Decoding and Targeting the Tumor Ecosystem in Hematological Malignancies: Microenvironmental Cues, Epigenetic Landscapes and Systems Biology Insights

Abstract

Hematological malignancies are not just beginning to be studied as being cell-autonomous, but rather as multi-cellular ecosystems in which malignant clones are able to usurp bone marrow and lymphoid niches to facilitate survival, immune evasion and resistance to treatment. Recent findings involve mutual interactions between stromal factors, immune subgroups, extracellular matrix, and tumor-produced extracellular vesicles as one of the key determinants of disease initiation, disease progression, and minimal residual disease. At the same time, epigenetic landscape perturbations, including DNA methylation, histone post-translational modifications, chromatin remodeling complexes, and non-coding RNAs, become the major regulators of malignant lineage identity, stemness and niche-guiding programs. These abnormalities in epigenetic regulations reflect and constitute microenvironmental conditions and hence they are modifiable therapeutic weaknesses. The current technological improvements in the fields of single-cell genomics, spatial transcriptomics, and integrative multi-omics have now rendered it possible to solve cellular phenotypes, signaling circuits, and epigenetic states on a tissue-scale level. These modalities, in combination with longitudinal sampling and liquid-biopsy designs, allow the reconstruction of clonal fates and niches remodelling underlying therapeutic failure. The use of such high-dimensional datasets necessitates systems-biology models and computational algorithms that can derive intercellular networks, predict ecosystem responses to perturbation and prioritize combination strategies that can effectively target malignant cells and supportive niches. Initial experiments with network biology and machine-learning-based biomarkers have the potential to allow the use of ecosystem-oriented and precision therapies, but clinical implementation is limited by spatial heterogeneity, model fidelity, and predictive validation. The review summarizes existing information about tumor-ecosystem biology in hematologic malignancies, identifies epigenetic processing that binds intrinsic and extrinsic disease advantages, surveys systems-level approaches to ecosystem decoding, and assesses new therapeutic approaches, which combines epigenetic, immune, metabolic and niche-guided methods. We conclude with a description of translational needs to achieve adaptive, ecosystem-directed interventions in clinical hematology.