# Spatially resolved single-cell patterns of drug-resistant ovarian cancers

> **NIH NIH F32** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $65,310

## Abstract

Project Summary
Ovarian cancers remain one of the deadliest cancers affecting women. Tumors commonly acquire
resistance to first-line chemotherapeutics, and only 30% of patients survive beyond 5 years. Novel
targeted combination therapies are needed to improve long-term survival outcomes and will depend on
an improved understanding of the molecular and genetic mechanisms of drug resistance. Previous
work has used next-generation bulk sequencing approaches to globally profile genetic signatures of
drug resistance in ovarian cancer tissue. However, rare cells in a highly heterogeneous 3D tissue
context may hold the key to understanding these complex processes, and such rare cells are
notoriously difficult to identify using these standard methods. We will use an in vivo patient derived
xenograft (PDX) model of high grade serous ovarian cancer (HSGSOC) representing multiple different
genetic backgrounds. HGSOC PDXs will be allowed to acquire resistance to PARP inhibitor talazoparib.
Transcriptional signatures of PARP inhibitor resistance will be assessed using single cell RNA
sequencing of single cells isolated from tumor tissue fully resistant to inhibitor as well as from tissue
collected at intermediate time points. scRNA seq data will be mined to gain transcriptional signatures
of (1) component cell populations and (2) candidate genes driving the resistant phenotype. We will use
multiplex single molecule FISH and light sheet microscopy to image target (n ~ 100) mRNA species in
thick tissue blocks. We will analyze 3D image datasets to identify resistant cancer cells and chart their
3D position in relation to supporting cell types that express relevant cell signaling ligands and/or
receptors and additional tumor features (e.g. stroma, blood vessels). Lastly, we will choose target genes
that will be functionally validated in relevant cell culture and additional PDX in vivo models. By
examining single cell transcriptional profiles, we will greatly advance our understanding of how the 3D
tumor tissue microenvironment allows and encourages rare cells with pre-resistant transcriptional
programs to escape PARP inhibitor treatment. Along with an improved understanding of the dynamics
of drug resistance, the proposed research has the potential to suggest novel combination treatments
that could be exploited in the future to more effectively eliminate HGSOC and prevent recurrence of
drug resistant tumors.

## Key facts

- **NIH application ID:** 9922656
- **Project number:** 5F32CA239394-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Benjamin Robert King
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $65,310
- **Award type:** 5
- **Project period:** 2019-05-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9922656

## Citation

> US National Institutes of Health, RePORTER application 9922656, Spatially resolved single-cell patterns of drug-resistant ovarian cancers (5F32CA239394-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9922656. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*