-
Zsuzsanna Hollander,
Virginia Chen,
Keerat Sidhu,
David Lin, Raymond T Ng,
Robert Balshaw,
Gabriela V Cohen-Freue,
Andrew Ignaszewski,
Carol Imai,
Annemarie Kaan,
Scott J Tebbutt,
Janet E Wilson-McManus,
Robert W McMaster,
Paul A Keown,
Bruce M McManus
[show abstract]
[hide abstract]
ABSTRACT: BACKGROUND: Acute rejection in cardiac transplant patients remains a contributory factor to limited survival of implanted hearts. Currently, there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplant acute cellular rejection. Such a development would be of great value in personalizing immunosuppressive treatment. METHODS: Recipient age, donor age, cold ischemic time, warm ischemic time, panel-reactive antibody, gender mismatch, blood type mismatch and human leukocyte antigens (HLA-A, -B and -DR) mismatch between recipients and donors were tested in 53 heart transplant patients for their power to predict post-transplant acute cellular rejection. Donor transplant biopsy and recipient pre-transplant blood were also examined for the presence of genomic biomarkers in 7 rejection and 11 non-rejection patients, using non-targeted data mining techniques. RESULTS: The biomarker based on the 8 clinical variables had an area under the receiver operating characteristic curve (AUC) of 0.53. The pre-transplant recipient blood gene-based panel did not yield better performance, but the donor heart tissue gene-based panel had an AUC = 0.78. A combination of 25 probe sets from the transplant donor biopsy and 18 probe sets from the pre-transplant recipient whole blood had an AUC = 0.90. Biologic pathways implicated include VEGF- and EGFR-signaling, and MAPK. CONCLUSIONS: Based on this study, the best predictive biomarker panel contains genes from recipient whole blood and donor myocardial tissue. This panel provides clinically relevant prediction power and, if validated, may personalize immunosuppressive treatment and rejection monitoring.
The Journal of heart and lung transplantation: the official publication of the International Society for Heart Transplantation 12/2012; · 3.54 Impact Factor
-
Oliver P Günther,
Virginia Chen,
Gabriela Cohen Freue,
Robert F Balshaw,
Scott J Tebbutt,
Zsuzsanna Hollander,
Mandeep Takhar,
W Robert McMaster,
Bruce M McManus,
Paul A Keown, Raymond T Ng
[show abstract]
[hide abstract]
ABSTRACT: BACKGROUND: Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble? RESULTS: The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity. CONCLUSION: Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.
BMC Bioinformatics 12/2012; 13(1):326. · 2.75 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Acute cardiac allograft rejection is a serious complication of heart transplantation. Investigating molecular processes in whole blood via microarrays is a promising avenue of research in transplantation, particularly due to the non-invasive nature of blood sampling. However, whole blood is a complex tissue and the consequent heterogeneity in composition amongst samples is ignored in traditional microarray analysis. This complicates the biological interpretation of microarray data. Here we have applied a statistical deconvolution approach, cell-specific significance analysis of microarrays (csSAM), to whole blood samples from subjects either undergoing acute heart allograft rejection (AR) or not (NR). We identified eight differentially expressed probe-sets significantly correlated to monocytes (mapping to 6 genes, all down-regulated in ARs versus NRs) at a false discovery rate (FDR) ≤ 15%. None of the genes identified are present in a biomarker panel of acute heart rejection previously published by our group and discovered in the same data***.
Bioinformatics and biology insights 01/2012; 6:49-61.
-
David Lin,
Zsuzsanna Hollander,
Anna Meredith,
Ellamae Stadnick,
Mayu Sasaki,
Gabriela Cohen Freue,
Pooran Qasimi,
Alice Mui, Raymond T Ng,
Robert Balshaw,
Janet E Wilson-McManus,
David Wishart,
David Hau,
Paul A Keown,
Robert McMaster,
Bruce M McManus
[show abstract]
[hide abstract]
ABSTRACT: To date, gene expression studies related to chronic heart failure (CHF) have mainly involved microarray analysis of myocardial tissues. The potential utility of blood to infer the etiology, pathogenesis, and course of CHF remains unclear. Further, the use of proteomic and metabolomic platforms for molecular profiling of CHF is relatively unexplored.
