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CancerSubminer: an integrated framework for cancer subtyping using supervised and unsupervised learning on DNA
Biorxiv : the Preprint Server for Biology
|November 24, 2025
Summary
CancerSubminer, a novel hybrid framework, improves cancer subtyping by integrating supervised and unsupervised learning to identify distinct prognostic subtypes. This approach enhances personalized therapy and survival prediction across diverse cancer types.
Area of Science:
- Computational biology
- Genomics
- Bioinformatics
Background:
- Human cancer's heterogeneity complicates prognosis and targeted therapy development.
- Molecular subtyping offers a path to personalized medicine by identifying homogeneous cancer subsets.
- Existing subtyping methods have limitations in generalizability, novel subtype discovery, and consistency with established classifications.
Purpose of the Study:
- To develop CancerSubminer, a hybrid framework integrating supervised and unsupervised learning for robust cancer molecular subtyping.
- To improve prognostic prediction and therapeutic strategy development by identifying clinically relevant cancer subtypes.
- To address challenges of batch effects and domain shift in multi-dataset cancer subtyping.
Main Methods:
- Developed CancerSubminer, a hybrid framework combining supervised classification with unsupervised clustering.
- Employed adversarial training to correct batch effects and learn domain-invariant features across datasets.
- Utilized semi-supervised fine-tuning for subtype alignment and novel subtype candidate identification.
- Validated on methylation data from five cancer types (breast, bladder, brain, kidney, thyroid) using TCGA and GEO datasets.
Main Results:
- CancerSubminer outperformed existing state-of-the-art subtyping and clustering methods.
- Demonstrated significant prognostic separation (p < 0.05) using Kaplan-Meier survival analysis across all evaluated cancer types.
- Identified significant prognostic subtypes in thyroid cancer, where predefined subtypes lacked prognostic value.
Conclusions:
- CancerSubminer effectively identifies distinct prognostic cancer subtypes, improving prognostic stratification.
- The framework successfully mitigates batch effects and enhances generalizability across heterogeneous datasets.
- CancerSubminer offers a valuable tool for advancing personalized cancer therapy and early diagnosis.
