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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.

Olivier Elemento^1,2,3, Chengqi Xu¹, Ilkay Us⁴

¹Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.

Research Square

|February 6, 2026

Summary

This study introduces PAIRWISE, a novel computational model for predicting synergistic drug combinations in cancer. PAIRWISE accurately identifies effective personalized cancer therapies, advancing precision oncology.

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Area of Science:

Computational biology
Oncology
Pharmacology

Background:

Combination therapies are crucial for enhancing cancer treatment efficacy and preventing recurrence.
Identifying optimal drug combinations is challenging due to numerous possibilities and tumor heterogeneity.
Preclinical screening can prioritize synergistic drug combinations, but personalized approaches are needed.

Purpose of the Study:

To develop a computational model, PAIRWISE, for predicting synergistic drug combinations in cancer.
To address the challenge of identifying personalized drug combinations tailored to specific cancer subtypes and patients.
To accelerate the development of precision oncology through effective nomination of drug combinations.

Main Methods:

Developed PAIRWISE, a model explicitly designed to predict synergistic effects of drug combinations.
Applied PAIRWISE to held-out cancer cell lines and an independent dataset of Diffuse Large B Cell Lymphoma (DLBCL) treated with Bruton Tyrosine Kinase (BTK) inhibitors.
Validated predictions using high-throughput screening (HTS) of approved or investigational agents for DLBCL.

Main Results:

PAIRWISE demonstrated superior performance compared to competing models, achieving an AUROC of 0.847 on cancer cell lines.
The model accurately predicted synergistic combinations in DLBCL with an AUROC of 0.720.
PAIRWISE showed strong concordance with in vitro screening results, validating its predictive capability.

Conclusions:

PAIRWISE effectively models synergistic drug effects and nominates personalized drug combinations for cancer treatment.
The model shows significant potential for accelerating precision oncology development.
This approach can guide the selection of effective combination therapies for individual patients.