# Core 3: Epigenomics Core

> **NIH NIH P01** · DANA-FARBER CANCER INST · 2020 · $308,602

## Abstract

Project Summary (Core 3)
The overall goal of the program is to study the key features of epigenetic circuitry in myeloma and improve our
understanding of global gene regulation to deploy novel therapeutics that target key epigenomic circuits. Cells
and cell states can be defined by their gene expression programs, and tumor cells commonly have deregulated
gene expression programs. This core will perform epigenomic analyses on clinical samples from Projects 1, 2,
and 4, as well as preclinical samples in Project 2 and 3. Specifically, samples will be obtained from patients
enrolled on the clinical trial (N=1260) evaluating an MRD-based therapeutic algorithm at time of diagnosis
(Project 1 and 2) and at relapse (Project 4). In each case, tumor cells will be isolated in Core 2 using well
established procedures. This core (Core 3) will perform analysis for select master transcription factors, their
chromatin cofactors, and the enhancer element marks. The core has capabilities to perform ChIP-seq using
low cell number (5000 cells; van Galen et al, Molecular Cell 2016 Jan;61(1):1-11), perform Single-cell ChIP-
seq (Rotem A et al Nat Biotechnol. 2015 Nov;33(11):1165-72) as well as adopt innovative variations
specifically needed to answer questions. The core will help accurately chart maps of histone modifications and
related chromatin structures.

## Key facts

- **NIH application ID:** 9987291
- **Project number:** 5P01CA155258-09
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Charles B Epstein
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $308,602
- **Award type:** 5
- **Project period:** 2011-12-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987291

## Citation

> US National Institutes of Health, RePORTER application 9987291, Core 3: Epigenomics Core (5P01CA155258-09). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9987291. Licensed CC0.

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