OncoLoop: A Network-Based Precision Cancer Medicine Framework-Reference-Cited by-全球学者库

OncoLoop: A Network-Based Precision Cancer Medicine Framework

Published:2022-11-14 Issue:2 Volume:13 Page:386-409
ISSN:2159-8274
Container-title:Cancer Discovery
language:en
Short-container-title:

Author:

Vasciaveo Alessandro¹^ORCID,Arriaga Juan Martín²^ORCID,de Almeida Francisca Nunes²^ORCID,Zou Min²^ORCID,Douglass Eugene F.¹,Picech Florencia²^ORCID,Shibata Maho³⁴⁵^ORCID,Rodriguez-Calero Antonio⁶⁷^ORCID,de Brot Simone⁸^ORCID,Mitrofanova Antonina¹^ORCID,Chua Chee Wai³⁴⁵^ORCID,Karan Charles¹⁹^ORCID,Realubit Ronald¹⁹^ORCID,Pampou Sergey¹⁹^ORCID,Kim Jaime Y.²^ORCID,Afari Stephanie N.²^ORCID,Mukhammadov Timur²^ORCID,Zanella Luca¹^ORCID,Corey Eva¹⁰^ORCID,Alvarez Mariano J.¹¹¹^ORCID,Rubin Mark A.⁶¹²^ORCID,Shen Michael M.¹³⁴⁵¹³^ORCID,Califano Andrea¹³⁹¹³¹⁴¹⁵^ORCID,Abate-Shen Cory¹²³⁵¹³¹⁶^ORCID

Affiliation:

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

2. 2Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

3. 3Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

4. 4Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

5. 5Department of Urology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

6. 6Department for Biomedical Research, University of Bern, Bern, Switzerland.

7. 7Institute of Pathology, University of Bern and Inselspital, Bern, Switzerland.

8. 8COMPATH, Institute of Animal Pathology, University of Bern, Bern, Switzerland.

9. 9J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, New York.

10. 10Department of Urology, University of Washington, Seattle, Washington.

11. 11DarwinHealth Inc., New York, New York.

12. 12Bern Center for Precision Medicine (BCPM), Bern, Switzerland.

13. 13Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York.

14. 14Department of Biochemistry and Molecular Biophysics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

15. 15Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

16. 16Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York.

Abstract

Abstract Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. Significance: OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247

Funder

National Cancer Institute

Publisher

American Association for Cancer Research (AACR)

Subject

Oncology

Link

https://aacrjournals.org/cancerdiscovery/article-pdf/doi/10.1158/2159-8290.CD-22-0342/3224728/cd-22-0342.pdf

Reference72 articles.

1. Establishing and maintaining an extensive library of patient-derived xenograft models;Mattar;Front Oncol,2018

2. Patient-derived tumour xenografts as models for oncology drug development;Tentler;Nat Rev Clin Oncol,2012

3. Xenograft and organoid model systems in cancer research;Bleijs;EMBO J,2019