EGFR (Epidermal growth factor receptor)

P3, N=614, Active, not recruiting, Merck Sharp & Dohme LLC | Recruiting --> Active, not recruiting

P1/2, N=315, Active, not recruiting, Dizal Pharmaceuticals | Trial completion date: Jul 2025 --> Dec 2025

Importantly, antimicrobial screening showed negligible activity against representative Gram-positive and Gram-negative strains, indicating a low risk of microbiota disruption - an important feature for cancer therapy. These findings position furanocoumarin derivatives, particularly compounds 4 and 6, as promising lead structures for the development of selective, microbiota-sparing anticancer agents.

Lastly, we evaluate its feasibility for multi-cancer early detection in population-scale screening using pooled plasma samples. The EC-SNV-Seq assay can enable highly sensitive and specific identification of low-frequency mutations, facilitating early cancer diagnosis and personalized treatment strategies.

FAM83A promotes MASH pathogenesis by interacting with RAF1 to activate ERK signaling, thereby stimulating fatty acid and cholesterol biosynthesis. Targeting this axis may offer therapeutic potential for MASH and metabolic dyslipidemia.

This study demonstrates the feasibility of localized tumor-normal sequencing in Nigerian BC patients, revealing actionable variants with clinical relevance. These findings highlight the need to integrate genomic profiling into routine cancer care and establish molecular tumor boards to advance precision oncology in Nigeria.

In CEACAM5 HE, the ORR was greater with high versus low cCEA. Associations were observed between cCEA and cCEACAM5; IHC CEACAM5, cCEA, and cCEACAM5; IHC CEACAM5 and CEACAM5 mRNA, but not between IHC CEACAM5 and oncogenic drivers.

Moreover, both products 8a and 8b were subjected to a molecular docking experiment and displayed good interaction with epidermal growth factor receptor (EGFR). These findings suggested that compounds 8a and 8b possess a valuable skeletal structure for the development of novel antitumor agents.

The iterative feedback mechanism further ensures chemical novelty and biological significance, showcasing the potential of EGFit to optimize compound generation for kinase-specific applications. This framework offers a scalable and effective solution to the challenges of kinase drug discovery, accelerating the development of novel therapeutics and paving the way for broader applications in future studies.

By leveraging the full spectrum of available patient data, MMLCA significantly enhances predictive accuracy. Our experiments show that MMLCA consistently outperforms traditional approaches, suggesting it could help clinicians make more accurate predictions and support more personalized treatment decisions.

ML-based models show promising ability to predict EGFR-TKI response in LCBM, supporting their potential to guide treatment selection. However, their use in clinical practice remains limited by small retrospective datasets and lack of external validation.

Additionally, EGFR-AS1 also facilitates displacing PARP1 from the sites of damaged DNA. Our findings demonstrate a lncRNA-associated PARP1 activation and displacement in DDR and highlight the potential of EGFR-AS1 as a target for cancer therapy.