Identification of key microRNAs as predictive biomarkers of Nilotinib response in chronic myeloid leukemia: a sub-analysis of the ENESTxtnd clinical trial

Clinical Insights into Structure, Regulation, and Targeting of ABL Kinases in Human Leukemia

Chronic myeloid leukemia is a multistep, multi-lineage myeloproliferative disease that originates from a translocation event between chromosome 9 and chromosome 22 within the hematopoietic stem cell compartment. The resultant fusion protein BCR::ABL1 is a constitutively active tyrosine kinase that can phosphorylate multiple downstream signaling molecules to promote cellular survival and inhibit apoptosis. Currently, tyrosine kinase inhibitors (TKIs), which impair ABL1 kinase activity by preventing ATP entry, are widely used as a successful therapeutic in CML treatment. However, disease relapses and the emergence of resistant clones have become a critical issue for CML therapeutics. Two main reasons behind the persisting obstacles to treatment are the acquired mutations in the ABL1 kinase domain and the presence of quiescent CML leukemia stem cells (LSCs) in the bone marrow, both of which can confer resistance to TKI therapy. In this article, we systemically review the structural and molecular properties of the critical domains of BCR::ABL1 and how understanding the essential role of BCR::ABL1 kinase activity has provided a solid foundation for the successful development of molecularly targeted therapy in CML. Comparison of responses and resistance to multiple BCR::ABL1 TKIs in clinical studies and current combination treatment strategies are also extensively discussed in this article.

Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching "chronic myeloid leukemia" and "artificial intelligence". The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the 'human' factor, which remains key in this domain.

Extracellular vesicles in the Chronic Myeloid Leukemia scenario: an update about the shuttling of disease markers and therapeutic molecules

Extracellular vesicles (EVs) are various sets of cell-derived membranous structures containing lipids, nucleic acids, and proteins secreted by both eukaryotic and prokaryotic cells. It is now well recognized that EVs are key intercellular communication mediators, allowing the functional transfer of bioactive chemicals from one cell to another in both healthy and pathological pathways. It is evident that the condition of the producer cells heavily influences the composition of EVs. Hence, phenotypic changes in the parent cells are mirrored in the design of the secreted EVs. As a result, EVs have been investigated for a wide range of medicinal and diagnostic uses in different hematological diseases. EVs have only recently been studied in the context of Chronic Myeloid Leukemia (CML), a blood malignancy defined by the chromosomal rearrangement t(9;22) and the fusion gene BCR-ABL1. The findings range from the impact on pathogenesis to the possible use of EVs as medicinal chemical carriers. This review aims to provide for the first time an update on our understanding of EVs as carriers of CML biomarkers for minimal residual disease monitoring, therapy response, and its management, as well as the limited reports on the use of EVs as therapeutic shuttles for innovative treatment approaches.

miR-181a plays the tumor-suppressor role in chronic myeloid leukemia CD34 + cells partially via SERPINE1

The formation of the BCR-ABL fusion gene drives human chronic myeloid leukemia (CML). The last 2 decades have witnessed that specific tyrosine kinase inhibitors (TKIs, e.g., imatinib mesylate, IM) against ABL1 improve disease treatment, although some patients still suffer from relapse and TKI resistance. Therefore, a better understanding of the molecular pathology of CML is still urgently needed. miR-181a-5p (miR-181a) acts as a tumor suppressor in CML; however, the molecular mechanism of miR-181a in CML stem/progenitor cells remains elusive. Herein, we showed that miR-181a inhibited the growth of CML CD34+ cells, including the quiescent subset, and sensitized them to IM treatment, while miR-181a inhibition by a sponge sequence collaborated with BCR-ABL to enhance the growth of normal CD34+ cells. Transcriptome data and biochemical analysis revealed that SERPINE1 was a bona fide and critical target of miR-181a, which deepened the understanding of the regulatory mechanism of SERPINE1. Genetic and pharmacological inhibition of SERPINE1 led to apoptosis mainly mediated by caspase-9 activation. The dual inhibition of SERPINE1 and BCR-ABL exhibited a significantly stronger inhibitory effect than a single agent. Taken together, this study demonstrates that a novel miR-181a/SERPINE1 axis modulates CML stem/progenitor cells, which likely provides an important approach to override TKI resistance.

Identification of multivariable microRNA and clinical biomarker panels to predict imatinib response in chronic myeloid leukemia at diagnosis

Imatinib Mesylate (imatinib) was once hailed as the magic bullet for chronic myeloid leukemia (CML) and remains a front-line therapy for CML to this day alongside other tyrosine kinase inhibitors (TKIs). However, TKI treatments are rarely curative and patients are often required to receive life-long treatment or otherwise risk relapse. Thus, there is a growing interest in identifying biomarkers in patients which can predict TKI response upon diagnosis. In this study, we analyze clinical data and differentially expressed miRNAs in CD34+ CML cells from 80 patients at diagnosis who were later classified as imatinib-responders or imatinib-nonresponders. A Cox Proportional Hazard (CoxPH) analysis identified 16 miRNAs that were associated with imatinib nonresponse and differentially expressed in these patients. We also trained a machine learning model with different combinations of the 16 miRNAs with and without clinical parameters and identified a panel with high predictive performance based on area-under-curve values of receiver-operating-characteristic and precision-recall curves. Interestingly, the multivariable panel consisting of both miRNAs and clinical features performed better than either miRNA or clinical panels alone. Thus, our findings may inform future studies on predictive biomarkers and serve as a tool to develop more optimized treatment plans for CML patients in the clinic.

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

The Double Face of miR-708: A Pan-Cancer Player with Dissociative Identity Disorder

Over the last decades, accumulating evidence has shown tumor-dependent profiles of miR-708, being either up- or downregulated, and thus, acting as a "Janus" regulator of oncogenic pathways. Herein, its functional duality was assessed through a thorough review of the literature and further validation in silico using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. In the literature, miR-708 was found with an oncogenic role in eight tumor types, while a suppressor tumor role was described in seven cancers. This double profile was also found in TCGA and GEO databases, with some tumor types having a high expression of miR-708 and others with low expression compared with non-tumor counterparts. The investigation of validated targets using miRBase, miRTarBase, and miRecords platforms, identified a total of 572 genes that appeared enriched for PI3K-Akt signaling, followed by cell cycle control, p53, Apellin and Hippo signaling, endocrine resistance, focal adhesion, and cell senescence regulations, which are all recognized contributors of tumoral phenotypes. Among these targets, a set of 15 genes shared by at least two platforms was identified, most of which have important roles in cancer cells that influence either tumor suppression or progression. In a clinical scenario, miR-708 has shown to be a good diagnostic and prognosis marker. However, its multitarget nature and opposing roles in diverse human tumors, aligned with insufficient experimental data and the lack of proper delivery strategies, hamper its potential as a sequence-directed therapeutic.