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Principles of Pharmacogenetics: Types of Genetic Variants

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients

Saud H AlDubayan^1,2,3,4, Jake R Conway^1,2,5, Sabrina Y Camp^1,2

¹Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, Massachusetts.

|November 17, 2020

Summary

Deep learning methods significantly improve the detection of germline pathogenic variants in cancer patients compared to standard genetic testing. This advancement holds promise for more accurate cancer risk assessment and personalized treatment strategies.

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Area of Science:

Genomics
Computational Biology
Oncology

Background:

Less than 10% of cancer patients have detectable pathogenic germline alterations, potentially due to limitations in current detection methods.
Accurate identification of germline pathogenic variants is crucial for cancer risk assessment and treatment selection.

Purpose of the Study:

To evaluate the efficacy of deep learning approaches in identifying more germline pathogenic variants in cancer patients compared to standard methods.
To assess the performance of deep learning in detecting variants across different gene sets, including cancer-predisposition, actionable, and mendelian genes.

Main Methods:

A cross-sectional study comparing standard germline detection with a deep learning method.
Utilized convenience cohorts of patients with prostate cancer and melanoma (US and Europe, 2010-2017).
Evaluated performance using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for variant detection.

Main Results:

Deep learning identified more patients with pathogenic variants in cancer-predisposition genes in both prostate cancer and melanoma cohorts.
The deep learning method demonstrated higher sensitivity and specificity across various gene sets, including actionable and mendelian genes.
Significant improvements in sensitivity and specificity were observed for deep learning in detecting germline pathogenic variants.

Conclusions:

Deep learning-based germline genetic testing is associated with higher sensitivity and specificity for detecting pathogenic variants compared to standard methods.
These findings suggest deep learning can enhance the identification of germline alterations in cancer patients.
Further research is warranted to correlate these improved detection rates with clinical outcomes.