Multi-lectin Affinity Chromatography and Quantitative Proteomic Analysis Reveal Differential Glycoform Levels between Prostate Cancer and Benign Prostatic Hyperplasia Sera.

Currently prostate-specific antigen is used for prostate cancer (PCa) screening, however it lacks the necessary specificity for differentiating PCa from other diseases of the prostate such as benign prostatic hyperplasia (BPH), presenting a clinical need to distinguish these cases at the molecular level. Protein glycosylation plays an important role in a number of cellular processes involved in neoplastic progression and is aberrant in PCa. In this study, we systematically interrogate the alterations in the circulating levels of hundreds of serum proteins and their glycoforms in PCa and BPH samples using multi-lectin affinity chromatography and quantitative mass spectrometry-based proteomics. Specific lectins (AAL, PHA-L and PHA-E) were used to target and chromatographically separate core-fucosylated and highly-branched protein glycoforms for analysis, as differential expression of these glycan types have been previously associated with PCa. Global levels of CD5L, CFP, C8A, BST1, and C7 were significantly increased in the PCa samples. Notable glycoform-specific alterations between BPH and PCa were identified among proteins CD163, C4A, and ATRN in the PHA-L/E fraction and among C4BPB and AZGP1 glycoforms in the AAL fraction. Despite these modest differences, substantial similarities in glycoproteomic profiles were observed between PCa and BPH sera

Sample datasets used in [Gil et al 2017] for AAAI 2017

<p>This file includes the pointer to the 42 patient ids and zip file names of the 84 genomic and proteomic datasets used for the paper "Gil, Y, Garijo, D, Ratnakar, V, Mayani, R, Adusumilli, A, Srivastava, R, Boyce, H, Mallick,P. Towards Continuous Scientific Data Analysis and Hypothesis Evolution", accepted in AAAI 2017.</p> <p>The datasets itself are not published due to their size and access conditions. They can be retrieved with the provided ids from TCGA (https://gdc-portal.nci.nih.gov/legacy-archive/search/f) and CPTAC (https://cptac-data-portal.georgetown.edu/cptac/s/S022) archives.</p> <p>These patient ids are a subset of the nearly 90 samples used in "Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., et al. Proteogenomic characterization of human colon and rectal cancer.  <em>Nature</em> 513,382–387", in order to test the system described in the AAAI 2017 paper. More samples were not included in the analysis due to time constraints.</p

Towards Continuous Scientific Data Analysis and Hypothesis Evolution

Scientific data is continuously generated throughout the world. However, analyses of these data are typically performed exactly once and on a small fragment of recently generated data. Ideally, data analysis would be a continuous process that uses all the data available at the time, and would be automatically re-run and updated when new data appears. We present a framework for automated discovery from data repositories that tests user-provided hypotheses using expert-grade data analysis strategies, and reassesses hypotheses when more data becomes available. Novel contributions of this approach include a framework to trigger new analyses appropriate for the available data through lines of inquiry that support progressive hypothesis evolution, and a representation of hypothesis revisions with provenance records that can be used to inspect the results. We implemented our approach in the DISK framework, and evaluated it using two scenarios from cancer multi-omics: 1) data for new patients becomes available over time, 2) new types of data for the same patients are released. We show that in all scenarios DISK updates the confidence on the original hypotheses as it automatically analyzes new data

Additional file 1: Table S1. of Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer

Subject characteristics of blood plasma samples from the Northern California site of the Breast Cancer Family Registry. Table S2 Targeted proteins in the antibody-based proteomics platforms. (DOCX 25 kb

Additional file 2: of Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer

Measured Protein Levels Across LC-MS/MS, Olink, and Myriad-RBM platforms. (XLSX 98 kb