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ABSTRACT: We discuss the feasibility of multiplex QD stain for four biomarkers and our progress in finding four suitable biomarkers from four different hosts. There is a demand for using more than three fluorescent probes on a single tissue sample for disease detection to offer a more reliable prediction of disease progression. We developed a protocol for targeting four biomarkers using four primary antibodies from four different animal hosts. We performed primary-secondary antibody binding assays on nitrocellulose paper and stained breast cancer microarray slides with known expression of ER, PR, and HER2. We identified the lack of a standard protocol and the limited supply of primary antibodies from hosts other than rabbit and mouse in the market as key challenges. The results show variable success in both assays, but indicate future potential for this protocol with more development.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:6576-9.
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ABSTRACT: In previous work, we reported the development of caCORRECT, a novel microarray quality control system built to identify and correct spatial artifacts commonly found on Affymetrix arrays. We have made recent improvements to caCORRECT, including the development of a model-based data-replacement strategy and integration with typical microarray workflows via caCORRECT's web portal and caBIG grid services. In this report, we demonstrate that caCORRECT improves the reproducibility and reliability of experimental results across several common Affymetrix microarray platforms. caCORRECT represents an advance over state-of-art quality control methods such as Harshlighting, and acts to improve gene expression calculation techniques such as PLIER, RMA and MAS5.0, because it incorporates spatial information into outlier detection as well as outlier information into probe normalization. The ability of caCORRECT to recover accurate gene expressions from low quality probe intensity data is assessed using a combination of real and synthetic artifacts with PCR follow-up confirmation and the affycomp spike in data. The caCORRECT tool can be accessed at the website: http://cacorrect.bme.gatech.edu.
We demonstrate that (1) caCORRECT's artifact-aware normalization avoids the undesirable global data warping that happens when any damaged chips are processed without caCORRECT; (2) When used upstream of RMA, PLIER, or MAS5.0, the data imputation of caCORRECT generally improves the accuracy of microarray gene expression in the presence of artifacts more than using Harshlighting or not using any quality control; (3) Biomarkers selected from artifactual microarray data which have undergone the quality control procedures of caCORRECT are more likely to be reliable, as shown by both spike in and PCR validation experiments. Finally, we present a case study of the use of caCORRECT to reliably identify biomarkers for renal cell carcinoma, yielding two diagnostic biomarkers with potential clinical utility, PRKAB1 and NNMT.
caCORRECT is shown to improve the accuracy of gene expression, and the reproducibility of experimental results in clinical application. This study suggests that caCORRECT will be useful to clean up possible artifacts in new as well as archived microarray data.
BMC Bioinformatics 09/2011; 12:383. · 2.75 Impact Factor
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ABSTRACT: The bag-of-features method has emerged as a useful and flexible tool that can capture medically relevant image characteristics.
In this paper, we study the effect of scale and rotation invariance in the bag-of-features framework for Renal Cell Carcinoma
subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations
of 3-fold cross validation. For a very heterogeneous dataset labeled by an expert pathologist, we achieve a classification
accuracy of 88% with four subtypes. Our study shows that rotation invariance is more important than scale invariance but combining
both properties gives better classification performance.
KeywordsBag-of-features Method–Texton-based Approach–Image Classification–Computer Aided Diagnosis
09/2011: pages 66-74;
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ABSTRACT: Robust behavior in metabolic pathways resembles stabilized performance in systems under autonomous control. This suggests we can apply control theory to study existing regulation in these cellular networks. Here, we use model-reference adaptive control (MRAC) to investigate the dynamics of de novo sphingolipid synthesis regulation in a combined theoretical and experimental case study. The effects of serine palmitoyltransferase over-expression on this pathway are studied in vitro using human embryonic kidney cells. We report two key results from comparing numerical simulations with observed data. First, MRAC simulations of pathway dynamics are comparable to simulations from a standard model using mass action kinetics. The root-sum-square (RSS) between data and simulations in both cases differ by less than 5%. Second, MRAC simulations suggest systematic pathway regulation in terms of adaptive feedback from individual molecules. In response to increased metabolite levels available for de novo sphingolipid synthesis, feedback from molecules along the main artery of the pathway is regulated more frequently and with greater amplitude than from other molecules along the branches. These biological insights are consistent with current knowledge while being new that they may guide future research in sphingolipid biology. In summary, we report a novel approach to study regulation in cellular networks by applying control theory in the context of robust metabolic pathways. We do this to uncover potential insight into the dynamics of regulation and the reverse engineering of cellular networks for systems biology. This new modeling approach and the implementation routines designed for this case study may be extended to other systems. Supplementary Material is available at www.liebertonline.com/cmb .
