# Systematic characterization of cancer variants using single-cell functional genomics

> **NIH NIH R33** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $418,693

## Abstract

PROJECT SUMMARY/ABSTRACT
Cancer is a genetic disease, and the set of mutations in a tumor affects both its behavior and its response to
therapies. Large sequencing initiatives have produced catalogs of gene variants arising in different cancers.
Substantial challenges remain, however, in interpreting their effects. First, even when variants affect the same
gene, their molecular phenotypes may be distinct. Second, many variants are common enough that they have
been identified, but still sufficiently rare that no targeted studies have characterized them. Finally, cancer in
general arises from cooperation among multiple mutations, so the function of a variant in one context—cell
type, genetic background, or environment—may only partly inform its behavior in another. The sheer number
of possible variants and contexts argues for taking a systematic approach to phenotyping. Here, we present
BEAT-seq (Base Editing Allele Transcriptome sequencing), a flexible, scalable, and robust approach for
engineering cancer-associated variants by CRISPR-mediated base editing and measuring the resulting effects
on cellular phenotype by single-cell RNA sequencing. Robustness follows from our development of a sensor
assay that can quantify the base editing efficiency of many sgRNAs in parallel, enabling us to identify those
that reliably introduce cancer variants. We then exploit an improved Perturb-seq protocol, enabling us to
introduce libraries of variants in pooled format and simultaneously capture both the sgRNAs, encoding the
programmed edits, and single-cell transcriptomes, carrying their phenotypic consequences. In Aim 1, we
credential BEAT-seq by generating validated sgRNAs targeting common cancer variants. We profile the effects
of these variants across different epithelial cell types—pancreatic and lung—and across different genetic
backgrounds to study the role of context. Finally, we explore whether BEAT-seq can assign function by
constructing a library targeting ~500 somatic and germline variants of unknown significance identified through
MSK-IMPACT sequencing. These tasks grow gradually in analytical complexity. In Aim 2, we establish rigorous
statistical pipelines for the interpretation of single-cell functional genomics experiments. We show that the
orthogonal characterization from the sensor assay enables a Bayesian approach to identify edited and
unedited cells, addressing a central challenge that affects many single-cell screens. We then develop a data
normalization procedure for representing perturbations’ effects in relative terms, enabling comparisons to be
made across contexts. Finally, in Aim 3 we conduct in vivo BEAT-seq experiments profiling cells carrying
dozens of p53 variants introduced by orthotopic transplantation into mouse pancreases. This work enables
parallelized characterization of cancer variants on a scale not previously feasible. Our results will provide
insight into how variants affect tumor phenotype in different contexts, illum...

## Key facts

- **NIH application ID:** 10795918
- **Project number:** 5R33CA267221-03
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** SCOTT W. LOWE
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $418,693
- **Award type:** 5
- **Project period:** 2022-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10795918

## Citation

> US National Institutes of Health, RePORTER application 10795918, Systematic characterization of cancer variants using single-cell functional genomics (5R33CA267221-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10795918. Licensed CC0.

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