19 research outputs found

    Targeting CDK2 overcomes melanoma resistance against BRAF and Hsp90 inhibitors

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    Novel therapies are undergoing clinical trials, for example, the Hsp90 inhibitor, XL888, in combination with BRAF inhibitors for the treatment of therapy-resistant melanomas. Unfortunately, our data show that this combination elicits a heterogeneous response in a panel of melanoma cell lines including PDX-derived models. We sought to understand the mechanisms underlying the differential responses and suggest a patient stratification strategy. Thermal proteome profiling (TPP) identified the protein targets of XL888 in a pair of sensitive and unresponsive cell lines. Unbiased proteomics and phosphoproteomics analyses identified CDK2 as a driver of resistance to both BRAF and Hsp90 inhibitors and its expression is regulated by the transcription factor MITF upon XL888 treatment. The CDK2 inhibitor, dinaciclib, attenuated resistance to both classes of inhibitors and combinations thereof. Notably, we found that MITF expression correlates with CDK2 upregulation in patients; thus, dinaciclib would warrant consideration for treatment of patients unresponsive to BRAF-MEK and/or Hsp90 inhibitors and/or harboring MITF amplification/overexpression

    Clinical patterns of hepatocellular carcinoma in nonalcoholic fatty liver disease: A multicenter prospective study

