Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning-based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and rising incidence rate in the developed world. Molecular profiling techniques have been used to study the variability between tumours as well as cancer models such as cell lines, but their translational value is incomplete with current methods. Moreover, first generation computational methods for subtype classification do not make use of multi-omics data in full scale. Drug discovery programs use cell lines as a proxy for human cancers to characterize their molecular makeup and drug response, identify relevant indications and discover biomarkers. In order to maximize the translatability and the clinical relevance of in vitro studies, selection of optimal cancer models is imperative. We present a novel subtype classification method based on deep learning and apply it to classify CRC tumors using multi-omics data, and further to measure the similarity between tumors and disease models such as cancer cell lines. Multi-omics Autoencoder Integration (maui) efficiently leverages data sets containing copy number alterations, gene expression, and point mutations, and learns clinically important patterns (latent factors) across these data types. Using these latent factors, we propose a refinement of the gold-standard CRC subtypes, and propose best-matching cell lines for the different subtypes. These findings are relevant for patient stratification and selection of cell lines for drug discovery pipelines, biomarker discovery, and target identification

Microestructura de quesos blancos turcos bajos en grasa producidos industrialmente, influencia de la homogenización de la crema

The microstructure and fat globule distribution of reduced and low fat Turkish white cheese were evaluated. Reduced and low fat cheeses were manufactured from 1.5% and 0.75% fat milk respectively which were standardized unhomogenized and homogenized cream in a dairy plant. Homogenized and non-homogenized creams and cheese whey were analyzed for fat globule distribution and cheese samples were also analyzed for microstructure characteristics. According to the results, the homogenization of cream decreased the size of fat globules; and showed that a large number of fat particles were dispersed in the in matrix and improved the lubrication of cheese microstructure. According to the micrographs for the fat, which was not removed, they exhibited a more extended matrix with a few small fat globules compared to the defatted micrographs. Homogenization of cream produces small fat globules and unclustured fat globules were found in the resulting whey. These results are important for dairy processors for using cream homogenization as a processing tool at the industrial level.Se estudia la microestructura y distribución de los glóbulos de grasa de quesos blancos turcos bajos en grasa. Quesos con reducida y baja cantidad en grasa fueron fabricados conteniendo entre el 1,5% y 0,75% de grasa de leche, respectivamente, y con cremas homogeneizadas y no homogeneizadas, en una planta de lácteos. Las cremas homogeneizadas y no homogeneizadas y el suero de los quesos se analizaron para determinar la distribución de los glóbulos de grasa y también se analizaron las características de la microestructura de muestras de queso. De acuerdo con los resultados, la homogeneización de la crema reduce el tamaño de los glóbulos de grasa, mostrando un gran número de partículas de grasa dispersa en la matriz de caseína que mejoró la lubricación de la microestructura del queso. De acuerdo con las micrografías de la grasa que no se elimina, estas exhiben una matriz más amplia en la que hay pocos glóbulos de grasa en comparación con las micrografías de las muestras desgrasadas. La homogenización de la crema produce pequeños glóbulos de grasa y el suero resultante contiene glóbulos de grasa no incrustados. Estos resultados son importantes para los procesadores de productos lácteos, y muestran la utilidad de la homogeneización de crema como una herramienta del procesamiento a nivel industrial

Head and Neck Cancers

This chapter in Cancer Concepts: A Guidebook for the Non-Oncologist presents an overview of head and neck cancers, including epidemiology, etiology, screening, pathology, staging, and treatment. The chapter focuses on cancers of the upper aerodigestive tract which are most often squamous cell carcinomas arising from the squamous epithelium that lines the tract.https://escholarship.umassmed.edu/cancer_concepts/1021/thumbnail.jp