Journal of computational biology: a journal of computational molecular cell biology 02/2011; 18(2):169-82. · 1.69 Impact Factor
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Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
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Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011 - 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III; 01/2011
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ABSTRACT: The bag-of-features method has emerged as a useful and flexible tool that can capture medically relevant image characteristics. In this paper, we study the effect of scale and rotation invariance in the bag-of-features framework for Renal Cell Carcinoma subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations of 3-fold cross validation. For a very heterogeneous dataset labeled by an expert pathologist, we achieve a classification accuracy of 88% with four subtypes. Our study shows that rotation invariance is more important than scale invariance but combining both properties gives better classification performance.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 3):66-74.
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ABSTRACT: Registration of high-resolution tissue images is a critical step in the 3D analysis of protein expression. Because the distance between images (~4-5μm thickness of a tissue section) is nearly the size of the objects of interest (~10-20μm cancer cell nucleus), a given object is often not present in both of two adjacent images. Without consistent correspondence of objects between images, registration becomes a difficult task. This work assesses the feasibility of current registration techniques for such images.
We generated high resolution synthetic 3-D image data sets emulating the constraints in real data. We applied multiple registration methods to the synthetic image data sets and assessed the registration performance of three techniques (i.e., mutual information (MI), kernel density estimate (KDE) method [1], and principal component analysis (PCA)) at various slice thicknesses (with increments of 1μm) in order to quantify the limitations of each method.
Our analysis shows that PCA, when combined with the KDE method based on nuclei centers, aligns images corresponding to 5μm thick sections with acceptable accuracy. We also note that registration error increases rapidly with increasing distance between images, and that the choice of feature points which are conserved between slices improves performance.
We used simulation to help select appropriate features and methods for image registration by estimating best-case-scenario errors for given data constraints in histological images. The results of this study suggest that much of the difficulty of stained tissue registration can be reduced to the problem of accurately identifying feature points, such as the center of nuclei.
Journal of pathology informatics. 01/2011; 2:S6.
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Leming Shi,
Gregory Campbell,
Wendell D Jones,
Fabien Campagne,
Zhining Wen,
Stephen J Walker,
Zhenqiang Su,
Tzu-Ming Chu,
Federico M Goodsaid,
Lajos Pusztai, [......],
Lun Yang,
Xiao Zeng,
Jialu Zhang,
Li Zhang,
Min Zhang,
Chen Zhao,
Raj K Puri,
Uwe Scherf,
Weida Tong,
Russell D Wolfinger
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ABSTRACT: Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
Nature Biotechnology 08/2010; 28(8):827-38. · 29.50 Impact Factor
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Jian Liu,
Stephen K Lau,
Vijay A Varma, Richard A Moffitt,
Matthew Caldwell,
Tao Liu,
Andrew N Young,
John A Petros,
Adeboye O Osunkoya,
Tracey Krogstad,
Brian Leyland-Jones,
May D Wang,
Shuming Nie
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ABSTRACT: Tumor heterogeneity is one of the most important and challenging problems not only in studying the mechanisms of cancer development but also in developing therapeutics to eradicate cancer cells. Here we report the use of multiplexed quantum dots (QDs) and wavelength-resolved spectral imaging for molecular mapping of tumor heterogeneity on human prostate cancer tissue specimens. By using a panel of just four protein biomarkers (E-cadherin, high-molecular-weight cytokeratin, p63, and alpha-methylacyl CoA racemase), we show that structurally distinct prostate glands and single cancer cells can be detected and characterized within the complex microenvironments of radical prostatectomy and needle biopsy tissue specimens. The results reveal extensive tumor heterogeneity at the molecular, cellular, and architectural levels, allowing direct visualization of human prostate glands undergoing structural transitions from a double layer of basal and luminal cells to a single layer of malignant cells. For clinical diagnostic applications, multiplexed QD mapping provides correlated molecular and morphological information that is not available from traditional tissue staining and molecular profiling methods.
ACS Nano 04/2010; 4(5):2755-65. · 10.77 Impact Factor
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ABSTRACT: Color variation in medical images degrades the classification performance of computer aided diagnosis systems. Traditionally, color segmentation algorithms mitigate this variability and improve performance. However, consistent and robust segmentation remains an open research problem. In this study, we avoid the tenuous phase of color segmentation by adapting a bag-of-features approach using scale invariant features for classification of renal cell carcinoma subtypes. Previous work shows that features from each subtype match those from expertly chosen template images. In this paper, we show that the performance of this match-based methodology greatly depends on the quality of the template images. To avoid this uncertainty, we propose a bag-of-features approach that does not require expert knowledge and instead learns a "vocabulary" of morphological characteristics from training data. We build a support vector machine using feature histograms and evaluate this method using 40 iterations of 3-fold cross validation. We achieve classification accuracy above 90% for a heterogeneous dataset labeled by an expert pathologist, showing its potential for future clinical applications.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:6749-52.
