# Secondary Use of Karyotype Data to Predict Outcomes in CLL

> **NIH NIH R03** · OHIO STATE UNIVERSITY · 2020 · $78,000

## Abstract

Project Summary
Individual genotypes influence disease incidence and severity. Personalized medicine seeks to
use genotype information to optimize an individual's chances of achieving or maintaining health.
Karyotyping, the practice of visually examining and recording chromosomal abnormalities, is
one of the earliest and most common genotyping techniques; it is part of the standard-of-care
for hematologic malignancies. As a result, large karyotype databases are available. For
example, the publicly available Mitelman database contains >65,000 karyotypes. However,
current clinical use of karyotype data is limited to patterns that are visually apparent to
cytogeneticists when scanning textual representations of karyotypes. Cytogeneticists record
karyotypes in a standardized notation, the International System for Human Cytogenetic
Nomenclature, which is human-readable but often difficult to analyze. As a result, clinically
relevant patterns hidden within long, complex karyotypes remain undiscovered. We have
developed a computational tool, CytoGenetic Pattern Sleuth (CytoGPS), which translates
“raw” karyotypes into a computable form that records loss, gain, or fusion (LGF) at the
resolution of cytogenetic bands. CytoGPS enables the secondary use of karyotype data by
facilitating the application of modern data mining tools. The long-term goal of this project is to
combine karyotype, genotype, and phenotype data to enable personalized cancer diagnostics
and therapeutics. Our main hypothesis is that CytoGPS can address important, clinically
actionable questions. Using Chronic Lymphocytic Leukemia (CLL) as a model disease, we will
analyze data on 1827 patients from an OSU research data repository to test this hypothesis with
the following Specific Aims:
Aim 1: To analyze cytogenetic data from CLL patients using CytoGPS in order to discover
novel, previously unrecognized, recurrent abnormalities or patterns of abnormalities in
stimulated cells from patients with CLL. We will also test whether patterns of abnormalities are
associated with disease onset or progression.
Aim 2: To use CytoGPS to construct, verify, and validate predictive models of CLL patient
response to the two most common therapies: Ibrutinib, or the combination therapy Fludarabine,
Cytoxan, Rituximab (FCR).

## Key facts

- **NIH application ID:** 9852428
- **Project number:** 5R03CA235101-02
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Kevin Robert Coombes
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $78,000
- **Award type:** 5
- **Project period:** 2019-01-21 → 2020-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9852428

## Citation

> US National Institutes of Health, RePORTER application 9852428, Secondary Use of Karyotype Data to Predict Outcomes in CLL (5R03CA235101-02). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9852428. Licensed CC0.

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