Microarray genomic, iTRAQ proteomic, and nuclear magnetic resonance metabolomic analyses were carried out on blood samples from 29 end-stage CHF patients (16 ischemic heart disease [IHD], 13 nonischemic cardiomyopathy [NICM]), and 20 normal cardiac function (NCF) controls. Robust statistical tests and bioinformatical tools were applied to identify and compare the molecular signatures among these subject groups.
No genes or proteins, and only two metabolites, were differentially expressed between IHD and NICM patients at end stage. However, CHF versus NCF comparison revealed differential expression of 7,426 probe sets, 71 proteins, and 8 metabolites. Functional enrichment analyses of the CHF versus NCF results revealed several in-common biological themes and potential mechanisms underlying advanced heart failure.
Multiple "-omic" analyses support the convergence of dramatic changes in molecular processes underlying IHD and NICM at end stage.
Journal of cardiac failure 10/2011; 17(10):867-74. · 3.25 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Oral epithelial dysplasias are believed to progress through a series of histopathological stages; from mild to severe dysplasia, to carcinoma in situ, and finally to invasive OSCC. Underlying this change in histopathological grade are gross chromosome alterations and changes in gene expression of both protein-coding genes and non-coding RNAs. Recent papers have described associations of aberrant expression of microRNAs, one class of non-coding RNAs, with oral cancer. However, expression profiling of long non-coding RNAs (lncRNAs) has not been reported. Long non-coding RNAs are a novel class of mRNA-like transcripts with no protein coding capacity, but with a variety of functions including roles in epigenetics and gene regulation. In recent reports, the aberrant expression of lncRNAs has been associated with human cancers, suggesting a critical role in tumorigenesis. Here, we present the first long non-coding RNA expression map for the human oral mucosa. We describe the expression of 325 long non-coding RNAs, suggesting lncRNA expression contributes significantly to the oral transcriptome. Intriguingly, ∼60% of the detected lncRNAs show aberrant expression in oral premalignant lesions. A number of these lncRNAs have been previously associated with other human cancers.
Oral Oncology 08/2011; 47(11):1055-61. · 2.86 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Background. We have shown that genomic biomarkers in peripheral blood provide evidence of early graft rejection and may offer an important option for posttransplant monitoring, and we are working to improve this signature to maximize assay performance.
Methods. This clinical refinement study (n=79) used gene expression profiling in a case-control design to compare whole blood samples between normal subjects (n=20) and patients with (n=20) or without (n=39) biopsy-confirmed acute rejection (BCAR).
Results. Gene expression in peripheral blood from subjects with BCAR before treatment differed significantly from that of normal subjects and transplant recipients without BCAR. Hierarchical clustering and principal component analysis showed that samples obtained 1 to 5 days after the start of treatment of BCAR were segregated across both groups before treatment or without BCAR and that this was closely related to the time lag between treatment and sampling. Genes differentially expressed during BCAR included FKSG49, LMAN2, NFYC, LIMK2, JUNB, NASP, MALAT1, ITGAX, HLA-J, FKBP1A, and RBMS1, and gene ontology analysis highlighted changes in networks related to cytoskeletal reorganization, apoptosis, and immune signaling, whereas after treatment change highlighted pathways of cellular metabolism, cell-cycle regulation, DNA damage, and apoptosis.
Conclusion. Gene expression in the peripheral blood is associated with BCAR, and the pattern of expression changes rapidly after treatment. This may offer a potential tool for diagnosis of rejection and immunologic monitoring of response to treatment, which is now being evaluated in a large multicenter international study.
Transplantation 02/2011; 91(3):323-329. · 4.00 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: We have shown that genomic biomarkers in peripheral blood provide evidence of early graft rejection and may offer an important option for posttransplant monitoring, and we are working to improve this signature to maximize assay performance.
This clinical refinement study (n=79) used gene expression profiling in a case-control design to compare whole blood samples between normal subjects (n=20) and patients with (n=20) or without (n=39) biopsy-confirmed acute rejection (BCAR).
Gene expression in peripheral blood from subjects with BCAR before treatment differed significantly from that of normal subjects and transplant recipients without BCAR. Hierarchical clustering and principal component analysis showed that samples obtained 1 to 5 days after the start of treatment of BCAR were segregated across both groups before treatment or without BCAR and that this was closely related to the time lag between treatment and sampling. Genes differentially expressed during BCAR included FKSG49, LMAN2, NFYC, LIMK2, JUNB, NASP, MALAT1, ITGAX, HLA-J, FKBP1A, and RBMS1, and gene ontology analysis highlighted changes in networks related to cytoskeletal reorganization, apoptosis, and immune signaling, whereas after treatment change highlighted pathways of cellular metabolism, cell-cycle regulation, DNA damage, and apoptosis.
