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. 2018 Jan 3;9(11):9714-9727.
doi: 10.18632/oncotarget.23882. eCollection 2018 Feb 9.

Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms

Guillermo Montalban-Bravo  1 , Koichi Takahashi  1   2 , Keyur Patel  3 , Feng Wang  2 , Song Xingzhi  4 , Graciela M Nogueras  5 , Xuelin Huang  5 , Ana Alfonso Pierola  1 , Elias Jabbour  1 , Simona Colla  1 , Irene Gañan-Gomez  1 , Gautham Borthakur  1 , Naval Daver  1 , Zeev Estrov  1 , Tapan Kadia  1 , Naveen Pemmaraju  1 , Farhad Ravandi  1 , Carlos Bueso-Ramos  3 , Ali Chamseddine  1 , Marina Konopleva  1 , Jianhua Zhang  4 , Hagop Kantarjian  1 , Andrew Futreal  2 , Guillermo Garcia-Manero  1
Affiliations

Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms

Guillermo Montalban-Bravo et al. Oncotarget. .

Abstract

The prognostic and predictive value of sequencing analysis in myelodysplastic syndromes (MDS) has not been fully integrated into clinical practice. We performed whole exome sequencing (WES) of bone marrow samples from 83 patients with MDS and 31 with MDS/MPN identifying 218 driver mutations in 31 genes in 98 (86%) patients. A total of 65 (57%) patients received therapy with hypomethylating agents. By univariate analysis, mutations in BCOR, STAG2, TP53 and SF3B1 significantly influenced survival. Increased number of mutations (≥ 3), but not clonal heterogeneity, predicted for shorter survival and LFS. Presence of 3 or more mutations also predicted for lower likelihood of response (26 vs 50%, p = 0.055), and shorter response duration (3.6 vs 26.5 months, p = 0.022). By multivariate analysis, TP53 mutations (HR 3.1, CI 1.3-7.5, p = 0.011) and number of mutations (≥ 3) (HR 2.5, CI 1.3-4.8, p = 0.005) predicted for shorter survival. A novel prognostic model integrating this mutation data with IPSS-R separated patients into three categories with median survival of not reached, 29 months and 12 months respectively (p < 0.001) and increased stratification potential, compared to IPSS-R, in patients with high/very-high IPSS-R. This model was validated in a separate cohort of 413 patients with untreated MDS. Although the use of WES did not provide significant more information than that obtained with targeted sequencing, our findings indicate that increased number of mutations is an independent prognostic factor in MDS and that mutation data can add value to clinical prognostic models.

Keywords: chronic myelomonocytic leukemia; mutations; myelodysplastic syndromes; prognosis; response.

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Conflict of interest statement

CONFLICTS OF INTEREST The authors do not disclose any conflicts of interest.

Figures

Figure 1
Figure 1. Mutational landscape of the studied MDS cohort
(A) Distribution and frequency of identified mutations by WHO Classification. The Y axis includes the percentage of patients harboring the specified mutation. Stacked columns display prevalence of each given mutation by WHO classification. (B) Distribution of mutations by pathway within the 114 MDS patients.
Figure 2
Figure 2. Association of mutations and pathway distribution
(A) Circos plot including all mutation associations among the discovery cohort. Colors are determined by functional pathway of each given gene. (B) Patterns of association of pathway abnormalities among studied patients. Areas shaded in pink represent co-occurrence. * = p < 0.05. + = p < 0.001 (C) Patterns of association of mutations and karyotype among studied patients. Areas shaded in pink represent co-occurrence and those in green mutual exclusiveness. Color palette determined by Pearson´s r correlation. * = p < 0.05. + = p < 0.001.
Figure 3
Figure 3. Graphical representation of clonal distribution
(A) Mean and standard deviation of VAF of identified mutations. (B) Distribution and frequency of identified mutations within major or minor clones among patients with clonal heterogeneity.
Figure 4
Figure 4. Overall survival outcomes by IPSS-R and molecular IPSS-R model in the discovery cohort
(A) Kaplan-Meier estimates of overall survival in the study cohort according to the integrated Molecular IPSS-R model. (B) Kaplan-Meier estimates of overall survival in the study cohort by IPSS-R scoring system.
Figure 5
Figure 5. Overall survival outcomes by IPSS-R and molecular IPSS-R model in the additional cohort
(A) Kaplan-Meier estimates of overall survival in the additional cohort according to the integrated Molecular IPSS-R model. (B) Kaplan-Meier estimates of overall survival in the additional cohort by IPSS-R scoring system.
See this image and copyright information in PMC

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