Epigenomic insights and computational advances in hematologic malignancies

Abstract

Hematologic malignancies (HMs) encompass a diverse spectrum of cancers originating from the blood, bone marrow, and lymphatic systems, with myeloid malignancies representing a significant and complex subset. This review provides a focused analysis of their classification, prevalence, and incidence, highlighting the persistent challenges posed by their intricate genetic and epigenetic landscapes in clinical diagnostics and therapeutics. The genetic basis of myeloid malignancies, including chromosomal translocations, somatic mutations, and copy number variations, is examined in detail, alongside epigenetic modifications with a specific emphasis on DNA methylation. We explore the dynamic interplay between genetic and epigenetic factors, demonstrating how these mechanisms collectively shape disease progression, therapeutic resistance, and clinical outcomes. Advances in diagnostic modalities, particularly those integrating epigenomic insights, are revolutionizing the precision diagnosis of HMs. Key approaches such as nano-based contrast agents, optical imaging, flow cytometry, circulating tumor DNA analysis, and somatic mutation testing are discussed, with particular attention to the transformative role of machine learning in epigenetic data analysis. DNA methylation episignatures have emerged as a pivotal tool, enabling the development of highly sensitive and specific diagnostic and prognostic assays that are now being adopted in clinical practice. We also review the impact of computational advancements and data integration in refining diagnostic and therapeutic strategies. By combining genomic and epigenomic profiling techniques, these innovations are accelerating biomarker discovery and clinical translation, with applications in precision oncology becoming increasingly evident. Comprehensive genomic datasets, coupled with artificial intelligence, are driving actionable insights into the biology of myeloid malignancies and facilitating the optimization of patient management strategies. Finally, this review emphasizes the translational potential of these advancements, focusing on their tangible benefits for patient care and outcomes. By synthesizing current knowledge and recent innovations, we underscore the critical role of precision medicine and epigenomic research in transforming the diagnosis and treatment of myeloid malignancies, setting the stage for ongoing advancements and broader clinical implementation.

Keywords: DNA methylation; Epigenetics; Hematologic malignancies; Machine learning.