Impact of decitabine on immunohistochemistry expression of the putative tumor suppressor genes FHIT, WWOX, FUS1 and PTEN in clinical tumor samples.

BackgroundSince tumor suppressor gene function may be lost through hypermethylation, we assessed whether the demethylating agent decitabine could increase tumor suppressor gene expression clinically. For fragile histidine triad (FHIT), WW domain-containing oxidoreductase (WWOX), fused in sarcoma-1 (FUS1) and phosphatase and tensin homolog (PTEN), immunohistochemistry scores from pre- and post-decitabine tumor biopsies (25 patients) were correlated with methylation of the long interspersed nuclear element-1 (LINE-1) repetitive DNA element (as a surrogate for global DNA methylation) and with tumor regression.ResultsWith negative staining pre-decitabine (score = 0), the number of patients converting to positive staining post-decitabine was 1 of 1 for FHIT, 3 of 6 for WWOX, 2 of 3 for FUS1 and 1 of 10 for PTEN. In tumors with low pre-decitabine tumor suppressor gene scores (≤150), expression was higher post-treatment in 8 of 8 cases for FHIT (P = 0.014), 7 of 17 for WWOX (P = 0.0547), 7 of 12 for FUS1 (P = 0.0726), and 1 of 16 for PTEN (P = 0.2034). If FHIT, WWOX and FUS1 were considered together, median pre- versus post-decitabine scores were 60 versus 100 (P = 0.0002). Overall, tumor suppressor gene expression change did not correlate with LINE-1 demethylation, although tumors converting from negative to positive had a median decrease in LINE-1 methylation of 24%, compared to 6% in those not converting (P = 0.069). Five of 15 fully evaluable patients had reductions in tumor diameter (range 0.2% to 33.4%). Of these, three had simultaneous increases in three tumor suppressor genes (including the two patients with the greatest tumor regression) compared to 2 of 10 with tumor growth (P = 0.25).ConclusionsIn tumors with low tumor suppressor gene expression, decitabine may be associated with increased expression of the tumor suppressor genes FHIT, FUS1, and WWOX, but not PTEN

Hyperparameter Importance Across Datasets

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Minin

High resolution chromosome 3p, 8p, 9q and 22q allelotyping analysis in the pathogenesis of gallbladder carcinoma

Our recent genome-wide allelotyping analysis of gallbladder carcinoma identified 3p, 8p, 9q and 22q as chromosomal regions with frequent loss of heterozygosity. The present study was undertaken to more precisely identify the presence and location of regions of frequent allele loss involving those chromosomes in gallbladder carcinoma. Microdissected tissue from 24 gallbladder carcinoma were analysed for PCR-based loss of heterozygosity using 81 microsatellite markers spanning chromosome 3p (n=26), 8p (n=14), 9q (n=29) and 22q (n=12) regions. We also studied the role of those allele losses in gallbladder carcinoma pathogenesis by examining 45 microdissected normal and dysplastic gallbladder epithelia accompanying gallbladder carcinoma, using 17 microsatellite markers. Overall frequencies of loss of heterozygosity at 3p (100%), 8p (100%), 9q (88%), and 22q (92%) sites were very high in gallbladder carcinoma, and we identified 13 distinct regions undergoing frequent loss of heterozygosity in tumours. Allele losses were frequently detected in normal and dysplastic gallbladder epithelia. There was a progressive increase of the overall loss of heterozygosity frequency with increasing severity of histopathological changes. Allele losses were not random and followed a sequence. This study refines several distinct chromosome 3p, 8p, 9q and 22q regions undergoing frequent allele loss in gallbladder carcinoma that will aid in the positional identification of tumour suppressor genes involved in gallbladder carcinoma pathogenesis

NeuroD1 regulation of migration accompanies the differential sensitivity of neuroendocrine carcinomas to TrkB inhibition

