Laying the foundation for genomically-based risk assessment in chronic myeloid leukemia

Abstract

Outcomes for patients with chronic myeloid leukemia (CML) have substantially improved due to advances in drug development and rational treatment intervention strategies. Despite these significant advances there are still unanswered questions on patient management regarding how to more reliably predict treatment failure at the time of diagnosis and how to select frontline tyrosine kinase inhibitor (TKI) therapy for optimal outcome. The BCR-ABL1 transcript level at diagnosis has no established prognostic impact and cannot guide frontline TKI selection. BCR-ABL1 mutations are detected in ~50% of TKI resistant patients but are rarely responsible for primary resistance. Other resistance mechanisms are largely uncharacterized and there are no other routine molecular testing strategies to facilitate the evaluation and further stratification of TKI resistance. Advances in next-generation sequencing technology has aided the management of a growing number of other malignancies, enabling the incorporation of somatic mutation profiles in diagnosis, classification, and prognostication. A largely unexplored area in CML research is whether expanded genomic analysis at diagnosis, resistance, and disease transformation can enhance patient management decisions, as has occurred for other cancers. The aim of this article is to review publications that reported mutated cancer-associated genes in CML patients at various disease phases. We discuss the frequency and type of such variants at initial diagnosis and at the time of treatment failure and transformation. Current limitations in the evaluation of mutants and recommendations for future reporting are outlined. The collective evaluation of mutational studies over more than a decade suggests a limited set of cancer-associated genes are indeed recurrently mutated in CML and some at a relatively high frequency. Genomic studies have the potential to lay the foundation for improved diagnostic risk classification according to clinical and genomic risk, and to enable more precise early identification of TKI resistance.

Figure 1.. Frequency of mutated cancer genes at diagnosis and AP/BC.

The data from 15 studies of patients at diagnosis and 20 studies at AP/BC are reported where cancer genes were mutated in more than one patient at diagnosis and/or BC.[14,20-32,42-51] Some variants were not included, such as the ASXL1 E1102D variant, which is reported in the population databases at a frequency suggesting it represents the germline and is not pathogenic. Other variants reported in PAX5, TP53 and TET2 were also not included for the same reason. Four other genes associated with hematologic malignancy were mutated in one patient each at BC: U2AF1, XPO1, NPMI and SETD2. The number of samples sequenced for individual genes is highly variable and ranges from 49 for IKZF1 exon deletions to 518 for ASXL1 exon 12 variants at diagnosis. BCR-ABL1 kinase domain mutation status was reported in few studies. Therefore, the confidence for the mutation frequency for some genes is low compared with other genes. Only genes listed in the COSMIC Cancer Gene Census are included.

Figure 2.. Comparison of variants in two studies of patients at BC.

(a) Mutant genes at BC reported in the study of Grossman et al. [47]. (b) Mutant genes at BC reported in the study of Branford et al. [22]. The most frequently mutated genes were common to both studies: RUNX1, ASXL1 and IKZF1 exon deletions. These were detected in >50% of patients in both studies. Genes mutated in both studies are indicated by matching colors. Light grey shading indicates genes that were sequenced in both studies but were only detected in one of the studies. Dark grey shading indicates the genes that were only sequenced in one study.

Figure 3.. Dynamics of somatic mutation profiles following TKI therapy.

Five patterns of mutation dynamics are noted according to their longitudinal changes in mutation burden following TKI therapy. Pattern 1 does not show any significant changes but all the cases responded to TKI therapy optimally, implying independence of the clone carrying the mutation from the Ph-positive clone. Pattern 2 represents new acquisition of mutations, associated with treatment failure. Pattern 3 shows a linear relationship between the reduction rate of mutation allele burden and BCR-ABL1 transcript level reduction, implying that this mutation arose from the Ph-positive clone. Modified from Kim et al. [31].

Figure 4.. Potential future model for an enhanced risk classification by incorporation of genomic factors.

Clinical risk assessed according to the EUTOS long-term survival score. Genomic risk may be classified as Low if no mutant genes of clinical relevance are detected, Intermediate if a mutation demonstrated to confer a moderate risk is detected, and High if specific mutations or multiple mutations are detected.