# Regulated cell death and responses to starvation in cancer

> **NIH NIH R35** · SLOAN-KETTERING INST CAN RESEARCH · 2022 · $920,209

## Abstract

Abstract
Cells respond to stress by upregulating adaptive mechanisms that promote survival or by
undergoing cell death when the stress is too severe. Cancer cells take advantage of stress
responses in order to survive within harsh cancer microenvironments, and understanding which
adaptive mechanisms are utilized to avoid cell death is critical to gaining new knowledge that may
be exploited for cancer therapy. It has also become clear that there is not one, but in fact many
different forms of cell death that can occur in response to stress, and our studies have contributed
significantly in this area. We have shown that some mechanisms have unique effects on the
dynamics of cell populations, and that some promote, while others may hinder, therapeutic
responses. Our proposed research program will focus on two major areas of discovery: (1) How
is cell death regulated in response to stress, and how do particular mechanisms contribute to
controlling population dynamics? (2) How do cells respond to nutrient starvation through adaptive
mechanisms that involve lysosomes? We will exploit recent findings and methods we have
developed to study these overarching questions through an integrated set of cell biological
approaches with a focus on imaging-based studies.

## Key facts

- **NIH application ID:** 10519322
- **Project number:** 1R35CA263846-01A1
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Michael H. Overholtzer
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $920,209
- **Award type:** 1
- **Project period:** 2022-09-09 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10519322

## Citation

> US National Institutes of Health, RePORTER application 10519322, Regulated cell death and responses to starvation in cancer (1R35CA263846-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10519322. Licensed CC0.

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