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Homologous Recombination Abnormalities Associated With BRCA1/2 Mutations as Predicted by Machine Learning of Targeted

Maher Albitar1, Hong Zhang1, Andrew Pecora2

  • 1Genomic Testing Cooperative, Irvine, CA, USA.

Breast Cancer : Basic and Clinical Research
|October 4, 2023
PubMed
Abstract

Insights

This study developed a machine learning model using next-generation sequencing data to accurately predict homologous recombination deficiency (HRD) in tumors. The model shows high sensitivity and specificity, aiding in identifying potential responses to double-strand break-inducing drugs.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Homologous recombination deficiency (HRD) is a key biomarker for predicting response to double-strand break (DSB)-inducing drugs, particularly in BRCA1/2-mutated cancers.
  • Identifying HRD in tumors with mutations in other homologous recombination repair (HRR) genes or in wild-type (WT) tumors is crucial for expanding therapeutic options.

Purpose of the Study:

  • To develop a practical, next-generation sequencing (NGS)-based approach using machine learning (ML) to predict HRD in any tumor.
  • To establish a method for identifying HRD similar to that observed in BRCA1/2 mutations across diverse tumor types.

Main Methods:

  • Utilized copy number alteration (CNA) data derived from targeted NGS of 434 genes.
  • Employed a modified naïve Bayesian model for HRD prediction, training the ML system on CNA log2 values.
  • Validated the approach using independent cohorts of breast, ovarian, and other wild-type cancers.

Main Results:

  • The ML model achieved high sensitivity (90%) and specificity (98%) in predicting HRD.
  • Demonstrated 39% HRD positivity in tumors with HRR gene mutations (excluding BRCA1/2) and 32% in WT cancers.
  • Showed 90% concordance with loss of heterozygosity (LOH) and validated in over 1300 samples.

Conclusions:

  • Copy number alterations combined with ML reliably predict BRCA1/2-level HRD with high specificity.
  • The developed ML model can effectively predict HRD in cancers with non-BRCA1/2 HRR gene mutations and in WT tumors.
  • This approach offers a promising tool for identifying patients who may benefit from DSB-inducing therapies.
Keywords:
BRCA1BRCA2Homologous recombination deficiencyPARP inhibitorscopy number variationdouble-strand breakmachine learningnext-generation sequencingpredictionresponse

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