Session: 617. Acute Myeloid Leukemia: Biology, Cytogenetics, and Molecular Markers in Diagnosis and Prognosis: Poster II
Hematology Disease Topics & Pathways:
AML, Diseases, Myeloid Malignancies
We collected data of targeted deep sequencing and whole exome sequencing of a cohort of 2188 AML cases from the Cleveland Clinic (n=855) and publicly available data. In total, we had 1636 primary (p), 433 secondary (s) and 119 (t) AML. Eighteen% of AML patients (n=388) carried a mutation in SF (SF mutant, SF-MT) while 83% were wild type (SF-WT AML, n=1800). More specifically, the frequency of SF-MT individuals was 14% in pAML (n=234) and tAML (n=17) while a significantly higher frequency was observed in sAML (32%, n=137) as compared to pAML + tAML (P<0.0001). Next, we compared the baseline, clinical and genomic characteristics of SF–MT vs SF-WT AML to uncover invariant phenotypic features and molecular associations that could potentially define SF-AML. SF-MT patients were significantly older than SF-WT AML [71 vs 60 years; P<0.0001] with an 83% having and age ≥60 years vs 56% in each group, respectively (P<0.0001). SF-MT AML were significantly of male predominance as compared to SF-WT AML (72 vs 50%, P<0.0001). Percentages of anemia and thrombocytopenia were similar in both groups. Median bone marrow blasts% was lower in SF-MT compared to SF-WT AML (49 vs 61; P<0.0001). In terms of cytogenetic profile, SF-mutant AML had lower odds of normal karyotype compared to SF-WT AML (50% vs 59%; P=0.002). Trisomy 8 was significantly frequent in SF-MT AML (15 vs 8%; P<0.0001) while -5/del(5q) was more common in SF-WT AML (10 vs 5%, P=0.008).
The molecular profile of SF-MT AML was characterized by significantly frequent mutations in ASXL1 (23 vs 5%, P<0.0001), BCOR/L1 (10 vs 3%, P<0.0001), CBL (5 vs 2%, P=0.001), ETV6 (4 vs 1%, P<0.0001), IDH2 (15 vs 10%, P=0.006), IDH2R140 (13 vs 8%, P=0.0009), JAK2 (5 vs 1%, P<0.0001), KRAS (6 vs 3%, P=0.03), MECOM (4 vs <1%, P=0.02), RUNX1 (29 vs 7%, P<0.0001), STAG2 (12 vs 5, P<0.0001), TET2 (19 vs 11%, P<0.0001) while mutations in DNMT3A (15 vs 26%, P<0.0001), FLT3ITD (18 vs 10%, P=0.005), NPM1 (32 vs 11%, P<0.0001), PTPN11 (8 vs 4%, P=0.005), TP53 (8 vs 4%, P=0.002) shaped the SF-WT AML profile. Survival analyses showed that SF-MT AML had dismal survival outcome compared to SF-WT AML (17.4 vs 26.2 mo., P<0.0001). We then used multivariate cox proportional hazards to identify genomic features associated with survival in each group separately and found that favorable features included NPM1MT in both groups and biallelic CEBPA in SF-WT AML. On the other hand, adverse genomic lesions included complex karyotype, DNMT3AMT, FLT3ITD-MT and TP53MT in both groups, in addition to KRASMT and PHF6MT in SF-MT while ASXL1MT, KDM6AMT, RUNX1MT in SF-WT AML. The comparison of OS between SF-MT vs SF-WT AML for the association of a specific genomic lesion (cytogenetic/gene abnormality) revealed that mutations in DNTM3A (17.8 vs 21.6 mo., P=0.02), ETV6 (17.4 vs 32.1 mo., P=0.04), KRAS (11.9 vs 28.7, P<0.0001), NRAS (12.9 vs 26.5 mo., P=0.0005), cohesins (12.1 vs 29.3 mo., P=0.0002) and TET2 (13.4 vs 28.1 mo., P=0.006) significantly contributed to shortened survival and worse outcomes.
In sum, we described the presence of SF mutations in AML and defined the phenotypic features of this subset and its genomic associations as compared to SF-WT AML. We further highlighted the unique genomic contributors associated with their survival outcomes. Perhaps, future larger scale studies of this AML group will delineate its potential to represent a unique well-defined entity of AML.
Disclosures: Maciejewski: Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria.
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