Prognostic interaction between ASXL1 and TET2 mutations in chronic myelomonocytic leukemia

Mutations involving epigenetic regulators (TET2~60% and ASXL1~40%) and splicing components (SRSF2~50%) are frequent in chronic myelomonocytic leukemia (CMML). On a 27-gene targeted capture panel performed on 175 CMML patients (66% males, median age 70 years), common mutations included: TET2 46%, ASXL1 47%, SRSF2 45% and SETBP1 19%. A total of 172 (98%) patients had at least one mutation, 21 (12%) had 2, 24 (14%) had 3 and 30 (17%) had >3 mutations. In a univariate analysis, the presence of ASXL1 mutations (P=0.02) and the absence of TET2 mutations (P=0.03), adversely impacted survival; while the number of concurrent mutations had no impact (P=0.3). In a multivariable analysis that included hemoglobin, platelet count, absolute monocyte count and circulating immature myeloid cells (Mayo model), the presence of ASXL1 mutations (P=0.01) and absence of TET2 mutations (P=0.003) retained prognostic significance. Patients were stratified into four categories: ASXL1wt/TET2wt (n=56), ASXL1mut/TET2wt (n=31), ASXL1mut/TET2mut (n=50) and ASXL1wt/TET2mut (n=38). Survival data demonstrated a significant difference in favor of ASXL1wt/TET2mut (38 months; P=0.016), compared with those with ASXL1wt/TET2wt (19 months), ASXL1mut/TET2wt (21 months) and ASXL1mut/TET2mut (16 months) (P=0.3). We confirm the negative prognostic impact imparted by ASXL1 mutations and suggest a favorable impact from TET2 mutations in the absence of ASXL1 mutations.


INTRODUCTION
Gene mutations are common (490%) in chronic myelomonocytic leukemia (CMML) and involve epigenetic regulators (TET2~60% and ASXL1~40%), spliceosome components (SRSF2~50%) and cell signaling (RAS~30% and CBL~15%). [1][2][3][4] Mutations involving ASXL1, TET2, RUNX1, CBL, SRSF2, RAS and IDH2 have demonstrated prognostic relevance on univariate survival analyses. 1,5,6 However, on multivariable analyses that have included additional CMML relevant factors, only ASXL1 mutations (frameshift and nonsense) have been shown to be prognostically detrimental. 1,2 This has led to the incorporation of ASXL1 mutations into molecular prognostic models such as the Molecular Mayo Model and the Groupe Francais des Myelodysplasies model. 1,2 TET2 mutations (chromosome 4q24) are frequent and are thought to be the driver mutations in CMML. 7 TET2 catalyzes the conversion of 5-methyl-cytosine to 5-hydroxymethyl-cytosine, regulating methylation and transcription. 8 The prognostic relevance of TET2 mutations remains unclear with some studies demonstrating favorable, 9 unfavorable 10 and no impact 1 on overall survival (OS). In vitro studies have shown that ASXL1 mutations enhance the de-ubiquitinase activity of the ASXL1-BAP1 (BRCA associated protein 1) complex, which then cooperates with loss of TET2 to skew towards myeloid development. 11 However, the mechanisms behind this effect and the prognostic interplay between TET2 and ASXL1 mutations remain unknown.
In the current study, we used a 27-gene panel assay to: (i) identify additional prognostically-relevant mutations in CMML, (ii) to determine if the number of mutations carries prognostic relevance and (iii) to study the prognostic interplay between TET2 and ASXL1 mutations.