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    107noNonalcoholic fatty liver disease (NAFLD) represents the hepatic manifestation of metabolic syndrome and may evolve into hepatocellular carcinoma (HCC). Only scanty clinical information is available on HCC in NAFLD. The aim of this multicenter observational prospective study was to assess the clinical features of patients with NAFLD-related HCC (NAFLD-HCC) and to compare them to those of hepatitis C virus (HCV)-related HCC. A total of 756 patients with either NAFLD (145) or HCV-related chronic liver disease (611) were enrolled in secondary care Italian centers. Survival was modeled according to clinical parameters, lead-time bias, and propensity analysis. Compared to HCV, HCC in NAFLD patients had a larger volume, showed more often an infiltrative pattern, and was detected outside specific surveillance. Cirrhosis was present in only about 50% of NAFLD-HCC patients, in contrast to the near totality of HCV-HCC. Regardless of tumor stage, survival was significantly shorter (P = 0.017) in patients with NAFLD-HCC, 25.5 months (95% confidence interval 21.9-29.1), than in those with HCV-HCC, 33.7 months (95% confidence interval 31.9-35.4). To eliminate possible confounders, a propensity score analysis was performed, which showed no more significant difference between the two groups. Additionally, analysis of patients within Milan criteria submitted to curative treatments did not show any difference in survival between NAFLD-HCC and HCV-HCC (respectively, 38.6 versus 41.0 months, P = nonsignificant) Conclusions: NAFLD-HCC is more often detected at a later tumor stage and could arise also in the absence of cirrhosis, but after patient matching, it has a similar survival rate compared to HCV infection; a future challenge will be to identify patients with NAFLD who require more stringent surveillance in order to offer the most timely and effective treatment. (Hepatology 2016;63:827-838)openopenPiscaglia F.; Svegliati-Baroni G.; Barchetti A.; Pecorelli A.; Marinelli S.; Tiribelli C.; Bellentani S.; Bernardi M.; Biselli M.; Caraceni P.; Domenicali M.; Garuti F.; Gramenzi A.; Lenzi B.; Magalotti D.; Cescon M.; Ravaioli M.; Del Poggio P.; Olmi S.; Rapaccini G.L.; Balsamo C.; Di Nolfo M.A.; Vavassori E.; Alberti A.; Benvegnau L.; Gatta A.; Giacomin A.; Vanin V.; Pozzan C.; Maddalo G.; Giampalma E.; Cappelli A.; Golfieri R.; Mosconi C.; Renzulli M.; Roselli P.; Dell'isola S.; Ialungo A.M.; Risso D.; Marenco S.; Sammito G.; Bruzzone L.; Bosco G.; Grieco A.; Pompili M.; Rinninella E.; Siciliano M.; Chiaramonte M.; Guarino M.; Camma C.; Maida M.; Costantino A.; Barcellona M.R.; Schiada L.; Gemini S.; Lanzi A.; Stefanini G.F.; Dall'aglio A.C.; Cappa F.M.; Suzzi A.; Mussetto A.; Treossi O.; Missale G.; Porro E.; Mismas V.; Vivaldi C.; Bolondi L.; Zoli M.; Granito A.; Malagotti D.; Tovoli F.; Trevisani F.; Venerandi L.; Brandi G.; Cucchetti A.; Bugianesi E.; Vanni E.; Mezzabotta L.; Cabibbo G.; Petta S.; Fracanzani A.; Fargion S.; Marra F.; Fani B.; Biasini E.; Sacco R.; Morisco F.; Caporaso N.; Colombo M.; D'ambrosio R.; Croce L.S.; Patti R.; Giannini E.G.; Loria P.; Lonardo A.; Baldelli E.; Miele L.; Farinati F.; Borzio M.; Dionigi E.; Soardo G.; Caturelli E.; Ciccarese F.; Virdone R.; Affronti A.; Foschi F.G.; Borzio F.Piscaglia, F.; Svegliati-Baroni, G.; Barchetti, A.; Pecorelli, A.; Marinelli, S.; Tiribelli, C.; Bellentani, S.; Bernardi, M.; Biselli, M.; Caraceni, P.; Domenicali, M.; Garuti, F.; Gramenzi, A.; Lenzi, B.; Magalotti, D.; Cescon, M.; Ravaioli, M.; Del Poggio, P.; Olmi, S.; Rapaccini, G. L.; Balsamo, C.; Di Nolfo, M. A.; Vavassori, E.; Alberti, A.; Benvegnau, L.; Gatta, A.; Giacomin, A.; Vanin, V.; Pozzan, C.; Maddalo, G.; Giampalma, E.; Cappelli, A.; Golfieri, R.; Mosconi, C.; Renzulli, M.; Roselli, P.; Dell'Isola, S.; Ialungo, A. M.; Risso, D.; Marenco, S.; Sammito, G.; Bruzzone, L.; Bosco, G.; Grieco, A.; Pompili, M.; Rinninella, E.; Siciliano, M.; Chiaramonte, M.; Guarino, M.; Camma, C.; Maida, M.; Costantino, A.; Barcellona, M. R.; Schiada, L.; Gemini, S.; Lanzi, A.; Stefanini, G. F.; Dall'Aglio, A. C.; Cappa, F. M.; Suzzi, A.; Mussetto, A.; Treossi, O.; Missale, G.; Porro, E.; Mismas, V.; Vivaldi, C.; Bolondi, L.; Zoli, M.; Granito, A.; Malagotti, D.; Tovoli, F.; Trevisani, F.; Venerandi, L.; Brandi, G.; Cucchetti, A.; Bugianesi, E.; Vanni, E.; Mezzabotta, L.; Cabibbo, G.; Petta, S.; Fracanzani, A.; Fargion, S.; Marra, F.; Fani, B.; Biasini, E.; Sacco, R.; Morisco, F.; Caporaso, N.; Colombo, M.; D'Ambrosio, R.; Croce, L. S.; Patti, R.; Giannini, E. G.; Loria, P.; Lonardo, A.; Baldelli, E.; Miele, L.; Farinati, F.; Borzio, M.; Dionigi, E.; Soardo, G.; Caturelli, E.; Ciccarese, F.; Virdone, R.; Affronti, A.; Foschi, F. G.; Borzio, F

    Identification of putative substrates for the periplasmic chaperone YfgM in Escherichia coli using quantitative proteomics

