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Updated: Mar 16, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data.

Michael R Young¹, David L Craft²

¹Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.; Department of Biomedical Engineering and Biotechnology, University of Massachusetts, Intercampus, MA, USA.

Cancer Informatics

|August 4, 2016

Summary

This summary is machine-generated.

We developed a Pathway-Informed Classification System (PICS) to classify cancers using gene expression. PICS reveals biological pathway involvement for distinct cancer subtyping and patient stratification.

Keywords:

biological pathways data-mining genomics-based optimization oncogenomics systems biology

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Area of Science:

Computational biology
Genomics
Cancer research

Background:

Accurate cancer classification is crucial for effective treatment.
Gene expression profiling offers a molecular basis for understanding cancer heterogeneity.

Purpose of the Study:

Introduce the Pathway-Informed Classification System (PICS) for cancer classification.
Utilize gene expression data to identify key biological pathways driving cancer subtypes.
Develop a computational method for patient stratification based on pathway activity.

Main Methods:

Developed PICS, a computational method using tumor sample gene expression levels.
Collapsed gene expression values into pathway scores to identify relevant biological activities.
Applied PICS to pan-cancer and individual cancer datasets, including those with and without non-cancerous samples.

Main Results:

PICS effectively separated a pan-cancer dataset by tissue of origin.
The method sub-classified individual cancer datasets into distinct survival groups.
Pathway activity scores revealed differential pathway involvement across cancer types (e.g., immune pathways in melanoma, signaling in lung cancer).

Conclusions:

Pathway-level genomic analysis is a valuable approach for cancer subtyping.
PICS demonstrates utility in classifying cancers based on biological pathway activity.
This approach may inform predictions of drug efficacy and side effects.