Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity

Sha Cao; Wennan Chang; Changlin Wan; Xiaoyu Lu; Pengtao Dang; Xinyu Zhou; Haiqi Zhu; Jian Chen; Bo Li; Yong Zang; Yijie Wang; Chi Zhang

Computational and Structural Biotechnology Journal (Jan 2023)

Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity

Sha Cao,
Wennan Chang,
Changlin Wan,
Xiaoyu Lu,
Pengtao Dang,
Xinyu Zhou,
Haiqi Zhu,
Jian Chen,
Bo Li,
Yong Zang,
Yijie Wang,
Chi Zhang

Affiliations

Sha Cao: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Biostatistics and Health Data Science, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Corresponding authors at: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA.
Wennan Chang: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Electronic Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
Changlin Wan: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Electronic Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
Xiaoyu Lu: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
Pengtao Dang: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Electronic Computer Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
Xinyu Zhou: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Computer Science, Indiana University, Bloomington, IN 47405, USA
Haiqi Zhu: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Computer Science, Indiana University, Bloomington, IN 47405, USA
Jian Chen: Shanghai pulmonary hospital, Shanghai 200082, China
Bo Li: School of Economics, Peking University, Beijing 100871, China
Yong Zang: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Biostatistics and Health Data Science, Indiana University, School of Medicine, Indianapolis, IN 46202, USA
Yijie Wang: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Computer Science, Indiana University, Bloomington, IN 47405, USA
Chi Zhang: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA; Corresponding authors at: Center for Computational Biology and Bioinformatics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA.

Journal volume & issue: Vol. 21
pp. 2160 – 2171

Abstract

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The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).

Published in Computational and Structural Biotechnology Journal

ISSN: 2001-0370 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Technology: Chemical technology: Biotechnology
Website: https://www.journals.elsevier.com/computational-and-structural-biotechnology-journal

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