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Related Experiment Video

Updated: Jan 7, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Learning Patient Similarity from Genomics for Precision Oncology.

Maha Shady^1,2,3,4, Brendan Reardon^3,4, Sharon Jiang^2,3,4

¹Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Medrxiv : the Preprint Server for Health Sciences

|December 25, 2025

Summary

This summary is machine-generated.

A new deep learning model identifies patient similarity using tumor genomic data. This approach aids treatment decisions, especially for patients lacking biomarkers or with rare cancers.

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Last Updated: Jan 7, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

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Published on: November 5, 2019

Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Precision oncology relies on molecular biomarkers, but many patients lack actionable targets or effective treatments.
Patient similarity approaches can enhance decision support by analyzing comprehensive tumor profiles and clinical data from large patient cohorts.

Purpose of the Study:

To develop a deep learning framework for measuring patient similarity using tumor genomic profiles.
To evaluate the association of patient subgroups and neighborhoods with therapeutic outcomes in breast cancer and pan-cancer settings.
To assess the model's utility for patients without actionable biomarkers and those with cancer of unknown primary (CUP).

Main Methods:

Utilized real-world clinicogenomic data from a tertiary cancer center.
Developed a deep learning model to embed tumor genomic profiles and measure patient similarity.
Evaluated patient subgroups and neighborhoods for associations with therapeutic outcomes.

Main Results:

The model identified clinically meaningful patient clusters with known and novel therapeutic associations.
Derived patient neighborhoods informed therapeutic trajectories more often than expected by chance.
Demonstrated utility for patients lacking actionable biomarkers and for cancer of unknown primary (CUP) diagnoses.
Showcased potential for continuous learning and analysis over time.

Conclusions:

The similarity-based framework translates complex data into actionable insights for precision oncology.
This approach augments clinician judgment and supports patient-centered decision-making.
Provides a foundation for a real-time learning decision support model in precision oncology.