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    How proteins are trafficked, folded, and assembled into functional units in the cell envelope of Gram-negative bacteria is of significant interest. A number of chaperones have been identified, however, the molecular roles of these chaperones are often enigmatic because it has been challenging to assign substrates. Recently we discovered a novel periplasmic chaperone, called YfgM, which associates with PpiD and the SecYEG translocon and operates in a network that contains Skp and SurA. The aim of the study presented here was to identify putative substrates of YfgM. We reasoned that substrates would be incorrectly folded or trafficked when YfgM was absent from the cell, and thus more prone to proteolysis (the loss-of-function rationale). We therefore used a comparative proteomic approach to identify cell envelope proteins that were lower in abundance in a strain lacking yfgM, and strains lacking yfgM together with either skp or surA. Sixteen putative substrates were identified. The list contained nine inner membrane proteins (CusS, EvgS, MalF, OsmC, TdcB, TdcC, WrbA, YfhB, and YtfH) and seven periplasmic proteins (HdeA, HdeB, AnsB, Ggt, MalE, YcgK, and YnjE), but it did not include any lipoproteins or outer membrane proteins. Significantly, AnsB (an asparaginase) and HdeB (a protein involved in the acid stress response), were lower in abundance in all three strains lacking yfgM. For both genes, we ruled out the possibility that they were transcriptionally down-regulated, so it is highly likely that the corresponding proteins are misfolded/mistargeted and turned-over in the absence of YfgM. For HdeB we validated this conclusion in a pulse-chase experiment. The identification of HdeB and other cell envelope proteins as potential substrates will be a valuable resource for follow-up experiments that aim to delineate molecular the function of YfgM

    Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

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    Purpose To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model. Methods Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared. Results Among 78 patients (median follow-up 262 days, IQR 73-957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p < 0.001) and AIC 89 for R1, AUC 0.81 (p = 0.001) and AIC 90.2 for R2. Conclusion The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability

    ROCK1 is a potential combinatorial drug target for BRAF mutant melanoma

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    Treatment of BRAF mutant melanomas with specific BRAF inhibitors leads to tumor remission. However, most patients eventually relapse due to drug resistance. Therefore, we designed an integrated strategy using (phospho)proteomic and functional genomic platforms to identify drug targets whose inhibition sensitizes melanoma cells to BRAF inhibition. We found many proteins to be induced upon PLX4720 (BRAF inhibitor) treatment that are known to be involved in BRAF inhibitor resistance, including FOXD3 and ErbB3. Several proteins were down-regulated, including Rnd3, a negative regulator of ROCK1 kinase. For our genomic approach, we performed two parallel shRNA screens using a kinome library to identify genes whose inhibition sensitizes to BRAF or ERK inhibitor treatment. By integrating our functional genomic and (phospho)proteomic data, we identified ROCK1 as a potential drug target for BRAF mutant melanoma. ROCK1 silencing increased melanoma cell elimination when combined with BRAF or ERK inhibitor treatment. Translating this to a preclinical setting, a ROCK inhibitor showed augmented melanoma cell death upon BRAF or ERK inhibition in vitro. These data merit exploration of ROCK1 as a target in combination with current BRAF mutant melanoma therapies

    ROCK1 is a potential combinatorial drug target for BRAF mutant melanoma

    No full text
    Treatment of BRAF mutant melanomas with specific BRAF inhibitors leads to tumor remission. However, most patients eventually relapse due to drug resistance. Therefore, we designed an integrated strategy using (phospho)proteomic and functional genomic platforms to identify drug targets whose inhibition sensitizes melanoma cells to BRAF inhibition. We found many proteins to be induced upon PLX4720 (BRAF inhibitor) treatment that are known to be involved in BRAF inhibitor resistance, including FOXD3 and ErbB3. Several proteins were down-regulated, including Rnd3, a negative regulator of ROCK1 kinase. For our genomic approach, we performed two parallel shRNA screens using a kinome library to identify genes whose inhibition sensitizes to BRAF or ERK inhibitor treatment. By integrating our functional genomic and (phospho)proteomic data, we identified ROCK1 as a potential drug target for BRAF mutant melanoma. ROCK1 silencing increased melanoma cell elimination when combined with BRAF or ERK inhibitor treatment. Translating this to a preclinical setting, a ROCK inhibitor showed augmented melanoma cell death upon BRAF or ERK inhibition in vitro. These data merit exploration of ROCK1 as a target in combination with current BRAF mutant melanoma therapies
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