PreprintCited by 51Computational Drug Discovery MethodsCancer Genomics and DiagnosticsBreast Cancer Treatment Studies

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

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Mundi, Prabhjot S., Dela Cruz, Filemon S., Grunn, Adina, Diolaiti, Daniel, Mauguen, Audrey, Rainey, Allison R., Guillan, Kristina C., Siddiquee, Armaan +45 more·2021·Cancer Discovery

Abstract

Predicting tumor sensitivity to antineoplastics remains an elusive challenge, with no methods demonstrating predictive power. Joint analysis of tumors—from patients with distinct malignancies who had progressed on multiple lines of therapy—and drug perturbation transcriptional profiles predicted sensitivity to 28 of 350 drugs, 26 of which (93%) were confirmed in low-passage, patient-derived xenograft (PDX) models. Drugs were prioritized based on their ability to either invert the activity of individual Master Regulator proteins, with available high-affinity inhibitors, or of the modules they comprise (Tumor-Checkpoints), based on de novo mechanism of action analysis. Of 138 PDX mice enrolled in 16 single and 18 multi-protein treatment arms, a disease control rate (DCR) of 68% and 91 %, and an objective response rate (ORR) of 12% and 17%, were achieved respectively, compared to 6% and 0% in the negative controls arm, with multi-protein drugs achieving significantly more durable responses. Thus, these approaches may effectively complement and expand current precision oncology approaches, as also illustrated by a case study.

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Crossref · 10.1101/2021.10.03.462951

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  • PDX model systems are valuable in basic science and personalized medicine because they have clinical and molecular features representative of human disease.

    “These PDX model systems are of particular value in both basic science and potentially personalized medicine research 180,183,222 as they have clinical and molecular features that are quite representative of the human disease 223,224,225,226 .”

    From Osteosarcoma (2022) · cites this paper as [226]

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Authors
Mundi, P. S., Dela Cruz, F. S., Grunn, A., Diolaiti, D., Mauguen, A., Rainey, A. R., Guillan, K. C., Siddiquee, A., You, D., Realubit, R., Karan, C., Ortiz, M. V., Accordino, M., Mistretta, S., Brogan, F., Bruce, J. N., Caescu, C. I., Carvajal, R., Crew, K., Decastro, G., Heaney, M., Henick, B., Hershman, D., Hou, J., Iwamoto, F., Jurcic, J., Kiran, R. P., Kluger, M., Kreisl, T., Lamanna, N., Lassman, A., Lim, E., Manji, G. A., McKhann, G., McKiernan, J., Neugut, A., Olive, K., Rosenblat, T., Schwartz, G. K., Shu, C., Sisti, M., Tergas, A., Vattakalam, R., Welch, M., Wenske, S., Wright, J. D., Hibshoosh, H., Kalinsky, K., Aburi, M., Sims, P. A., Alvarez, M. J., Kung, A. L., & Califano, A.
Affiliations
  • Columbia University Irving Medical Center (US)
  • Memorial Sloan Kettering Cancer Center (US)
  • Emory University (US)
Corresponding author
  • Alvarez, Mariano J.
  • Kung, Andrew L.
  • Califano, Andrea
Venue
Cancer Discovery
Publisher
openRxiv
Published
2021-10-03
Volume
13
Issue
6
Pages
1386-1407
ISSN
2159-8274 (ISSN-L)
OpenAlex
W4365997950
Cited by
51
References
85
Citations by year
2026: 15 · 2025: 14 · 2024: 7 · 2023: 10 · 2022: 5
FWCI
6.58
Type
journal article
Language
en
Open access
Locations
  • Cancer Discovery (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • CureSearch for Children's Cancer
  • Memorial Sloan-Kettering Cancer Center (CA008748, P30 CA008748)
  • National Institutes of Health (1s10od021764-01, 1s10od032433-01a1, 1s10od012351-01, CA008748, CA013696)
  • Irving Medical Center, Columbia University (P30 CA008748)
  • NIH Office of the Director (S10OD021764, S10 OD012351)
  • National Cancer Institute (S10OD021764, S10 OD012351, U54CA209997, CA008748, CA013696, OD012351, CA209997, U01CA217858, CA217858, OD021764, P30CA008748, U01CA272610, R35CA197745, P30CA013696)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
precision oncology, patient-derived xenografts, oncotarget, drug response prediction, basket trials, umbrella trials, disease control rate
MeSH terms
Antineoplastic Agents, Humans, Medical Oncology, Neoplasms, Precision Medicine, Transcriptome

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