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Genomic signatures for predicting survival and adjuvant chemotherapy benefit in patients with non-small-cell lung cancer
Ryan K Van Laar
BMC Medical Genomics , 2012, DOI: 10.1186/1755-8794-5-30
Abstract: A novel prognostic algorithm was identified using genomic profiles from 332 stage I-III adenocarcinomas and independently validated on a separate series of 264 patients with stage I-II tumors, compiled from five previous studies. The prognostic algorithm was used to interrogate genomic data from a series of patients treated with adjuvant chemotherapy. Those genes associated with outcome in the adjuvant treatment setting, independent to prognosis were used to train an algorithm able to classify a patient as either a responder or non-responder to ACT. The performance of this signature was independently validated on a separate series of genomic profiles from patients enrolled in a randomized controlled trial of cisplatin/vinorelbine vs. observation alone (JBR.10).NSCLC patients exhibiting the high-risk, poor-prognosis form of the 160-gene prognosis signature experienced a 2.80-times higher rate of 5-year disease specific death (log rank P?<?0.0001) compared to those with the low-risk, good prognosis profile, adjusted for covariates. The prognosis signature was found to especially accurate at identifying early stage patients at risk of disease specific death within 24?months of diagnosis when compared to traditional methods of outcome prediction.Separately, NSCLC patients with the 37-gene ACT-response signature (n?=?70, 64?%), benefited significantly from cisplatin/vinorelbine (adjusted HR: 0.23, P?=?0.0032). For those patients predicted to be responders, receiving this form of ACT conferred a 25?% improvement in the probability of 5-year-survival, compared to observation alone and adjusted for covariates. Conversely, in those patients predicted to be non-responders, ACT was observed to offer no significant survival benefit (adjusted HR: 0.55, P?=?0.32).The two gene signatures overlap by one gene only SPSB3, which interacts with the oncogene MET. In this study, higher levels of SPSB3 which were associated with favorable prognosis and benefit from ACT.These complimentary
Predicting discovery rates of genomic features  [PDF]
Simon Gravel,NHLBI GO Exome Sequencing Project
Quantitative Biology , 2014,
Abstract: Successful sequencing experiments require judicious sample selection. However, this selection must often be performed on the basis of limited preliminary data. Predicting the statistical properties of the final sample based on preliminary data can be challenging, because numerous uncertain model assumptions may be involved. Here, we ask whether we can predict ``omics" variation across many samples by sequencing only a fraction of them. In the infinite-genome limit, we find that a pilot study sequencing $5\%$ of a population is sufficient to predict the number of genetic variants in the entire population within $6\%$ of the correct value, using an estimator agnostic to demography, selection, or population structure. To reach similar accuracy in a finite genome with millions of polymorphisms, the pilot study would require about $15\%$ of the population. We present computationally efficient jackknife and linear programming methods that exhibit substantially less bias than the state of the art when applied to simulated data and sub-sampled 1000 Genomes Project data. Extrapolating based on the NHLBI Exome Sequencing Project data, we predict that $7.2\%$ of sites in the capture region would be variable in a sample of $50,000$ African-Americans, and $8.8\%$ in a European sample of equal size. Finally, we show how the linear programming method can also predict discovery rates of various genomic features, such as the number of transcription factor binding sites across different cell types.
A Reverse Hierarchy Model for Predicting Eye Fixations  [PDF]
Tianlin Shi,Liang Ming,Xiaolin Hu
Computer Science , 2014,
Abstract: A number of psychological and physiological evidences suggest that early visual attention works in a coarse-to-fine way, which lays a basis for the reverse hierarchy theory (RHT). This theory states that attention propagates from the top level of the visual hierarchy that processes gist and abstract information of input, to the bottom level that processes local details. Inspired by the theory, we develop a computational model for saliency detection in images. First, the original image is downsampled to different scales to constitute a pyramid. Then, saliency on each layer is obtained by image super-resolution reconstruction from the layer above, which is defined as unpredictability from this coarse-to-fine reconstruction. Finally, saliency on each layer of the pyramid is fused into stochastic fixations through a probabilistic model, where attention initiates from the top layer and propagates downward through the pyramid. Extensive experiments on two standard eye-tracking datasets show that the proposed method can achieve competitive results with state-of-the-art models.
