# (PQ4) Quantitative and multiplexed analysis of gene function in cancer in vivo

> **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $461,991

## Abstract

PROJECT SUMMARY
 Genome sequencing has catalogued the somatic alterations in human cancers and identified many
putative driver genes. However, human cancers generally evolve through the sequential acquisition of multiple
genomic alterations and simply identifying recurrent genomic alterations does not necessarily reveal their
functional importance to cancer growth. Genetically engineered mouse models have become a mainstay for the
analysis of gene function in cancer in vivo, however the breadth of their utility is limited by the fact that they are
neither readily scalable nor sufficiently quantitative. To increase the scope and precision of in vivo cancer
modeling, we previously integrated conventional genetically-engineered mouse models, CRISPR/Cas9-based
somatic genome engineering, and quantitative genomics with mathematical approaches. We developed
methods to inactivate multiple genes in parallel in mouse models of lung cancer using pools of barcoded sgRNA-
containing lentiviral vectors. This tumor barcoding with sequencing (Tuba-seq) approach uncovers the size of
each tumor, enables the parallel investigation of multiple tumor genotypes in individual mice, and allows the
generation of large-scale maps of gene function within autochthonous cancer models. Our preliminary data and
novel genetic systems, as well as our dedicated and collaborative team of investigators with expertise in cancer
genetics, mouse models, genome-editing, clinical cancer care, and quantitative modeling make us uniquely
positioned to conduct these studies. In this proposal, we will extend Tuba-seq to quantify the effect of
combinatorial genetic alterations through the development and validation of a platform for the rapid and
quantitative analysis of interactions between genetic alterations on tumor growth in vivo. To enable multiplexed
and quantitative analysis of the impact of temporally controlled genomic alterations on cancer cell growth in vivo,
we will also develop a system for inducible genome editing in established lung tumors. Finally, we will develop
novel in vivo approaches to comprehensively and broadly uncover the gene expression programs in cancer cells
of different genotypes in parallel. Through multiplexed in vivo genetic alterations, the effect of putative cancer
drivers can be uncovered at an unprecedented scale and resolution. The results of this proposal will be significant
because innovative methods for the cost-effective, quantitative, and multiplexed analysis of the genetic
determinants of cancer pathogenesis will illuminate novel aspects of tumorigenesis and accelerate our ability to
understand cancer evolution, drug responses, and therapy resistance.

## Key facts

- **NIH application ID:** 9997822
- **Project number:** 5R01CA231253-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Dmitri Petrov
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $461,991
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9997822

## Citation

> US National Institutes of Health, RePORTER application 9997822, (PQ4) Quantitative and multiplexed analysis of gene function in cancer in vivo (5R01CA231253-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9997822. Licensed CC0.

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