Angel Janevski

DNA methylation patterns in luminal breast cancers differ from non-luminal subtypes and can identify relapse risk independent of other clinical variables

The diversity of breast cancers reflects variations in underlying biology and affects the clinical implications for patients. Gene expression studies have identified five major subtypes– Luminal A, Luminal B, basal‐like, ErbB2+ and Normal‐Like. We set out to determine the role of DNA methylation in subtypes by performing genome‐wide scans of CpG methylation in breast cancer samples with known expression‐based subtypes. Unsupervised hierarchical clustering using a set of most varying loci clustered the tumors into a Luminal A majority (82%) cluster, Basal‐like/ErbB2+ majority (86%) cluster and a non‐specific cluster with samples that were also inconclusive in their expression‐based subtype correlations. Contributing methylation loci were both gene associated loci (30%) and non‐gene associated (70%), suggesting subtype dependant genome‐wide alterations in the methylation landscape. The methylation patterns of significant differentially methylated genes in luminal A tumors are similar to those identified in CD24 + luminal epithelial cells and the patterns in basal‐like tumors similar to CD44 + breast progenitor cells. CpG islands in the HOXA cluster and other homeobox (IRX2, DLX2, NKX2‐2) genes were significantly more methylated in Luminal A tumors. A significant number of genes (2853, p < 0.05) exhibited expression–methylation correlation, implying possible functional effects of methylation on gene expression. Furthermore, analysis of these tumors by using follow‐up survival data identified differential methylation of islands proximal to genes involved in Cell Cycle and Proliferation (Ki‐67, UBE2C, KIF2C, HDAC4), angiogenesis (VEGF, BTG1, KLF5), cell fate commitment (SPRY1, OLIG2, LHX2 and LHX5) as having prognostic value independent of subtypes and other clinical factors.

Personalized news through content augmentation and profiling

This paper is concerned with the topic of personalized news assembly at the set-top box, based on augmented video. This is video complemented with additional information that is somehow relevant to the semantic video content. We touch upon the technique that is used for video augmentation, which is video subject detection followed by information searches on the subject. The focus of this paper is on subject detection implemented using traditional text analysis tools; video segmentation is based on these results and visual processing. We describe the architecture of such a system and the benefits to the consumer. Further we discuss a preliminary system that shows the viability of the concept.

Translating next generation sequencing to practice: opportunities and necessary steps.

Next‐generation sequencing (NGS) approaches for measuring RNA and DNA benefit from greatly increased sensitivity, dynamic range and detection of novel transcripts. These technologies are rapidly becoming the standard for molecular assays and represent huge potential value to the practice of oncology. However, many challenges exist in the transition of these technologies from research application to clinical practice. This review discusses the value of NGS in detecting mutations, copy number changes and RNA quantification and their applications in oncology, the challenges for adoption and the relevant steps that are needed for translating this potential to routine practice.

A microRNA panel to discriminate carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissue

Objective It is a challenge to differentiate invasive carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues. In this study, microRNA profiles were evaluated in the transformation of colorectal carcinogenesis to discover new molecular markers for identifying a carcinoma in colonoscopy biopsy tissues where the presence of stromal invasion cells is not detectable by microscopic analysis. Methods The expression of 723 human microRNAs was measured in laser capture microdissected epithelial tumours from 133 snap-frozen surgical colorectal specimens. Three well-known classification algorithms were used to derive candidate biomarkers for discriminating carcinomas from adenomas. Quantitative reverse-transcriptase PCR was then used to validate the candidates in an independent cohort of macrodissected formalin-fixed paraffin-embedded colorectal tissue samples from 91 surgical resections. The biomarkers were applied to differentiate carcinomas from high-grade intraepithelial neoplasms in 58 colonoscopy biopsy tissue samples with stromal invasion cells undetectable by microscopy. Results One classifier of 14 microRNAs was identified with a prediction accuracy of 94.1% for discriminating carcinomas from adenomas. In formalin-fixed paraffin-embedded surgical tissue samples, a combination of miR-375, miR-424 and miR-92a yielded an accuracy of 94% (AUC=0.968) in discriminating carcinomas from adenomas. This combination has been applied to differentiate carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues with an accuracy of 89% (AUC=0.918). Conclusions This study has found a microRNA panel that accurately discriminates carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues. This microRNA panel has considerable clinical value in the early diagnosis and optimal surgical decision-making of colorectal cancer.