HOT or not: examining the basis of high-occupancy target regions

High-occupancy target (HOT) regions are segments of the genome with unusually high number of transcription factor binding sites. These regions are observed in multiple species and thought to have biological importance due to high transcription factor occupancy. Furthermore, they coincide with house-keeping gene promoters and consequently associated genes are stably expressed across multiple cell types. Despite these features, HOT regions are solemnly defined using ChIP-seq experiments and shown to lack canonical motifs for transcription factors that are thought to be bound there. Although, ChIP-seq experiments are the golden standard for finding genome-wide binding sites of a protein, they are not noise free. Here, we show that HOT regions are likely to be ChIP-seq artifacts and they are similar to previously proposed 'hyper-ChIPable' regions. Using ChIP-seq data sets for knocked-out transcription factors, we demonstrate presence of false positive signals on HOT regions. We observe sequence characteristics and genomic features that are discriminatory of HOT regions, such as GC/CpG-rich k-mers, enrichment of RNA-DNA hybrids (R-loops) and DNA tertiary structures (G-quadruplex DNA). The artificial ChIP-seq enrichment on HOT regions could be associated to these discriminatory features. Furthermore, we propose strategies to deal with such artifacts for the future ChIP-seq studies

Global identification of functional microRNA-mRNA interactions in Drosophila

MicroRNAs (miRNAs) are key mediators of post-transcriptional gene expression silencing. So far, no comprehensive experimental annotation of functional miRNA target sites exists in Drosophila. Here, we generated a transcriptome-wide in vivo map of miRNA-mRNA interactions in Drosophila melanogaster, making use of single nucleotide resolution in Argonaute1 (AGO1) crosslinking and immunoprecipitation (CLIP) data. Absolute quantification of cellular miRNA levels presents the miRNA pool in Drosophila cell lines to be more diverse than previously reported. Benchmarking two CLIP approaches, we identify a similar predictive potential to unambiguously assign thousands of miRNA-mRNA pairs from AGO1 interaction data at unprecedented depth, achieving higher signal-to-noise ratios than with computational methods alone. Quantitative RNA-seq and sub-codon resolution ribosomal footprinting data upon AGO1 depletion enabled the determination of miRNA-mediated effects on target expression and translation. We thus provide the first comprehensive resource of miRNA target sites and their quantitative functional impact in Drosophila

methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles

DNA methylation is a chemical modification of cytosine bases that is pivotal for gene regulation, cellular specification and cancer development. Here, we describe an R package, methylKit, that rapidly analyzes genome-wide cytosine epigenetic profiles from high-throughput methylation and hydroxymethylation sequencing experiments. methylKit includes functions for clustering, sample quality visualization, differential methylation analysis and annotation features, thus automating and simplifying many of the steps for discerning statistically significant bases or regions of DNA methylation. Finally, we demonstrate methylKit on breast cancer data, in which we find statistically significant regions of differential methylation and stratify tumor subtypes. methylKit is available at http://code.google.com/p/methylkit

Development of a high throughput, molecular diagnostic assay for predicting telomerase activity in breast cancer cell lines and tissues

Telomerase is a cellular enzyme that helps to provide genomic stability in tumor cells by maintaining the integrity of telomeres. Telomerase is an RNA-dependent DNA polymerase that contains a protein component (hTERT) and an associated RNA (hTR), which is used as a template for telomere repeat addition. Telomerase activity, while not detectable in most normal human somatic cells, is associated with approximately 85% of malignant human cancers overall, including over 90% of breast cancers. We have optimized a novel, quantitative, high-throughput telomerase activity assay using fluorescently labelled primers and Real Time quantitation via the ABI Prism 7700 (a.k.a., the TaqMan). Using established breast cancer cell lines and a subset of breast tumors, we demonstrate that telomerase levels quantitated from the TaqMan-based assay closely correlate with values obtained using the traditional, gel-based telomerase activity assay (TRAP). In addition, we have assessed the levels of both hTERT mRNA and hTR in each of our samples via RT-PCR to determine whether relative amounts or a ratio of the two telomerase components correlate with activity in a given sample. Our ultimate goal is to develop a Real Time, fluorescent RT-PCR assay to simultaneously measure hTERT and hTR messages in breast tumor samples, in an attempt to convert the enzymatic telomerase activity assay into a quantitative nucleic acid test to predict levels of activity in routinely processed clinical specimens