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1990 Genomic Landscape of Splicing Factor Mutant Acute Myeloid Leukemia

Program: Oral and Poster Abstracts
Session: 617. Acute Myeloid Leukemia: Biology, Cytogenetics, and Molecular Markers in Diagnosis and Prognosis: Poster II
Hematology Disease Topics & Pathways:
AML, Diseases, Myeloid Malignancies
Sunday, December 6, 2020, 7:00 AM-3:30 PM

Hassan Awada, MD1, Arda Durmaz2*, Cassandra M. Kerr, MS3*, Jaroslaw P. Maciejewski, MD, PhD3 and Valeria Visconte, PhD1

1Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic Foundation, Cleveland, OH
2Department of Systems biology and Bioinformatics, CWRU, cleveland
3Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH

Mutations in components of the RNA-splicing machinery are among the most prevalent genomic alterations in myelodysplastic syndromes (MDS, 45-85% based on disease subtype). With the advent of new powered genomic technologies, spliceosomal mutations have been also detected in acute myeloid leukemia (AML). AML cells have shown to express a large number of alternative spliced events with splicing factor (SF) mutations altering pre-mRNA splicing of downstream targets. How these mutations mediate, modify or support leukemogenic transformation is still unknown. One could think that indirectly the targets of SF alterations could be unveiled through detailed analysis of the phenotype of AML patients carrying those events. Therefore, we aimed to describe the frequency of mutations, clinical associations and molecular cooperative lesions of the most common SF genes (SF3B1, SRSF2, U2AF1, ZRSR2) in AML to speculate unique molecular combinations contributing to disease phenotypes.

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.

*signifies non-member of ASH