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Updated: May 21, 2025
A Mouse Model of Incompletely Resected Soft Tissue Sarcoma for Testing Neoadjuvant Therapies
Published on: July 28, 2020
Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatments
Sharon Pei Yi Chan1, Masturah Bte Mohd Abdul Rashid2, Jhin Jieh Lim2
1Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Drive, #12-01 Centre for Translational Medicine, Singapore, 117599, Republic of Singapore.
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.
Insights
This study shows the Quadratic Phenotypic Optimization Platform (QPOP) can predict soft tissue sarcoma (STS) treatment responses. QPOP identified a novel drug combination with superior efficacy for STS patients.
Area of Science:
- Oncology
- Pharmacology
- Biotechnology
Background:
- Soft tissue sarcomas (STS) are rare, heterogeneous cancers with poor prognoses.
- Current treatments, mainly cytotoxic chemotherapy, offer limited efficacy due to lack of targeted therapies.
Purpose of the Study:
- To evaluate the Quadratic Phenotypic Optimization Platform (QPOP) for predicting treatment response in STS.
- To explore QPOP's utility in designing effective combination therapies for STS.
Main Methods:
- QPOP was used to screen 45 primary STS patient samples against various agents.
- Drug sensitivity data was correlated with patient outcomes.
- A combination of AZD5153 (BET inhibitor) and pazopanib (multi-kinase blocker) was identified and validated.
Main Results:
- QPOP predictions showed improved or concordant patient outcomes.
- The identified drug combination demonstrated superior efficacy compared to standard regimens.
- In vitro and in vivo models confirmed synergistic interaction and repression of MYC pathways.
Conclusions:
- QPOP shows promise as a predictive tool for STS treatment response.
- This platform can guide the development of novel, effective combination therapies for STS.
- Preliminary clinical evidence supports QPOP's role in personalized STS treatment strategies.
