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  • br Many of the top scoring cancer specific

    2020-03-17


    Many of the top-scoring, cancer-specific markers have also been experimentally validated as significantly elevated in a bio-fluid of subjects harboring that particular type of cancer, some of which are currently used in the clinic for diagnosis. For example, four of the top five scoring candidates for liver hepato-cellular carcinoma (LIHC) have been experimentally validated as biofluid LIHC biomarkers (ESM1, AFP, GPC3, and MDK); AFP is the most commonly used serological LIHC marker in the clinic (Capurro et al., 2003; Lou et al., 2017; Spangenberg et al., 2006; Yang et al., 2017; Zhu et al., 2013). Likewise, two (ANGPT2 [Gayed et al., 2015] and ANGPTL4 [Dong et al., 2017]) of the top five candidates for kidney renal clear cell carcinoma (KIRC) and the top candidate (LAMC2 [Kosanam et al., 2013]) for pancreatic ductal adenocarcinoma (PAAD) have been measured at signifi-cantly higher concentrations in the plasma, serum, or urine of subjects harboring the respective cancer types compared to non-cancer controls. Extension to an Experimentally Defined Secretome
    The biomarker analysis described here included only classically secreted Echinomycin that contain a signal peptide, but many pro-teins are secreted through unconventional routes and possess similar diagnostic potential (Rabouille, 2017). However, defining a list of unconventionally secreted proteins is non-trivial due to the many secretion routes available (e.g., exosomes, pore-medi-ated translocation, ATP-driven transport [Rabouille, 2017]), as well as the variation in their protein cargo across different cell 
    types or conditions (Vlassov et al., 2012). We therefore used the Human Cancer Secretome Database (HCSD) (Feizi et al., 2015) to generate a list of all of the proteins (regardless of signal peptide) that had been experimentally detected in the secretome among any of the 35 studies encompassed by the database. This yielded an ‘‘experimental secretome’’ consisting of 6,500 pro-teins, 800 of which were present in our signal peptide-derived secretome. The results and associated consensus ranks for the experimental secretome are presented in Table S3.
    Exploration of the ‘‘Core’’ Cancer Secretome Definition of the Core Secretome
    Shifts in secretome expression associated with malignant trans-formation can be used to identify candidate cancer biomarkers; however, based on our global analysis across different cancer types, it is also possible to address the more fundamental ques-tion of why cancer cells restructure their secretome profile throughout tumorigenesis. We therefore sought to investigate the biological features underlying the altered secretome expres-sion. Motivated by the large number of multi-cancer candidates in our biomarker analysis, we first explored the core cancer se-cretome—the subset of the secretome exhibiting strong differ-ential expression across most or all of the cancer types studied. Secretome genes were ranked based on the magnitude and sig-nificance of their expression fold changes (tumor versus paired normal) across all cancer types, referred to here as the PF rank (STAR Methods).
    Members of the Core Secretome
    Upon inspection of the genes populating the top 1% (16 of 1,563 genes) of the pan-cancer PF ranks, two key features were imme-diately apparent (Figure 2A). First, each gene exhibited an expression change in the same direction across all (or nearly all) of the cancer types, despite ignoring the fold change direc-tion in the rank calculation. This is supportive of an important and defined tumorigenic role for each of the associated encoded proteins, independent of the tissue or cell type from which it orig-inates. Second, 15 of the 16 top-ranked genes exhibited an expression decrease across all or nearly all of the cancer types, suggesting that cancer type-independent shifts in secretome expression tend to be decreases.
    The only top-ranking core secretome gene exhibiting an increased expression was MMP11, which was also one of the MMPs that scored highly as a potential candidate biomarker for many cancer types. In addition to the tumor-specific