Abstract:We present a case of metastatic spreading to the testicle in a 46-year-old patient with renal cell carcinoma, 'clear-cell' type, during interleukin-2 combined subcutaneous plus aerosol treatment. Testicular metastasis occurred while the patient showed a response to the treatment with disappearance of lung lesions and reduction of lymph-nodes lesions. After orchiectomy with spermatic cord resection and disease re-evaluation confirming the previous response, the patient re-started immunotherapy. The contrast between systemic disease response to treatment and disease testicular progression might be explained by a relative insensitivity of the testicle to interleukin-2 immunotherapy as a result of a possible establishment of an immunosuppressive microenvironment. We believe that the rarity of this metastatic site and the intriguing possible mechanisms at its base, makes an interesting case for clinicians.
Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.
Comprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.
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BackgroundThe human epidermal growth factor receptor 2 (HER2) and p53 pathways may be involved in chemotherapy sensitivity and/or resistance. We explore the value of HER2 and p53 status to foretell docetaxel sensitivity in advanced breast cancer.MethodsHER2 and p53 expression was analysed in 36 (median age 55 yrs; range 37-87) metastatic breast cancer patients receiving docetaxel-based first-line chemotherapy. HER2 was determined by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), p53 was tested by IHC. We correlate the expression of study parameters with pathologic parameters, RECIST response and survival. The standard cut-off value of 2 was used to determine HER2 overexpression while p53 mean expression level was used to divide low/high expressors tumors.ResultsMedian time to progression and overall survival were 9 (range 2 - 54) and 20 (range 3 - 101) months. Overall response rate was 41.6%. Nine cases showed HER2 overexpression. HER2 was more frequently overexpressed in less differentiated (p = 0.05) and higher stage (p = 0.003) disease. Mean FISH-HER2 values were significantly higher in responder than in non-responder pts (8.53 ± 10.21 vs 2.50 ± 4.12, p = 0.027). Moreover, HER2 overexpression correlates with treatment response at cross-tabulation analysis (p = 0.046). p53 expression was only associated with higher stage disease (p = 0.02) but lack of any significant association with HER status or docetaxel response. No significant relation with survival was observed for any parameter.ConclusionOur data seem to indicate that FISH-determined HER2 status but not p53 is associated with docetaxel sensitivity in metastatic breast cancer.
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