# Bay Area Cancer Target Discovery and Development

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $969,145

## Abstract

PROJECT SUMMARY
Our general strategy is to take advantage of novel tools and methodologies that we have developed
during our first two CTD^2 funding periods– more specifically pioneering and applying CRISPR based
technologies to aid the discovery and characterization of novel cancer targets and their modulators–
using innovative high throughput technologies. Our end goal is to uncover optimal combinations of
targets with the potential to eliminate all cancer cells, despite their clonal heterogeneity and
environmental context. This requires us to better understand tumor biogenesis, namely the
combinations of genes that drive oncogenesis, and tumor heterogeneity which complicates effective
therapeutic treatment.
In this proposal we build upon exciting systems allowing us to quantitate genotypic and phenotypic cell
heterogeneity in cell culture and in vivo. The overall goal is to identify synthetic gene combinations
necessary for clinical resistance and related to inter- and intra-tumor heterogeneity. We hypothesize
that altered cell states such as inflammatory phenotypes and lineage plasticity fuels therapy tolerance
and resistance. We apply single-cell approaches and cutting-edge lineage tracing tools to investigate
the genesis of pathogenic cellular state changes and use genetic screening, computational and
pharmacologic approaches, and clinically relevant in vitro and in vivo tumor models to identify
mechanistically calibrated, specific therapeutic vulnerabilities. These approaches will be applied to two
cancer, lung and breast adenocarcinoma.
Tumor biogenesis and evolution is a challenging area of research, largely due to the complexity of cell
types and behaviors and the combinations of genes that drive cancer types and subtypes is poorly
understood. We have developed next generation GEMMs to interrogate gene combinations that
promote cancer. In this aim, mouse models will be generated that contain combinations of genetic
perturbations of the top 30 TCGA recurrent mutations. These studies will associate the combination of
perturbagens with specific cell states, despite their clonal heterogeneity and cell state and lay a solid
foundation for identifying which combinations of recurrent genes respond to which therapy, thus
helping to stratify patients. This part of the research program focuses on lung cancer as it synergizes
with other components of the proposal. We apply an evolved lineage tracing technology with single
cell RNA-seq readout that lets us follow tumor evolution with unprecedented resolution. These studies
will help us understand how tumor plasticity enables cancers to evade therapeutic challenges. And
importantly, how the loss of tumor suppressor genes or gene combinations, alters the preferred
evolutionary paths a single transformed cell takes to reach aggressive and metastatic states.

## Key facts

- **NIH application ID:** 10504993
- **Project number:** 1U01CA272546-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Sourav Bandyopadhyay
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $969,145
- **Award type:** 1
- **Project period:** 2022-09-13 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10504993

## Citation

> US National Institutes of Health, RePORTER application 10504993, Bay Area Cancer Target Discovery and Development (1U01CA272546-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10504993. Licensed CC0.

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