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.
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http://dx.doi.org/10.1002/1878-0261.13159 | DOI Listing |
Eur J Nucl Med Mol Imaging
December 2024
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, Sichuan, 610041, China.
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Front Genet
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Department of Plastic Surgery, The Affiliated Friendship Plastic Surgery Hospital of Nanjing Medical University, Nanjing, China.
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Front Immunol
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Department of Orthopedics, The First Affiliated Hospital of YangTze University, Jingzhou, Hubei, China.
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Front Immunol
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Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, ;China.
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We present, to our knowledge, the first methodological study aimed at enhancing the prognosticpower of Cox regression models, widely used in survival analysis, through optimized data selection. Ourapproach employs a novel two-stage mechanism: by framing the prognostic stratum matching problemintuitively, we select prognostically representative patient observations to create a more balanced trainingset. This enables the model to assign equal attention to distinct prognostic subgroups.
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