# Signaling Drivers of Sarcoma Drug Resistance

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2022 · $334,806

## Abstract

PROJECT SUMMARY
Sarcomas are a heterogenous group of rare cancers affecting about 16,000 people each year in the US. Bone
sarcomas are particularly rare, with 3,500 diagnoses a year. Resistance to therapy is a critical clinical
challenge in bone sarcoma management, limiting the utility of systemic treatment in the management of
relapsing and metastatic disease. There is limited data on drivers of bone sarcoma innate and acquired drug
resistance. As part of the parent grant, we have established a pipeline to develop personalized bone sarcoma
organoids to screen hundreds of drugs and determine a drug resistance and sensitivity profile for each tumor.
We pair this with whole-genome sequencing to identify mutational correlates of drug sensitivity. Here, we will
complement this work by interrogating disruptions in signaling pathways at the RNA and protein level to identify
correlates of drug resistance in pre- and post-neoadjuvant samples (Aim 1) as well as in different metastatic
lesions from the same patient (Aim 2). The combination of spatial and temporal genomic, transcriptomic and
proteomic analyses with functional drug testing will allow us to determine the factors impacting drug resistance
and develop biomarkers of response.

## Key facts

- **NIH application ID:** 10437547
- **Project number:** 3R01CA244729-03S1A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Paul Christopher Boutros
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $334,806
- **Award type:** 3
- **Project period:** 2020-07-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10437547

## Citation

> US National Institutes of Health, RePORTER application 10437547, Signaling Drivers of Sarcoma Drug Resistance (3R01CA244729-03S1A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10437547. Licensed CC0.

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