The accumulation of genetic and epigenetic alterations in cancer cells rewires cellular signalling pathways through changes in the patterns of protein-protein interactions (PPIs). Understanding these patterns may facilitate the design of tailored cancer therapies. Here, we show that single-molecule pull-down and co-immunoprecipitation techniques can be used to characterize signalling complexes of the human epidermal growth-factor receptor (HER) family in specific cancers. By analysing cancer-specific signalling phenotypes, including post-translational modifications and PPIs with downstream interactions, we found that activating mutations of the epidermal growth-factor receptor (EGFR) gene led to the formation of large protein complexes surrounding mutant EGFR proteins and to a reduction in the dependency of mutant EGFR signalling on phosphotyrosine residues, and that the strength of HER-family PPIs is correlated with the strength of the dependence of breast and lung adenocarcinoma cells on HER-family signalling pathways. Furthermore, using co-immunoprecipitation profiling to screen for EGFR-dependent cancers, we identified non-small-cell lung cancers that respond to an EGFR-targeted inhibitor. Our approach might help predict responses to targeted cancer therapies, particularly for cancers that lack actionable genomic mutations.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1038/s41551-018-0212-3 | DOI Listing |
Alzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
View Article and Find Full Text PDFComb Chem High Throughput Screen
January 2025
Department of Pharmacy, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangsu, China.
Objective: This study aimed to explore the active components and potential mechanism of Tanre Qing Injection (TRQI) in the treatment of Acute Respiratory Distress Syndrome (ARDS) using network pharmacology, molecular docking, and animal experiments.
Methods: The targets of active ingredients were identified using the TCMSP and Swiss Target Prediction databases. The targets associated with ARDS were obtained from the GeneCards database, Mala card database, and Open Targets Platform.
Comb Chem High Throughput Screen
January 2025
Department of Emergency, First Medical Center of Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
Background: The prevalence of depression in COVID-19 patients is notably high, disrupting daily life routines and compounding the burden of other chronic health conditions. In addition, to elucidate the connection between COVID-19 and depression, we conducted an analysis of commonly differentially expressed genes [co-DEGs], uncovering potential biomarkers and therapeutic avenues specific to COVID-19-related depression.
Methods: We obtained gene expression profiles from the Gene Expression Omnibus [GEO] database with strategic keyword searches ["COVID-19", "depression," and "SARS"].
Ren Fail
December 2025
Department of Nephrology, Affiliated Hospital of Nantong University, Nantong, China.
Background: Chronic kidney disease (CKD) represents a significant global public health challenge. This study aims to identify biomarkers of renal fibrosis and elucidate the relationship between unilateral ureteral obstruction (UUO), immune infiltration, and cell death.
Methods: Gene expression matrices for UUO were retrieved from the gene expression omnibus (GSE36496, GSE79443, GSE217650, and GSE217654).
J Transl Med
January 2025
Medical College of YiChun University, Xuefu Road No 576, Yichun, 336000, Jiangxi, People's Republic of China.
Background: Artificial sweeteners (AS) have been widely utilized in the food, beverage, and pharmaceutical industries for decades. While numerous publications have suggested a potential link between AS and diseases, particularly cancer, controversy still surrounds this issue. This study aims to investigate the association between AS consumption and cancer risk.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!