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  • 1
    In: Oncotarget, Impact Journals, LLC, Vol. 8, No. 17 ( 2017-04-25), p. 28812-28825
    Type of Medium: Online Resource
    ISSN: 1949-2553
    URL: Issue
    Language: English
    Publisher: Impact Journals, LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2560162-3
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  • 2
    In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 2839-2839
    Abstract: Early therapeutic decision-making is crucial in patients with higher-risk MDS where median survival is only around one year. Azacitidine prolongs survival for these patients (Fenaux et al, Lancet Oncology 2009) but clinically relevant biomarkers remain to be identified. We evaluated retrospectively, the impact of clinical parameters and mutational profiles in 134 consecutive patients treated with a median number of 7 cycles of Azacitidine (range 1-45), in accordance to European guidelines. The vast majority (n=114) had higher-risk disease i.e. MDS with IPSS int-2 or high, AML with multilinear dysplasia and 20-30% blasts or CMML-II. We combined an initial cohort from Karolinska University Hospital (n=89) with a validating cohort from King's College Hospital, London (n=45). Studied endpoints were response, as defined by the IWG criteria, and survival. Since prolonged survival is the main goal for this cohort of patients, we believe that survival is the most relevant endpoint, supported by the fact that even non-responding patients have a survival benefit from Azacitidine (Gore et al, Haematologica, 2013). While neither clinical parameters nor mutations had a significant impact on response rate, both karyotype and mutational profile were strongly associated with survival from the start of treatment, see Table 1 and Figure 1-2. IPSS high-risk cytogenetics was negatively associated with survival (median 20 vs 10 months; p 〈 0.001), whereas mutations in histone modulators (ASXL1, EZH2, MLL) were associated with prolonged survival (22 vs 12 months, p=0.001). This positive association was present in both cohorts and remained highly significant in the multivariate cox model. Importantly, patients with mutations in histone modulators lacking high-risk cytogenetics showed a survival of 29 months (response rate 73%) compared to 10 months (response rate 49%) in patients with the opposite pattern, see Figure 3. While TP53 was negatively associated with survival in the univariate analysis, neither RUNX1-mutations nor the number of mutations, previously reported as negative prognostic markers, appeared to influence survival in this cohort. In contrast, disease duration and cellularity showed a weak negative correlation with survival. We propose a model combining histone modulator mutational screening with cytogenetics in the clinical decision-making process for higher-risk MDS eligible for treatment with Azacitidine. Table 1. Variables associated with survival. Univariate analyses used the log-rank test. The cox model included all listed variables except response rate in a multivariate analyses. Estimated median survival (months) Univariate p-value Cox regression p-value Hazard ratio (95% CI) Response rate: CR / mCR vs PR/HI vs SD/PD 20 vs 20 vs 10 〈 0.001 IPSS cytogenetic risk group: Favorable vs Int vs Adverse 20 vs 20 vs 10 〈 0.001 〈 0.001* 3.00 (1.9-4.7) Disease duration ≥ 4 months: Yes vs No 14 vs 17 0.44 0.05** 1.01 (1.00-1.02) Marrow blasts ≥ 11%: Yes vs No 14 vs 14 0.7 Cellularity ≥ 70%: Yes vs No 14 vs 20 0.2 0.02 1.013 (1.002-1.023)** ANC ≥ 1.3: Yes vs No 14 vs 17 0.32 Platelets ≥ 60: Yes vs No 17 vs 12 0.07 Transfusion dependent: Yes vs No 13 vs 17 0.43 Therapy related: Yes vs No 17 vs 14 0.44 Number of mutations: 0 vs 1 vs ≥ 2 17 vs 12 vs 17 0.64 Epigenetic mutation: Yes vs No 19 vs 12 0.03 DNA methylation mutation: Yes vs No 14 vs 14 0.64 Histone modulator mutation: Yes vs No 22 vs 12 0.001 0.007 0.499 (0.3-0.83) Splicing factor mutation: Yes vs No 13 vs 17 0.31 ASXL1 mutation: Yes vs No 29 vs 12 0.03 TET2 mutation: Yes vs No 13 vs 16 0.45 EZH2 mutation: Yes vs No 20 vs 14 0.37 SF3B1 mutation: Yes vs No 13 vs 16 0.35 RUNX1 mutation: Yes vs No 17 vs 14 0.76 SRSF2 mutation: Yes vs No 20 vs 14 0.5 TP53 mutation: Yes vs No 9 vs 17 〈 0.001 *Comparing adverse cytogenetics vs the other groups. ** Disease duration, marrow blasts, cellularity, ANC and TPK were analyzed as a continuous variable in the cox model Figure 1. Kaplan-Meier estimated survival stratified for response and pre-treatment parameters Figure 1. Kaplan-Meier estimated survival stratified for response and pre-treatment parameters Figure 2. Forest plot indicating hazard ratio including confidence interval for all pre-treatment variables. The hazard ratios were retrieved using cox univariate regression models for each variable analyzed separately. Figure 2. Forest plot indicating hazard ratio including confidence interval for all pre-treatment variables. The hazard ratios were retrieved using cox univariate regression models for each variable analyzed separately. Figure 3. Kaplan-Meier estimated survival stratified for the two dominant predictors in the cox regression model: Adverse cytogenetics and histone modulator mutations Figure 3. Kaplan-Meier estimated survival stratified for the two dominant predictors in the cox regression model: Adverse cytogenetics and histone modulator mutations Disclosures McLornan: Novartis: Research Funding, Speakers Bureau. Jädersten:Celgene: Other: speakers fee. Kulasekararaj:Alexion: Consultancy. Mufti:Celgene: Consultancy, Other: Speakers fee. Hellström-Lindberg:Celgene Corporation: Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2015
