Abstract
Myelodysplastic syndromes (MDS) arise in older adults through the stepwise acquisition of multiple somatic mutations. The genetic heterogeneity that results includes mutations of diverse genes and their combinations, clonal hierarchy, genetic configuration (e.g., bi-allelic or compound heterozygous, hemizygous lesions), specific positions within a gene including canonical hotspots vs. other positions, and types of mutation (truncations vs. missense), all of which could differentially affect pathogenesis. Given the binary status (e.g. mutated vs. wild-type) used in many clinical analyses, the true impact of specific types of mutations may be obscured and their specific roles underestimated.
Deep targeted NGS was carried out for a panel of the 36 most frequently mutated genes in 1,809 MDS patients (low-risk MDS (n=839) vs. high-risk MDS (n=607), MDS/MPN (n=212), and sAML n=151). Copy number alterations (CNA) were also evaluated by combining karyotyping, microarray, and digital copy number analysis. With a mean coverage of 862x, after removing SNPs and errors, 3,971 somatic mutations were identified, the most common (>10% of cases) being TET2, SF3B1, ASXL1, del(5q), SRSF2, complex karyotype, and del(7q).
For the purpose of this proof of concept analysis we focused on illustrative genes (TP53, RUNX1, TET2, and EZH2) affected by 2 recurrent hits. Bi-allelic TET2 or TP53 mutations were found in 15% (271/1,809) and 4% (72/1,809) of patients, respectively. TET2 and RUNX1 were most likely biallelic, whereas TP53 and EZH2 were most often affected by mutations and somatic deletion. Comparing the distribution of canonical vs. other types of mutations in genes, DNMT3A mutations affected the canonical site (R882) in 17% (35/203) of patients, were truncating in 39% (79/203) and missense in 44% (89/203) have also been found; deletions affecting the DNMT3A locus are rare. Within U2AF1, U2AF1Q157 are more frequent than U2AF1S34 (54% vs. 35%).
Next, we checked correlation between these different types of mutations of one gene. 78 significant combinations were found. For instance, U2AF1Q157 mutations more commonly accompanied ASXL1 mutations and del(7q) and less frequently DNMT3A and BCOR mutations, trisomy8 and del(20) when compared to U2AF1S34 mutations [ASXL1 mutations 53% (42/80) in U2AF1Q157 vs. 16% (8/49) in U2AF1S34, P < .0001]. TET2 Bi-allelic mutations were more commonly associated with ZRSR2 and SRSF2 mutations, and less frequently del(5q) when compared to TET2 mono-allelic mutations [SRSF2 mutations 29% (80/276) in TET2-bi vs. 15% (34/227) in TET2-mono, P = .003]. In addition, patients with SRSF2 missense mutations were more likely to have RUNX1 bi-allelic mutations than those with SRSF2 in-frame mutations.
We evaluated the impact of different types of mutations and combinations of them on disease phenotypes and survival. We then evaluated the impact of different types of mutations and their combinations on clinical phenotypes including dichotomous morphological (MDS vs. MDS/MPN) features, progressive (low- vs. high risk) subtypes. EZH2 bi-allelic alterations were more commonly associated with myleoproliferative features` compared to EZH2 mono-allelic alteration (q=.016). TET2 bi-allelic alterations and truncating mutations were found more frequently in higher-risk subtypes than TET2 mono-allelic and missense mutations (q<.001). In survival analyses, patients with DNMT3AR882 mutations had a poorer prognosis than those with truncating and the other missense mutations [P = .033, HR 1.86 (1.05-3.3)].
Next, using the PyClone bioanalytic pipeline, we recapitulated for each patient the clonal hierarchy and defined "dominant" vs. "secondary" mutations. DNMT3AR882 mutations were likely to be dominant/founder lesions compared to truncating or the other missense mutations: 77% (27/35) for R882 vs. 51% (40/79) for truncating vs. 45% (47/98) for the other missense, p=.0046. Specific dominant and secondary mutational pairs also differentially affected survival compared to the reverse configuration (q<.1) including EZH2 and RUNX1 or BCOR and U2AF1 or RUNX1 and BCOR.
In conclusion, we report a comprehensive analysis of various types and configurations of lesions of individual commonly affected genes. Our results indicate that establishment of clinical or phenotypic correlations requires consideration of the type, rank and configuration of somatic mutations.
Disclosures
Mukherjee: McGraw Hill Hematology Oncology Board Review: Other: Editor; Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy. Nazha:Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; Jazz Pharmacutical: Research Funding; Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau. Sekeres:Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Ogawa:Asahi Genomics: Equity Ownership; Dainippon-Sumitomo Pharmaceutical, Inc.: Research Funding; Qiagen Corporation: Patents & Royalties; RegCell Corporation: Equity Ownership; ChordiaTherapeutics, Inc.: Consultancy, Equity Ownership; Kan Research Laboratory, Inc.: Consultancy. Maciejewski:Novartis: Consultancy; Alexion: Consultancy.
