# Project 2: Functional Genetic Networks for Systems-Guided Precision Medicine

> **NIH NIH U54** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $501,398

## Abstract

CCMI v2.0
Project 2: Functional Genetic Networks for Systems-Guided Precision Medicine
Project Leads: Prashant Mali and Stephanie Fraley; Co-Investigators: Alan Ashworth, Jennifer Grandis,
Silvio Gutkind, Trey Ideker, and Laura van ’t Veer.
SUMMARY
Precision medicine aims to tailor therapies to the genetic and molecular background of a patient’s tumor. The
development of such therapies faces numerous obstacles, many deriving from our ignorance of the genetic
networks underlying tumorigenesis and the mechanisms of possible interventions. To clarify the genetic logic
that governs therapeutic efficacy, Project 2 will use CRISPR/Cas9 genetic perturbation methodologies in an
ensemble of combinatorial, functional, and mechanistic screens. Screens will focus on the PI3K pathway, the
p53 tumor suppressor, and the protein systems mutated in invasive breast cancer (BRCA), head and neck
squamous cell carcinoma (HNSCC), and lung squamous cell carcinoma (LUSC), pathways and diseases that
together result in well over one million deaths each year worldwide. This focus will enable us to interrogate a
broad collection of cell lines, experiments, and microenvironmental conditions. First, we will build on significant
preliminary data to establish maps of synthetic lethal and epistatic genetic interactions centered on frequently
mutated genes and therapeutic targets in the above pathways and cancer subtypes (Aim 1). Second, we will
couple CRISPR/Cas9 screening to a panel of scalable functional assays for large-scale measurement of cancer
phenotypes beyond cell proliferation (Aim 2). Third, we will pilot a new technology, STAG-CRISPR, to link
CRISPR/Cas9 screening to real time molecular events in living cells, providing access to an even deeper array
of phenotypes that have been recalcitrant to systems genetics thus far (Aim 3). Finally, to facilitate clinical
translation of the identified gene-gene, gene-phenotype, gene-mechanism and gene-drug interactions, we will
apply our extensive library of BRCA, HNSCC, and LUSC patient-derived xenograft (PDX) models to test
compelling leads in vivo. We will also validate the identified interaction networks with patient data from the BRCA
I-SPY 2 trial and from HNSCC patients at UCSD (Aim 4). Taken together, our integrated approach establishes
a network of extensively validated interactions among genes, drugs and multiple phenotypic endpoints to
advance the practice of precision oncology.

## Key facts

- **NIH application ID:** 10704609
- **Project number:** 5U54CA274502-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Stephanie Irene Fraley
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $501,398
- **Award type:** 5
- **Project period:** 2022-09-14 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10704609

## Citation

> US National Institutes of Health, RePORTER application 10704609, Project 2: Functional Genetic Networks for Systems-Guided Precision Medicine (5U54CA274502-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10704609. Licensed CC0.

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