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IntroductionAmyloidosis typing is crucial to determine the best therapeutic strategy for patients. Since conventional histological techniques often fail, the identification of amyloid precursors by mass spectrometry became the new standard. However, without quantification, selecting the amyloid precursor from proteins that may be ubiquitous under non-pathological conditions may be equivocal. Therefore, we quantified protein enrichment in amyloid deposits to improve typing.MethodsProtein enrichment was measured by extracted ion chromatogram based label free quantification by comparing a microdissected amyloid area with a non-amyloid area. We assessed the discrimination ability of candidate precursors with this approach compared to the two practiced identification methods.ResultsAs proof of concept, we selected seven cases, 5 typical of the most common amyloidosis subtypes and typed by immunostainings, 2 inconclusive after immunohistochemistry. Proteins associated with amyloid deposits were identified in all samples confirming the pathology. When the routine clinical mass spectrometric identification techniques allowed unambiguous conclusions for 2/3 of 7 cases, quantification of the enrichment ratio in the amyloid deposit allowed unambiguous precursor selection in all cases.ConclusionQuantification of precursor enrichment in amyloid deposits is a promising optimization for amyloidosis typing. Incorporated into routine clinical processes, it will improve patient care in difficult diagnostic situations.

International audience; Combined therapy with anti-BRAF plus anti-MEK is currently used as first-line treatment of patients with metastatic melanomas harboring the somatic BRAF V600E mutation. However, the main issue with targeted therapy is the acquisition of tumor cell resistance. In a majority of resistant melanoma cells, the resistant process consists in epithelial-to-mesenchymal transition (EMT). This process called phenotype switching makes melanoma cells more invasive. Its signature is characterized by MITF low, AXL high, and actin cytoskeleton reorganization through RhoA activation. In parallel of this phenotype switching phase, the resistant cells exhibit an anarchic cell proliferation due to hyper-activation of the MAP kinase pathway. We show that a majority of human melanoma overexpress discoidin domain receptor 2 (DDR2) after treatment. The same result was found in resistant cell lines presenting phenotype switching compared to the corresponding sensitive cell lines. We demonstrate that DDR2 inhibition induces a decrease in AXL expression and reduces stress fiber formation in resistant melanoma cell lines. In this phenotype switching context, we report that DDR2 control cell and tumor proliferation through the MAP kinase pathway in resistant cells in vitro and in vivo. Therefore, inhibition of DDR2 could be a new and promising strategy for countering this resistance mechanism.

Background and Aims : Through an exploratory proteomic approach based on typical hepatocellular adenomas (HCAs), we previously identified a diagnostic biomarker for a distinctive subtype of HCA with high risk of bleeding, already validated on a multicenter cohort. We hypothesized that the whole protein expression deregulation profile could deliver much more informative data for tumor characterization. Therefore, we pursued our analysis with the characterization of HCA proteomic profiles, evaluating their correspondence with the established genotype/phenotype classification and assessing whether they could provide added diagnosis and prognosis values. Approach and Results : From a collection of 260 cases, we selected 52 typical cases of all different subgroups on which we built a reference HCA proteomics database. Combining laser microdissection and mass-spectrometry–based proteomic analysis, we compared the relative protein abundances between tumoral (T) and nontumoral (NT) liver tissues from each patient and we defined a specific proteomic profile of each of the HCA subgroups. Next, we built a matching algorithm comparing the proteomic profile extracted from a patient with our reference HCA database. Proteomic profiles allowed HCA classification and made diagnosis possible, even for complex cases with immunohistological or genomic analysis that did not lead to a formal conclusion. Despite a well-established pathomolecular classification, clinical practices have not substantially changed and the HCA management link to the assessment of the malignant transformation risk remains delicate for many surgeons. That is why we also identified and validated a proteomic profile that would directly evaluate malignant transformation risk regardless of HCA subtype. Conclusions : This work proposes a proteomic-based machine learning tool, operational on fixed biopsies, that can improve diagnosis and prognosis and therefore patient management for HCAs.

Metastasis is the leading cause of cancer-related deaths. During this process, tumor cells acquire invasive and migratory capacities in order to invade the surrounding tissues. To achieve this, the tumor microenvironment including the extracellular matrix (ECM) is altered to facilitate cancer cell proliferation and dissemination. Extracellular vesicles (EVs), such as exosomes or migrasomes, are already known to induce pro-tumor features such as migration, promoting tumor development and metastasis formation. Here we highlight a new type of EVs, referred as tracks, released by cancer cells during migration and specifically attached along collagen fibers. These tracks, identified by discoidin receptor 1 (DDR1) enrichment, are promoted when cell-ECM interactions are increased, such as in tumors. We characterized these tracks, their ultrastructure as well as their molecular composition in terms of proteins and nucleic acids, showing that they are different from classical EVs known so far. Moreover, these tracks are very stable structures and can be internalized by surrounding cells. After internalization, they modify the differentiation status and the phenotype of recipient cells, promoting epithelial to mesenchymal transition, matrix degradation and invasion. Thus, we identified a new class of collagen-associated EVs, tracks, that play a role in cell-cell communication by transferring invasive properties. Consequently, these cancer-related tracks could be a new player in the tumor invasion process.