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ABSTRACT: Drug bioavailability is a major failing point of new pharmaceuticals i.e. drugs fail to reach their target or fail to stay there long enough for therapeutic effect. Compounding this issue, significant variability exists between patients and how they metabolize and distribute a drug. We present WebPK, a web-based tool for simulation of custom pharmacokinetic models. Model parameters can be entered manually or uploaded as a file. Simulation computations are performed on the server side, and thus require minimal client resources, which makes WebPK suitable for mobile devices. Time series biodistribution data are returned to the user in graphical and numerical form for quick interpretation or archiving. Results generated from WebPK are consistent with previously published pharmacokinetic models. This work is expected to provide physicians with access to easy simulation of patient pharmacokinetic profiles, which will allow for the prescription of more efficient and personalized drug regimens. URL: http://webpk.bme.gatech.edu.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:1494-7.
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ABSTRACT: The differential diagnosis of clear cell, papillary, and chromophobe renal cell carcinoma is clinically important, because these tumor subtypes are associated with different pathobiology and clinical behavior. For cases in which histopathology is equivocal, immunohistochemistry and quantitative reverse transcriptase-polymerase chain reaction can assist in the differential diagnosis by measuring expression of subtype-specific biomarkers. Several renal tumor biomarkers have been discovered in expression microarray studies. However, due to heterogeneity of gene and protein expression, additional biomarkers are needed for reliable diagnostic classification. We developed novel bioinformatics systems to identify candidate renal tumor biomarkers from the microarray profiles of 45 clear cell, 16 papillary, and 10 chromophobe renal cell carcinomas; the microarray data was derived from 2 independent published studies. The ArrayWiki biocomputing system merged the microarray data sets into a single file, so gene expression could be analyzed from a larger number of tumors. The caCORRECT system removed non-random sources of error from the microarray data, and the omniBioMarker system analyzed data with several gene-ranking algorithms to identify algorithms effective at recognizing previously described renal tumor biomarkers. We predicted these algorithms would also be effective at identifying unknown biomarkers that could be verified by independent methods. We selected 6 novel candidate biomarkers from the omniBioMarker analysis and verified their differential expression in formalin-fixed paraffin-embedded tissues by quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry. The candidate biomarkers were carbonic anhydrase IX, ceruloplasmin, schwannomin-interacting protein 1, E74-like factor 3, cytochrome c oxidase subunit 5a, and acetyl-CoA acetyltransferase 1. Quantitative reverse transcriptase-polymerase chain reaction was performed on 17 clear cell, 13 papillary and 7 chromophobe renal cell carcinoma. Carbonic anhydrase IX and ceruloplasmin were overexpressed in clear cell renal cell carcinoma; schwannomin-interacting protein 1 and E74-like factor 3 were overexpressed in papillary renal cell carcinoma; and cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1 were overexpressed in chromophobe renal cell carcinoma. Immunohistochemistry was performed on tissue microarrays containing 66 clear cell, 16 papillary, and 12 chromophobe renal cell carcinomas. Cytoplasmic carbonic anhydrase IX staining was significantly associated with clear cell renal cell carcinoma. Strong cytoplasmic schwannomin-interacting protein 1 and cytochrome c oxidase subunit 5a staining were significantly more frequent in papillary and chromophobe renal cell carcinoma, respectively. In summary, we developed a novel process for identifying candidate renal tumor biomarkers from microarray data, and verifying differential expression in independent assays. The tumor biomarkers have potential utility as a multiplex expression panel for classifying renal cell carcinoma with equivocal histology. Biomarker expression assays are increasingly important for renal cell carcinoma diagnosis, as needle core biopsies become more common and different therapies for tumor subtypes continue to be developed.
Human pathology 09/2009; 40(12):1671-8. · 3.03 Impact Factor
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ABSTRACT: Recent advances in biomarker discovery, biocomputing and nanotechnology have raised new opportunities in the emerging fields of personalized medicine (in which disease detection, diagnosis and therapy are tailored to each individual's molecular profile) and predictive medicine (in which genetic and molecular information is used to predict disease development, progression and clinical outcome). Here, we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, in addition to the prospects for and challenges involved in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases.
