# Computational analysis of tumor ecosystems and their regulation and association with outcomes

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $577,261

## Abstract

Project Summary
 The cellular makeup of tumors can radically influence response to treatment, and
survival outcomes. Biomarkers derived from tumor biopsies have had modest success in
their clinical utility for prognosis or guiding treatment decisions, being confounded by
factors such as cellular composition of tissues Moreover, different biomarkers may be
needed in female vs male patients. In prior work we showed how meta-analysis of large
clinically annotated public cancer datasets with clinical annotations can robustly identify
specific genes and processes associated with survival for patients in both pan-cancer and
cancer-specific ways. Here we still systematically investigate cancer-specific prognostic
cell types through integration of single cell RNA-seq (scRNAseq) with bulk RNA-seq and
methylation data. We will validate selected findings in tissue microarrays.
 First, we will identify cancer-specific cell transcriptional states and ecosystems
associated with survival and treatment response, extending prior work that identified 10
different “ecotypes” of co-occurring cell states across carcinomas. Second, we will extend
our framework to isolate cancer-specific cell-type-specific methylation profiles and their
correlation with imputed gene expression across populations using paired bulk RNA-seq
and methylation from TCGA. Third, we will validate survival associations of cancer-
specific cell states by staining human tissue microarrays. We will focus on high grade
serous ovarian cancer (HGSOC), which has dire prognosis, and non small-cell lung
cancer (NSCLC) for which we have extensive information on immunotherapy response.
We will use CODEX imaging on large tissue sections to assess the spatial organization
of outcome-related cell states in NSCLC and HGSOC. Overall, we will comprehensively
map cancer-specific cell states and ecotypes across malignancies, identifying potential
biomarkers and possible new therapeutic targets.

## Key facts

- **NIH application ID:** 10789916
- **Project number:** 5R01CA276828-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Andrew J. Gentles
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $577,261
- **Award type:** 5
- **Project period:** 2023-05-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10789916

## Citation

> US National Institutes of Health, RePORTER application 10789916, Computational analysis of tumor ecosystems and their regulation and association with outcomes (5R01CA276828-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10789916. Licensed CC0.

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