Footprints: Stamping hallmarks of lung cancer with patient-derived models, from molecular mechanisms to clinical translation

Front Bioeng Biotechnol. 2023 Feb 21:11:1132940. doi: 10.3389/fbioe.2023.1132940. eCollection 2023.

Abstract

The conventional two-dimensional (2D) tumor cell lines in Petri dishes have played an important role in revealing the molecular biological mechanism of lung cancer. However, they cannot adequately recapitulate the complex biological systems and clinical outcomes of lung cancer. The three-dimensional (3D) cell culture enables the possible 3D cell interactions and the complex 3D systems with co-culture of different cells mimicking the tumor microenvironments (TME). In this regard, patient-derived models, mainly patient-derived tumor xenograft (PDX) and patient-derived organoids discussed hereby, are with higher biological fidelity of lung cancer, and regarded as more faithful preclinical models. The significant Hallmarks of Cancer is believed to be the most comprehensive coverage of current research on tumor biological characteristics. Therefore, this review aims to present and discuss the application of different patient-derived lung cancer models from molecular mechanisms to clinical translation with regards to the dimensions of different hallmarks, and to look to the prospects of these patient-derived lung cancer models.

Keywords: Hallmarks of cancer; NSCLC; SCLC; clinical application; precision medicine.

Publication types

  • Review

Grants and funding

This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB36000000), the National High Level Hospital Clinical Research Funding (No. 2022-PUMCH-B-011), the CAMS Innovation Fund for Medical Sciences (CIFMS) (No. 2020-I2M-C&T-A-003), Chinese Society of Clinical Oncology fund (No. Y-MSD2020-0270), Beijing Health Promotion Association (No. BJHPA-FW-XHKT-2020040400344), Ministry of Science and Technology of the People's Republic of China, Special Data Service for Oncology, The National Population and Health Scientific Data Sharing Platform (No. NCMI-ABD02-201809, NCMI-YF02N-201906).