# Characterization of high-grade serous ovarian cancer subtypes via single-cell profiling

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2021 · $598,647

## Abstract

High-grade serous ovarian cancer (HGSOC) subtypes have been identified across multiple studies; however,
the biologic basis of these subtypes remains poorly understood. The central hypothesis of this proposal is that
differences in the cellular composition of HGSOC tumors drives the expression patterns that characterize at
least some of the previously described HGSOC subtypes. New technologies that barcode antibodies and
transcripts from individual cells before sequencing can characterize gene expression at single cell resolution
and detect cell types, which allows the central hypothesis to be directly tested. These combined advances lay
the groundwork to identify the basis, in terms of cell composition and pathway expression, of HGSOC subtypes
through two aims.
Aim 1: Characterize transcriptomes and selected proteins at single cell resolution, and deconvolve
existing tumor gene expression data. Single cell RNA and surface protein abundances will be measured at
the single cell level for high-grade serous ovarian cancers, unsupervised analysis will be used to identify cell
populations, cell surface proteins will be analyzed to characterize the immune compartment, cell-type marker
genes will be defined, and marker genes will be used deconvolve matched bulk RNA-seq samples. This will
allow existing data from larger cohorts to be deconvolved allowing survival analyses to be performed on tumors
stratified by cell composition.
Aim 2: Characterize the transcriptomic profile of cancer cells within HGSOC tumors to identify
pathways that are variably expressed within cancer cells. Gene expression within cancer cells will be
measured using two complementary approaches: (i) orthotopic patient derived xenografts (PDXs) and (ii)
single cell RNAseq. For each pathway, an enrichment score will be generated and pathways with expression
levels that vary substantially across the cohort will be identified. Combining the expression levels of genes
within variable pathways with cancer cell fraction estimates from existing datasets will enable inference of the
extent to which these variable pathways differ between reported subtypes after controlling for cancer cell
abundances.
The proposal is expected to result in two primary outcomes: 1) an understanding of the extent to which cell
composition and pathway expression contribute to HGSOC gene expression subtypes; and 2) estimates of the
proportions of cell types in existing studies with public gene expression data. A short-term impact is expected
through improved survival predictors of HGSOC subtypes based on variation identified from cell composition
and pathway expression and the work is expected to be impactful in the longer-term because determining the
biologic basis of subtypes is a key step towards developing treatments that target their specific vulnerabilities.

## Key facts

- **NIH application ID:** 10407165
- **Project number:** 7R01CA237170-03
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Jennifer Anne Doherty
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $598,647
- **Award type:** 7
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10407165

## Citation

> US National Institutes of Health, RePORTER application 10407165, Characterization of high-grade serous ovarian cancer subtypes via single-cell profiling (7R01CA237170-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10407165. Licensed CC0.

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