# In vivo CRISPR-screening of novel cancer cell-intrinsic targets that sensitize to local ionizing radiation, and possible combination with systemic checkpoint blockade.

> **NIH NIH R21** · UNIVERSITY OF CHICAGO · 2022 · $240,831

## Abstract

Project Summary
While many promising candidate radiosensitizers have been pursued, the development of a clinically approved
radiosensitizer remains a “holy grail” of clinical radiobiology. Our proposal represents an exciting union of
traditional translational basic science with state-of-the-art technology: the use of a CRISPR screening library to
identify new molecular targets that would radiosensitize cancer cells in vivo, both intrinsically and through the
involvement of the immune system (Aim 1), and testing in both in vivo mouse tumor models and using a unique
clinical dataset (Aim 2). This collaboration between the Weichselbaum lab, which has pioneered radiation
therapy breakthroughs for over 40 years, and the Manguso lab, featuring the next generation of investigators
who are world leaders in the nascent field of in vivo CRISPR screenings, makes us uniquely qualified to use
cutting-edge technology to solve a long-standing problem that would immediately improve clinical radiotherapy.
 Specifically, we plan to use in vivo CRISPR-screening for novel cancer cell-intrinsic genetic targets that
increase or decrease the efficacy of radiation combined or not with checkpoint blockade, directly or through the
participation of the immune system. By determining which sgRNAs are depleted in treated vs. untreated mice,
which would indicate that a sensitizing loss-of-function was introduced, we can identify putative targets for RT
alone or in combinational strategies. sgRNAs will be ranked by degree of depletion. Candidate targets will be
selected based on (i) having highest cumulative scores, (ii) novelty, and (iii) being depleted in both MC38 and
LLC tumor models. We will use SBRT-like and fractionated IR schemes +/- checkpoint blockade (anti-
PD1/anti-CTLA-4) to test in vivo three targets that increase the therapeutic effect of radiation directly, i.e., in
immunodeficient mice, and three targets that increase the therapeutic effect of IR combined with checkpoint
blockade in immunocompetent mice. Finally, we will examine target amplification in exome sequencing data
from patients treated in our institution with RT or radio-immunotherapy. Additionally, although not the main
focus for this proposal, an inspection of sgRNAs enriched instead of depleted in irradiated mice will point at
genes whose targeting could play an unknown role in cancer radio-resistance.
 The findings we present supporting our proposal highlight the key role of the local interaction between
cancer cells and immune cells in the irradiated tumor microenvironment to determine treatment outcome after
radiotherapy alone, or combined with immunotherapy. Unbiased approaches are required to discover novel
targets and better prioritize combination strategies to improve treatment. Successful completion of our studies
will address the long sought unmet need of a radiosensitizer to improve outcomes for cancer patients. This
would be the first in vivo CRISPR-based screening of genetic targets tha...

## Key facts

- **NIH application ID:** 10512896
- **Project number:** 1R21CA267607-01A1
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** RALPH R WEICHSELBAUM
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $240,831
- **Award type:** 1
- **Project period:** 2022-08-16 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10512896

## Citation

> US National Institutes of Health, RePORTER application 10512896, In vivo CRISPR-screening of novel cancer cell-intrinsic targets that sensitize to local ionizing radiation, and possible combination with systemic checkpoint blockade. (1R21CA267607-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10512896. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*