Gene expression in the peripheral blood is associated with BCAR, and the pattern of expression changes rapidly after treatment. This may offer a potential tool for diagnosis of rejection and immunologic monitoring of response to treatment, which is now being evaluated in a large multicenter international study.
Transplantation 02/2011; 91(3):323-9. · 4.00 Impact Factor
-
Farshid S Garmaroudi,
David Marchant,
Xiaoning Si,
Abbas Khalili,
Ali Bashashati,
Brian W Wong,
Aline Tabet, Raymond T Ng,
Kevin Murphy,
Honglin Luo,
Kevin A Janes,
Bruce M McManus
[show abstract]
[hide abstract]
ABSTRACT: Signal transduction networks can be perturbed biochemically, genetically, and pharmacologically to unravel their functions. But at the systems level, it is not clear how such perturbations are best implemented to extract molecular mechanisms that underlie network function. Here, we combined pairwise perturbations with multiparameter phosphorylation measurements to reveal causal mechanisms within the signaling network response of cardiomyocytes to coxsackievirus B3 (CVB3) infection. Using all possible pairs of six kinase inhibitors, we assembled a dynamic nine-protein phosphorylation signature of perturbed CVB3 infectivity. Cluster analysis of the resulting dataset showed repeatedly that paired inhibitor data were required for accurate data-driven predictions of kinase substrate links in the host network. With pairwise data, we also derived a high-confidence network based on partial correlations, which identified phospho-IκBα as a central "hub" in the measured phosphorylation signature. The reconstructed network helped to connect phospho-IκBα with an autocrine feedback circuit in host cells involving the proinflammatory cytokines, TNF and IL-1. Autocrine blockade substantially inhibited CVB3 progeny release and improved host cell viability, implicating TNF and IL-1 as cell autonomous components of CVB3-induced myocardial damage. We conclude that pairwise perturbations, when combined with network-level intracellular measurements, enrich for mechanisms that would be overlooked by single perturbants.
Proceedings of the National Academy of Sciences 09/2010; 107(39):17053-8. · 9.68 Impact Factor
-
Gabriela V Cohen Freue,
Mayu Sasaki,
Anna Meredith,
Oliver P Günther,
Axel Bergman,
Mandeep Takhar,
Alice Mui,
Robert F Balshaw, Raymond T Ng,
Nina Opushneva,
Zsuzsanna Hollander,
Guiyun Li,
Christoph H Borchers,
Janet Wilson-McManus,
Bruce M McManus,
Paul A Keown,
W Robert McMaster
[show abstract]
[hide abstract]
ABSTRACT: Acute graft rejection is an important clinical problem in renal transplantation and an adverse predictor for long term graft survival. Plasma biomarkers may offer an important option for post-transplant monitoring and permit timely and effective therapeutic intervention to minimize graft damage. This case-control discovery study (n = 32) used isobaric tagging for relative and absolute protein quantification (iTRAQ) technology to quantitate plasma protein relative concentrations in precise cohorts of patients with and without biopsy-confirmed acute rejection (BCAR). Plasma samples were depleted of the 14 most abundant plasma proteins to enhance detection sensitivity. A total of 18 plasma proteins that encompassed processes related to inflammation, complement activation, blood coagulation, and wound repair exhibited significantly different relative concentrations between patient cohorts with and without BCAR (p value <0.05). Twelve proteins with a fold-change >or=1.15 were selected for diagnostic purposes: seven were increased (titin, lipopolysaccharide-binding protein, peptidase inhibitor 16, complement factor D, mannose-binding lectin, protein Z-dependent protease and beta(2)-microglobulin) and five were decreased (kininogen-1, afamin, serine protease inhibitor, phosphatidylcholine-sterol acyltransferase, and sex hormone-binding globulin) in patients with BCAR. The first three principal components of these proteins showed clear separation of cohorts with and without BCAR. Performance improved with the inclusion of sequential proteins, reaching a primary asymptote after the first three (titin, kininogen-1, and lipopolysaccharide-binding protein). Longitudinal monitoring over the first 3 months post-transplant based on ratios of these three proteins showed clear discrimination between the two patient cohorts at time of rejection. The score then declined to baseline following treatment and resolution of the rejection episode and remained comparable between cases and controls throughout the period of quiescent follow-up. Results were validated using ELISA where possible, and initial cross-validation estimated a sensitivity of 80% and specificity of 90% for classification of BCAR based on a four-protein ELISA classifier. This study provides evidence that protein concentrations in plasma may provide a relevant measure for the occurrence of BCAR and offers a potential tool for immunologic monitoring.
