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Related Experiment Video

Updated: Dec 29, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

Wei Jiao¹, Gurnit Atwal^1,2,3, Paz Polak^4,5,6,7

¹Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON, Canada.

Nature Communications

|February 7, 2020

Summary

This summary is machine-generated.

This study developed a deep learning classifier to predict cancer type from somatic passenger mutations in whole genome sequencing data. The model accurately identifies cancer origins, aiding in diagnosing metastatic cancers with unknown primary sites.

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Area of Science:

Genomics
Computational Biology
Oncology

Background:

Determining the primary tumor site is crucial for cancer treatment, but challenging in metastatic cases.
Histopathology and organ of origin are key factors, yet 3% of patients present with metastatic tumors lacking an obvious primary.
The International Cancer Genome Consortium (ICGC)/The Cancer Genome Atlas (TCGA) Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium provides a valuable dataset for cancer research.

Purpose of the Study:

To develop and validate a deep learning classifier for predicting cancer type using somatic passenger mutation patterns.
To assess the classifier's accuracy on independent primary and metastatic tumor samples.
To explore the clinical applicability of mutation pattern analysis for identifying cancer origins, particularly in metastatic disease.

Main Methods:

Training a deep learning classifier on whole genome sequencing (WGS) data from 2606 tumors across 24 cancer types.
Utilizing patterns of somatic passenger mutations as input features for the classifier.
Evaluating classifier performance on held-out tumor samples and independent primary and metastatic samples.

Main Results:

The deep learning classifier achieved 91% accuracy on held-out samples, 88% on primary tumors, and 83% on metastatic tumors.
The classifier's accuracy significantly outperformed that of trained pathologists in identifying metastatic tumors without primary site information.
Inclusion of driver mutation data unexpectedly decreased classifier accuracy.

Conclusions:

Somatic passenger mutation patterns effectively encode the cell of origin, enabling accurate cancer type prediction.
The developed deep learning model shows significant clinical potential for diagnosing metastatic cancers of unknown primary.
This approach can inform strategies for detecting the origin of circulating tumor DNA (ctDNA).