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Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.

Yifang Wei¹, Lingmei Li¹, Xin Zhao¹

¹Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China.

Briefings in Bioinformatics

|November 26, 2022

Summary

A new hierarchical multi-kernel learning (hMKL) method accurately identifies cancer subtypes by integrating diverse omics data. This approach improves upon existing methods, especially with heterogeneous data, aiding personalized cancer treatment.

Keywords:

cancer subtyping data heterogeneity hierarchical multi-kernel learning kernel fusion multi-omics data integration

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate cancer subtyping is essential for personalized treatment and improved patient prognosis.
Integrating multi-omics data offers a comprehensive view of cancer biology for diagnosis and treatment.
Data heterogeneity across different omics types presents a significant challenge in cancer subtyping.

Purpose of the Study:

To develop a novel cancer molecular subtyping method using a hierarchical multi-kernel learning (hMKL) approach.
To effectively integrate heterogeneous multi-omics data for robust cancer subtype identification.
To improve upon existing cancer subtyping methods by addressing data heterogeneity.

Main Methods:

Proposed a two-stage hierarchical multi-kernel learning (hMKL) strategy for cancer subtyping.
Stage 1: Optimized individual omics kernel parameters using a Cancer Integration via Multi-Kernel Learning (CIMLR) approach.
Stage 2: Fused individual kernels using unsupervised multiple kernel learning and applied k-means clustering.

Main Results:

hMKL demonstrated superior performance compared to the one-stage CIMLR method, particularly with heterogeneous data.
The method accurately estimated the number of cancer subtypes (clusters), a critical challenge in the field.
Application to real datasets identified meaningful cancer subtypes and key cancer-associated biomarkers.

Conclusions:

hMKL provides a robust toolkit for integrating heterogeneous multi-omics data for cancer subtype identification.
The proposed method enhances personalized cancer treatment strategies by revealing distinct molecular subtypes.
hMKL offers a promising advancement in computational oncology and biomarker discovery.