Molecular & Cellular Proteomics 09/2010; 9(9):1954-67. · 7.40 Impact Factor
-
Xin Dong,
Jun Guan,
John C English,
Julia Flint,
John Yee,
Kenneth Evans,
Nevin Murray,
Calum Macaulay, Raymond T Ng,
Peter W Gout,
Wan L Lam,
Janessa Laskin,
Victor Ling,
Stephen Lam,
Yuzhuo Wang
[show abstract]
[hide abstract]
ABSTRACT: Current chemotherapeutic regimens have only modest benefit for non-small cell lung cancer (NSCLC) patients. Cumulative toxicities/drug resistance limit chemotherapy given after the first-line regimen. For personalized chemotherapy, clinically relevant NSCLC models are needed for quickly predicting the most effective regimens for therapy with curative intent. In this study, first generation subrenal capsule xenografts of primary NSCLCs were examined for (a) determining responses to conventional chemotherapeutic regimens and (b) selecting regimens most effective for individual patients.
Pieces (1x3x3 mm(3)) of 32 nontreated, completely resected patients' NSCLCs were grafted under renal capsules of nonobese diabetic/severe combined immunodeficient mice and treated with (A) cisplatin+vinorelbine, (B) cisplatin+docetaxel, (C) cisplatin+gemcitabine, and positive responses (treated tumor area <or=50% of control, P < 0.05) were determined. Clinical outcomes of treated patients were acquired.
Xenografts from all NSCLCs were established (engraftment rate, 90%) with the retention of major biological characteristics of the original cancers. The entire process of drug assessment took 8 weeks. Response rates to regimens A, B, and C were 28% (9 of 32), 42% (8 of 19), and 44% (7 of 16), respectively. Certain cancers that were resistant to a particular regimen were sensitive to others. The majority of responsive tumors contained foci of nonresponding cancer cells, indicative of tumor heterogeneity and potential drug resistance. Xenografts from six of seven patients who developed recurrence/metastasis were nonresponsive.
Models based on first generation NSCLC subrenal capsule xenografts have been developed, which are suitable for quick assessment (6-8 weeks) of the chemosensitivity of patients' cancers and selection of the most effective regimens. They hold promise for application in personalized chemotherapy of NSCLC patients.
Clinical Cancer Research 02/2010; 16(5):1442-51. · 7.74 Impact Factor
-
Raj Chari,
Kim M Lonergan,
Larissa A Pikor,
Bradley P Coe,
Chang Qi Zhu,
Timothy H W Chan,
Calum E MacAulay,
Ming-Sound Tsao,
Stephen Lam, Raymond T Ng,
Wan L Lam
[show abstract]
[hide abstract]
ABSTRACT: An important consideration when analyzing both microarray and quantitative PCR expression data is the selection of appropriate genes as endogenous controls or reference genes. This step is especially critical when identifying genes differentially expressed between datasets. Moreover, reference genes suitable in one context (e.g. lung cancer) may not be suitable in another (e.g. breast cancer). Currently, the main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set. A caveat here is the requirement for transcript normalization prior to analysis, and measurements obtained are relative, not absolute. Alternatively, as sequencing-based technologies provide digital quantitative output, absolute quantification ensues, and reference gene identification becomes more accurate.
Serial analysis of gene expression (SAGE) profiles of non-malignant and malignant lung samples were compared using a permutation test to identify the most stably expressed genes across all samples. Subsequently, the specificity of the reference genes was evaluated across multiple tissue types, their constancy of expression was assessed using quantitative RT-PCR (qPCR), and their impact on differential expression analysis of microarray data was evaluated.
We show that (i) conventional references genes such as ACTB and GAPDH are highly variable between cancerous and non-cancerous samples, (ii) reference genes identified for lung cancer do not perform well for other cancer types (breast and brain), (iii) reference genes identified through SAGE show low variability using qPCR in a different cohort of samples, and (iv) normalization of a lung cancer gene expression microarray dataset with or without our reference genes, yields different results for differential gene expression and subsequent analyses. Specifically, key established pathways in lung cancer exhibit higher statistical significance using a dataset normalized with our reference genes relative to normalization without using our reference genes.
