Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatments

Soft tissue sarcomas (STS) are rare, heterogeneous tumors with poor survival outcomes, primarily due to reliance on cytotoxic chemotherapy and lack of targeted therapies. Given the uniquely individualized nature of STS, we hypothesized that the ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), can predict treatment response and enhance combination therapy design for STS. Using QPOP, we screened 45 primary STS patient samples, and showed improved or concordant patient outcomes that are attributable to QPOP predictions. From a panel of approved and investigational agents, QPOP identified AZD5153 (BET inhibitor) and pazopanib (multi-kinase blocker) as the most effective combination with superior efficacy compared to standard regimens. Validation in a panel of established patient lines and in vivo models supported its synergistic interaction, accompanied by repressed oncogenic MYC and related pathways. These findings provide preliminary clinical evidence for QPOP to predict STS treatment outcomes and guide the development of novel therapeutic strategies for STS patients.

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Publication Details

Journal
npj Precision Oncology
Published
2025-03-22
DOI
https://doi.org/10.1038/s41698-025-00851-7
Citations
4
Primary Topic
Protein Degradation and Inhibitors
Type
article
Field-Weighted Citation Impact
3.22
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article

Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatments

Janice Jia Ni Goh, Xin Xiu Sam, London Lucien Ooi, Andre Villanueva et al.
4 citations
npj Precision Oncology
Protein Degradation and Inhibitors
3.22
article

Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatments

Janice Jia Ni Goh, Xin Xiu Sam, London Lucien Ooi, Andre Villanueva, Edward Kai‐Hua Chow, Masturah Bte Mohd Abdul Rashid, Wai Yee Wong, Tan Boon Toh, Nur Nadiah Ismail, Xing Yi Woo, Timothy Kwang Yong Tay, Ming‐Hui Yong, Baiwen Luo, Claramae Shulyn Chia, Sharon Pei Yi Chan, Valerie Shiwen Yang, Chin‐Ann Johnny Ong, Benjamin Jieming Chen, Brandon Xuan Ming Phua, Jhin Jieh Lim, Lissa Hooi, Krishan Kumar, Nur Fazlin Bte Mohamed Noor, Suraya Zainul Abidin
article en
4 citations

Abstract

Soft tissue sarcomas (STS) are rare, heterogeneous tumors with poor survival outcomes, primarily due to reliance on cytotoxic chemotherapy and lack of targeted therapies. Given the uniquely individualized nature of STS, we hypothesized that the ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), can predict treatment response and enhance combination therapy design for STS. Using QPOP, we screened 45 primary STS patient samples, and showed improved or concordant patient outcomes that are attributable to QPOP predictions. From a panel of approved and investigational agents, QPOP identified AZD5153 (BET inhibitor) and pazopanib (multi-kinase blocker) as the most effective combination with superior efficacy compared to standard regimens. Validation in a panel of established patient lines and in vivo models supported its synergistic interaction, accompanied by repressed oncogenic MYC and related pathways. These findings provide preliminary clinical evidence for QPOP to predict STS treatment outcomes and guide the development of novel therapeutic strategies for STS patients.

npj Precision OncologyVol. 9(1)
Agency for Science, Technology and Research (SG), SingHealth (SG), National University of Singapore (SG), Singapore General Hospital (SG), SingHealth Duke-NUS Academic Medical Centre (SG), National University Cancer Institute, Singapore (SG), Université Bourgogne Franche-Comté (FR), Duke-NUS Medical School (SG), National Neuroscience Institute (SG), National Cancer Centre Singapore (SG), Bioinformatics Institute (SG), Institute of Molecular and Cell Biology (SG)
Good health and well-being
Openalex Percentile: Top 9%
Protein Degradation and Inhibitors
3.22
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