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Updated: Aug 22, 2025

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OncoLoop: A Network-Based Precision Cancer Medicine Framework

Alessandro Vasciaveo1, Juan Martín Arriaga2, Francisca Nunes de Almeida2

  • 1Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

Cancer Discovery
|November 14, 2022
PubMed

Insights

OncoLoop is a precision medicine framework that predicts patient-specific drug sensitivity using cognate models. This approach successfully identified and validated effective cancer treatments, enhancing existing therapies.

Area of Science:

  • Cancer research
  • Precision medicine
  • Translational oncology

Background:

  • Prioritizing cancer treatments for individual patients is challenging.
  • Real-time coclinical studies with patient-derived models are often unfeasible.

Purpose of the Study:

  • Introduce OncoLoop, a precision medicine framework to predict drug sensitivity in human tumors and their cognate models.
  • Validate the framework's efficacy in prostate cancer using genetically engineered mouse models (GEMM).

Main Methods:

  • Leveraged drug perturbation profiles to predict drug sensitivity.
  • Applied Master Regulator (MR) conservation analysis to human prostate cancer cohorts and GEMM-derived tumors (GEMM-DT).
  • Validated predicted drugs in various preclinical tumor models (allograft, syngeneic, PDX) and assessed combination therapy efficacy.

Main Results:

  • Most advanced prostate cancer patients were represented by at least one cognate GEMM-DT.
  • Drugs predicted to invert MR activity showed successful validation in preclinical models.
  • OncoLoop-predicted drugs enhanced the efficacy of nivolumab (PD-1 inhibitor) and enzalutamide (AR inhibitor).

Conclusions:

  • OncoLoop is a transcriptomic-based framework for rapid-turnaround coclinical studies to identify and validate patient-specific drugs.
  • The framework can be adapted for clinical practice and applied to various cancer types with available models and drug perturbation data.

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