Therapy resistance mechanisms in hematological malignancies

疾病 医学 癌症 表观遗传学 免疫疗法 靶向治疗 生物信息学 免疫学 肿瘤科 生物 内科学 生物化学 基因
作者
Wolf‐Karsten Hofmann,Andreas Trumpp,Carsten Müller‐Tidow
出处
期刊:International Journal of Cancer [Wiley]
卷期号:152 (3): 340-347 被引量:27
标识
DOI:10.1002/ijc.34243
摘要

Hematologic malignancies are model diseases for understanding neoplastic transformation and serve as prototypes for developing effective therapies. Indeed, the concept of systemic cancer therapy originated in hematologic malignancies and has guided the development of chemotherapy, cellular therapies, immunotherapy and modern precision oncology. Despite significant advances in the treatment of leukemias, lymphomas and multiple myelomas, treatment resistance associated with molecular and clinical relapse remains very common. Therapy of relapsed and refractory disease remains extremely difficult, and failure of disease control at this stage remains the leading cause of mortality in patients with hematologic malignancies. In recent years, many efforts have been made to identify the genetic and epigenetic mechanisms that drive the development of hematologic malignancies to the stage of full-blown disease requiring clinical intervention. In contrast, the mechanisms responsible for treatment resistance in hematologic malignancies remain poorly understood. For example, the molecular characteristics of therapy-resistant persisting cells in minimal residual disease (MRD) remain rather elusive. In this mini-review we want to discuss that cellular heterogeneity and plasticity, together with adaptive genetic and epigenetic processes, lead to reduced sensitivity to various treatment regimens such as chemotherapy and pathway inhibitors such as tyrosine kinase inhibitors. However, resistance mechanisms may be conserved across biologically distinct cancer entities. Recent technological advances have made it possible to explore the underlying mechanisms of therapy resistance with unprecedented resolution and depth. These include novel multi-omics technologies with single cell resolution combined with advanced biocomputational approaches, along with artificial intelligence (AI) and sophisticated disease models for functional validation.
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