Interventions to Improve Colorectal Cancer Screening
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Neoplasms
PROVIDER: 2056477 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:The aim of this project is to assess the impact on health and economics of the implementation of text messaging (SMS) in cancer screening programs. Three interventions with SMS will be evaluated through community trials. In the colorectal cancer screening program the following interventions will be tested: a) Participation reminder: six weeks after sending the invitation letter of the colorectal cancer program if there has not been a response, a reminder SMS will be sent in front of the usual method by letter; b) Reminder to return the fecal occult blood test: SMS reminder of test delivery versus no intervention. This reminder will be sent to the individuals who have gone to the pharmacy to pick up a fecal occult blood test and they have not returned it after 14 days. The impact on participation will be analyzed and, if applicable, the proportion of advanced neoplasms will be calculated by increase in participation. In the breast cancer screening program, the invitation by SMS versus the usual invitation by letter will be studied in women who had participated in the previous screening round. The impact on participation will be analyzed. A cost-effectiveness analysis of the three interventions will be carried out. The incremental cost ratio of the interventions between cost variation and effectiveness variation will be calculated.
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Project description:This project aims to evaluate different approaches to increase colorectal cancer screening among primary care patients at Penn Medicine through a centralized screening outreach program. In a pragmatic trial, we will evaluate different approaches to increase response rate to mailed fecal immunochemical test (FIT) kits among eligible patients, including differentiated packaging, sending text reminders, and personalized reminders.
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Project description:Analysis of large-scale data-independent acquisition mass spectrometry (DIA-MS) metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS (DDA-MS), protein identification and quantification using DIA-MS, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-minute diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate (FDR) of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67–0.90) in genus diversity and showed a high correlation (rSpearman = 0.73–0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia (DLP) patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.
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Project description:Fecal immunochemical tests (FIT) detecting hemoglobin in stool are widely used for non-invasive colorectal cancer (CRC) screening, but their sensitivity leaves room for improvement. Our aim is to identify novel protein biomarkers in stool that outperform or complement hemoglobin in detecting CRCs and advanced adenomas (AAs). Stool samples (one series of 12 CRCs and 10 controls, and a second series of 81 CRCs, 40 AAs, 43 non-advanced adenomas and 129 controls) were analyzed by mass-spectrometry and searched for human proteins. Classification and regression tree and logistic regression analyses were performed to identify protein combinations that differentiated CRCs and/or AAs from controls. Antibody-based assays for four selected proteins were performed on an independent series of FIT samples (14 CRCs, 16 AAs, 18 non-advanced adenomas and 24 controls) Results: In total, 834 human proteins were identified, of which 29 were significantly enriched in CRCs versus controls in both stool sample series. Combinations of four proteins reached sensitivities of 80% and 45% for detecting CRCs and AAs, at 95% specificity, which was higher than hemoglobin alone.
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