Proteomic Profiling of Colorectal Adenomas Identifies a Predictive Risk Signature for Development of Metachronous Advanced Colorectal Neoplasia.

BACKGROUND & AIMS: Colonic adenomatous polyps, or adenomas, are frequent precancerous lesions and the origin of most cases of colorectal adenocarcinoma. However, we know from epidemiologic studies that although most colorectal cancers (CRCs) originate from adenomas, only a small fraction of adenomas (3%-5%) ever progress to cancer. At present, there are no molecular markers to guide follow-up surveillance programs. METHODS: We profiled, by mass spectrometry-based proteomics combined with machine learning analysis, a selected cohort of formalin-fixed, paraffin-embedded high-grade (HG) adenomas with long clinical follow-up, collected as part of the Danish national screening program. We grouped subjects in the cohort according to their subsequent history of findings: a nonmetachronous advanced neoplasia group (G0), with no new HG adenomas or CRCs up to 10 years after polypectomy, and a metachronous advanced neoplasia group (G1) where individuals developed a new HG adenoma or CRC within 5 years of diagnosis. RESULTS: We generated a proteome dataset from 98 selected HG adenoma samples, including 20 technical replicates, of which 45 samples belonged to the nonmetachronous advanced neoplasia group and 53 to the metachronous advanced neoplasia group. The clear distinction of these 2 groups seen in a uniform manifold approximation and projection plot indicated that the information contained within the abundance of the ∼5000 proteins was sufficient to predict the future occurrence of HG adenomas or development of CRC. CONCLUSIONS: We performed an in-depth analysis of quantitative proteomic data from 98 resected adenoma samples using various novel algorithms and statistical packages and found that their proteome can predict development of metachronous advanced lesions and progression several years in advance.

A streamlined mass spectrometry-based proteomics workflow for large-scale FFPE tissue analysis.

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis, and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Herein we describe a MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from histopathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including low-input cancer tissue isolated by laser microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent acquisition (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total workup took less than a day. We observed a moderate trend towards lower protein identification in long-term stored samples (>15 years), but clustering into distinct proteomic subtypes was independent of archival time. Our results underscore the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and they prove the feasibility of analyzing large tissue cohorts in a robust, timely, and streamlined manner. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Sesquiterpene lactone mix as a diagnostic tool for Asteraceae allergic contact dermatitis: chemical explanation for its poor performance and Sesquiterpene lactone mix II as a proposed improvement.

BACKGROUND: Two preparations are currently in use for the diagnosis of allergic contact dermatitis caused by Asteraceae: (i) Sesquiterpene lactone (SL) mix [three pure sesquiterpene lactones (STLs)], whose use has been questioned, owing to an insufficient rate of true-positive results; and (ii) Compositae mix, consisting of five Asteraceae extracts, which is problematic because of lack of standardization and questionable reproducibility. OBJECTIVES: To analyse the reasons for the narrow sensitivity of SL mix from a chemoinformatic point of view, and to propose a solution by rational selection of alternative constituents for a new SL mix II covering a broader cohort of allergic patients. MATERIALS/METHODS: Structural and biological information on allergenic STLs was retrieved from databases and the literature, and molecular modelling and chemoinformatic computations were performed. RESULTS: An explanation for the insufficient hit rate of SL mix is that the three constituents possess extremely similar molecular structures/properties and do not represent well the structural diversity of allergenic STLs. STLs that are known as constituents of Compositae mix plants show much a wider diversity, which explains the higher positive rate. CONCLUSIONS: On the basis of their positions in chemical property space, a new collection of STLs that more evenly cover the overall structural diversity spectrum is proposed. SL mix II is likely to detect a larger number of patients sensitized to Asteraceae.