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G. Nteliopoulos et al.
Functional associations of variants with chronic myeloid leukemia
To further investigate any association of the altered genes with CML a protein-protein-interaction (PPI) net- work of P210BCRABL1 with the 21 coded proteins affected by the somatic variants was constructed. Twenty-one of 23 proteins were parts of the network with six proteins including those encoded by ASXL1, IKZF1, EP300 and RUNX1 interacting directly with P210BCRABL1 suggesting a functional association (Online Supplementary Table S7 and Online Supplementary Figure S7).
Finally, we studied whether the presence of a somatic variant in epigenetic modifiers influenced the DNA methylation signature in the imatinib cohort. Hierarchical clustering based on 1,028 differentially methylated posi- tions (DMP) (see criteria in the Online Supplementary Results) clearly separated the 12 variant and 30 non-variant CMP-CP subjects (Online Supplementary Figure S8). Functional annotation of DMP showed the imatinib phar- macokinetics/pharmacodynamics pathway being among the top ten hits of over-represented pathways (P=0.0016) (Online Supplementary Table S8).
Discussion
The successful introduction of TKI in CML therapy has resulted in an excellent outcome for approximately 90%
of individuals, who have a life expectancy approaching that of unaffected individuals.35 However, the remaining 10% should ideally be identified at diagnosis and offered more potent TKI immediately or early allogeneic-stem cell transplantation if they demonstrate TKI resistance. The most widely used biomarker for outcomes in CML-CP, namely BCRABL1 transcript levels after three months on TKI (BCRABL1 ≤10%), identifies a cohort with an excel- lent prognosis. However, patients with BCRABL1 >10% at three months may or may not respond to 2G-TKI. Our aim was to investigate associations between somatic vari- ants in epigenetic modifiers and response to imatinib and 2G-TKI given from diagnosis of CML-CP.
Others have explored the predictive value of a number of different biomarkers at the time of diagnosis including gene expression,36-38 protein expression,39 DNA methyla- tion,40 miRNA expression,41 and SNP analysis,42-44 but none has proved sufficiently accurate and precise for clinical decision-making. We previously identified different genome-wide DNA methylation and gene expression pat- terns between CML-CP and normal individuals,32 but this did not correlate clearly with TKI response. However, this prompted us to investigate the role of genetic variants in epigenetic modifiers in greater detail.
We used targeted amplicon sequencing to detect genetic variants that might correlate with TKI response. To opti- mize the opportunity to assess differences in genetic vari- ants between responders and non-responders to TKI-ther-
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Figure 3. Association of occur- rence of somatic variants with clinical outcome of individuals starting on second-generation tyrosine kinase inhibitor (2G- TKI) treatment. Kaplan-Meier survival analyses in 2G-TKI- treated subjects with somatic variants (red-dashed line) ver- sus non-variant (black-solid line). The end points used were cumulative incidence of major molecular response MR3 at five years (A) and probabilities of event-free survival (EFS) (B), progression-free survival (PFS) (C) and chronic myeloid leukemia (CML)-related sur- vival at six years after start of therapy (D). HR (95% CI) derived from Cox proportional hazard regression models and the P-value calculated by the Log Rank test also shown. Number of subjects (N) per group is also shown. Notably, one subject has been excluded from the survival analysis due to non-CML-related death, whereas 12 subjects have been excluded from the EFS and five from the major molec- ular response (MR3) analyses because of 2G-TKI failure due to intolerance.
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haematologica | 2019; 104(12)