Chronic myeloid leukaemia (CML) is a myeloproliferative disorder arising in the haemopoietic stem cell (HSC) compartment. This disease is characterised by a reciprocal t(9;22) chromosomal translocation, resulting in the formation of the Philadelphia (Ph) chromosome containing the BCR-ABL1 gene. As such, diagnosis and monitoring of disease involves detection of BCR-ABL1. It is the BCR-ABL1 protein, in particular its constitutively active tyrosine kinase activity, that forges the pathogenesis of CML. This aberrant kinase signalling activates downstream targets that reprogram the cell to cause uncontrolled proliferation and results in myeloid hyperplasia and 'indolent' symptoms of chronic phase (CP) CML. Without successful intervention, the disease will progress into blast crisis (BC), resembling an acute leukaemia. This advanced disease stage takes on an aggressive phenotype and is almost always fatal. The cell biology of CML is also centred on BCR-ABL1. The presence of BCR-ABL1 can explain virtually all the cellular features of the leukaemia (enhanced cell growth, inhibition of apoptosis, altered cell adhesion, growth factor independence, impaired genomic surveillance and differentiation). This article provides an overview of the clinical and cell biology of CML, and highlights key findings and unanswered questions essential for understanding this disease.

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MiRNAs post-transcriptionally repress gene expression by binding to mRNA 3′UTRs, but the extent to which they act through protein coding regions (CDS regions) is less well established. MiRNA interaction studies show a substantial proportion of binding occurs in CDS regions, however sequencing studies show much weaker effects on mRNA levels than from 3′UTR interactions, presumably due to competition from the translating ribosome. Consequently, most target prediction algorithms consider only 3′UTR interactions. However, the consequences of CDS interactions may have been underestimated, with the reporting of a novel mode of miRNA-CDS interaction requiring base pairing of the miRNA 3′ end, but not the canonical seed site, leading to repression of translation with little effect on mRNA turnover. Using extensive reporter, western blotting and bioinformatic analyses, we confirm that miRNAs can indeed suppress genes through CDS-interaction in special circumstances. However, in contrast to that previously reported, we find repression requires extensive base-pairing, including of the canonical seed, but does not strictly require base pairing of the 3′ miRNA terminus and is mediated through reducing mRNA levels. We conclude that suppression of endogenous genes can occur through miRNAs binding to CDS, but the requirement for extensive base-pairing likely limits the regulatory impacts to modest effects on a small subset of targets.

Background: A single genomic event is sufficient to cause CML; Ph translocation and the resulting BCR-ABL fusion. Additional genomic lesions accompany progression, which occurs very rapidly after diagnosis (dx) in a minority. Identification at dx of patients (pts) with poor prognosis remains an important goal and new sequencing technology enhances the prospects of uncovering pathologically relevant lesions for early warning of disease progression. Aim: To determine the somatic genomic landscape at dx, the risk conferred by genomic lesions towards blast crisis (BC), and whether mechanisms that underlie CML progression are shared by other malignancies. Method: Sequencing the whole exome (WES) and transcriptome (RNAseq) of paired tumor-normal samples (bone marrow mesenchymal stromal cells or remission) identified somatic single nucleotide variants, indels and gene fusions. Twenty-eight chronic phase (CP) first line imatinib (IM) treated pts were tested: 14 had BC at a median of 9 mo, r 3-60; and 14 had good response (MMR by 6 mo). Also tested were 4 pts diagnosed in advanced phase (2 accelerated phase [AP]; 2 BC) and 5 historical pts with BC at a median of 64 mo, r 38-100. Results: At dx, a median of 33 somatic variants were detected per pt (r 1-62). The number of variants did not correlate with response, or CP vs AP/BC, but increased with age (r =0.48, P =.007), consistent with accumulation of variants in stem cells with aging and suggesting that many may be "passenger mutations". Non synonymous protein coding variants were present at a median of 7 per pt at dx (r 0-17), again without difference between groups. Most variants had an allele frequency close to 50%, indicating their likely presence in all leukemic cells. However, polyclonality at dx was evident by variants with low allele frequency that either expanded or diminished at BC, Fig A. All 4 pts in AP/BC at dx had mutations in genes implicated in cancer pathogenesis (cancer genes) at dx; CBFB-MYH11 fusion, BCORL1, GATA2 and PTPRT, and SMARCA1. Of the 28 pts with first line IM, 11 had 15 somatic and 1 germline non synonymous variants/fusions at dx of known/potential significance: oncogenic mutations in IDH1 (R132H) and TP53 (germline R248Q); 6 frameshift/stop/splice site mutations in ASXL1, 1 EZH2 stop, 1 SETD1B stop, 1 MLL2 frameshift, 1 CHD1 splice site; and 4 novel fusions. Two of the fusions were generated by inversions of 2-13 MB of chr 22: PPM1F-SPECC1L (truncating the protein phosphatase PPM1F) and MYH9-BCR (truncating MYH9, reported to regulate p53 stability). Of these 11 pts, 9 had BC at a median of 6 mo of IM, r 3-39, and 2 had MMR by 6 mo. The 2 good response pts had ASXL1 mutations (both stop) and 1 also had a fusion involving chr 9 and 22 (TNRC6B-NEK6). The frequency of BC in CP pts with potentially pathogenic variants at dx was significantly higher than pts without such variants; 9/11 (82%) vs 5/17 (29%), P =.02. At BC, 18 pts had WES performed. A median of 6 non synonymous variants were gained (r 0-15) including 1-4 mutations in cancer genes in 15/18 pts, Fig B. Six of 13 first line IM pts also had 11 BCR-ABL KD mutations at BC (8 P loop) and 5/6 were among the pts who acquired mutations in cancer genes. The pt with the germline oncogenic TP53 mutation acquired a novel ANKRD11-UBQLN1 fusion at BC at 5 mo. Interestingly, ANKRD11 is a key regulator of the oncogenic potential of this mutation. In total, 6 genes were recurrently mutated; ASXL1, BCORL1, RUNX1, GATA2, MLL and UBE2A. Mutations occurred in genes that primarily belonged to classes mutated in AML, Fig B. Of the 23 AP/BC samples, 22 had non synonymous variants in genes involved in epigenetic regulation/chromatin modification. Variants were also detected in genes involved in ubiquitination and nuclear export, including an XPO1 variant reported in CLL. Nucleocytoplasmic transport has been implicated in IM resistance. Conclusion: Risk of BC was significantly associated with cancer gene mutation or novel fusions at dx. Some mutated pathways in CML were common to other cancers. Epigenetic regulation/chromatin modification appears to play a central role in CML pathogenesis, and ubiquitination and nuclear export may be of emerging relevance. Notably, most pts with BCR-ABL KD mutations at BC also acquired cancer associated mutations, indicating multiple mechanisms may contribute to progression. Future testing at dx will likely include tumor/normal exome/transcriptome sequencing to aid risk stratification. Figure 1. Figure 1. Disclosures Branford: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Research Funding; Qiagen: Membership on an entity's Board of Directors or advisory committees. Yeung:Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Hughes:ARIAD: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.

