http://www.raredr.com/news/11-subgroups-aml
Study Identifies 11 Subgroups of Acute Myeloid Leukemia

James Radke, PhD

A new study published in the New England Journal of Medicine may change the way we manage acute myeloid leukemia (AML) patients and how we develop future, more targeted therapies.
 
The study analyzed data from 1540 patient with AML from 3 clinical trials and the researchers identified 5234 driver mutations across 76 genes or genomic regions. Patterns of co-mutation compartmentalized the cohort into 11 classes, each with distinct diagnostic features and clinical outcomes.
 
The study was co-led by Elli Papaemmanuil, PhD, of Memorial Sloan Kettering Cancer Centre in New York and Peter Campbell, MB, PhD, of Britain's Wellcome Trust Sanger Institute.
 
The genomic subgroups included:
 Subgroup  % of patients
 AML with MPM1 mutation 27
 AML with mutated chromatin, RNA-splicing genes, or both 19
 AML with PT53 mutations, chromosomal aneuploidy, or both 13
 AML with inv(16)(p13.1qww) or t(16;16)(p13.1;q22); CBFB-M 5
 AML with biallelic CEBPA mutations 4
 AML with t(15;17)(q22;q12); PML–RARA 4
 AML with t(8;21)(q22;q22); RUNX1–RUNX1T1 4
 AML with MLL fusion genes; t(x;11)(x;q23) 3
 AML with inv(3)(q21q26.2) or t(3;3)(q21;q26.2); GATA2,MECOM(EVI1) 1
 AML with IDH2R172 mutations and no other class-defining lesions 1
 AML with t(6;9)(p23;q34); DEK–NUP214 1
   
 AML with driver mutations but no detected class-defining lesions 11
 AML with no detected driver mutations 4
 AML meeting criteria for ≥2 genomic subgroups 4

The researchers noted that in their study, 48% of the AML patients they looked at would not have been classified using the WHO’s 2008 classification for AML. Furthermore, the characterization of many new leukemia genes, multiple driver mutations per patient, and complex co-mutation patterns prompted the authors to go back to the drawing board in how to classify AML patients. They developed a Bayesian statistical model to compartmentalize AML into mutually exclusive subtypes on the basis of patterns of co-mutation and from that model, they were able to come up with the 11 subgroups of AML.
 
The largest group were those with NPM1-mutated AML (accounting for 27% of the cohort).
 
The second largest subgroup, accounting for 18% of the cohort (and a group that is not part of the 2008 WHO classification) was defined by mutations in genes regulating RNA splicing (SRSF2, SF3B1, U2AF1, and ZRSR2), chromatin (ASXL1, STAG2, BCOR, MLLPTD, EZH2, and PHF6), or transcription (RUNX1). In contrast to the WHO classes of AML, no single genomic lesion defines this group.
 
The third largest group were those with mutations in TP53, complex karyotype alterations, cytogenetically visible copy-number alterations (aneuploidies), or a combination.

What does it mean?

A better understanding of the genetic mutations involved with each AML patient can not only help target therapies but also predict outcomes.
 
The authors noted that in their model that included genetic, clinical, and diagnostic variables, they found that genomic features were the best predictors of survival.
 
As more studies validate this model and help ascertain the ways that it can guide treatment and predict outcomes, it will be curious to see value of this model in the clinic.

Reference

Papaemmanuil E, Gerstung M, Bullinger L, et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med 2016; 374:2209-2221 DOI: 10.1056/NEJMoa1516192
 
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