# Rapid ex vivo biosensor cultures to assess dependencies in gastroesophageal cancer

> **NIH NIH R01** · BROAD INSTITUTE, INC. · 2021 · $566,213

## Abstract

The ability to predict dependencies given the molecular features of a
patient’s tumor is central to cancer precision medicine. The systematic use of CRISPR/Cas9 and pharmacologic
tools in established cancer models is showing great potential to discover new targets. However, existing model
development approaches require long periods of culture time during which evolutionary pressures
reduce heterogeneity. And, it remains challenging to create long-term models for certain tumor types and
genotypes, making it challenging to use perturbational tools to experimentally map dependencies.
To address these challenges, our overarching goal is to develop ‘rapid ex vivo tumor biosensors’ whereby
we would be able to interrogate cancer dependencies in an immediate short-term ‘culture’ of cancer cells taken
from a patient biopsy/surgery/fluid collection as a novel research-grade experimental model of cancer. In doing
so, we aim to couple the timing of drug or CRISPR/Cas9 perturbation with the preservation of subcellular
heterogeneity. If successful, we hypothesize that this modelling approach will more accurately recapitulate
patient tumors and may ultimately serve as a stronger foundation for preclinical therapeutic studies. This work
should also substantially expand the fraction of patient samples that can be interrogated.
Here, we propose using gastroesophageal adenocarcinoma (GEA) as a test case for this strategy due to our
experience as well as the existence of marked intra-tumor heterogeneity. However, once established, this novel
modeling platform should enable a wide range of basic and translational questions (both for GEA and other
tumors) that require model formats that include heterogeneous cell populations.
Our goal will be achieved via two Specific Aims including (1) using patient-derived organoids created on
rapid time frames for CRISPR/Cas9 editing to validate emerging GEA dependencies; and (2) developing the
ability to directly visualize and perturb single cells from matching patient ascites fluid or disaggregated primary
tumors ex vivo using label-free imaging methods. We will benchmark these approaches against each other using
the same clinically annotated, serially collected patient samples. In following the instructions for this RFP, we
focus on technology-development focused goals as opposed to deeper mechanistic studies. We focus on
benchmarking predictions and assessing reproducibility, sensitivity and specificity. This work is innovative, in
that it brings together expertise at the intersection of functional genomics, advanced computational approaches
for image-analysis and GEA genomics. If successful, this effort could have significant impact by establishing a
foundation to expand this approach to other disease (tumor and non-cancer) indications.

## Key facts

- **NIH application ID:** 10115675
- **Project number:** 5R01CA248280-02
- **Recipient organization:** BROAD INSTITUTE, INC.
- **Principal Investigator:** Jesse Samuel Boehm
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $566,213
- **Award type:** 5
- **Project period:** 2020-04-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10115675

## Citation

> US National Institutes of Health, RePORTER application 10115675, Rapid ex vivo biosensor cultures to assess dependencies in gastroesophageal cancer (5R01CA248280-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10115675. Licensed CC0.

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