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

Laise, Pasquale; Stanifer, Megan L.; Bosker, Gideon; Sun, Xiaoyun; Triana, Sergio; Doldan, Patricio; Manna, Federico La; Menna, Marta De; Realubit, Ronald; Pampou, Sergey; Karan, Charles; Alexandrov, Theodore; Julio, Marianna Kruithof-de; Califano, Andrea; Boulant, Steeve; Alvarez, Mariano J.

doi:10.1038/s42003-022-03663-8

Cited by 3 publications

(2 citation statements)

References 60 publications

(125 reference statements)

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“…In addition, analysis of DU145 perturbation profiles identified four drugs capable of activating Master Regulator protein modules (MR-blocks) that are inactivated in the most aggressive subtype of prostate cancer, thus inhibiting growth, immune evasion, and cell migration hallmarks 25 . Supporting PANACEA's ability to extend to disease other than cancer, it should be noted that analysis of its perturbational profiles, for lung and colon cancer cell lines, was instrumental in identifying 18 drugs predicted to invert the activity if Master Regulator regulating host cell response to SARS-CoV-2 infection, 80% of which were experimentally validated 74 . Despite these success stories, however, none of these individual studies would have supported the large-scale analysis of MoA context-specificity nor the creation of a large-scale, tissue-specific MoA similarity and polypharmacology map discussed in this study.…”

Section: Discussionmentioning

confidence: 99%

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium

Hu,

Douglass,

Turunen

et al. 2023

Preprint

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The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have implemented an efficient integrative experimental and computational framework leveraging the systematic generation and analysis of drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Protein activity-based analyses revealed highly reproducible, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide polypharmacology map, characterization of MoA-related drug clusters and off-target effects, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins. The proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, could help support more systematic and quantitative approaches to precision oncology.

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Section: Discussionmentioning

confidence: 99%

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium

Hu,

Douglass,

Turunen

et al. 2023

Preprint

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show abstract

“…We have used this concept to develop the New York State CLIA-certified DarwinOncoTreat algorithm, which prioritizes drugs based on their predicted capacity to invert the activity pattern of an individual patient's tumor checkpoints (Alvarez et al, 2018). In addition, this approach has been extended to target checkpoints associated with specific biological hallmarks, such as cell migration (Paull et al, 2021), and even to block the phenotypic transition permissive of viral replication induced in human host cells by viral infections (Laise et al, 2022).…”

Section: Part 4 Of the Pmp Protocol: Prioritization Of Drugs Predicte...mentioning

confidence: 99%

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

et al. 2022

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The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six‐step patient‐to‐model‐to‐patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context–specific drug mechanism of action (MOA) and permit precise, biomarker‐based preclinical validations of drug efficacy. The combination of determination of a drug's context‐specific MOA and precise identification of patients’ tumor checkpoints provides a personalized, mechanism‐based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

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Developmental and MAPK-responsive transcription factors drive distinct malignant subtypes and genetic dependencies in pancreatic cancer

Laise

Turunen

Garcia³

et al. 2020

Preprint

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Despite extensive efforts to characterize the transcriptional landscape of pancreatic ductal adenocarcinoma (PDA), reproducible assessment of subtypes with actionable dependencies remains challenging. Systematic, network-based analysis of regulatory protein activity stratified PDA tumours into novel functional subtypes that were highly conserved across multiple cohorts, including at the single cell level and in laser capture microdissected (LCM) samples. Identified subtypes were characterized by activation of master regulator proteins representing either gastrointestinal lineage markers or transcriptional effectors of morphogen pathways. Single cell analysis confirmed the existence of Lineage and Morphogenic states but also revealed a dominant population of more differentiated Oncogenic Precursor (OP) cells , present in all sampled patients, yet not apparent from bulk tumor analysis. Master regulators were validated by pooled, CRISPR/Cas9 screens, demonstrating both subtype-specific and universal dependencies. Conversely, ectopic expression of Lineage MRs, such as OVOL2, was sufficient to reprogram Morphogenic cells, thus providing a roadmap for the future targeting of patient-specific dependencies in PDA.

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

Cited by 3 publications

References 60 publications

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium

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

Developmental and MAPK-responsive transcription factors drive distinct malignant subtypes and genetic dependencies in pancreatic cancer

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