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Clinical Proteomics in Colorectal Cancer, a Promising Tool for Improving Personalised Medicine.

ABSTRACT: Colorectal cancer is the third most common and the fourth most lethal cancer worldwide. In most of cases, patients are diagnosed at an advanced or even metastatic stage, thus explaining the high mortality. The lack of proper clinical tests and the complicated procedures currently used for detecting this cancer, as well as for predicting the response to treatment and the outcome of a patient's resistance in guiding clinical practice, are key elements driving the search for biomarkers. In the present overview, the different biomarkers (diagnostic, prognostic, treatment resistance) discovered through proteomics studies in various colorectal cancer study models (blood, stool, biopsies), including the different proteomic techniques used for the discovery of these biomarkers, are reviewed, as well as the various tests used in clinical practice and those currently in clinical phase. These studies define the limits and perspectives related to proteomic biomarker research for personalised medicine in colorectal cancer.

SUBMITTER: Chauvin A

PROVIDER: S-EPMC6313903 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Clinical Proteomics in Colorectal Cancer, a Promising Tool for Improving Personalised Medicine.

Chauvin Anaïs A Boisvert François-Michel FM

Proteomes 20181202 4

Colorectal cancer is the third most common and the fourth most lethal cancer worldwide. In most of cases, patients are diagnosed at an advanced or even metastatic stage, thus explaining the high mortality. The lack of proper clinical tests and the complicated procedures currently used for detecting this cancer, as well as for predicting the response to treatment and the outcome of a patient's resistance in guiding clinical practice, are key elements driving the search for biomarkers. In the pres ...[more]

PMID: 30513835

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Project description:Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these models should focus on the critical STNs for each cancer, include prognostic genes and proteins, and correctly predict patient-specific differences in STN activity. Focussing on colorectal cancer and the WNT STN, we used mechanism-based machine learning models to identify genes and proteins with significant associations to event-free patient survival and predictive power for explaining patient-specific differences of STN activity. First, we identified the WNT pathway as the most significant pathway associated with event-free survival. Second, we built linear-regression models that incorporated both genes and proteins from established mechanistic models in the literature and novel genes with significant associations to event-free patient survival. Data from The Cancer Genome Atlas and Clinical Proteomic Tumour Analysis Consortium were used, and patient-specific STN activity scores were computed using PROGENy. Three linear regression models were built, based on; (1) the gene-set of a state-of-the-art mechanistic model in the literature, (2) novel genes identified, and (3) novel proteins identified. The novel genes and proteins were genes and proteins of the extant WNT pathway whose expression was significantly associated with event-free survival. The results show that the predictive power of a model that incorporated novel event-free associated genes is better compared to a model focussing on the genes of a current state-of-the-art mechanistic model. Several significant genes that should be integrated into future mechanistic models of the WNT pathway are DVL3, FZD5, RAC1, ROCK2, GSK3B, CTB2, CBT1, and PRKCA. Thus, the study demonstrates that using mechanistic information in combination with machine learning can identify novel features (genes and proteins) that are important for explaining the STN heterogeneity between patients and their association to clinical outcomes.

Project description:Background:One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). Methods:A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. Results:Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. Conclusion:MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

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Dataset Information

Clinical Proteomics in Colorectal Cancer, a Promising Tool for Improving Personalised Medicine.

Publications

Clinical Proteomics in Colorectal Cancer, a Promising Tool for Improving Personalised Medicine.

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