E-cadherin is a robust prognostic biomarker in colorectal cancer and low expression is associated with sensitivity to inhibitors of topoisomerase, aurora and HSP90 in preclinical models.
Jarle BruunPeter W EideChristian Holst BergslandOscar BruckAud SvindlandMariliina ArjamaKatja VälimäkiMerete BjørnslettMarianne G GurenOlli KallioniemiArild NesbakkenRagnhild A LotheTeijo PellinenPublished in: Molecular oncology (2021)
Cell-cell and cell-matrix adhesion proteins that have been implicated in colorectal epithelial integrity and epithelial-to-mesenchymal transition could be robust prognostic and potential predictive biomarkers for standard and novel therapies. We analyzed in situ protein expression of E-cadherin (ECAD), integrin β4 (ITGB4), zonula occludens-1 (ZO-1) and cytokeratins in a single-hospital series of Norwegian patients with colorectal cancer (CRC) stages I-IV (n=922) using multiplex fluorescence-based immunohistochemistry (mfIHC) on tissue microarrays. Pharmacoproteomic associations were explored in 35 CRC cell lines annotated with drug sensitivity data on >400 approved and investigational drugs. ECAD, ITGB4, and ZO-1 were positively associated with survival, while cytokeratins were negatively associated with survival. Only ECAD showed independent prognostic value in multivariable Cox models. Clinical and molecular associations for ECAD were technically validated on a different mfIHC platform, and the prognostic value was validated in another Norwegian series (n=798). In preclinical models, low and high ECAD expression differentially associated with sensitivity to topoisomerase, aurora and HSP90 inhibitors, and EGFR inhibitors. E-cadherin protein expression is a robust prognostic biomarker with potential clinical utility in CRC.
Keyphrases
- cell therapy
- single cell
- poor prognosis
- small cell lung cancer
- healthcare
- heat shock protein
- high throughput
- clinical trial
- binding protein
- heat stress
- emergency department
- epidermal growth factor receptor
- escherichia coli
- machine learning
- staphylococcus aureus
- artificial intelligence
- pseudomonas aeruginosa
- long non coding rna
- quantum dots
- climate change
- biofilm formation
- cystic fibrosis
- open label
- drug induced
- deep learning
- phase ii
- cell adhesion
- phase iii