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classifieR a flexible interactive cloud-application for functional annotation of cancer transcriptomes.

Gerard P Quinn1, Tamas Sessler1, Baharak Ahmaderaghi2

  • 1Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK.

BMC Bioinformatics
|April 1, 2022
PubMed
Summary
This summary is machine-generated.

classifieR is a web application that simplifies cancer sub-typing using gene expression data. It provides rapid annotation for colorectal and breast cancers, aiding prognosis and treatment discovery for labs without bioinformatics expertise.

Keywords:
Cancer SubtypeColorectal Shiny CMS CRIS ImmuneFunctional AnnotationGene expressionShiny application

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Transcriptionally informed predictions are crucial for cancer sub-typing, understanding disease biology, and developing novel treatments.
  • Established molecular sub-types like Consensus Molecular Subgroups (CMS), Intrinsic Subtypes (CRIS), and PAM50 have prognostic and predictive value in cancers such as colorectal and breast cancer.
  • Current methods for assigning samples to these subtypes are time-consuming, require significant bioinformatics expertise, and lack a universal approach for diverse platforms or integrated functional annotations.

Purpose of the Study:

  • To develop an accessible R-Shiny based web application, classifieR, for rapid single-sample annotation of transcriptional profiles from diverse cancer patient samples.
  • To provide a flexible framework that integrates established gene classifier sets (CMS, CRIS, PAM50, OncotypeDX) with functional annotations.
  • To enable laboratories without dedicated bioinformaticians to gain insights into the molecular makeup of their samples.

Main Methods:

  • Development of an R-Shiny based web application, classifieR, with specialized versions for colorectal (classifieRc) and breast (classifieRb) cancers.
  • Implementation of algorithms for disease-relevant transcriptional subgroup classification (CMS/CRIS, PAM50/OncotypeDX).
  • Integration of tools for estimating cellular composition (MCP-counter, xCell), performing single-sample Gene Set Enrichment Analysis (ssGSEA), and predicting transcription factor activity (DoRothEA).

Main Results:

  • classifieR facilitates rapid annotation of transcriptional profiles from diverse platforms, addressing a key bottleneck in cancer research.
  • The framework successfully demonstrates utility in classifying colorectal and breast cancer subtypes using established gene sets.
  • Integrated functional annotations provide insights into cellular composition, pathway enrichment, and transcription factor activity for individual samples.

Conclusions:

  • classifieR offers a user-friendly framework for molecular sub-typing and functional annotation of cancer samples, accessible to labs lacking bioinformatics support.
  • The tool provides valuable insights into patient prognosis and potential druggability, serving as a powerful aid for analysis and discovery.
  • Online applications for classifieRc and classifieRb are available, promoting broader accessibility and application in cancer research.