# Project 2: Probing the role of the p53 network in ferroptosis

> **NIH NIH P01** · COLUMBIA UNIV NEW YORK MORNINGSIDE · 2020 · $352,898

## Abstract

Project Summary
We hypothesize the existence of a p53-regulated nutrient-sensing metabolism checkpoint, in which
dysregulation of key nutrients needed for oxidative metabolism drives accumulation of lipid peroxides.
These lipid peroxides may enable activation of scavenging mechanisms that resolve the nutrient scarcity,
induce alternative metabolic pathways that bypass the need for the scarce nutrients, and/or drive activation
of ferroptotic cell death to eliminate cells damaged by nutrient scarcity. We suggest that each of these
outputs can lead to a tumor suppressive phenotype, and that understanding the mechanisms that govern
these processes is critical for understanding the evolution of human cancers and how they may be
addressed with precision therapeutics. We focus here on the role of the p53 network in this checkpoint and
its impact on tumor suppression. We have two major goals—to define how regulation of the mevalonate
pathway by p53 alters sensitivity to ferroptosis in hepatocellular carcinomas and to define the regulatory
mechanisms governing polyunsaturated fatty acid metabolism in lymphomas. Together, we suggest that
these studies will define a critical new axis of p53-mediated tumor suppression and provide a new avenue
for creation of precision cancer medicines.

## Key facts

- **NIH application ID:** 9905341
- **Project number:** 5P01CA087497-19
- **Recipient organization:** COLUMBIA UNIV NEW YORK MORNINGSIDE
- **Principal Investigator:** Brent R. Stockwell
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $352,898
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9905341

## Citation

> US National Institutes of Health, RePORTER application 9905341, Project 2: Probing the role of the p53 network in ferroptosis (5P01CA087497-19). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9905341. Licensed CC0.

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