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Mutagenicity and Carcinogenicity

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Updated: Jun 1, 2025

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

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An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods

Subrata Das¹, Vatsal Patel¹, Shouvik Chakravarty^1,2

¹Biotechnology Research and Innovation Council-National Institute of Biomedical Genomics (BRIC-NIBMG), National Institute of Biomedical Genomics, Kalyani, West Bengal, India.

|January 20, 2025

Summary

This study developed an ensemble machine learning approach to rank cancer mutation pathogenicity scoring algorithms. The top-performing algorithms accurately distinguish driver mutations from passenger mutations, improving cancer gene prioritization.

Keywords:

Driver mutation Machine learning Pathogenecity prediction algorithm

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate identification of cancer driver mutations is crucial for patient management.
Existing mutation pathogenicity algorithms show significant performance variation, even for known drivers.
Inconsistencies in mutation assessment hinder reliable clinical application.

Purpose of the Study:

To develop an ensemble machine learning (ML) approach for evaluating pathogenic and conservation scoring algorithms (PCSAs).
To rank PCSAs based on their ability to differentiate pathogenic driver mutations from benign passenger mutations in head and neck squamous cell carcinoma (HNSC).
To identify the most effective PCSAs for prioritizing cancer-driving mutations.

Main Methods:

Utilized a dataset of 502 HNSC patients with mutations classified across 299 high-confidence cancer driver genes.
Annotated each mutation with 41 PCSAs.
Employed logistic regression, random forest, and support vector machine ML algorithms with recursive feature elimination to rank PCSAs, using rank-average-sort and rank-sum-sort for final ranking.

Main Results:

The random forest algorithm achieved the highest performance (AUC-ROC 0.89) in distinguishing driver from passenger mutations.
The top 11 PCSAs, identified via rank-sum distribution, significantly outperformed the remaining 30 PCSAs (p < 2.22e-16).
Top-ranked PCSAs demonstrated robust performance on validation cohorts across HNSC, breast, lung, and colorectal cancers.

Conclusions:

An ensemble ML approach effectively evaluates and ranks PCSAs for differentiating pathogenic driver mutations from benign passenger mutations.
Data-driven selection of PCSAs is essential, as some popular algorithms performed poorly.
The validated top PCSAs offer a more reliable tool for cancer mutation analysis and prioritization across various cancer types.