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Multi-Omics Data Fusion for Cancer Molecular Subtyping Using Sparse Canonical Correlation Analysis.

Lin Qi1, Wei Wang1, Tan Wu1

  • 1Department of Biomedical Sciences, City University of Hong Kong, Shenzhen, China.

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|August 9, 2021
PubMed
Summary

We developed a new bioinformatics framework, sparse canonical correlation analysis for cancer classification (SCCA-CC), to integrate multi-omics data for more accurate cancer subtyping and improved clinical outcome prediction.

Keywords:
breast cancercancer subtypingcanonical correlation analysisdata fusionmulti-omicsovarian cancer

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Area of Science:

  • Bioinformatics
  • Cancer Research
  • Genomics
  • Molecular Biology

Background:

  • Major malignancies are heterogeneous, presenting challenges for individualized therapy.
  • Previous cancer subtyping relied heavily on transcriptomics, overlooking other (epi)genetic regulatory levels.
  • Integrating multiple omics data types offers a more comprehensive view of cancer biology and heterogeneity.

Purpose of the Study:

  • To develop and validate an integrated bioinformatics framework, sparse canonical correlation analysis for cancer classification (SCCA-CC), for molecular subtyping.
  • To improve the identification of biologically meaningful and clinically relevant cancer subtypes by integrating multi-omics data.
  • To demonstrate the effectiveness of SCCA-CC in classifying both single-omics and multi-omics data.

Main Methods:

  • Proposed sparse canonical correlation analysis for cancer classification (SCCA-CC) to project single-omics data onto a unified space for data fusion.
  • Integrated mRNA and miRNA profiles for molecular classification of ovarian (n=462) and breast (n=451) cancers as case studies.
  • Applied clustering and classification analysis on the fused data to identify cancer subtypes.

Main Results:

  • Identified cancer subtypes that recapitulated previously recognized subtypes (P < 0.001) but showed stronger clinical associations.
  • In ovarian cancer, SCCA-CC identified subtypes significantly associated with overall survival, unlike TCGA classifications (P=0.039 vs. 0.12).
  • SCCA-CC demonstrated superior performance compared to iCluster, yielding subtypes with higher coherence, clinical relevance, and efficiency.

Conclusions:

  • Developed SCCA-CC, an effective integrated bioinformatics framework for cancer molecular subtyping.
  • SCCA-CC successfully identified biologically meaningful and clinically relevant subtypes in breast and ovarian cancers.
  • The framework's ability to classify both single- and multi-omics data enhances its applicability and efficient use of omics resources.