Lung cancer organoids: models for preclinical research and precision medicine

Front Oncol. 2023 Oct 24:13:1293441. doi: 10.3389/fonc.2023.1293441. eCollection 2023.

Abstract

Lung cancer is a malignancy with high incidence and mortality rates globally, and it has a 5-year survival rate of only 10%-20%. The significant heterogeneity in clinical presentation, histological features, multi-omics findings, and drug sensitivity among different lung cancer patients necessitate the development of personalized treatment strategies. The current precision medicine for lung cancer, primarily based on pathological and genomic multi-omics testing, fails to meet the needs of patients with clinically refractory lung cancer. Lung cancer organoids (LCOs) are derived from tumor cells within tumor tissues and are generated through three-dimensional tissue culture, enabling them to faithfully recapitulate in vivo tumor characteristics and heterogeneity. The establishment of a series of LCOs biobanks offers promising platforms for efficient screening and identification of novel targets for anti-tumor drug discovery. Moreover, LCOs provide supplementary decision-making factors to enhance the current precision medicine for lung cancer, thereby addressing the limitations associated with pathology-guided approaches in managing refractory lung cancer. This article presents a comprehensive review on the construction methods and potential applications of LCOs in both preclinical and clinical research. It highlights the significance of LCOs in biomarker exploration, drug resistance investigation, target identification, clinical precision drug screening, as well as microfluidic technology-based high-throughput drug screening strategies. Additionally, it discusses the current limitations and future prospects of this field.

Keywords: biobanks; biomarker exploration; drug screening; lung cancer organoids; precision medicine.

Publication types

  • Review

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Postdoctoral Project of Qingdao (QDBSH20220202207).