Classes of ITD predict outcomes in AML patients treated with FLT3 inhibitors Academic Article uri icon

Overview

MeSH Major

  • Anaplastic Lymphoma Kinase
  • Carcinoma, Non-Small-Cell Lung
  • Chromosome Breakpoints
  • Lung Neoplasms

abstract

  • © 2018 American Association for Cancer Research. Purpose: Recurrent internal tandem duplication (ITD) mutations are observed in various cancers including acute myeloid leukemia (AML), where ITD mutations in tyrosine kinase receptor FLT3 are associated with poor prognostic outcomes. Several FLT3 inhibitors (FLT3i) are in clinical trials for high-risk FLT3-ITD–positive AML. However, the variability of survival following FLT3i treatment suggests that the mere presence of FLT3-ITD mutations might not guarantee effective clinical response. Motivated by the heterogeneity of FLT3-ITD mutations, we investigated the effects of FLT3-ITD structural features on the response of AML patients to treatment. Experimental Design: We developed the HeatITup (HEAT diffusion for Internal Tandem dUPlication) algorithm to identify and quantitate ITD structural features including nucleotide composition. Using HeatITup, we studied the impact of ITD structural features on the clinical response to FLT3i and induction chemotherapy in FLT3-ITD–positive AML patients. Results: HeatITup accurately identifies and classifies ITDs into newly defined categories of "typical" or "atypical" based on their nucleotide composition. A typical ITD's insert sequence completely matches the wild-type FLT3, whereas an atypical ITD's insert contains nucleotides exogenous to the wild-type FLT3. Our analysis shows marked divergence between typical and atypical ITD mutation features. Furthermore, our data suggest that AML patients carrying typical FLT3-ITDs benefited significantly more from both FLT3i and induction chemotherapy treatments than patients with atypical FLT3-ITDs. Conclusions: These results underscore the importance of structural discernment of complex somatic mutations such as ITDs in progressing toward personalized treatment of AML patients, and enable researchers and clinicians to unravel ITD complexity using the provided software.

publication date

  • January 15, 2019

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC6335170

Digital Object Identifier (DOI)

  • 10.1158/1078-0432.CCR-18-0655

PubMed ID

  • 30181385

Additional Document Info

start page

  • 573

end page

  • 583

volume

  • 25

number

  • 2