# Integrating targeted and immunotherapy to treat genetically heterogeneous cancers

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $1,010,431

## Abstract

Identification of cancer drug targets using high throughput screens of tumor cell lines has led to a number of
agents presently in clinical trials. In addition, recent advances in drugs that attack immune cells within tumors,
such as αCTLA4 and αPD-1, have highlighted the importance of immune modulation as a strategy for cancer
therapy. The next phase of cancer drug target discovery will seek to integrate these strategies to identify
combinations of drugs that most efficiently target both tumor cells and the immune components in advanced
cancers. The goal of this proposal is to identify and validate these combinations using large-scale data mining
and mouse pre-clinical cancer models that mimic the major genetic features of human cancer. This proposal
addresses both mechanisms of immune escape by a) finding genetic targets that may enhance tumor mutation
load, and b) carrying out high throughout screens in T cells or myeloid cells for targets that promote immune
cell infiltration. We will exploit unique mouse models that mirror major genetic categories of human cancer –
high vs low mutation load, and strong vs weak immune infiltrate. Applying single-cell RNAseq and mass
cytometric proteomic analyses, cutting edge immune composition databases and novel computational network
approaches to cancer target discovery using existing large databases, we propose to identify vulnerabilities
addressed by combining small molecule drugs with immunotherapy. We will make immunologically “cold”
tumors, that do not engage the immune system, into “hot” tumors that present more or stronger antigens, or
that encourage infiltration by immune effector cells. To achieve this goal, we propose three highly innovative
aims centered on perturbation of specific targets: first by a CRISP/Cas9 screen in immune cells of the tumor
microenvironment, second through increasing antigen load in tumors to optimize immune recognition and
finally through a network-based identification of tumor-expressed targets that may confer susceptibility to
existing immune-oncology therapies. This represents a true `network' of our collective expertise as well as a
measured collection of candidate and screening approaches.
AIM 1 –We will perform CRISPR screens in monocytes and T-cells to identify genes associated with tumor
entry and function in two distinct tumor types.
AIM 2– We will use genetic or pharmacological perturbation of newly generated candidate genes involved in
metabolic stress and ROS-induced DNA damage to increase mutation load and antigen abundance in a tumor-
specific manner, leading to improved responses to immunotherapy.
AIM 3 – We will exploit gene expression networks to identify druggable targets and pathways that augment
immune responses.
This proposal identifies pathways and perturbants for accelerating immunotherapies.

## Key facts

- **NIH application ID:** 10199951
- **Project number:** 5U01CA217864-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** ALLAN BALMAIN
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,010,431
- **Award type:** 5
- **Project period:** 2017-08-17 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10199951

## Citation

> US National Institutes of Health, RePORTER application 10199951, Integrating targeted and immunotherapy to treat genetically heterogeneous cancers (5U01CA217864-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10199951. Licensed CC0.

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