Somatic mutations identified at diagnosis by exome sequencing can predict response to imatinib in chronic phase chronic myeloid leukemia (CML) patients
Authors: Luca Mologni, Rocco Piazza, Praveen Khandelwal, Alessandra Pirola, Carlo Gambacorti‐Passerini
Journal: American Journal of Hematology (2017)

First-line treatment of chronic myeloid leukemia (CML) with imatinib mesylate leads to hematologic remission in >95% of cases by 12 months and a normal life expectancy in chronic phase (CP)-CML patients.1, 2 Despite excellent prognosis, treatment failure is possible. Second- and third-generation tyrosine kinase inhibitors (TKIs) achieve a complete cytogenetic response (CCyR) rate of 40%-60% in imatinib-resistant patients. Recently, additional genetic alterations were detected in CP-CML samples at diagnosis, including ASXL1, DNMT3A, RUNX1, TET2, and JAK2 mutations3 and chromosomal abnormalities such as trisomy of chromosomes 8 and 19, monosomy of chromosome 7 and loss of chromosome Y,4 through targeted resequencing or conventional cytogenetics. We applied a global, unbiased approach to CML mutational landscape at diagnosis with bioinformatic analyses to the identification of the risk of failing first-line imatinib therapy. Nineteen consecutive CP-CML patients referred to our center were prospectively studied between 2007 and 2013 after obtaining informed consent (median age 49 years; Supporting Information Table S1). The Institutional Ethics Committee approved the study. The cytogenetic profile and BCR-ABL1 transcript level were periodically examined in accordance with NCCN/ELN Clinical Practice Guidelines. Myeloid cells were collected at diagnosis from bone marrow or peripheral blood (tumor sample). Normal lymphocytes were expanded in vitro with IL-2 as controls. All patients were prescribed imatinib (400 mg) as first-line TKI treatment. Six patients were subsequently shifted to second- and third-line TKI therapy because of primary or acquired resistance to imatinib. Only 1 patient eventually progressed to AP/BC. To test whether additional abnormalities co-exist along with BCR-ABL1 fusion gene in the Ph+ clone at onset, we performed whole-exome sequencing on matched tumor/normal pairs, at mean exon coverage of 84-fold per sample. Variants were called when present in >35% of tumor reads and <10% of control reads. Synonymous substitutions, non-coding changes and known SNPs were discarded. All candidate somatic mutations identified by WES were confirmed by Sanger sequencing. We identified a total of 81 non-synonymous mutations (median 4 per patient, range 0–11; Supporting Information Table S2). All mutations were not previously described in CP-CML, with the exception of ASXL1 (Q829*) in 1 patient. A weak positive correlation between the number of mutations and patients' age was found (r = 0.4179; P = .075), suggesting that some variants may be passengers. Unfortunately, unless biological data are available, it is not easy to discriminate between true oncogenic mutations and passenger events expanded by neoplastic transformation. In order to evaluate the oncogenic potential of each mutation, we applied OncoScore, a recently developed cancer scoring system based on automatic literature data mining.5 Genes with a score >22 were considered as potential cancer-causing genes. Using these settings, 45% of the mutations (37/81) were predicted to be driver variants (OncoScore >22), with some of them such as ASXL1, KAT6A, PATZ1, DLG5, TEP1, and KAT7 showing very high scores. The oncogenic potential in each patient was calculated either by annotating the sum of OncoScores of all the identified somatic variants (total OncoScore, tOS); or considering only those genes passing the driver threshold (OS22 score); or by 2topOS, which is the sum of scores from the 2 highest-ranking mutated genes in each patient. We found a positive correlation between tOS and Sokal score,6 suggesting that somatic variants of oncogenic potential may be associated with high-risk patients (Figure 1A). Indeed, patients in the high-risk Sokal group had significantly higher tOS compared to patients in the low-risk group (mean ± SEM, 126 ± 20 vs. 63 ± 9, P = .0473). (A) Pearson correlation and linear regression analysis of tOS versus Sokal score in 19 consecutive CP CML patients. (B) Box-and-whiskers graph of OncoScore and Sokal score values from patients who remained on imatinib therapy (IM) and patients who were shifted to second/third line TKI therapy (second) because of resitance to imatinib. The boxes extend from the 25th to 75th percentiles; the middle line