ClassifieR 2.0 - expanding interactive gene expression-based stratification to prostate and high-grade serous ovarian cancer

Sep 21, 2026

Uploaded by Quinn

Transcriptional profiling has enabled discovery of molecular subtypes across and within tissue-based cancer categories, offering value for prognosis improvement and treatment guidance. However, assigning samples to these subtypes and related transcriptionally inferred predictions is labor-intensive and requires substantial bioinformatics expertise. ClassifieR 2.0 expands an existing interactive cloud platform by adding modules for prostate cancer and high-grade serous ovarian cancer, streamlining computational stratification workflows.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

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Results

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Background

McCabe et al. BMC Bioinformatics (2024) 25:362 BMC Bioinformatics [https://doi.org/10.1186/s12859-024-05981-6](https://doi.org/10.1186/s12859-024-05981-6) SOFTWARE Open Access ClassifieR 2.0: expanding interactive gene expression-based stratification to prostate and high-grade serous ovarian cancer Aideen McCabe 1,2*†, Gerard P. Quinn3,4†, Suneil Jain4,5, Micheál Ó Dálaigh2,6,7, Kellie Dean 1, Ross G. Murphy4,8† and Simon S. McDade3,4*† | †Aideen McCabe, Gerard
P. Quinn, Ross G. Murphy and Simon S. McDade have contributed equally to this work. | | --- | | *Correspondence: [120224381@umail. ucc.ie](120224381@umail. ucc.ie); [s.mcdade@qub.ac.uk](s.mcdade@qub.ac.uk) | 1 School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland 2 The SFI Centre for Research Training in Genomics Data Science, Galway, Ireland 3 BlokBio, Ormeau Labs, Belfast, Northern Ireland, UK 4 The Patrick G Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast, Northern Ireland, UK 5 Department of Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, UK 6 School of Biological and Chemical Sciences, University of Galway, Galway, Ireland 7 School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland 8 The Centre for Genomic Medicine, Ulster University, Coleraine, Northern Ireland, UK Abstract Background: Advances in transcriptional profiling methods have enabled the discovery of molecular subtypes within and across traditional tissue-based cancer classifications. Such molecular subgroups hold potential for improving patient outcomes by guiding treatment decisions and revealing physiological distinctions and targetable pathways. Computational methods for stratifying transcriptomic data into molecular subgroups are increasingly abundant. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time-consuming and requires significant bioinformatics expertise. To address this need, we recently reported“ClassifieR,” a flexible, interactive cloud application for the functional annotation of colorectal and breast cancer transcriptomes. Here, we report“ClassifieR 2. 0”which introduces additional modules for the molecular subtyping of prostate and high-grade serous ovarian cancer (HGSOC) . Results: ClassifieR 2.0 introduces ClassifieRp and ClassifieRov, two specialised modules specifically designed to address the challenges of prostate and HGSOC molecular classification. ClassifieRp includes sigInfer, a method we developed to infer commercial prognostic prostate gene expression signatures from publicly available gene-lists or indeed any user-uploaded gene-list. ClassifieRov utilizes consensus molecular subtyping methods for HGSOC, including tools like consensusOV, for accurate ovarian cancer stratification. Both modules include functionalities present in the original ClassifieR framework for estimating cellular composition, predicting transcription factor (TF) activity and single sample gene set enrichment analysis (ssGSEA) . Conclusions: ClassifieR 2.0 combines molecular subtyping of prostate cancer and HGSOC with commonly used sample annotation tools in a single, user-friendly platform, allowing scientists without bioinformatics training to explore prostate and HGSOC transcriptional data without the need for extensive bioinformatics knowledge or manual data handling to operate various packages. Our sigInfer method within ClassifieRp enables the inference of commercially available gene signatures for prostate cancer, while ClassifieRov incorporates consensus molecular subtyping for HGSOC. Overall, ClassifieR 2.0 aims to make molecular subtyping more accessible to the wider research community. This is crucial for increased understanding © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,

FAQs

What problem does ClassifieR 2.0 address?

It addresses the time-consuming, expertise-heavy process of assigning transcriptomic samples to molecular subtypes and other transcriptionally inferred predictions.

Which specialized modules are introduced in ClassifieR 2.0?

ClassifieR 2.0 introduces ClassifieRp and ClassifieRov, tailored for prostate and high-grade serous ovarian cancer molecular classification.

How does ClassifieRp infer prostate gene expression signatures?

It includes sigInfer to infer prognostic prostate gene expression signatures from publicly available gene lists or user-uploaded gene lists.

Sep 21, 2026

Uploaded by Quinn