Integrating genetic and epigenetic factors in chronic myeloid leukemia risk assessment: toward gene expression-based biomarkers.

Cancer treatment is constantly evolving from a one-size-fits-all towards bespoke approaches for each patient. In certain solid cancers, including breast and lung, tumor genome profiling has been incorporated into therapeutic decision-making. For chronic phase chronic myeloid leukemia (CML), while tyrosine kinase inhibitor therapy is the standard treatment, current clinical scoring systems cannot accurately predict the heterogeneous treatment outcomes observed in patients. Biomarkers capable of segregating patients according to outcome at diagnosis are needed to improve management, and facilitate enrollment in clinical trials seeking to prevent blast crisis transformation and improve the depth of molecular responses. To this end, gene expression (GE) profiling studies have evaluated whether GE signatures at diagnosis are clinically informative. Patient material from a variety of sources has been profiled using microarrays, RNA sequencing and, more recently, single-cell RNA sequencing. However, differences in the cell types profiled, the technologies used, and the inherent complexities associated with the interpretation of genomic data pose challenges in distilling GE datasets into biomarkers with clinical utility. The goal of this paper is to review previous studies evaluating GE profiling in CML, and explore their potential as risk assessment tools for individualized CML treatment. We also review the contribution that acquired mutations, including those seen in clonal hematopoiesis, make to GE profiles, and how a model integrating contributions of genetic and epigenetic factors in resistance to tyrosine kinase inhibitors and blast crisis transformation can define a route to GE-based biomarkers. Finally, we outline a four-stage approach for the development of GE-based biomarkers in CML.

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

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.