MATERIALS AND METHODS
One-hundred and seventy five patients with CMML were included in the study. All patients had bone marrow biopsies and cytogenetic studies performed at diagnosis. The diagnosis of CMML, including subclassification into CMML-1 or CMML-2, and leukemic transformation were according to the 2008 World Health Organization criteria. 12 Risk stratification was per the Mayo-French cytogenetic system, 13 the Mayo model, 14 the Groupe Francais des Myelodysplasies model 1 and the Molecular Mayo model. 2 Twenty-seven gene panel targeted capture assays were carried out on bone marrow DNA specimens obtained at diagnosis for the following genes : TET2, DNMT3A, IDH1, IDH2, ASXL1, EZH2, SUZ12, SRSF2, SF3B1, ZRSR2,  U2AF1, PTPN11, Tp53, SH2B3, RUNX1, CBL, NRAS, JAK2, CSF3R, FLT3, KIT,  CALR, MPL, NPM1, CEBPA, IKZF and SETBP1. Paired-end indexed libraries were prepared from individual patient DNA in the Mayo Clinic Genomic Sequencing Core Laboratory using the NEBNext Ultra Library prep protocol on the Agilent Bravo liquid handler (NEB, Ipswich, MA, USA/Agilent Technologies Inc., Santa Clara, CA, USA). Capture libraries were assembled according to the Nimblegen standard library protocol (Roche Nimblegen, Inc., Basel, Switzerland). A panel including the regions of 27 heme-related genes was selected for custom target capture using the Agilent SureSelect Target Enrichment Kit (Agilent Technologies Inc Genesifter software was utilized (PerkinElmer, Danvers, MA, USA) to analyze targeted sequence data. Reads from the sequencing in fastq format were aligned using the Burrows-Wheeler Aligner against the 1  Based on prior observations, only frame shift and nonsense ASXL1 mutations were considered pathogenic. 2,14 For TET2, frame shift, nonsense, missense, insertions and deletions were considered pathogenic. Previously annotated single nucleotide polymorphisms (http//www.hapmap.org) in all the aforementioned genes were considered nonpathogenic.
All statistical analyses considered parameters obtained at time of referral to the Mayo Clinic, which in most instances coincided with time of bone marrow biopsy. Differences in the distribution of continuous variables between categories were analyzed by either Mann-Whitney (for comparison of two groups) or Kruskal-Wallis (comparison of three or more groups) test. Patient groups with nominal variables were compared by the chi-square test. Overall survival was calculated from the date of first referral to date of death (uncensored) or last contact (censored). Leukemiafree survival (LFS) was calculated from the date of first referral to date of leukemic transformation (uncensored) or death/last contact (censored). Overall and LFS curves were prepared by the Kaplan-Meier method and compared by the log-rank test. Cox proportional hazard regression model was used for multivariable analysis. P o 0.05 were considered significant. The Stat View (SAS Institute, Cary, NC, USA) statistical package was used for all calculations.
TET2 (ten-eleven translocation (TET) oncogene family member 2) is a member of the TET family of proteins. 22 Although TET2 mutations are widely prevalent in CMML, thus far, they have not been shown to independently impact either OS or LFS. 1 In the current study, TET2 mutations were seen in 46% of CMML patients and the absence of TET2 mutations negatively impacted OS. Additionally, the presence of clonal TET2 mutations, in the absence of clonal ASXL1 mutations (ASXL1wt/TET2mut), had a favorable impact on OS. The mechanism behind this association is unclear. In MDS and younger patients with CMML (age o 65 years), the presence of clonal TET2 mutations, in the absence of clonal ASXL1 mutations, have been associated with response to hypomethylating agents (5-azacitidine and decitabine). 5,23 Treatment data on our cohort of patients were incomplete and it is currently unknown as to whether this favorable impact was an effect of better responses to hypomethylating agents or not.
Approximately, 80% of patients with MDS have one or more oncogenic driver mutations (SF3B1~24%, TET2~22%,    ASXL1 and TET2 mutations in CMML MM Patnaik et al SRSF2~15% and ASXL1~15%). 4 In a large study (n = 738), Papaemmanuil et al. 4 demonstrated that driver mutations had an equivalent prognostic significance and LFS steadily declined as the number of driver mutations increased. 78% had at least one oncogenic mutation, while 43% had 2 or 3 and 10% had 4-8 mutations. Variants of unclear significance in oncogenic genes such as ASXL1 also adversely impacted outcomes. In the current study, 98% of the CMML patients had at least one mutation, 12% had 2, 14% had 3 and 17% had 43 mutations. The number of oncogenic mutations in CMML did not impact either the LFS or OS. In summary, nearly all patients with CMML express one or more myeloid neoplasm-relevant mutations. Similar to prior studies, the three most frequent mutations include TET2, ASXL1 and SRSF2. 1,2 Unlike in MDS, survival outcomes in CMML were not affected by the number of concurrent driver mutations. We confirm the negative prognostic impact on OS imparted by ASXL1 mutations 1,2 and also suggest a favorable prognostic impact from TET2 mutations, unless accompanied by ASXL1 mutations. These findings need validation in a larger data set.