Incidence of the V600K mutation among melanoma patients with BRAF mutations, and potential therapeutic response to the specific BRAF inhibitor PLX4032

Activating mutations in BRAF kinase are common in melanomas. Clinical trials with PLX4032, the mutant-BRAF inhibitor, show promising preliminary results in patients selected for the presence of V600E mutation. However, activating V600K mutation is the other most common mutation, yet patients with this variant are currently excluded from the PLX4032 trials. Here we present evidence that a patient bearing the BRAF V600K mutation responded remarkably to PLX4032, suggesting that clinical trials should include all patients with activating BRAF V600E/K mutations

Shifting patterns of genomic variation in the somatic evolution of papillary thyroid carcinoma

Shows Single Nucleotide Substitutions in the MAPK Pathway. (XLSX 43 kb

Signal Transmission in the Auditory System

Contains table of contents for Section 3, an introduction and reports on six research projects.Health Sciences FundNational Institutes of Health Grant 5 R01 DC00194National Institutes of Health Grant 8 P01 DC00119National Institutes of Health Grant 5 R01 DC00473National Institutes of Health Grant 5 R01 DC00238National Institutes of Health Grant 5 T32 DC00006National Institutes of Health Grant 5 P01 DC00361National Institutes of Health Grant 5 R01 DC00235Peoples Republic of China FellowshipUnisys Corporation Doctoral FellowshipWhitaker Health Sciences Fellowshi

Демографический потенциал Республики Беларусь

Материалы III Междунар. науч. конф. студентов, аспирантов и молодых ученых, Гомель, 20 мая 2010 г

Genome-wide methylation and expression profiling identifies promoter characteristics affecting demethylation-induced gene up-regulation in melanoma

Abstract Background Abberant DNA methylation at CpG dinucleotides represents a common mechanism of transcriptional silencing in cancer. Since CpG methylation is a reversible event, tumor supressor genes that have undergone silencing through this mechanism represent promising targets for epigenetically active anti-cancer therapy. The cytosine analog 5-aza-2'-deoxycytidine (decitabine) induces genomic hypomethylation by inhibiting DNA methyltransferase, and is an example of an epigenetic agent that is thought to act by up-regulating silenced genes. Methods It is unclear why decitabine causes some silenced loci to re-express, while others remain inactive. By applying data-mining techniques to large-scale datasets, we attempted to elucidate the qualities of promoter regions that define susceptibility to the drug's action. Our experimental data, derived from melanoma cell strains, consist of genome-wide gene expression data before and after treatment with decitabine, as well as genome-wide data on un-treated promoter methylation status, and validation of specific genes by bisulfite sequencing. Results We show that the combination of promoter CpG content and methylation level informs the ability of decitabine treatment to up-regulate gene expression. Promoters with high methylation levels and intermediate CpG content appear most susceptible to up-regulation by decitabine, whereas few of those highly methylated promoters with high CpG content are up-regulated. For promoters with low methylation levels, those with high CpG content are more likely to be up-regulated, whereas those with low CpG content are underrepresented among up-regulated genes. Conclusions Clinically, elucidating the patterns of action of decitabine could aid in predicting the likelihood of up-regulating epigenetically silenced tumor suppressor genes and others from pathways involved with tumor biology. As a first step toward an eventual translational application, we build a classifier to predict gene up-regulation based on promoter methylation and CpG content, which achieves a performance of 0.77 AUC.</p

Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.

BACKGROUND AND OBJECTIVES: Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon cancer that is, captured by these features, whereas microsatellite instability (MSI) is an established biomarker traditionally assessed by immunohistochemistry or polymerase chain reaction. METHODS: H&E-stained slides from The Cancer Genome Atlas (TCGA) colon cohort are passed through the CNN. Resulting imaging features are used to cluster morphologically similar slide regions. Tile-level pairwise similarities are calculated and used to generate a tumor heterogeneity score (THS). Patient-level THS is then correlated with TCGA-reported biomarkers, including MSI-status. RESULTS: H&E-stained images from 313 patients generated 534 771 tiles. Deep learning automatically identified and annotated cells by type and clustered morphologically similar slide regions. MSI-high tumors demonstrated significantly higher THS than MSS/MSI-low (p \u3c 0.001). THS was higher in MLH1-silent versus non-silent tumors (p \u3c 0.001). The sequencing derived MSIsensor score also correlated with THS (r = 0.51, p \u3c 0.0001). CONCLUSIONS: Deep learning provides spatially resolved visualization of imaging-derived biomarkers and automated quantification of tumor heterogeneity. Our novel THS correlates with MSI-status, indicating that with expanded training sets, translational tools could be developed that predict MSI-status using H&E-stained images alone

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

&lt;p&gt;Processed data:&lt;/p&gt;&lt;p&gt;inferCNV_output_WM4237.zip, inferCNV_output_WM4007.zip - InferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;ad_all_human_clustered_cnv_WM4237.h5ad, ad_all_human_clustered_cnv_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of the inferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;ad_all_human_clustered_im_st_WM4237.h5ad, ad_all_human_clustered_im_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for 12 Visium samples of model WM4237 or WM4007. "_im" for imaging, "_st" for Visium.&lt;/p&gt;&lt;p&gt;ad_all_human_clustered_im_ad_WM4237_m.h5ad, ad_all_human_clustered_im_ad_WM4007_m.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for additional H&amp;E slides (non-Visium) of model WM4237 or WM4007. "_im" for imaging, "_m" for Macenko normalization of the H&amp;E slides with STQ.&lt;/p&gt;&lt;p&gt;ids_WM4237_AD.txt, ids_WM4007_AD.txt, ids_WM4237_ST.txt, ids_WM4237_ST.txt - list of identifiers of all samples (tissue sections).&lt;/p&gt;&lt;p&gt;ad_all_human_clustered_st_WM4237.h5ad, ad_all_human_clustered_st_WM4007.h5ad - &nbsp;AnnData object containing integration, dimensionality reduction and clustering of RNA profiles for 12 Visium samples of model WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;CNV_burden_WM4237.csv, CNV_burden_WM4007.csv - &nbsp;Per-spot values of InferCNV-derived CNV burden for samples of models WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;ad_all_scaled_filtered_st_WM4237.h5ad, ad_all_scaled_filtered_st_WM4007.h5ad - &nbsp;AnnData objects containing concatenated and pre-processed samples of WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;WM4237_3_AD_m-Imaging-STQ.zip, WM4007_3_AD_m-Imaging-STQ.zip - Imaging portion of STQ pipeline output derived from additional (non-Visium) H&amp;E-stained tissue sections of model WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;WM4237_ST-Imaging-STQ.tar.gz, WM4007_ST-Imaging-STQ.tar.gz - &nbsp;Imaging portion of STQ pipeline output derived from Visium H&amp;E-stained tissue sections of model WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;WM4237-ST-downstream-output.tar.gz, WM4007-ST-downstream-output.tar.gz - ST-downstream-processing pipeline output for Visium samples of model WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;WM4237-STQ-sequencing.tar.gz, WM4007-STQ-sequencing.tar.gz - RNA sequencing portion of STQ pipeline output derived from Visium samples of model WM4237 or WM4007.&lt;/p&gt;&lt;p&gt;rna-pseudotime-ordered.zip - &nbsp;Pseudotime ordering of RNA profiles of spots for each time point (T0, T1, T2, T3, T4, TC) for samples of models WM4237 and WM4007.&lt;/p&gt