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Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

Madison Darmofal1,2, Shalabh Suman3, Gurnit Atwal4,5,6

  • 1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York.

Cancer Discovery
|February 28, 2024
PubMed
Summary

A new deep learning model, Genome-Derived-Diagnosis Ensemble (GDD-ENS), accurately predicts tumor type using targeted gene panel sequencing. This approach rivals whole-genome sequencing methods and aids in diagnosing rare cancers for improved patient treatment.

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Histology-based cancer diagnosis is challenging, yet tumor type is crucial for treatment decisions.
  • Genomic alterations are highly diagnostic of tumor type, but current methods are often not clinically feasible.
  • Existing tumor-type classifiers face limitations with whole-genome sequencing data or restricted cancer type predictions.

Purpose of the Study:

  • To develop a clinically feasible and accurate tumor-type classification model using targeted cancer gene panel sequencing data.
  • To create a deep neural network-based hyperparameter ensemble for enhanced diagnostic performance.
  • To enable real-time tumor-type predictions for guiding clinical treatment decisions.

Main Methods:

  • Utilized genomic features from 39,787 solid tumors sequenced via a targeted cancer gene panel.
  • Developed Genome-Derived-Diagnosis Ensemble (GDD-ENS), a deep neural network ensemble model.
  • Trained and validated the model for classifying tumor types across 38 distinct cancer categories.

Main Results:

  • GDD-ENS achieved 93% accuracy for high-confidence predictions across 38 cancer types.
  • The model's performance rivals that of whole-genome sequencing-based methods.
  • GDD-ENS demonstrated utility in diagnosing rare tumor types and cancers of unknown primary, with potential for incorporating clinical information.

Conclusions:

  • GDD-ENS provides a clinically feasible and accurate method for tumor-type prediction using targeted gene panels.
  • The model's performance rivals more complex whole-genome sequencing approaches.
  • Integrating GDD-ENS into clinical workflows can enhance real-time diagnostic capabilities and guide cancer treatment decisions.