Our analyses found NDUFA1, RPL19, RAB5C, and RPS18 to occupy the top ranking positions among 15 suitable reference genes optimal for normalization of lung tissue expression data. Significantly, the approach used in this study can be applied to data generated using new generation sequencing platforms for the identification of reference genes optimal within diverse contexts.
BMC Medical Genomics 01/2010; 3:32. · 3.69 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Non-small cell lung cancer (NSCLC) presents as a progressive disease spanning precancerous, preinvasive, locally invasive, and metastatic lesions. Identification of biological pathways reflective of these progressive stages, and aberrantly expressed genes associated with these pathways, would conceivably enhance therapeutic approaches to this devastating disease.
Through the construction and analysis of SAGE libraries, we have determined transcriptome profiles for preinvasive carcinoma-in-situ (CIS) and invasive squamous cell carcinoma (SCC) of the lung, and compared these with expression profiles generated from both bronchial epithelium, and precancerous metaplastic and dysplastic lesions using Ingenuity Pathway Analysis. Expression of genes associated with epidermal development, and loss of expression of genes associated with mucociliary biology, are predominant features of CIS, largely shared with precancerous lesions. Additionally, expression of genes associated with xenobiotic metabolism/detoxification is a notable feature of CIS, and is largely maintained in invasive cancer. Genes related to tissue fibrosis and acute phase immune response are characteristic of the invasive SCC phenotype. Moreover, the data presented here suggests that tissue remodeling/fibrosis is initiated at the early stages of CIS. Additionally, this study indicates that alteration in copy-number status represents a plausible mechanism for differential gene expression in CIS and invasive SCC.
This study is the first report of large-scale expression profiling of CIS of the lung. Unbiased expression profiling of these preinvasive and invasive lesions provides a platform for further investigations into the molecular genetic events relevant to early stages of squamous NSCLC development. Additionally, up-regulated genes detected at extreme differences between CIS and invasive cancer may have potential to serve as biomarkers for early detection.
PLoS ONE 01/2010; 5(2):e9162. · 4.09 Impact Factor
-
Oliver P Günther,
Robert F Balshaw,
Andreas Scherer,
Zsuzsanna Hollander,
Alice Mui,
Timothy J Triche,
Gabriela Cohen Freue,
Guiyun Li, Raymond T Ng,
Janet Wilson-McManus,
W Robert McMaster,
Bruce M McManus,
Paul A Keown
[show abstract]
[hide abstract]
ABSTRACT: Acute graft rejection is an important clinical problem in renal transplantation and an adverse predictor for long-term graft survival. Peripheral blood biomarkers that provide evidence of early graft rejection may offer an important option for posttransplant monitoring, optimize the utility of graft biopsy, and permit timely and effective therapeutic intervention to minimize the graft damage.
In this feasibility study (n=58), we have used gene expression profiling in a case-control design to compare whole blood samples between normal subjects (n=20) and patients with (n=11) or without (n=22) biopsy-confirmed acute rejection (BCAR) or borderline changes (n=5).
A total of 183 probe sets representing 160 genes were differentially expressed (false discovery rate [FDR] <0.01) between subjects with or without BCAR, from which linear discriminant analysis and cross-validation identified an initial gene signature of 24 probe sets, and a more refined set of 11 probe sets found to classify subject samples correctly. Cross-validation suggested an out-of-sample sensitivity of 73% and specificity of 91% for identification of samples with or without BCAR. An increase in classifier gene expression correlated closely with acute rejection during the first 3 months posttransplant. Biological evaluation indicated that the differentially expressed genes encompassed processes related to immune response, signal transduction, and cytoskeletal reorganization.
Preliminary evidence indicates that gene expression in the peripheral blood may yield a relevant measure for the occurrence of BCAR and offer a potential tool for immunologic monitoring. These results now require confirmation in a larger cohort.
