Abstract 371P
Background
Lung cancer research, driven by numerous large-scale studies conducted across various laboratories, has generated an extensive volume of data from both in vitro and in vivo experiments. Consolidating these datasets into a unified and accessible format is pivotal for encompassing a broader spectrum of tumor types and represents a critical step towards achieving a comprehensive understanding of lung cancer.
Methods
Using transcriptomic data from 203 lung cancer cell lines provided by the Cancer Cell Line Encyclopedia (CCLE), we inferred a highly specific lung cancer gene regulatory network (GRN) employing the hLICORN algorithm and the CoRegNet R package. This GRN was applied to a lung cancer metacohort comprising 42 datasets and 5,487 patients, leading to the identification of seven distinct clusters that capture the histological, clinical, molecular, and survival heterogeneity of lung cancer.
Results
We present the lung.cregmap portal, an interactive tool enabling researchers to explore a lung cancer coregulatory network reflecting disease heterogeneity and plasticity. Using this platform, adenocarcinomas (Adk) are classified into good (median survival: 96 months, EGFR mutations: 3%, KRAS mutations: 47%) and poor prognosis groups (34 months, EGFR mutations: 27%, KRAS mutations: 27%). Squamous cell carcinomas (Sq) form a Sq-rich group, defined by a proliferative signature and frequent CSMD3, KMT2D, and NFE2L2 mutations (54%, 31%, and 25%, respectively). The neuroendocrine (NE) group shows poor prognosis (32 months), high tumor purity, NE markers (ASCL1, INSM1, NEUROD1), and frequent RB1 (50%) and TP53 (96%) mutations. Two histology-mixed groups are identified: Mixed/NE-like, expressing NE markers and a proliferative signature, and Mixed/Mes-like, with high stromal/immune scores and EMT-related signatures. Lastly, carcinoid tumors form a distinct group with favorable prognosis (>14 years) and minimal proliferation.
Conclusions
Lung coregulatory network studies unravel lung cancer heterogeneity and plasticity, offering a unique framework to uncover latent molecular mechanisms and advancing cancer biology and precision oncology.
Funding
Institut National du Cancer (INCa), French National Research Agency, Centre national de la recherche scientifique (CNRS)
Disclosure
All authors have declared no conflicts of interest.