Clara E. Isaza

Identification of potential biomarkers from microarray experiments using multiple criteria optimization

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.

Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge

Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization‐based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.

An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer

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.

Biological signaling pathways and potential mathematical network representations: biological discovery through optimization

Establishing the role that different genes play in the development of cancer is a daunting task. A step toward this end is the detection of genes that are important in the illness from high‐throughput biological experiments. Furthermore, it is safe to say that it is highly unlikely that these show expression changes independently, even with a list of potentially important genes. A biological signaling pathway is a more plausible underlying mechanism as favored in the literature. This work attempts to build a mathematical network problem through the analysis of microarray experiments. A preselection of genes is carried out with a multiple criteria optimization framework previously published by our research group . Afterward, application of the Traveling Salesperson Problem and Minimum Spanning Tree network optimization models are proposed to identify potential signaling pathways via the most correlated path among the genes of interest. Biological evidencing is provided to assess the effectiveness of the proposed methods. The capability of our analysis strategy is also demonstrated through the undertaking of meta‐analysis studies. Three important aspects are novel in this work: (1) our joint analyses of different groups of lung cancer states reveal new correlations, biologically evidenced, and previously undocumented; (2) computation of the correlation coefficients from expression differences leads to an effective use of network optimization methods; and (3) the methods yield mathematically optimal correlation structures: no other configuration is better correlated using the available information.

Baseline Detection of Potential Cancer Biomarkers with Linear Models from Microarray Experiments

An important research objective in biology and the medical sciences is the search for genes whose change in relative expression is an indication of a particular state of an organism such as cancer. These genes are known as biomarkers. Microarray experiments have played an important role in the identification of this type of genes. The successful identification of potential biomarker genes can lead to an eventual clinical confirmation and thus to enhanced disease diagnosis and prognosis capabilities.

Abstract P5-06-05: miRNAs associated with DNA repair capacity in Puerto Rican women with breast cancer

BACKGROUND: MicroRNAs (miRNA) are short non-protein-coding RNAs that regulate gene expression at the post-transcriptional level via binding to 3′-untranslated regions of protein-coding transcripts. Some miRNAs have been used as diagnostic, prognostic and therapeutic markers of breast cancer (BC). It is well established that dysregulation of DNA repair capacity (DRC) is an important risk factor of BC. However, there is little published information as to what specific miRNAs are associated with DRC in women with BC. OBJECTIVE : The main objective of this study was to identify candidate miRNAs associated with dysregulation of DRC in women with BC. METHODS: Plasma samples from 30 BC cases and 30 controls selected based on their DRC levels (low, high) using a proprietary algorithm. Samples were analyzed for miRNA expression utilizing protocols from Applied Biosystems (Life Technologies). The miRNA expression profiling was performed utilizing the RT-PCR TaqMan Array Human MicroRNA A Cards v 2.0 (Applied Biosystems) containing 383 miRNA probes. Single-stranded cDNA was synthesized from 200 ng of total RNA in 8 Multiplex RT primer pool reactions containing stem-looped RT primers that were specific to mature miRNAs. U6 snRNA-001973 was selected for normalization based on our own experimental validations. To quantify the association of the miRNA expression the fold change () was estimated for every detector with the p-values calculated using the t-test. RESULTS: Candidate miRNAs that showed a statistically significant expression were: miR-146, miR-34a, miR-221, Let-7b, miR-193b, miR-132, miR-192, miR-21, miR-197, miR-24, miR-26b, miR-29c were identified based on a false discovery rate of 4%. The results showed that twelve miRNAs differentially expressed in patients with BC. Candidate miRNAs have been reported associated with the expression of twenty seven DNA repair genes. Two of these genes are part of the NER pathway which has been identified by previous studies as important in BC. CONCLUSION: Our preliminary data suggests that differential expression of specific miRNAs might be associated with dysregulation of DRC in BC. The molecular mechanisms by which miRNAs regulate DNA repair genes remain to be elucidated. However, our results lend further promise to the concept of miRNAs as a tool to study the regulation of DRC. We see potentialfuture applications in prognosis and therapy of women with BC. Supported by grants S06 GM008239-20 and 1SCA157250 from the NCI Center to Reduce Health Disparities and NIH-MBRS Program (NIGMS) and NIH-NIGMS #GM082406 (CO). Citation Format: Jaime Matta, Clara Isaza, Carmen Ortiz, Erick Suarez, Luisa Morales. miRNAs associated with DNA repair capacity in Puerto Rican women with breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P5-06-05.

Abstract B45: An optimization-based approach to potential biomarker identification with microarray data

The purpose of this work is to introduce an approach to identify potential biomarkers from the analysis of microarray experiments. The proposed approach does not require the adjustment of any parameters, thus favoring results’ consistency and reproducibility across different analysts. The key idea in this approach is to represent the biomarker identification task as a multicriteria mathematical optimization problem. The solution of this problem to optimality implies the determination of a set of genes that significantly change their expression level across different experiments. In order to enable such representation, a series of performance measures are obtained for each gene under analysis. It is proposed that these performance measures be statistical p values obtained through nonparametric statistical comparison. A gene with minimal p values across all instances can be considered a potentially dominant gene. Due to the nature of data-based analysis, however, considering multiple p values at a time will lead to conflicting behavior among them. This conflicting behavior introduces a considerable amount of subjectivity in the selection of important genes since different researchers will use different techniques to deal with it. It is our hypothesis that an optimization-based approach can resolve this conflict with consistent and objective results. Preliminary results on the application of the proposed approach to cervix cancer publicly available data from Wong, et al (2003), include the following prioritized lists of accession numbers for potential biomarkers of cervix cancer. The list with the first priority includes {AA488645, H22826}. The list with the second priority includes {AI553969, AA243749, T71316, AA460827}. The list with the third priority is {AA454831, AA913408}. The list with the fourth priority is {AA487237, AA446565}, and the list with the fifth priority includes {H23187}. A literature search on these genes indicates that there is a high likelihood for some of them to be biomarkers with at least H23187 being a confirmed biomarker for gastrointestinal stromal tumors as reported by Parkkila, et al (2010). The initial results are encouraging in terms of the high discrimination rate shown by the approach, the lack of parameters involved to identify potential biomarkers, and the hierarchical structure in which the final information can be shown. A validation experiment on the list of genes here presented is currently under consideration by our research group. The proposed method can be used to perform secondary analysis of existing microarray databases with more objectivity. References: 1. Y. F. Wong, et al, “Expression Genomics of Cervical Cancer: Molecular Classification and Prediction of Radiotherapy Response by DNA Microarray,” Clinical Cancer Research, vol 9, pp. 5486-5492. July 2003. 2. Parkkila S, et al, ”Carbonic anhydrase II. A novel biomarker for gastrointestinal stromal tumors,” Modern Pathology (2010) 23, 743-750. Citation Information: Cancer Prev Res 2010;3(12 Suppl):B45.