Functional genomic screening reveals new synthetic lethality with and modes of resistance to epidermal growth factor receptor (EGFR) targeted therapy in EGFR-mutant human lung cancer.

Lineage transformation in lung cancer is dictated by ERK activity and requires permissive chromatin alterations in the context of targeted therapy resistance.

It provides evidence for the feasibility of using combined graph convolutional neural network and transformers’ based systems with patient demographics, and CT-derived imaging features as inputs into the transformer model for survival prediction in early stage lung cancer patients.

Combined EGFR and SOS1 inhibition synergize to inhibit NSCLC spheroid growth.

In small cell lung cancer, the transition from a neuroendocrine state to a more neuronal state endows these cancer cells with increased migration and metastatic potential.

In metastatic NSCLC patients, CHIP showed minimal impact on immunotherapy response but was more prevalent and associated with inflammatory pathways in myeloid cells, particularly in lung squamous cell carcinoma.

As macrophages are involved in various aspects of non-small cell lung cancer development and progression it is crucial to develop appropriate 3D co-culture platforms for modeling immune cell-tumor cell interactions, leading to more efficacious macrophage-focused immunotherapies.

Integration of language model and geometric deep learning enables accurate and efficient genome-scale annotation of comprehensive protein-ligand binding sites.

Targeting aberrant CRTC-CREB activation blocks the growth of LKB1-inactivated lung cancer.

Prediagnostic RNA levels show early lung cancer progression.