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Caie, P. D.

Publications and source records attributed to Caie, P. D..

2 recordsLinked to original sources

Identification of plasma proteins associated with oesophageal cancer chemotherapeutic treatment outcomes using SWATH-MS

Oesophageal adenocarcinoma (OAC) is an aggressive cancer with a five-year survival of <15%. Current chemotherapeutic strategies only benefit a minority (20-30%) of patients and there are no methods available to differentiate between responders and non-responders. We performed quantitative proteomics using Sequential Window Acquisition of all THeoretical fragment-ion spectra-Mass Spectrometry (SWATH-MS) on albumin/IgG-depleted and non-depleted plasma samples from 23 patients with locally advanced OAC prior to treatment. Individuals were grouped based on tumour regression (TRG) score (TRG1/2/3 vs TRG4/5) after chemotherapy, and differentially abundant proteins were compared. Protein depletion of highly abundant proteins led to the identification of around twice as many proteins. SWATH-MS revealed significant quantitative differences in the abundance of several proteins between the two groups. These included complement c1q subunit proteins, C1QA, C1QB and C1QC, which were of higher abundance in the low TRG group. Of those that were found to be of higher abundance in the high TRG group, GSTP1 was found to exhibit the lowest p-value and highest classification accuracy and Cohens kappa value. Concentrations of these proteins were further examined using ELISA-based assays. This study provides quantitative information relating to differences in the plasma proteome that underpin response to chemotherapeutic treatment in oesophageal cancers.

cancer biology↗

Identification of immunological features enables survival prediction of muscle-invasive bladder cancer patients using machine learning

AO_SCPLOWBSTRACTC_SCPLOWClinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insights into patient prognosis. In this paper, we apply multiplex immunofluorescence on MIBC tissue sections to capture whole slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine learning based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance (p value < 1e - 05). Critical to improving MIBC survival rates, our method classifies correctly 71.4% of the patients who succumb to MIBC within 5 years, significantly higher than the 28.6% of the current clinical gold standard, the TNM staging system.

cancer biology↗