Trends in Biotechnology 07/2009; 27(6):350-8. · 9.15 Impact Factor
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ABSTRACT: The process of developing molecular assays for disease diagnosis and prognosis requires cross-disciplinary research which monitors quality and reproducibility at all levels. This paper discusses challenges in the quality control of highly multiplexed Quantum Dot (QD) staining and provides a method for improving accuracy of QD quantification in two phases. Phase one is the estimation of unintended crosstalk between multiplexed QD-antibody reporters, and phase two is digital correction of this crosstalk. Results show that crosstalk varies among tissues and reagents, and in some cases it can be on the same order of magnitude as the original intended signal. In cases where target protein expression is assumed to be independent, crosstalk can be empirically estimated from imaging data and corrected for. This work is expected to improve the overall reproducibility and quantification of multiplexed QD staining.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:6739-42.
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ABSTRACT: Clinical Histopathology is based on the analysis of immunohistochemistry (IHC) stained tissue images. Selection of antibodies for detecting the presence, type, and grade of cancerous tissue has a great influence on the diagnostic potential of IHC tests. Automated evaluation methods for tissue microarrays applied to many combinations of antibody and tissue type can speed development of new clinical assays. We present an automatic method that successfully quantifies stain intensity, fraction of cells stained and sub-cellular location of staining in tissue microarray images. The method combines an opponent color preprocessor and a novel statistical approach for identifying brown and blue staining, followed by multilevel morphological processing. We verify the capability of our method by comparing the results to manually annotated image databases. We also demonstrate cross-tissue robustness using two clinical case study data.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:3665-8.
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ABSTRACT: Microarray technology has enabled us to simultaneously measure the expression of thousands of genes. Using this high-throughput technology, we can examine subtle genetic changes between biological samples and build predictive models for clinical applications. Although microarrays have dramatically increased the rate of data collection, sample size is still a major issue when selecting features. Previous methods show that combining multiple microarray datasets improves feature selection using simple methods such as fold change. We propose a wrapper-based gene selection technique that combines bootstrap estimated classification errors for individual genes across multiple datasets and reduces the contribution of datasets with high variance. We use the bootstrap because it is an unbiased estimator of classification error that is also effective for small sample data. Coupled with data combination across multiple datasets, we show that our meta-analytic approach improves the biological relevance of gene selection using prostate and renal cancer microarray data.
Computer and Computational Sciences, International Multi-Symposiums on. 10/2008;
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ABSTRACT: In this paper, we present a simple method for the processing and quantification of multiplexed Quantum Dot (QD) labeled images of clinical cancer tissue samples. QDs provide several features which make them ideal for reliable quantification, including long-term signal stability, high signal-to-noise ratios, as well as narrow emission bandwidths. Deconvolution of QD spectra is accomplished in a batch mode in which unmixing parameters are preserved across samples to allow for quantitative and reproducible comparisons. After unmixing the QD images, we segment each one to exclude acellular regions. We use a simple average intensity to quantify the level of QD staining for each image. We illustrate the viability of this approach by testing it on 28 tissue samples using a tissue microarray. We show that using as few as two QD protein targets (MDM-2, and B-actin), the Renal Cell Carcinoma (RCC) samples are distinguishable from adjacent normal tissue samples. A simple linear discriminant results in 100% classification of 25 RCC samples and 3 normal samples. This suggests that multiplexed QDs can be used to properly diagnose RCC from otherwise healthy tissue. We expect to apply this work to larger panels of more robust QD biomarker targets to aid in clinical decision-making for the diagnosis and prognosis of diseases, such as cancer.
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE; 09/2008
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Proceedings of the 8th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2008, October 8-10, 2008, Athens, Greece; 01/2008
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ABSTRACT: Quality assurance of high throughput "-omics" data is a major concern for biomedical discovery and translational medicine, and is considered a top priority in bioinformatics and systems biology. Here, we report a web-based bioinformatics tool called caCORRECT for chip artifact detection, analysis, and CORRECTion, which removes systematic artifactual noises that are commonly observed in microarray gene expression data. Despite the development of major databases such as GEO arrayExpress, caArray, and the SMD to manage and distribute microarray data to the public, reproducibility has been questioned in many cases, including high-profile papers and datasets. Based on both archived and synthetic data, we have designed the caCORRECT to have several advanced features: (1) to uncover significant, correctable artifacts that affect reproducibility of experiments; (2) to improve the integrity and quality of public archives by removing artifacts; (3) to provide a universal quality score to aid users in their selection of suitable microarray data; and (4) to improve the true-positive rate of biomarker selection verified by test data. These features are expected to improve the reproducibility of Microarray study. caCORRECT is freely available at: http://caCORRECT.bme.gatech.edu.
Annals of Biomedical Engineering 07/2007; 35(6):1068-80. · 2.37 Impact Factor
