# Statistical Methods for Characterizing Tumor Heterogeneity at the Single Cell Level

> **NIH NIH K00** · HARVARD UNIVERSITY · 2020 · $90,694

## Abstract

Chronic lymphocytic leukemia (CLL) is a cancer that exhibits genetic and transcriptional heterogeneity
along with a highly variable disease course among patients that remains poorly understood. Previous research
has highlighted vast inter- and intra-patient genetic heterogeneity, with subclonal evolution commonly occurring
in treatment settings leading to therapeutic resistance and relapse in many cases. In addition, our understanding
of the role of co-existing non-cancer cells in the tumor-microenvironment remains limited. Therefore,
characterization of these subclonal populations and their corresponding microenvironment will be paramount to
enabling precision medicine and synergistic treatment combinations that target subclonal drivers and eliminate
aggressive subpopulations thereby improving clinical outcome. In order to accurately dissect the genetic
landscape and reconstruct the underlying subclonal architecture in CLL, measurements must be made on the
single cell level.
In the F99-phase of this proposed research, Jean Fan will continue developing statistical methods and
computational software to analyze single cell RNA-seq data derived from CLL patient samples. Specifically, Jean
will develop methods to identify aspects of genetic heterogeneity, such as the presence of small single nucleotide
mutations and regions of copy number variation, in single cells. Jean will then reconstruct the genetic subclonal
architecture and characterize the gene expression profiles of identified subclonal populations.
In the K00-phase of this proposed research, Jean will characterize heterogeneity in the tumor-microenvironment
and develop methods to assess potential reciprocal interactions between subclones and their
microenvironment over time in response to therapy.
The proposed work will yield innovative statistical methods to enable the identification and
characterization of subpopulations in cancer and yield open-source software that can be tailored and applied to
diverse cancer types. Ultimately, application of these developed methods to CLL will provide a better
understanding of CLL development and progression.

## Key facts

- **NIH application ID:** 9898349
- **Project number:** 5K00CA222750-04
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** Jean Fan
- **Activity code:** K00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $90,694
- **Award type:** 5
- **Project period:** 2018-05-01 → 2020-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9898349

## Citation

> US National Institutes of Health, RePORTER application 9898349, Statistical Methods for Characterizing Tumor Heterogeneity at the Single Cell Level (5K00CA222750-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9898349. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*