Individualized Prognostic Framework Predicts Risk in Acute Leukemia
Researchers have developed a novel prognostic framework based on comprehensive genomic that predicts risk acute myeloid leukemia.
ORLANDO – Researchers have developed a novel prognostic framework based on comprehensive genomic and clinical data that predicts risk in patients with acute myeloid leukemia (AML), a study presented at the American Society of Hematology (ASH) 57th Annual Meeting & Exposition has shown.1
”We can correctly predict the risk status of 72% of patients,” Moritz Gerstung, PhD, of the European Bioinformatics Institute EMBL-EBI in Cambridge, UK, said during his presentation.
Although AML is a disease often driven by various concurrently occurring genomic lesions, there is a lack of data surrounding how a particular combination of genomic risk factors impacts a patient's outcome in combination with common clinical variable like blood counts.
Therefore, researchers sought to construct a prognostic framework based on genomic sequencing data of 111 cancer genes and diagnostic, treatment, and survival data from 1540 patients with AML.
Results showed that patient risk is impacted by a combination of mutations in NPM1, CEBPA-/-, FLT3ITD, and TP53, fusion genes generated by t(15;17), inv(16), and inv(3) rearrangements, as well as complex karyotype, del(5q) and trisomy 21. Researchers found that multiple risk factors act additively the majority of the time, except when there is an interaction between NPM1, FLT3ITD, and DNMT3A.
“Most patients have unique constellations of driver genes,” Dr Gerstung noted. “They may be grouped into 11 categories.”
Furthermore, prognosis seemed to be determined mostly by genomic factors with the remainder being attributed to diagnostic blood counts, demographic data, and treatment.
“Two-thirds of risk came from genomics, while one-third came from clinical characteristics,” Dr. Gerstung said.
Researchers then used models to make detailed predictions about the probability of being alive or dead during induction therapy, first complete remission, and after relapse. The model personalizes predictions based on a patient's constellation of risk factors, thereby providing a unique quantitative risk assessment to inform treatment decisions.
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“Predictions are expected to become more accurate with more patients,” Dr Gerstung concluded. “About 10,000 cases are needed for precision medicine.”
- Gerstung M, Papaemmanuil E, Martincorena I, et al. Personally tailored risk prediction of AML based on comprehensive genomic and clinical data. Oral presentation at: the American Society of Hematology (ASH) 57th Annual Meeting & Exposition ; December 5-8, 2015; Orlando, FL.