Histopathological and Genomic Grading Provide Complementary Prognostic Information in Breast Cancer: A Study on Publicly Available Datasets  [PDF]
Nilotpal Chowdhury
Pathology Research International , 2011, DOI: 10.4061/2011/890938
Abstract: The genomic grade (GG) for breast cancer is thought to be the genomic counterpart of histopathological grade (HG). The motivation behind this study was to see whether HG retains its prognostic impact even when adjusted for GG, or whether it can be replaced by the latter. Four publicly available gene expression datasets were analyzed. Kaplan-Meier curves, log rank test, and Cox regression were used to study recurrence-free survival (RFS) and distant metastasis-free survival (DMFS). HG remained a significant prognostic indicator in low GG tumors (P = 0.003 for DMFS, P 0.001 for RFS) but not in high GG tumors. HG grade 2 tumors differed significantly from HG grade 1 tumors, underlining the prognostic role of intermediate HG tumors. Additionally, GG could stratify HG 1 as well as HG 2 tumors into distinct prognostic groups. HG and GG add independent prognostic information to each other. However, the prognostic effects of both HG and GG are time varying, with the hazard ratios of high HG and GG tumors being markedly attenuated over time. 1. Introduction The genomic grade [1] is a recently characterized grading system for breast cancer which has shown early promise in prognostic classification of breast cancer patients. It is a two-tier grading system which has appealed to researchers in doing away with an intermediate grade, ostensibly easing the decision-making process. Furthermore, it has a strong theoretical basis, since it is judged to be a gene expression signature of the histopathological grade, a proven independent predictor of breast cancer survival. The genomic grade has met with favorable academic response and has been licensed for commercial use. Early studies suggested that it contained most of the prognostic information of histopathological grade [1], thus leading to suggestions that it may supplant histopathological grading altogether [2, 3]. One study found that the genomic grade was a better prognostic indicator than histopathological grade [4]. However, it was not mentioned whether histopathological grade had any prognostic effect in breast cancer patients when adjusted for genomic grade. The datasets used in the last mentioned study were put in the public domain in an easily analyzable format. The same datasets were used in the present study to see whether histopathological grading provides any additional information over and above the genomic grade. This would serve to point out whether gene expression-based signatures could act as a competitive replacement for histopathological grade, or whether both genomic and histopathological grade
Using the Gene Ontology Hierarchy when Predicting Gene Function  [PDF]
Sara Mostafavi,Quaid Morris
Computer Science , 2012,
Abstract: The problem of multilabel classification when the labels are related through a hierarchical categorization scheme occurs in many application domains such as computational biology. For example, this problem arises naturally when trying to automatically assign gene function using a controlled vocabularies like Gene Ontology. However, most existing approaches for predicting gene functions solve independent classification problems to predict genes that are involved in a given function category, independently of the rest. Here, we propose two simple methods for incorporating information about the hierarchical nature of the categorization scheme. In the first method, we use information about a gene's previous annotation to set an initial prior on its label. In a second approach, we extend a graph-based semi-supervised learning algorithm for predicting gene function in a hierarchy. We show that we can efficiently solve this problem by solving a linear system of equations. We compare these approaches with a previous label reconciliation-based approach. Results show that using the hierarchy information directly, compared to using reconciliation methods, improves gene function prediction.
GIST: Genomic island suite of tools for predicting genomic islands  [cached]
Mohammad Shabbir Hasan,Qi Liu,Han Wang,John Fazekas
Bioinformation , 2012,
Abstract: Genomic Islands (GIs) are genomic regions that are originally from other organisms, through a process known as Horizontal Gene Transfer (HGT). Detection of GIs plays a significant role in biomedical research since such align genomic regions usually contain important features, such as pathogenic genes. We have developed a use friendly graphic user interface, Genomic Island Suite of Tools (GIST), which is a platform for scientific users to predict GIs. This software package includes five commonly used tools, AlienHunter, IslandPath, Colombo SIGI-HMM, INDeGenIUS and Pai-Ida. It also includes an optimization program EGID that ensembles the result of existing tools for more accurate prediction. The tools in GIST can be used either separately or sequentially. GIST also includes a downloadable feature that facilitates collecting the input genomes automatically from the FTP server of the National Center for Biotechnology Information (NCBI). GIST was implemented in Java, and was compiled and executed on Linux/Unix operating systems
Survival prediction from clinico-genomic models - a comparative study
Hege M B?velstad, St?le Nyg?rd, ?rnulf Borgan
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-413
Abstract: We propose a way to combine classical clinical covariates with genomic data in a clinico-genomic prediction model based on the Cox regression model. The prediction model is obtained by a simultaneous use of both types of covariates, but applying dimension reduction only to the high-dimensional genomic variables. We describe how this can be done for seven well-known prediction methods: variable selection, unsupervised and supervised principal components regression and partial least squares regression, ridge regression, and the lasso. We further perform a systematic comparison of the performance of prediction models using clinical covariates only, genomic data only, or a combination of the two. The comparison is done using three survival data sets containing both clinical information and microarray gene expression data. Matlab code for the clinico-genomic prediction methods is available at http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/ webcite.Based on our three data sets, the comparison shows that established clinical covariates will often lead to better predictions than what can be obtained from genomic data alone. In the cases where the genomic models are better than the clinical, ridge regression is used for dimension reduction. We also find that the clinico-genomic models tend to outperform the models based on only genomic data. Further, clinico-genomic models and the use of ridge regression gives for all three data sets better predictions than models based on the clinical covariates alone.Predicting the outcome of a disease or some disease related phenotype based on microarrays or other high-throughput data is an important application of genomic data. One particular instance of this problem is the prediction of the time to some disease specific event like death or relapse, often referred to by the technical term survival time or failure time. The most widely used model for survival data is the Cox proportional hazards model [1] which describes the
An FPT Approach for Predicting Protein Localization from Yeast Genomic Data  [PDF]
Jin Wang,Chunhe Li,Erkang Wang,Xidi Wang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0014449
Abstract: Accurately predicting the localization of proteins is of paramount importance in the quest to determine their respective functions within the cellular compartment. Because of the continuous and rapid progress in the fields of genomics and proteomics, more data are available now than ever before. Coincidentally, data mining methods been developed and refined in order to handle this experimental windfall, thus allowing the scientific community to quantitatively address long-standing questions such as that of protein localization. Here, we develop a frequent pattern tree (FPT) approach to generate a minimum set of rules (mFPT) for predicting protein localization. We acquire a series of rules according to the features of yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regression under various statistical measures. Our results show that mFPT gave better performance than other approaches in predicting protein localization. Meanwhile, setting 0.65 as the minimum hit-rate, we obtained 138 proteins that mFPT predicted differently than the simple naive bayesian method (SNB). In our analysis of these 138 proteins, we present novel predictions for the location for 17 proteins, which currently do not have any defined localization. These predictions can serve as putative annotations and should provide preliminary clues for experimentalists. We also compared our predictions against the eukaryotic subcellular localization database and related predictions by others on protein localization. Our method is quite generalized and can thus be applied to discover the underlying rules for protein-protein interactions, genomic interactions, and structure-function relationships, as well as those of other fields of research.
Gene expression profiles of lung adenocarcinoma linked to histopathological grading and survival but not to EGF-R status: a microarray study
Jens Neumann, Friedrich Feuerhake, Gian Kayser, Thorsten Wiech, Konrad Aumann, Bernward Passlick, Paul Fisch, Martin Werner, Axel zur Hausen
BMC Cancer , 2010, DOI: 10.1186/1471-2407-10-77
Abstract: Affymetrix Human Genome U133A platform was used to obtain gene expression profiles of 28 pathologically and clinically annotated adenocarcinomas of the lung. EGFR status was determined by fluorescent in situ hybridization and immunohistochemistry.Using unsupervised clustering algorithms, the predominant gene expression signatures correlated with the histopathological grade but not with EGFR protein expression as detected by immunohistochemistry. In a supervised analysis, the signature of high grade tumors but not of EGFR overexpressing cases showed significant enrichment of gene sets reflecting MAPK activation and other potential signaling cascades downstream of EGFR. Out of four different previously published gene sets that had been linked to prognosis, three showed enrichment in the gene expression signature associated with favorable prognosis.In this dataset, histopathological tumor grades but not EGFR status were associated with dominant gene expression signatures and gene set enrichment reflecting oncogenic pathway activation, suggesting that high immunohistochemistry EGFR scores may not necessarily be linked to downstream effects that cause major changes in gene expression patterns. Published gene sets showed association with patient survival; however, the small sample size of this study limited the options for a comprehensive validation of previously reported prognostic gene expression signatures.Lung cancer is the most common invasive cancer worldwide. In the year 2005 approximately 172.570 new cases were diagnosed in the United States [1]. In addition, it is the leading cause of cancer associated death [2]. Lung cancer includes a broad variety of histological subtypes classified either as small cell lung cancer (SCLC) or non-small cell lung cancer (NSCLC). NSCLC comprises approx. 80% of all lung cancers and is further divided into lung adenocarcinoma (LAC) (~28%), squamous cell carcinoma (SCC) (~44%), and large cell carcinoma (LC) (~9%). However, many tumor
Impact of Histopathological Diagnosis with Ancillary Immunohistochemical Studies on Lung Cancer Subtypes Incidence and Survival: A Population-Based Study  [PDF]
Andrea Bordoni,Massimo Bongiovanni,Luca Mazzucchelli,Alessandra Spitale
Journal of Cancer Epidemiology , 2011, DOI: 10.1155/2011/275758
Abstract: Purpose. The aim of this study was to assess the impact of immunohistochemical- (IHC-) studies on incidence and survival of lung cancer histotypes. Patients and Methods. Lung cancers occurred in southern Switzerland between 1996 and 2010 were selected by the Ticino Cancer Registry and categorised into adenocarcinoma (AC), squamous-cell-carcinoma (SqCC), small-cell-carcinoma (SmCC), and large-cell carcinoma/non-small-cell lung cancer (LCC/NSCLC). Incidence rates, annual-percentage-change (APC), and two-year overall survival (OS) (follow-up: 31.12.2010) were performed. Results. 2467 cases were selected: 997 (40.4%) AC; 522 (21.2%) LCC/NSCLC, 378 (15.3%) SmCC, and 570 (23.1%) SqCC. Trend-analysis showed significant increase in AC (APC: 4.6; 95% CI: 3.1; 6.0) and decrease of LCC/NSCLC, with significant joinpoint in 2003 (APC: ?14.7; 95% CI: ?21.6; ?7.1). Improved OS and decreased OS were detected in SqCC and LCC/NSCLC, respectively. Conclusions. This study highlights that diagnosis with ancillary immunohistochemical studies will change incidence and survival of precisely defined lung cancer subtypes. It calls attention to the need for cautious interpretation of studies and clinical trials, where the diagnosis was based on histology unaccompanied by IHC studies, and to the need of standardised diagnostic procedures. 1. Introduction Lung cancer is one of most common cancers in the world, representing 17.1% of all cancers in men, 6.7% in women, and 12.2% in both sexes [1]. Of the histological types, adenocarcinoma (AC) has remained the most prevalent among women over the past three decades, with incidence rates increasing slowly over time in many countries. In contrast, squamous cell carcinoma (SqCC) has historically been the predominant tumour type in men, but the incidence has declined and converged with the corresponding incidence in women, which has remained fairly stable [2]. Traditionally, lung carcinoma was classified into histological types using standard histological techniques. The most critical step in histopathological diagnosis was to distinguish small cell carcinoma (SmCC) from the other lung carcinomas, which were collectively called the non-small cell lung carcinomas (NSCLCs); patients with the former were referred to chemotherapy, whereas patients with the latter were potentially eligible for surgery or different chemotherapies. Over the past few years, the emergence of targeted or combination treatment strategies has created new demands on histopathological diagnostics, as it is now recognised that the efficacy and toxicity of some new drugs
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