Special Issue "Advanced Methods in Microarrays for Cancer Research"

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A special issue of Microarrays (ISSN 2076-3905).

Deadline for manuscript submissions: closed (31 January 2015)

Special Issue Editor

Guest Editor
Dr. Shu-Kay Ng (Website)

School of Medicine, Griffith University, Meadowbrook QLD 4131, Australia
Interests: statistical modelling; bioinformatics; pattern recognition; microarrays; cancer research; multivariate data analysis

Special Issue Information

Dear Colleagues,

The advent of high-throughput microarray technologies has revolutionized molecular biology and has great impact on the way genetic diseases such as cancer are diagnosed, classified, and treated. New methods emerge for the processing and the analysis of microarray data in an attempt to understand the regulation of gene targets and associating pathways, detect differentially-expressed biomarkers, and classify disease subtypes.

This special issue invites contributions to the advanced development and applications of profiling techniques to characterize microarray profiles of cancer patients and new methodologies for the identification of biomarkers relevant to various aspects of cancer screening, growth, diagnosis, treatment therapy, and prognosis. This special issue aims to stimulate further multidisciplinary research on this emerging science.

Dr. Shu-Kay Ng
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microarrays is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • microarrays
  • cancer research
  • bioinformatics
  • differential expression
  • profiling techniques
  • network analysis
  • biomarkers

Published Papers (3 papers)

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Research

Open AccessArticle Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology
Microarrays 2015, 4(3), 339-369; doi:10.3390/microarrays4030339
Received: 9 April 2015 / Accepted: 3 August 2015 / Published: 12 August 2015
Cited by 1 | PDF Full-text (2271 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are [...] Read more.
DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer. Full article
(This article belongs to the Special Issue Advanced Methods in Microarrays for Cancer Research)
Figures

Open AccessArticle An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer
Microarrays 2015, 4(2), 287-310; doi:10.3390/microarrays4020287
Received: 22 February 2015 / Revised: 27 April 2015 / Accepted: 13 May 2015 / Published: 28 May 2015
PDF Full-text (1170 KB) | HTML Full-text | XML Full-text
Abstract
Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High‑throughput biological experiments have played a critical role in providing information in this regard. A special [...] Read more.
Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High‑throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path. Full article
(This article belongs to the Special Issue Advanced Methods in Microarrays for Cancer Research)
Figures

Open AccessArticle Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies
Microarrays 2015, 4(2), 162-187; doi:10.3390/microarrays4020162
Received: 30 January 2015 / Revised: 23 March 2015 / Accepted: 25 March 2015 / Published: 2 April 2015
Cited by 1 | PDF Full-text (1369 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
New minimal invasive diagnostic methods for early detection of lung cancer are urgently needed. It is known that the immune system responds to tumors with production of tumor-autoantibodies. Protein microarrays are a suitable highly multiplexed platform for identification of autoantibody signatures against [...] Read more.
New minimal invasive diagnostic methods for early detection of lung cancer are urgently needed. It is known that the immune system responds to tumors with production of tumor-autoantibodies. Protein microarrays are a suitable highly multiplexed platform for identification of autoantibody signatures against tumor-associated antigens (TAA). These microarrays can be probed using 0.1 mg immunoglobulin G (IgG), purified from 10 µL of plasma. We used a microarray comprising recombinant proteins derived from 15,417 cDNA clones for the screening of 100 lung cancer samples, including 25 samples of each main histological entity of lung cancer, and 100 controls. Since this number of samples cannot be processed at once, the resulting data showed non-biological variances due to “batch effects”. Our aim was to evaluate quantile normalization, “distance-weighted discrimination” (DWD), and “ComBat” for their effectiveness in data pre-processing for elucidating diagnostic immune‑signatures. “ComBat” data adjustment outperformed the other methods and allowed us to identify classifiers for all lung cancer cases versus controls and small-cell, squamous cell, large-cell, and adenocarcinoma of the lung with an accuracy of 85%, 94%, 96%, 92%, and 83% (sensitivity of 0.85, 0.92, 0.96, 0.88, 0.83; specificity of 0.85, 0.96, 0.96, 0.96, 0.83), respectively. These promising data would be the basis for further validation using targeted autoantibody tests. Full article
(This article belongs to the Special Issue Advanced Methods in Microarrays for Cancer Research)

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Potential biomarkers and their most correlated path in Lung Cancer
Authors: 1Katia I. Cáceres-Camacho, 1Enery Lorenzo, 1Juan Rosas, 1Michael Ortiz, 1Lynn Perez, 1Juan Acevedo, 1Juan Irizarry, 1Valerie González, 1Mauricio Cabrera-Ríos, 1,2Clara Isaza,
Affiliations: 1 The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico at Mayagüez
2
Department of Pharmacology, Physiology and Toxicology, Ponce Health Sciences University and School of Medicine
E-Mails: applied.optmization@gmail.com
Abstract: This work presents a two-stage analysis pipeline for microarray data. The first stage detects the most differentially expressed genes through multiple criteria optimization and the second one elicits their most correlated path through integer network programming. This pipeline does not require the adjustment of any parameters by the user, the definition of significance thresholds or the use of any data normalization procedure, thereby preserving analysis objectivity and repeatability. A study on lung cancer is presented to demonstrate the capabilities of the method. In particular, the results confirm genes RAGE, SPP1 as importantly related to lung cancer, while providing evidence to include XIST, ADH1B, RPS4Y1, CYP1B1, CEACAM6, MSMB, SCGB1A1, FABP4, KRT15 and FGG for their potential role in lung cancer for different groups of study.
Keywords: microarrays, cancer research, biomarkers, network analysis

Title: Identification of Copy Number Aberrations in Breast Cancer Subtypes using Persistence Topology
Authors:
Javier Arsuaga
Affiliations:
Mathematics and Molecular and Cellular Biology, University of California at Davis
Abstract:
DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Their identification however remains a challenging task because they are hidden by CNAs that are the product of random events that occur during tumor evolution. Experimental detection of CNAs is commonly accomplished through microarray Comparative Genomic Hybridization (mCGH) assays followed by supervised or unsupervised statistical methods that interrogate the segmented profile of each patient. Here we present a new supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a 2-dimensional (2D) point cloud to each CGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph, for different segmentation values. Identification of CNAs is achieved by interrogating the topological properties of these simplicial complexes. We tested our approach on a published data set with the goal of identifying breast cancer CNAs associated with specific molecular subtypes. Our results confirmed all regions found in the original publication except for 17q in luminal B and detected 33 additional regions. Most of the additional regions have been reported in other independent studies. Three not previously reported CNAs, gain of 1p, gain of 2p and loss of 14q, were found on basal subtypes and validated on a second published data set. We therefore suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.
Keywords: Copy number aberrations; breast cancer; topological data analysis

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