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 3
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 4613-4613
    Abstract: Introduction: Azacytidine (Aza) is first-line treatment for patients with higher-risk MDS but only around 50% of patients respond to therapy. Overall survival for this patient group is short and clinical decision-making tools are highly warranted. As Aza may improve survival also in patients with hematologic improvement or stable disease, survival may be a better response predictor than response rate. Methods: We evaluated the impact of clinical parameters (n=134), mutations (n=90) and DNA methylation profiles (n=42) on response and survival in a cohort of consecutive patients with higher-risk MDS treated with Aza. Targeted sequencing of 42 genes involved in myeloid disease and Illumina 450 methylation arrays were applied for mutational assessment and methylation profiling, respectively. The IWG criteria were used for response scoring. Results: Patients were eligible for analysis if they had received ≥1 dose of Aza. Median number of cycles given was 6 (range 1-29). Responses were scored as CR (22%), mCR (11%), PR (3%), HI (13%), SD (27%) and PD (13%). Fifteen patients (11%) were not evaluated for response due to early death. Disease duration was negatively associated with both response (p=0.035) and survival (p=0.001). Adverse cytogenetics and high absolute neutrophil count was associated with shorter survival (p=0.03 and p=0.02) but not with response. No single mutation or group of mutations was associated with response although there was a weak positive trend for TET2 and ASXL1. When using survival as endpoint, ASXL1 showed a strong trend towards prolonged survival (median 29 vs 14 months, p=0.07) and, importantly, the group of patients with any mutation in histone modulators (ASXL1, EZH2, MLL) had a significant longer survival (median 28 vs 13 months, p=0.01). This remained significant in the cox regression model (HR 0.3223 (0.16-0.70 95% CI); p=0.002). No other mutations or group of mutations were associated with survival. Interestingly, previously reported negative prognostic factors including RUNX1 (p=0.82), TP53 (p=0.54), and the number of mutations (p=0.37), were not associated with survival in this Aza-treated cohort DNA methylation profiling identified 233 differentially methylated regions (DMRs) between responders and non-responders, corresponding to 200 genes, including six HOX-genes, which were highly enriched for gene ontology pathways involved in development and differentiation. High methylation of HOXA5, the most significant DMR, was associated with prolonged survival (22 vs 12 months, p=0.03). We also studied the methylation level of HOXA5 in CD34+ cells from patients with high-risk MDS and sorted compartments during myeloid differentiation in normal bone marrow. The methylation profile in responding patients was closer to that of differentiated cells while non-responding cells were closer to progenitor cells. Discussion: Single mutations have a limited impact on response rates. Howver, we demonstrate a clear survival benefit for patients with mutations in histone modulators, which previously have been reported as negative prognostic factors (Bejar, NEJM 2013; Haferlach, Leukemia 2014). Moreover, several negative risk factors, such as RUNX1, TP53, and the number of mutations were neutralized by Aza. Histone modulation mutations may therefore be used in the clinical decision-making for higher-risk MDS. We demonstrate for the first time that methylation profiles in genes involved in differentiation and development differ between responders and non-responders and that hypermethylation of HOXA5 is positively associated with survival (p=0.03). Since methylation pattern in HOXA5 is linked to differentiation status, we hypothesize that non-responding patients are skewed towards more immature differentiation. Figure 1: Survival curves Figure 1:. Survival curves Figure 2: DNA methylation levels at the HOXA5 locus. Squares represent gene location with light green=TSS-1500; Dark green=TSS-200; Red=Gene body; Magenta=1st Exon; Dark blue=5’UTR; Cyan=3’UTR and diamonds represent sample values. A=Median methylation level of responders illustrated with orange diamonds (MNCs) and non-responders with blue diamonds (MNCs). B=Added CD34+ cells with red diamonds. C=All patients. D=Normal bone marrow with PMN illustrated with brown diamonds and CMP with green diamonds. Figure 2:. DNA methylation levels at the HOXA5 locus. Squares represent gene location with light green=TSS-1500; Dark green=TSS-200; Red=Gene body; Magenta=1st Exon; Dark blue=5’UTR; Cyan=3’UTR and diamonds represent sample values. A=Median methylation level of responders illustrated with orange diamonds (MNCs) and non-responders with blue diamonds (MNCs). B=Added CD34+ cells with red diamonds. C=All patients. D=Normal bone marrow with PMN illustrated with brown diamonds and CMP with green diamonds. Figure 3 Figure 3. Disclosures No relevant conflicts of interest to declare.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 4
    In: Oncotarget, Impact Journals, LLC, Vol. 7, No. 16 ( 2016-04-19), p. 22103-22115
    Type of Medium: Online Resource
    ISSN: 1949-2553
    URL: Issue
    Language: English
    Publisher: Impact Journals, LLC
    Publication Date: 2016
    detail.hit.zdb_id: 2560162-3
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