A challenge in achieving optimal management of cancer is the discovery of secreted biomarkers that represent useful surrogates for the disease and could be measured noninvasively. Because of the problems encountered in the proteomic interrogation of plasma, secretomes have been proposed as an alternative source of tumor markers that might be enriched with secreted proteins relevant to the disease. However, secretome analysis faces analytical challenges that interfere with the search for true secreted tumor biomarkers. Here, we have addressed two of the main challenges of secretome analysis in comparative discovery proteomics. First, we carried out a kinetics experiment whereby secretomes and lysates of tumor cells were analyzed to monitor cellular viability during secretome production. Interestingly, the proteomic signal of a group of secreted proteins correlated well with the apoptosis induced by serum starvation and could be used as an internal cell viability marker. We then addressed a second challenge relating to contamination of serum proteins in secretomes caused by the required use of serum for tumor cell culture. The comparative proteomic analysis between cell lines labeled with SILAC showed a number of false positives coming from serum and that several proteins are both in serum and being secreted from tumor cells. A thorough study of secretome methodology revealed that under optimized experimental conditions there is a substantial fraction of proteins secreted through unconventional secretion in secretomes. Finally, we showed that some of the nuclear proteins detected in secretomes change their cellular localization in breast tumors, explaining their presence in secretomes and suggesting that tumor cells use unconventional secretion during tumorigenesis. The unconventional secretion of proteins into the extracellular space exposes a new layer of genome post-translational regulation and reveals an untapped source of potential tumor biomarkers and drug targets. Molecular & Cellular

The study of the cancer secretome suggests that a fraction of the intracellular proteome could play unanticipated roles in the extracellular space during tumorigenesis. A project aimed at investigating the invasive secretome led us to study the alternative extracellular function of the nuclear protein high mobility group A1 (HMGA1) in breast cancer invasion and metastasis. Antibodies against HMGA1 were tested in signaling, adhesion, migration, invasion, and metastasis assays using breast cancer cell lines and xenograft models. Fluorescence microscopy was used to determine the subcellular localization of HMGA1 in cell lines, xenograft, and patient-derived xenograft models. A cohort of triple-negative breast cancer (TNBC) patients was used to study the correlation between subcellular localization of HMGA1 and the incidence of metastasis. Our data show that treatment of invasive cells with HMGA1-blocking antibodies in the extracellular space impairs their migration and invasion abilities. We also prove that extracellular HMGA1 (eHMGA1) becomes a ligand for the Advanced glycosylation end product-specific receptor (RAGE), inducing pERK signaling and increasing migration and invasion. Using the cytoplasmic localization of HMGA1 as a surrogate marker of secretion, we showed that eHMGA1 correlates with the incidence of metastasis in a cohort of TNBC patients. Furthermore, we show that HMGA1 is enriched in the cytoplasm of tumor cells at the invasive front of primary tumors and in metastatic lesions in xenograft models. Our results strongly suggest that eHMGA1 could become a novel drug target in metastatic TNBC and a biomarker predicting the onset of distant metastasis.

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This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models firstly to the generalized linear model framework. Then a nonparametric version of these models is proposed by means of local fitting. Distances between individuals are the only predictor information needed to fit these models. Therefore they are applicable, among others, to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. An implementation is provided by the R package dbstats, which also implements other distancebased prediction methods. Supplementary material for this article is available online, which reproduces all the results of this article.

Purpose: The lack of secreted biomarkers measurable by noninvasive tests hampers the development of effective targeted therapies against cancer. Our hypothesis is that cetuximab (an anti-EGFR mAb) induces a specific secretome in colorectal cancer cells that could be exploited for biomarker discovery.Experimental Design: Considering the strong correlation between mutated KRAS and a lack of response to cetuximab therapy, we addressed whether performing secretome-based proteomics on isogenic colorectal cancer cells sharing the KRAS mutations found on patients would yield candidate-secreted biomarkers useful in the clinical setting. Because 2D culture did not optimally model the sensitivity/resistance to cetuximab observed in colorectal cancer patients, we moved to 3D spheroids, developing a methodology for both cell-based assays and quantitative proteomics.Results: A large comparative quantitative proteomic analysis of the 3D secretomes of colorectal cancer isogenic cells treated with cetuximab uncovered an EGFR pathway-centric secretome found only when cells grow in 3D. The validation of the secretome findings in plasma of colorectal cancer patients, suggests that phosphorylated-EGFR (pEGFR) is a candidate-secreted biomarker of response to cetuximab.Conclusions: We have proved that 3D spheroids from colorectal cancer cells generate secretomes with a drug-sensitivity profile that correlates well with patients with colorectal cancer, illustrating molecular connections between intracellular and extracellular signaling. Furthermore, we show how the secretion of pEGFR is associated with the sensitivity of colorectal cancer cells to cetuximab and the response of patients with colorectal cancer to the drug. Our work could allow the noninvasive monitoring of anti-EGFR treatment in patients with colorectal cancer. Clin Cancer Res; 20(24); 6346-56. Ó2014 AACR.

Secretome profiling has become a methodology of choice for the identification of tumor biomarkers. We hypothesized that due to the dynamic nature of secretomes cellular perturbations could affect their composition but also change the global amount of protein secreted per cell. We confirmed our hypothesis by measuring the levels of secreted proteins taking into account the amount of proteome produced per cell. Then, we established a correlation between cell proliferation and protein secretion that explained the observed changes in global protein secretion. Next, we implemented a normalization correcting the statistical results of secretome studies by the global protein secretion of cells into a generalized linear model (GLM). The application of the normalization to two biological perturbations on tumor cells resulted in drastic changes in the list of statistically significant proteins. Furthermore, we found that known epithelial-to-mesenchymal transition (EMT) effectors were only statistically significant when the normalization was applied. Therefore, the normalization proposed here increases the sensitivity of statistical tests by increasing the number of true-positives. From an oncology perspective, the correlation between protein secretion and cellular proliferation suggests that slow-growing tumors could have high-protein secretion rates and consequently contribute strongly to tumor paracrine signaling.

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