Effective normalization for copy number variation detection from whole genome sequencing

BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

PAPAyA: a platform for breast cancer biomarker signature discovery, evaluation and assessment

BackgroundThe decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, therefore, a need for translational clinical tools based on molecular bioinformatics, particularly in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions.ResultsWe present a platform for molecular signature discovery and clinical decision support that relies on genomic and epigenomic measurement modalities as well as clinical parameters such as histopathological results and survival information. Our P hysician A ccessible P reclinical A naly tics A pplication (PAPAyA) integrates a powerful set of statistical and machine learning tools that leverage the connections among the different modalities. It is easily extendable and reconfigurable to support integration of existing research methods and tools into powerful data analysis and interpretation pipelines. A current configuration of PAPAyA with examples of its performance on breast cancer molecular profiles is used to present the platform in action.ConclusionPAPAyA enables analysis of data from (pre)clinical studies, formulation of new clinical hypotheses, and facilitates clinical decision support by abstracting molecular profiles for clinicians.

Web information extraction for content augmentation

Today, users have to cope with an overwhelming number of TV channels and Web content sources. We introduce automatic content augmentation as a novel approach to contextual information extraction on behalf of the user where the context is provided by the primary content source (i.e. TV channel) and tailored by the users preferences. A key aspect of this approach is Web information extraction (WebIE) which automatically derives structured information from unstructured Web documents. Our system executes WebIE tasks, each an instantiation of WebIE rules, our generic document processors. We present two WebIE approaches: diffusion WebIE that crawls a wide set of Web pages and extracts information from a subset of the pertinent pages; and laser WebIE that accesses a select set of Web pages and extracts narrowly defined information. We describe the architecture and the implementation details of the system and provide detailed laser WebIE examples.

MyInfo: a personal news interface

We present a novel interface design for MyInfo, a personal news application that processes and combines content from TV and the web. MyInfo provides personalized content selectable by topic such as weather or traffic. In addition, users can play back a personal news program as a TV show, leaving themselves free to complete tasks such as making breakfast. We detail our design process from concept generation to focus group exploration to final design. The main design challenges include (i) understanding what kinds of TV/Web applications people want, and (ii) developing an interface that fits peoples lifestyles.

Brief‐exposure to preoperative bevacizumab reveals a TGF‐β signature predictive of response in HER2‐negative breast cancers

To best define biomarkers of response, and to shed insight on mechanism of action of certain clinically important agents for early breast cancer, we used a brief‐exposure paradigm in the preoperative setting to study transcriptional changes in patient tumors that occur with one dose of therapy prior to combination chemotherapy. Tumor biopsies from breast cancer patients enrolled in two preoperative clinical trials were obtained at baseline and after one dose of bevacizumab (HER2‐negative), trastuzumab (HER2‐positive) or nab‐paclitaxel, followed by treatment with combination chemo‐biologic therapy. RNA‐Sequencing based PAM50 subtyping at baseline of 46 HER2‐negative patients revealed a strong association between the basal‐like subtype and pathologic complete response (pCR) to chemotherapy plus bevacizumab (p ≤ 0.0027), but did not provide sufficient specificity to predict response. However, a single dose of bevacizumab resulted in down‐regulation of a well‐characterized TGF‐β activity signature in every single breast tumor that achieved pCR (p ≤ 0.004). The TGF‐β signature was confirmed to be a tumor‐specific read‐out of the canonical TGF‐β pathway using pSMAD2 (p ≤ 0.04), with predictive power unique to brief‐exposure to bevacizumab (p ≤ 0.016), but not trastuzumab or nab‐paclitaxel. Down‐regulation of TGF‐β activity was associated with reduction in tumor hypoxia by transcription and protein levels, suggesting therapy‐induced disruption of an autocrine‐loop between tumor stroma and malignant cells. Modulation of the TGF‐β pathway upon brief‐exposure to bevacizumab may provide an early functional readout of pCR to preoperative anti‐angiogenic therapy in HER2‐negative breast cancer, thus providing additional avenues for exploration in both preclinical and clinical settings with these agents.

Media Augmentation and Personalization Through Multimedia Processing and Information Extraction

This chapter details the value and methods for content augmentation and personalization among different media such as TV and Web. We illustrate how metadata extraction can aid in combining different media to produce a novel content consumption and interaction experience. We present two pilot content augmentation applications. The first, called MyInfo, combines automatically segmented and summarized TV news with information extracted from Web sources. Our news summarization and metadata extraction process employs text summarization, anchor detection and visual key element selection. Enhanced metadata allows matching against the user profile for personalization. Our second pilot application, called InfoSip, performs person identification and scene annotation based on actor presence. Person identification relies on visual, audio, text analysis and talking face detection. The InfoSip application links person identity information with filmographies and biographies extracted from the Web, improving the TV viewing experience by allowing users to easily query their TVs for information about actors in the current scene.