# (PQ4) - Tools for simultaneous disruption of multiple epigenetically silenced genes for studying their roles in tumorigenesis using ex vivo human and mouse colon organoid and in vivo mouse models

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $374,578

## Abstract

We will address PQ4 – the need to develop tools for simultaneous manipulation of multiple genes in human
cancer-relevant models – because a key challenge in the field is to understand how multiple genes affected by
epigenetic or copy number alterations act in concert to influence the process of tumor development. For the
epigenetically affected genes, we have shown that multiple important tumor suppressor and developmental
regulator genes are maintained in a permanently silenced state by promoter CpG-island (CGI) DNA
hypermethylation across various cancer types. Importantly many genes from the same biological pathways are
epigenetically silenced within the same patient sample. The cumulative effect of multiple epigenetically
silenced genes (ESGs) on tumor development remains an unaddressed aspect of cancer biology, mainly
because of a lack of tools for targeting multiple genes, tractable biological models and assays to measure
tumor development. In this proposal we seek to fill these gaps by leveraging our current work in human cancer
relevant models. Our motivation to pursue these studies originate from our work in the past 3-4 years where
we have modeled the early stages of human colorectal cancer (CRC) development by oncogenic BRAFV600E
and KRASG12D mutations. We show that in mouse proximal colon-derived organoids, induction of BrafV600E, but
not mutant KrasG12D, results in Wnt pathway activation, stem cell phenotype and transformation. Tumor
explants of the transformed organoids phenocopy human proximal CRC, inclusive of mucinous histopathology
and CGI hypermethylator phenotype (CIMP). We show that an aging-like acquisition of CGI hypermethylation
in organoids, not unlike that in aging human colon, promotes BrafV600E-mediated tumorigenesis by inducing
early Wnt pathway activation and stem cell phenotype, which are early features during tumorigenesis in this
model. Our studies, in concert with work from other labs, show that the organoids are a highly relevant model
that phenocopies features of human tumor progression. Further, we have established Cas9 and Cpf1-based
screening in organoids to identify loss of ESGs that promote tumorigenesis. The major goal of this project is to
develop tools for simultaneous inactivation of multiple ESGs to study their combined roles in cancers. In this
regard, we have developed inducible-CRISPR based human colon-derived organoids for multiple gene
knockout to study the collusion between ESGs and cancer driver mutations. We will continue on these by
developing human and mouse colon organoid system with inducible Cas9/Cpf1 for systematically screening
random combinatorial inactivation of multiple ESGs that confer stem-cell maintenance and block differentiation.
Next we will focus on important candidate genes, bioinformatically mined form cancer databases, to study the
synergy of their simultaneous loss in cancer initiation. Finally, we will apply and validate our studies in in-vivo
mouse models. In summary, thi...

## Key facts

- **NIH application ID:** 10232133
- **Project number:** 5R01CA230995-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** STEPHEN B. BAYLIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $374,578
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10232133

## Citation

> US National Institutes of Health, RePORTER application 10232133, (PQ4) - Tools for simultaneous disruption of multiple epigenetically silenced genes for studying their roles in tumorigenesis using ex vivo human and mouse colon organoid and in vivo mouse models (5R01CA230995-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10232133. Licensed CC0.

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