# A Big Data Approach to BCR ABL Leukemias

> **NIH NIH R21** · GEORGIA INSTITUTE OF TECHNOLOGY · 2020 · $190,454

## Abstract

BCR-ABL1 positive leukemias account for a substantial portion of adult leukemia. Tyrosine kinase 
inhibitors (TKIs) have dramatically changed survival outlook. However, current protocols recommend 
patients receive TKI chemotherapy agents indefinitely, causing long-term toxicity and substantive 
quality of life deficits, leading to decreased TKI compliance. Moreover, >50% of patients who stop 
TKIs ultimately relapse and are not as responsive to post-relapse treatment; additionally, mutation 
resistance is becoming an increasing issue with TKIs as patients live longer. It is hypothesized 
patient heterogeneity within BCR-ABL1 leukemias are a major driving factor on outcome and strongly 
influences optimal TKI selection and cessation. However, small cohort size and disease rarity has 
impacted large, pragmatic clinical trials, necessitating a big data approach. The overall goal is 
to quilt together individual studies to produce a comprehensive view of BCR-ABL1 leukemias that 
includes epidemiology, etiology, assessment, and therapy, as well their inter-relationships. With 
a comprehensive view, personalized, predictive medicine becomes possible. This project utilizes 
literature mining and “big data” techniques to analyze four major categories: epidemiology (who 
gets BCR-ABL1 leukemias, how response correlates to patient characteristics, etc.); etiology (what 
factors trigger mutation, mechanisms to improve TKI specificity, preclinical model metrics, 
prognostic indicators of recurrence/relapse, etc.); assessment (identifying new 
diagnostic/prognostic metrics, improving polymerase chain reaction (PCR) protocols, objective 
staging criteria); and treatment (aggregate effect sizes among different therapies, short and 
long-term side effect profiles, TKI selection protocols, adjunctive and combination therapies, 
criteria for TKI cessation, etc.). The specific aims of the project include: 1) prototype a data 
path and construct infrastructure for BCR-ABL1 data curation from literature and/or clinical 
sources; 2) construct literature ontological field map to quantify topic depth, aggregate data, and 
identify relationships within and between categories; 3) perform exploratory analysis to assess 
aggregate statistical power and prototype predictive models for TKI optimization. In summary, the 
present project delivers a 21st century, big data approach for BCR-ABL1 leukemia to optimize 
clinical management and expedite basic preclinical research.

## Key facts

- **NIH application ID:** 9869875
- **Project number:** 5R21CA232249-02
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Cassie S Mitchell
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $190,454
- **Award type:** 5
- **Project period:** 2019-04-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9869875

## Citation

> US National Institutes of Health, RePORTER application 9869875, A Big Data Approach to BCR ABL Leukemias (5R21CA232249-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9869875. Licensed CC0.

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