Long-term cellular memory of hypoxia leads to sustained suppression of tumor-intrinsic type I IFN signaling and the antigen presentation pathway that facilitates tumorigenesis and metastasis.

A CRISPR screen reveals AFDN loss as a mediator of liver tropism in colorectal cancer metastasis by decreasing tight junctions in the primary tumor and increasing interactions between cancer cells and hepatocytes.

Prime-boost vaccination shapes the mucosal microenvironment and reprograms central memory T cells to generate lung resident memory T cells that protect against lung metastasis, providing insights for the optimization of vaccine strategies.

CD47-SIRPα blockade increases surface CD11b/CD18 on macrophages to enhance adhesion to cancer cells, resulting in robust synergistic phagocytosis in combination with cetuximab treatment in head and neck squamous cell carcinoma.

Phase separation of NAT10 enables acetylation of SRSF2 that enhances YTHDF1 exon 4 skipping, which is a tumor-promoting axis in gastric cancer that represents potential therapeutic targets and prognostic biomarkers.

GLS1 and c-Myc form a positive feedback loop that promotes head and neck cancer metastasis and can be targeted as a promising therapeutic strategy for this disease.

CCN6 deficiency drives metaplastic breast carcinoma growth and metastasis by increasing Wnt/β-catenin activation to upregulate EZH2, identifying EZH2 inhibition as a mechanistically guided treatment strategy for this deadly form of breast cancer.

Two FDA-approved compounds, AKT inhibitors and pitavastatin, synergize to induce cell death in triple-negative breast cancer, motivating evaluation of the efficacy of this combination in clinical trials.

Characterization of the proteomic composition of circulating extracellar vesicles in breast cancer patients identifies signatures for diagnosing primary and metastatic tumors and reveals tumor-promoting cargo that can be targeted to improve outcomes.

The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients.

Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type–specific combination treatment strategies.