# Exploring Collateral Lethality for Development of Cancer Therapeutics

> **NIH NIH R01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2020 · $474,053

## Abstract

Project Summary
Pancreatic cancer remains the most lethal disease with no effective therapeutics. We have recently made
conceptual advances in targeting signature genomic deletions, a hallmark of human cancers. We
demonstrated earlier that passenger deletions could confer cancer cell specific vulnerabilities, which we
termed “Collateral Lethality”. We deployed this concept in targeting the SMAD4 deletion, which occurs
frequently in pancreatic cancer, and have identified malic enzyme (ME) 3 as a target for collateral lethality in
SMAD4-deleted pancreatic tumor cells harboring adjacent deletion of malic enzyme 2. We unexpectedly
discovered that mitochondrial malic enzymes are required for the uptake of branched chain amino acids
(BCAAs) in pancreatic cancer, which has been implicated as a diagnostic plasma marker for early detection of
pancreatic cancer. Our overall goals are: to validate ME3 as a therapeutic target for pancreatic cancer in vitro
and in PDX models (Aim 1) and in vivo using chimeric PDAC model (Aim 2); and to identify useful therapeutic
agents that specifically target ME2 deleted cells (Aim 3). Our goals align well with the NCI directive on
“Scientific Framework for Pancreatic Ductal Adenocarcinoma.”

## Key facts

- **NIH application ID:** 9899100
- **Project number:** 5R01CA225955-03
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** RONALD ANTHONY DEPINHO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $474,053
- **Award type:** 5
- **Project period:** 2018-04-09 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9899100

## Citation

> US National Institutes of Health, RePORTER application 9899100, Exploring Collateral Lethality for Development of Cancer Therapeutics (5R01CA225955-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9899100. Licensed CC0.

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