A Transcriptome-Based Precision Oncology Platform for Patient–Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies-Reference-Cited by-同舟云学术

A Transcriptome-Based Precision Oncology Platform for Patient–Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies

Published:2023-04-16 Issue:6 Volume:13 Page:1386-1407
ISSN:2159-8274
Container-title:Cancer Discovery
language:en
Short-container-title:

Author:

Mundi Prabhjot S.¹²^ORCID,Dela Cruz Filemon S.³^ORCID,Grunn Adina¹^ORCID,Diolaiti Daniel³^ORCID,Mauguen Audrey⁴^ORCID,Rainey Allison R.³,Guillan Kristina³^ORCID,Siddiquee Armaan³^ORCID,You Daoqi³^ORCID,Realubit Ronald¹^ORCID,Karan Charles¹²^ORCID,Ortiz Michael V.³^ORCID,Douglass Eugene F.¹^ORCID,Accordino Melissa²⁵^ORCID,Mistretta Suzanne²^ORCID,Brogan Frances²^ORCID,Bruce Jeffrey N.²⁶^ORCID,Caescu Cristina I.¹^ORCID,Carvajal Richard D.²⁵^ORCID,Crew Katherine D.²⁵^ORCID,Decastro Guarionex²⁷^ORCID,Heaney Mark²⁵,Henick Brian S.²⁵^ORCID,Hershman Dawn L.²⁵⁸^ORCID,Hou June Y.⁹^ORCID,Iwamoto Fabio M.²¹⁰^ORCID,Jurcic Joseph G.²⁵^ORCID,Kiran Ravi P.²¹¹^ORCID,Kluger Michael D.²¹¹^ORCID,Kreisl Teri¹²^ORCID,Lamanna Nicole²⁵^ORCID,Lassman Andrew B.²¹⁰^ORCID,Lim Emerson A.²⁵^ORCID,Manji Gulam A.²⁵^ORCID,McKhann Guy M.²⁶^ORCID,McKiernan James M.²⁷^ORCID,Neugut Alfred I.²⁵⁸^ORCID,Olive Kenneth P.²⁵^ORCID,Rosenblat Todd²⁵^ORCID,Schwartz Gary K.²⁵^ORCID,Shu Catherine A.²⁵^ORCID,Sisti Michael B.²⁶¹²¹³^ORCID,Tergas Ana⁹^ORCID,Vattakalam Reena M.²⁹^ORCID,Welch Mary²¹⁰^ORCID,Wenske Sven²⁷^ORCID,Wright Jason D.²⁹^ORCID,Canoll Peter²¹⁴,Hibshoosh Hanina²¹⁴^ORCID,Kalinsky Kevin²¹⁵^ORCID,Aburi Mahalaxmi¹^ORCID,Sims Peter A.¹¹⁶^ORCID,Alvarez Mariano J.¹¹⁷^ORCID,Kung Andrew L.³^ORCID,Califano Andrea¹²⁵¹⁶¹⁸¹⁹^ORCID

Affiliation:

1. 1Department of Systems Biology, Columbia University Irving Medical Center, New York, New York.

2. 2Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York.

3. 3Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York.

4. 4Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.

5. 5Department of Medicine, Columbia University Irving Medical Center, New York, New York.

6. 6Department of Neurological Surgery, Columbia University Irving Medical Center, New York, New York.

7. 7Department of Urology, Columbia University Irving Medical Center, New York, New York.

8. 8Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.

9. 9Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, New York.

10. 10Department of Neurology, Columbia University Irving Medical Center, New York, New York.

11. 11Department of Surgery, Columbia University Irving Medical Center, New York, New York.

12. 12Department of Otolaryngology Head and Neck Surgery, Columbia University Irving Medical Center, New York, New York.

13. 13Department of Radiation Oncology, Columbia University Irving Medical Center, New York, New York.

14. 14Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, New York.

15. 15Winship Cancer Institute of Emory University and Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, Georgia.

16. 16Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, New York.

17. 17DarwinHealth Inc., New York, New York.

18. 18Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York.

19. 19J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, New York.

Abstract

Abstract Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study. Significance: Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275

Funder

National Cancer Institute

NIH Office of the Director

CureSearch for Children's Cancer

Publisher

American Association for Cancer Research (AACR)

Subject

Oncology

Link

https://aacrjournals.org/cancerdiscovery/article-pdf/doi/10.1158/2159-8290.CD-22-1020/3319737/cd-22-1020.pdf

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