Transplantation 10/2009; 88(7):942-51. · 4.00 Impact Factor
-
David Lin,
Zsuzsanna Hollander, Raymond T Ng,
Carol Imai,
Andrew Ignaszewski,
Robert Balshaw,
Gabriela Cohen Freue,
Janet E Wilson-McManus,
Pooran Qasimi,
Anna Meredith,
Alice Mui,
Tim Triche,
Robert McMaster,
Paul A Keown,
Bruce M McManus
[show abstract]
[hide abstract]
ABSTRACT: Significant progress has been made in cardiac transplantation over the past 30 years; however, the means for detection of acute cardiac allograft rejection remains in need of improvement. At present, the endomyocardial biopsy, an invasive and inconvenient procedure for patients, is required for the surveillance and diagnosis of acute cardiac allograft rejection. In the Biomarkers in Transplantation initiative, we investigated gene expression profiles in peripheral blood of cardiac transplant subjects as potential biomarkers for diagnosis of allograft rejection.
Whole blood samples were obtained from 28 cardiac transplant subjects who consented to the study. Serial samples were collected from pre-transplant through 3 years post-transplant according to the standard protocol. Temporally correspondent biopsies were also collected, reviewed in a blinded manner, and graded according to current ISHLT guidelines. Blood samples were analyzed using Affymetrix microarrays. Genomic profiles were compared in subjects with acute rejection (AR; ISHLT Grade > or =2R) and no rejection (NR; Grade 0R). Biomarker panel genes were identified using linear discriminant analysis.
We found 1,295 differentially expressed probe-sets between AR and NR samples and developed a 12-gene biomarker panel that classifies our internal validation samples with 83% sensitivity and 100% specificity.
Based on our current results, we believe whole blood genomic biomarkers hold great potential in the diagnosis of acute cardiac allograft rejection. A prospective, Canada-wide trial will be conducted shortly to further evaluate the classifier panel in diverse patients and a range of clinical programs.
The Journal of heart and lung transplantation: the official publication of the International Society for Heart Transplantation 10/2009; 28(9):927-35. · 3.54 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Analysis of array comparative genomic hybridization (aCGH) data for recurrent DNA copy number alterations from a cohort of patients can yield distinct sets of molecular signatures or profiles. This can be due to the presence of heterogeneous cancer subtypes within a supposedly homogeneous population.
We propose a novel statistical method for automatically detecting such subtypes or clusters. Our approach is model based: each cluster is defined in terms of a sparse profile, which contains the locations of unusually frequent alterations. The profile is represented as a hidden Markov model. Samples are assigned to clusters based on their similarity to the cluster's profile. We simultaneously infer the cluster assignments and the cluster profiles using an expectation maximization-like algorithm. We show, using a realistic simulation study, that our method is significantly more accurate than standard clustering techniques. We then apply our method to two clinical datasets. In particular, we examine previously reported aCGH data from a cohort of 106 follicular lymphoma patients, and discover clusters that are known to correspond to clinically relevant subgroups. In addition, we examine a cohort of 92 diffuse large B-cell lymphoma patients, and discover previously unreported clusters of biological interest which have inspired followup clinical research on an independent cohort.
Software and synthetic datasets are available at http://www.cs.ubc.ca/ approximately sshah/acgh as part of the CNA-HMMer package.
Supplementary data are available at Bioinformatics online.
Bioinformatics 07/2009; 25(12):i30-8. · 5.47 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The study of oral premalignant lesions (OPL) is crucial to the identification of initiating genetic events in oral cancer. However, these lesions are minute in size, making it a challenge to recover sufficient DNA from microdissected cells for comprehensive genomic analysis. As a step toward identifying genetic aberrations associated with oral cancer progression, we used tiling-path array comparative genomic hybridization to compare alterations on chromosome 3p for 71 OPLs against 23 oral squamous cell carcinomas. 3p was chosen because although it is frequently altered in oral cancers and has been associated with progression risk, its alteration status has only been evaluated at a small number of loci in OPLs. We identified six recurrent losses in this region that were shared between high-grade dysplasias and oral squamous cell carcinomas, including a 2.89-Mbp deletion spanning the FHIT gene (previously implicated in oral cancer progression). When the alteration status for these six regions was examined in 24 low-grade dysplasias with known progression outcome, we observed that they occurred at a significantly higher frequency in low-grade dysplasias that later progressed to later-stage disease (P < 0.003). Moreover, parallel analysis of all profiled tissues showed that the extent of overall genomic alteration at 3p increased with histologic stage. This first high-resolution analysis of chromosome arm 3p in OPLs represents a significant step toward predicting progression risk in early preinvasive disease and provides a keen example of how genomic instability escalates with progression to invasive cancer.
