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. 2018 Aug;103(8):e351-e355.
doi: 10.3324/haematol.2018.190926. Epub 2018 Mar 22.

Automated decision tree to evaluate genetic abnormalities when determining prognostic risk in acute myeloid leukemia

Kevin Watanabe-Smith  1 , Brian J Druker  1   2   3 , Jeffrey W Tyner  1   4 , David K Edwards 5th  5
Affiliations

Automated decision tree to evaluate genetic abnormalities when determining prognostic risk in acute myeloid leukemia

Kevin Watanabe-Smith et al. Haematologica. 2018 Aug.
No abstract available

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Figures

Figure 1.
Figure 1.
Experimental design to determine the impact of genetic mutation co-occurrence on prognostic risk in acute myeloid leukemia (AML). Patient information from three AML datasets were combined and mutation status and karyotype were extracted. These records were analyzed using an automated script that classified each patient based on their European LeukemiaNet (ELN) prognostic risk score, and the prognostic impact of each genetic test was evaluated. *“Other abnormalities” is defined as the presence of one or two abnormalities. **“Complex karyotype” is defined as the presence of three or more abnormalities.
Figure 2.
Figure 2.
Mutational co-occurrence within cytogenetic prognostic risk categories in 1682 acute myeloid leukemia patients. Patients were grouped into four categories according to their karyotype: Adverse, Intermediate, or Favorable (which contain abnormalities that categorize them accordingly, based on the 2017 European LeukemiaNet guidelines); and Normal (which contains no abnormalities). (A) Patients were categorized as having TP53, RUNX1, or ASXL1 mutations, and, for the remaining patients, subdivided by sequential presence or absence of NPM1 mutations, FLT3-ITD, and CEBPA mutations. The area of each circle is proportional to the number of patients within that category. (B) The frequency of TP53, RUNX1, and ASXL1 was tabulated across different karyotype risk categories. Proportional Venn diagrams were drawn showing the overlap between the presence of complex karyotype, an adverse risk gene mutation, and all other adverse cytogenetic abnormalities.
Figure 3.
Figure 3.
Automated decision tree determines that genetic abnormalities have different prognostic significance individually and sequentially in acute myeloid leukemia patients. (A) Overview of automated decision tree to determine acute myeloid leukemia prognostic risk. After automatically identifying the prognostically significant karyotypic abnormalities, the decision tree returns the assigned prognostic risk category based on the hierarchy of abnormalities described in the European LeukemiaNet (ELN) guidelines (see Online Supplementary Methods). (B) Prognostic impact of individual genetic abnormalities, as measured by the omission of data from each of the seven prognostically significant genetic test and the assignment of patients into prognostic risk categories. k: karyotype; f: FLT3-ITD; n: NPM1; c: CEBPA; p: TP53; r: RUNX1; a: ASXL1. (C) Sequential analysis of every possible arrangement of the genetic tests to best determine overall prognostic risk. To mimic an unknown test result, each patient was considered to be wild type for gene mutations and have normal karyotype. For each test in a particular sequence, the prognostic risk category was calculated after successively adding in the test results, and the percentage of patients with a correctly called prognostic risk was recorded. Every possible test sequence (5040 total) was graphed and the optimized sequence to identify patients with favorable (green), intermediate (yellow and dark yellow), and adverse (red) risk were labeled. For all three categories, the optimized sequence began with karyotype, which is labeled in black. (D–F) The sequential analysis for all possible arrangement of tests to best identify patients with (D) favorable, (E) intermediate, and (F) adverse prognostic risk. For these sequences, the percentage of correctly called prognostic risk patients was normalized due to the differences in population size across categories.
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