Applied models and molecular characteristics of small cell lung cancer

被引：2

作者：

Fur, Gabriella Mihalekne ^{[1
]}

Nemes, Kolos ^{[1
]}

Mago, Eva ^{[1
,2
]}

Beno, Alexandra A. ^{[1
]}

Topolcsanyi, Petronella ^{[1
]}

Moldvay, Judit ^{[3
,4
]}

Pongor, Lorinc S. ^{[1
]}

机构：

[1] Hungarian Ctr Excellence Mol Med HCEMM, Canc Genom & Epigenet Core Grp, Szeged, Hungary

[2] Hungarian Ctr Excellence Mol Med HCEMM, Genome Integr & DNA Repair Core Grp, Szeged, Hungary

[3] Szeged Univ, Szent Gyorgy Albert Med Sch, Dept Pulmonol, Szeged, Hungary

[4] Natl Koranyi Inst Pulmonol, Dept Pulmonol 1, Budapest, Hungary

来源：

PATHOLOGY & ONCOLOGY RESEARCH | 2024年 / 30卷

关键词：

SCLC; drug response; ctDNA; liquid biopsy; databases; CIRCULATING TUMOR-CELLS; NEUROENDOCRINE DIFFERENTIATION; PRECLINICAL MODELS; DRUG-SENSITIVITY; XENOGRAFT MODEL; GENE-THERAPY; IDENTIFICATION; HETEROGENEITY; DNA; EXPRESSION;

D O I：

10.3389/pore.2024.1611743

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care.

引用

页数：13

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