represents the median; whiskers show the range (min to max). tOS, sum of the OncoScores of all mutated genes; OS22, sum of all the OncoScores from genes with score >22; 2topOS, sum of the OncoScores derived from the 2 top-scoring genes. Statistical analysis was run by unpaired 2-tailed t-test, where P < .05 was considered as statistically significant. (C) Scatter dot plot of OS22 scores from the 2 group of patients, showing the cut-off value calculated by Jouden's analysis. (D) Kaplan-Meyer curves showing time to loss of response to imatinib for patients showing OS22 > 66.4 (red, n = 8) or < 66.4 (blue, n = 11); P value represents the log-rank test. (E) Box-and-whiskers graphs analysis according to cytogenetic status at long-term follow-up in 19 patients on first-line imatinib: the distribution of OS22 and 2topOS scores is shown for patients with (CCyR, n = 17) and without (non-CCyR, n = 2) complete cytogenetic remission at 2 years of follow-up. A significance test was calculated by 2-tailed unpaired t-test method. P < .05 is considered as statistically significant Next, we asked whether mutations with high OncoScore could affect response to treatment. At 3 months following diagnosis, no significant differences were observed between patients with (10/19, 52.6%) or without CCyR, in average number of mutations or mean oncogenic scores, and no correlation was detected between quantitative real-time PCR values at 3 months and OncoScores. On long-term follow-up (median 5 years, range 2–8), 13/19 patients remained on imatinib while 6 were shifted to another regimen (either second-generation TKIs or imatinib dose increase). We found that the 6 imatinib-resistant patients possessed significantly higher OncoScores than the 13 patients still on imatinib therapy (Figure 1B). The distribution of OS22 values (48.1 ± 11.6 vs. 130.4 ± 14.4 for imatinib responding and resistant patients, respectively; P = .0007) allowed the definition of a cut-off threshold to discriminate between the 2 groups of patients, by ROC curve analysis (Figure 1C). An OS22 cut-off value of 66.4 correctly detects 17/19 outcomes (sensitivity 100%, specificity 85%, accuracy 89%; Youden index J = 0.8462, area under the ROC curve = 0.897; 95% CI = 0.672–0.988; P < .0001). Indeed, the 8 patients with OS22 > 66.4 had a markedly increased probability to lose response to imatinib, compared to those with a low OS22 (median time to loss of response, 15 months vs. not reached; Figure 1D). In contrast, we found no significant difference in Sokal score between imatinib-resistant and responding patients, indicating that Sokal score was less accurate than OncoScore as a prognostic factor in our patients population. These results were maintained also when we repeated all statistical analyses including genes that were found mutated at lower frequency (>25%) in the tumor sample. Interestingly, using this lower threshold, we identified recurrent mutations in the BCLAF1 gene in 2 patients. Whether this finding is significant for disease progression is unknown. At the same time point, 2 patients who never achieved CCyR had ∼2-fold higher mean OncoScores than patients (17/19) who eventually achieved CCyR (OS22, 158.9 ± 31.3 vs. 64.1 ± 11.7, P = .017; 2topOS, 125.0 ± 2.7 vs. 57.5 ± 7.8, P = .011; Figure 1E). Only 1 patient progressed to AP/BC and therefore no comparison was possible with nonprogressing patients. In conclusion, we found that additional mutations were present in BCR-ABL1 positive CP-CML patients at diagnosis, and that patients resistant to first-line imatinib treatment had an increased burden of mutations in cancer-related genes, which could be relevant to treatment response and possibly to the progression of CML. Prioritization of mutated genes by in silico scoring allowed the identification of high-risk patients. Interestingly, Sokal score could not differentiate between the 2 populations, suggesting that genetic analyses may have superior value compared with clinical scoring in identifying patients at high-risk of imatinib failure. If confirmed in larger cohorts these data could allow a better stratification of patients at diagnosis and potentially a more rational treatment selection. We thank Ms Michela Viltadi for assistance in the collection of samples. The authors declare that they have no conflicts of interest with the contents of this article. Additional Supporting Information may be found online in the supporting information tab for this article. Supporting Information Table 1 Supporting Information Table 2 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.