The developmental transcription factor NeuroD1 is anomalously expressed in a subset of aggressive neuroendocrine tumors. Previously, we demonstrated that TrkB and neural cell adhesion molecule (NCAM) are downstream targets of NeuroD1 that contribute to the actions of neurogenic differentiation 1 (NeuroD1) in neuroendocrine lung. We found that several malignant melanoma and prostate cell lines express NeuroD1 and TrkB. Inhibition of TrkB activity decreased invasion in several neuroendocrine pigmented melanoma but not in prostate cell lines. We also found that loss of the tumor suppressor p53 increased NeuroD1 expression in normal human bronchial epithelial cells and cancer cells with neuroendocrine features. Although we found that a major mechanism of action of NeuroD1 is by the regulation of TrkB, effective targeting of TrkB to inhibit invasion may depend on the cell of origin. These findings suggest that NeuroD1 is a lineage-dependent oncogene acting through its downstream target, TrkB, across multiple cancer types, which may provide new insights into the pathogenesis of neuroendocrine cancers

Mechanisms of FUS1/TUSC2 deficiency in mesothelioma and its tumorigenic transcriptional effects

<p>Abstract</p> <p>Background</p> <p>FUS1/TUSC2 is a novel tumor suppressor located in the critical 3p21.3 chromosomal region frequently deleted in multiple cancers. We previously showed that Tusc2-deficient mice display a complex immuno-inflammatory phenotype with a predisposition to cancer. The goal of this study was to analyze possible involvement of TUSC2 in malignant pleural mesothelioma (MPM) - an aggressive inflammatory cancer associated with exposure to asbestos.</p> <p>Methods</p> <p>TUSC2 insufficiency in clinical specimens of MPM was assessed via RT-PCR (mRNA level), Representational Oligonucleotide Microarray Analysis (DNA level), and immunohistochemical evaluation (protein level). A possible link between TUSC2 expression and exposure to asbestos was studied using asbestos-treated mesothelial cells and ROS (reactive oxygen species) scavengers. Transcripional effects of TUSC2 in MPM were assessed through expression array analysis of TUSC2-transfected MPM cells.</p> <p>Results</p> <p>Expression of TUSC2 was downregulated in ~84% of MM specimens while loss of TUSC2-containing 3p21.3 region observed in ~36% of MPMs including stage 1 tumors. Exposure to asbestos led to a transcriptional suppression of TUSC2, which we found to be ROS-dependent. Expression array studies showed that TUSC2 activates transcription of multiple genes with tumor suppressor properties and down-regulates pro-tumorigenic genes, thus supporting its role as a tumor suppressor. In agreement with our knockout model, TUSC2 up-regulated IL-15 and also modulated more than 40 other genes (~20% of total TUSC2-affected genes) associated with immune system. Among these genes, we identified CD24 and CD274, key immunoreceptors that regulate immunogenic T and B cells and play important roles in systemic autoimmune diseases. Finally, clinical significance of TUSC2 transcriptional effects was validated on the expression array data produced previously on clinical specimens of MPM. In this analysis, 42 TUSC2 targets proved to be concordantly modulated in MM serving as disease discriminators.</p> <p>Conclusion</p> <p>Our data support immuno-therapeutic potential of TUSC2, define its targets, and underscore its importance as a transcriptional stimulator of anti-tumorigenic pathways.</p

Reanalysis of the NCCN PD-L1 Companion Diagnostic Assay Study for Lung Cancer in the Context of PD-L1 Expression Findings in Triple-Negative Breast Cancer

The companion diagnostic test for checkpoint inhibitor immune therapy is an immunohistochemical test for PD-L1. The test has been shown to be reproducible for expression in tumor cells, but not in immune cells. Immune cells were used in the IMpassion130 trial which showed PD-L1 expression was associated with a better outcome. Two large studies have been done assessing immune cell PD-L1 expression in lung cancer. Here, we reanalyze one of those studies, to show that, even with an easier scoring method, there is still only poor agreement between assays and pathologist for immune cell PD-L1 expression