Cancer Prevention Research 11/2008; 1(6):424-9. · 4.91 Impact Factor
-
K-John J Cheung,
Sohrab P Shah,
Christian Steidl,
Nathalie Johnson,
Thomas Relander,
Adele Telenius,
Betty Lai,
Kevin P Murphy,
Wan Lam,
Abdulwahab J Al-Tourah,
Joseph M Connors, Raymond T Ng,
Randy D Gascoyne,
Douglas E Horsman
[show abstract]
[hide abstract]
ABSTRACT: The secondary genetic events associated with follicular lymphoma (FL) progression are not well defined. We applied genome-wide BAC array comparative genomic hybridization to 106 diagnostic biopsies of FL to characterize regional genomic imbalances. Using an analytical approach that defined regions of copy number change as intersections between visual annotations and a Hidden Markov model-based algorithm, we identified 71 regional alterations that were recurrent in at least 10% of cases. These ranged in size from approximately 200 kb to 44 Mb, affecting chromosomes 1, 5, 6, 7, 8, 10, 12, 17, 18, 19, and 22. We also demonstrated by cluster analysis that 46.2% of the 106 cases could be sub-grouped based on the presence of +1q, +6p/6q-, +7, or +18. Survival analysis showed that 21 of the 71 regions correlated significantly with inferior overall survival (OS). Of these 21 regions, 16 were independent predictors of OS using a multivariate Cox model that included the international prognostic index (IPI) score. Two of these 16 regions (1p36.22-p36.33 and 6q21-q24.3) were also predictors of transformation risk and independent of IPI. These prognostic features may be useful to identify high-risk patients as candidates for risk-adapted therapies.
Blood 08/2008; 113(1):137-48. · 9.90 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Disruptions of beta-catenin and the canonical Wnt pathway are well documented in cancer. However, little is known of the non-canonical branch of the Wnt pathway. In this study, we investigate the transcript level patterns of genes in the Wnt pathway in squamous cell lung cancer using reverse-transcriptase (RT)-PCR. It was found that over half of the samples examined exhibited dysregulated gene expression of multiple components of the non-canonical branch of the WNT pathway. In the cases where beta catenin (CTNNB1) was not over-expressed, we identified strong relationships of expression between wingless-type MMTV integration site family member 5A (WNT5A)/ frizzled homolog 2 (FZD2), frizzled homolog 3 (FZD3) / dishevelled 2 (DVL2), and low density lipoprotein receptor-related protein 5 (LRP5)/ secreted frizzled-related protein 4 (SFRP4). This is one of the first studies to demonstrate expression of genes in the non-canonical pathway in normal lung tissue and its disruption in lung squamous cell carcinoma. These findings suggest that the non-canonical pathway may have a more prominent role in lung cancer than previously reported.
Clinical Medicine: Oncology 04/2008; 2008(2):169-179.
-
[show abstract]
[hide abstract]
ABSTRACT: Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms.
To address this issue, we have developed an integrative genomic analysis platform MD-SeeGH, a software tool that allows users to rapidly and directly analyze genomic datasets spanning multiple genomic experiments. With MD-SeeGH, users have the flexibility to easily update datasets in accordance with new genomic builds, make a quality assessment of data using the filtering features, and identify genetic alterations within single or across multiple experiments. Multiple sample analysis in MD-SeeGH allows users to compare profiles from many experiments alongside tracks containing detailed localized gene information, microRNA, CpG islands, and copy number variations.
MD-SeeGH is a new platform for the integrative analysis of diverse microarray data, facilitating multiple profile analyses and group comparisons.
BMC Bioinformatics 02/2008; 9:243. · 2.75 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems.
We propose a novel multivariate approach to evaluate the quality of an array that examines the 'Mahalanobis distance' of its quality attributes from those of other arrays. Thus, we call it Mahalanobis Distance Quality Control (MDQC) and examine different approaches of this method. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Using two case studies, we show that a multivariate analysis gives substantially richer information than analyzing each parameter of the QC report in isolation. Moreover, once the QC report is produced, our quality assessment method is computationally inexpensive and the results can be easily visualized and interpreted. Finally, we show that computing these distances on subsets of the quality measures in the report may increase the method's ability to detect unusual arrays and helps to identify possible reasons of the quality problems.
The library to implement MDQC will soon be available from Bioconductor.
Bioinformatics 01/2008; 23(23):3162-9. · 5.47 Impact Factor
