Therapy resistance mechanisms in hematological malignancies.
Int J Cancer
; 152(3): 340-347, 2023 02 01.
Article
en En
| MEDLINE
| ID: mdl-35962946
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Neoplasias Hematológicas
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Int J Cancer
Año:
2023
Tipo del documento:
Article
País de afiliación:
Alemania