OncoLoop: A network-based precision cancer medicine framework

Abstract

Prioritizing cancer treatment at the individual patient level remains challenging and performing co-clinical studies using patient-derived models in real-time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework to predict drug sensitivity in both a human tumor and its highest-fidelity (cognate) model(s), for contextual in vivo validation, by leveraging perturbational profiles of clinically-relevant oncology drugs. As proof-of-concept, we applied OncoLoop to prostate cancer using a series of genetically engineered mouse models (GEMMs) that capture the broad spectrum of disease states, including metastatic, castration-resistant, and neuroendocrine prostate cancer. Interrogation of published cohorts revealed that most patients were represented by at least one cognate GEMM-derived tumor (GEMM-DT), based on Master Regulator (MR) conservation analysis. Drugs recurrently predicted to invert MR protein activity in patients and their cognate GEMM-DTs were successfully validated, including in two cognate allografts and one patient derived xenograft (PDX). OncoLoop is highly generalizable and can be extended to other cancers and other pathologies.

A modular master regulator landscape controls cancer transcriptional identity

“…Analysis of the counts were performed using the DEbrowser tool (Kucukural et al, 2019). MRB:14 drug prioritization We used a dataset of protein activity profiles of drug response, as inferred from a screening of 120 FDA-approved drugs and 217 latestage experimental compounds (in Phase 2 and 3 trials) in the DU145 prostate cancer cell line (Vasciaveo et al, 2020). Profiles were generated at 24 h following perturbation with the compound's IC20 concentration determined at 48 h by 7-point dose response curves.…”

A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection

“…Although this mode of action may be inferred from our model, one limitation of the study is lack of direct experimental evidence confirming that reactivation of such specific MRs is the mechanism mediating drug-induced effects on infectivity in the experimental setting. In this regard, it should be noted that when such a model has been applied in the oncology setting, drugs predicted to inhibit tumor growth do so in association with the expected inversion of MRs in the tumor checkpoint activity pattern in vivo 34,45 . However, confirming inversion of critical MRs in our virus-based model presents a number of technical hurdles that require intensive optimization.…”

“…5c-h). We have previously shown that recapitulation of MR protein activity by the drug perturbed models is important to maximize drug MoA conservation and OncoTreat analysis sensitivity 30,34 . Based on these results and considering availability of a compatible cell line as a relevant validation model to experimentally assess ViroTreat-predicted drugs, for this model we focused our validation efforts on the GI context.…”

A Patient‐to‐Model‐to‐Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture

“…Given the potential differences in drug pharmacodynamics between in vitro models and in vivo conditions and the potential drug pharmacokinetic issues inherent to in vivo conditions, a drug's context‐specific MOA inferred from cell line models might not always be conserved in vivo . To address these potential discrepancies and to ensure that the experimental results and analyses generated by the PMP protocol are optimally applicable to the needs of preclinical and clinical cancer drug discovery and validation, drug pharmacodynamics, pharmacokinetics, and anti‐tumor effects must be assessed within the context of in vivo models, most commonly by using genetically engineered mouse models (GEMMs) (Vasciaveo et al., 2022) or PDX models (Mundi et al., 2021; Vasciaveo et al., 2022). We will focus the discussion here on pharmacodynamics studies aimed at (a) elucidating the drug's context‐specific MOA and (b) validating the inversion of the tumor checkpoint activity pattern in vivo , two criteria that must be evaluated to predict drug efficacy and translational relevance in the clinical setting.…”

“…High‐fidelity (cognate) in vivo tumor models for the study of drug MOA and drug response can be identified by assessing the conservation of tumor checkpoints from patient tumor samples (see part 1 of the PMP protocol, above) in the corresponding animal models (Alvarez et al., 2019; Vasciaveo et al., 2022) (Fig. 2B).…”