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Sara J. Cooper

Publications and source records attributed to Sara J. Cooper.

2 recordsLinked to original sources

RNA sequencing-based cell proliferation analysis across 19 cancers identifies a subset of proliferation-informative cancers with a common survival signature

Despite advances in cancer diagnosis and treatment strategies, robust prognostic signatures remain elusive in most cancers. Cell proliferation has long been recognized as a prognostic marker in cancer, but it has not been thoroughly investigated across multiple cancers. Here we explore the role of cell proliferation across 19 cancers (n=6,581 patients) using tissue-based RNA sequencing from The Cancer Genome Atlas project by employing a proliferative index derived from gene expression associated with PCNA expression. This proliferative index is significantly associated with patient survival (Cox, p-value<0.05) in 7/19 cancers, which we have defined as proliferation-informative cancers (PICs). In PICs the proliferative index is strongly correlated with tumor stage and nodal invasion. PICs paradoxically demonstrate reduced baseline expression of proliferation machinery relative to non-PICs suggesting that non-PICs saturate their proliferative capacity early in tumor development and allow other factors to dictate prognostic outcomes. We also identify chemotherapies whose efficacy is correlated with proliferation index and highlight drugs capable of inhibiting proliferation associated expression. Additionally, we find that proliferative index is significantly associated with gross somatic mutation burden (Spearman, p=1.76x10-23) as well mutations in individual driver genes. This analysis provides a comprehensive characterization of tumor proliferation rates and their association with disease progression and prognosis across cancer types and highlights specific cancers that may be particularly susceptible to improved targeting of this classic cancer hallmark.

Genomics

aRNApipe: A balanced, efficient and distributed pipeline for processing RNA-seq data in high performance computing environments

SummaryThe wide range of RNA-seq applications and their high computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project-oriented pipeline for processing of RNA-seq data in high performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing, and sequence variant calling. aRNApipe is project-oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis.\n\nAvailability and documentationhttps://github.com/HudsonAlpha/aRNAPipe; DOI:10.5281/zenodo.202950\n\nContactrmyers@hudsonalpha.org\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics