential transcript usage (DTU) occurs when the relative transcript abundance of a gene changes between different conditions. Existing approaches to analyze DTU often rely on computational procedures that can have speed and scalability issues as the number of samples increases. In this paper, we propose a new method, termed CompDTU, that utilizes compositional regression to model transcript-level relative abundance proportions that are of interest in DTU analyses. This procedure does not suffer from speed and scalability issues due to the relative computational simplicity, making it ideally suited for DTU analysis with large sample sizes. The method also allows for the testing of and controlling for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty present in RNA-Seq data, where prior work has shown that accounting for such uncertainty may improve testing performance. We extend our CompDTU method to incorporate quantification uncertainty using bootstrap replicates of abundance estimates from Salmon and term this method CompDTUme. Through several power analyses, we show that CompDTU improves sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty while maintaining favorable speed and scalability.

genomics↗

bioRxiv2020

Model-based Feature Selection and Clustering of RNA-seq Data for Unsupervised Subtype Discovery

Clustering is a form of unsupervised learning that aims to un-cover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and utilized the quadratic penalty method with a SCAD penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

genomics↗

bioRxiv2019

Efficient Detection and Classification of Epigenomic Changes Under Multiple Conditions

SO_SCPLOWUMMARYC_SCPLOWEpigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively-parallel sequencing (ChIP-seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP-seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, mixNBHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.

bioinformatics↗