Background: Scoring systems at CML diagnosis, such as Sokal risk, provide important response prediction for imatinib (IM) treated patients (pts). Specific treatment policies have been suggested for high risk pts to optimize otherwise inferior outcomes. However, responses among pts with high risk are heterogeneous and new biomarkers are required to facilitate rational selection of optimal therapy. Biological factors, such as germline genetic variation, may play a role in therapy response dynamics. We aimed to identify predictive biomarkers of response to IM at CML diagnosis to aid selection of front line therapy for optimal treatment outcomes. Methods: Targeted amplicon sequencing using a custom Ion AmpliSeq panel and the Ion Proton was performed for 35 genes: 10 BCL2 family genes involved in TKI initiated apoptosis (including BIM, BAD and BCL2); 5 drug metabolism genes; and 20 genes implicated in hematologic malignancies (including ASXL1 and TET2). Genotypes were determined for 200 candidate single nucleotide variants (SNPs) for 528 front line IM and 83 front line NIL treated pts. For the IM pts, baseline variables were assessed for association with outcome: Sokal risk, age, gender, assigned IM dose (400, 600 or 800 mg); and genotype. Results: SNPs significantly associated with outcome in univariate analyses were assessed in multivariate models with the other baseline variables. The Sokal risk, ASXL1 rs4911231 and BIM rs686952 SNPs were independent predictors of 12 mo MMR, 48 mo MR4, MR4.5 and failure free survival (FFS, loss of any response, death, progression to AP/BC). For the ASXL1 SNP, the homozygous T genotype (155/508 evaluable pts, 30%), and for the BIM SNP, the A allele (249/507 evaluable pts, 49%) were associated with superior outcomes. We explored the additive effect of combining the genotypes of the ASXL1 and BIM SNPs on outcome. Three risk groups were readily identified (defined in Fig): Good (16% of evaluable pts), Average (46%) and Poor (37%). There were significant differences in the cumulative incidence of 3 mo EMR, 12 mo MMR, and 48 mo MR4, MR4.5 and FFS, as stratified by these SNP risk groups in IM treated pts (Table and Fig A). No significant association was found for progression to AP/BC or survival for any baseline variable. To examine the predictive power of SNP genotype within the high Sokal risk group, high risk pts were stratified by SNP genotype group. Significant differences were observed for EMR, MMR, MR4, MR4.5 and FFS (Table and Figure B), demonstrating the ability of the SNP genotype within high Sokal risk pts to predict response. Moreover, high Sokal risk pts harboring a poor risk SNP genotype had a significantly higher risk of progression to AP/BC vs high Sokal risk pts with an average/good risk genotype, 12% vs 2% (P =.03). The impact of SNP genotype risk on achieving 12 mo MMR was examined in the 83 pts treated with frontline NIL (median 24 mo follow up). In contrast to the significant difference observed for IM pts, there was no significant difference for NIL pts: 75% vs 73% vs 64% for good, average and poor risk, respectively, P =.34, suggesting the poor risk conferred by genotype may be abrogated by more potent TKI. Conclusion: Our data suggest inherent genetic variation contributes to the heterogeneity of response to IM. An intronic SNP in BIM, a key initiator of TKI induced apoptosis, and a synonymous SNP in ASXL1 exon 12, a region commonly mutated in hematologic cancers, were strong biomarkers of IM response. The mechanism by which these SNPs affect response awaits further clinical and experimental evaluation. Among pts with high Sokal risk, the genotype of these 2 SNPs delineated response and identified a good risk subgroup where more potent TKI may not be required for optimal outcomes. Assessment of genetic variation at diagnosis may contribute to a prognostic score that will allow for optimization of therapy. Disclosures Yeung: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel grant international meeting, Research Funding. Hughes:Bristol-Myers Squibb: Honoraria, Research Funding; ARIAD: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Branford:BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Qiagen: Membership on an entity's Board of Directors or advisory committees.

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