Clinicopathologic Analysis of Localized Nasal/Paranasal Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) comprises 2 molecularly distinct subgroups of non-germinal center B-cell-like (non-GCB) and germinal center B-cell-like (GCB) DLBCLs, with the former showing relatively poor prognosis. In the present study, we analyzed the clinicopathological features of 39 patients with localized nasal/paranasal DLBCL. Immunohistochemistry-based subclassification revealed that 11 patients (28%) were of the GCB-type according to Hans’ algorithm and 11 (28%) were of the GCB-type according to Choi’s algorithm. According to both Hans’ and Choi’s algorithms, the non-GCB type was predominant. Nevertheless, prognosis was good. Overall survival did not differ significantly between the GCB and non-GCB subgroups (Hans’ algorithm: p = 0.57, Choi’s algorithm: p = 0.99). Furthermore, the prognosis of localized nasal/paranasal DLBCL was better than that of other localized extranodal DLBCLs. The prognosis of extranodal DLBCL is usually considered poorer than that of nodal DLBCL. However, in our study, no difference was noted between patients with localized nasal/paranasal DLBCL and patients with localized nodal DLBCL. In conclusion, although the non-GCB subtype is thought to show poor prognosis, in our study, the prognosis for localized nasal/paranasal DLBCL patients was good irrespective of subclassification.     

Commentary on the WHO classification of tumors of lymphoid tissues (2008): aggressive B-cell lymphomas

In the novel WHO classification 2008, the classification of aggressive B-cell lymphoma has been revised for several categories with the aim to define “clean” entities. Within large B-cell lymphoma, a few distinct clinico-pathological entities have been recognized with more clinically defined entities than pathologically defined ones. The majority of known morphological variations were not considered to merit more than classification as a variant of DLBCL, not otherwise specified. Specifically, a biological subgrouping of DLBCL on the basis of molecular (activated B-cell versus germinal center B-cell) or immunophenotypic (CD5+) features was felt to be too immature to include at this stage. The role of EBV in aggressive B-cell lymphoma has been explored in more depth with the recognition of several novel and re-defined clinico-pathological entities. Also, in these diseases, clinical definitions play a very dominant role in the WHO classification 2008.     

Case-based prediction of survival in colorectal cancer patients

OBJECTIVE: To develop an approach to the prediction of survival in patients with colorectal cancer using nearest neighbor analysis and case-based reasoning. STUDY DESIGN: A total of 216 patients with full clinicopathologic records and five-year follow-up were the subjects of this study. They were divided into a core database of 162 cases and a test group of 54 cases, with follow-up on all patients. When the patient was still alive at the end of the follow-up period, censored survival time was used. For each of the test cases, the four closest neighbors from the database were retrieved and their median survival time recorded and used as the predicted estimate of survival. Case matching was based on a Euclidean multivariate distance measure for the three best predictor variables: patient age, Dukes stage and tubule configuration. Cases with the smallest distance from the test case were considered to be the most similar. The predicted survival times for the test cases were compared with the actual, observed survival in the test cases to determine the success of this approach. RESULTS: The results showed reasonable concordance between observed and predicted survival figures, although there was a large degree of spread. Classification of cases into < or = 60 and > 60 months' survival showed a correct classification rate of 63%. For the prediction of survival time, the distribution of differences between observed and predicted survival times for the uncensored test cases had a median value of--5 months but also showed a wide dispersion of values. Correlation of observed and predicted survival times, while not reaching statistical significance at P < .05, did show a strong positive association. CONCLUSION: Case-based approaches to the prediction of survival times in cancer patients are important. The results of the current study illustrate the difficulties in applying this approach to survival data and highlight the complexity of patient information and the inability to accurately predict patient outcome on a small subset of clinicopathologic features. While extensive work needs to be carried out to improve prediction power, this study illustrates the potential for case-based analyses. The ability to retrieve feature-matched cases from hospital patient databases has clear, independent advantages in patient management, but the ability to provide reliable, targeted prognostic estimates on individual cases should be a common goal in medical research.     

Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

Background  

Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA) algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis) using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes.     

Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data

MOTIVATION: An important application of microarray technology is to relate gene expression profiles to various clinical phenotypes of patients. Success has been demonstrated in molecular classification of cancer in which the gene expression data serve as predictors and different types of cancer serve as a categorical outcome variable. However, there has been less research in linking gene expression profiles to the censored survival data such as patients' overall survival time or time to cancer relapse. It would be desirable to have models with good prediction accuracy and parsimony property. RESULTS: We propose to use the L(1) penalized estimation for the Cox model to select genes that are relevant to patients' survival and to build a predictive model for future prediction. The computational difficulty associated with the estimation in the high-dimensional and low-sample size settings can be efficiently solved by using the recently developed least-angle regression (LARS) method. Our simulation studies and application to real datasets on predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed procedure, which we call the LARS-Cox procedure, can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. The LARS-Cox regression gives better predictive performance than the L(2) penalized regression and a few other dimension-reduction based methods. CONCLUSIONS: We conclude that the proposed LARS-Cox procedure can be very useful in identifying genes relevant to survival phenotypes and in building a parsimonious predictive model that can be used for classifying future patients into clinically relevant high- and low-risk groups based on the gene expression profile and survival times of previous patients.     

肺鳞状细胞癌发生的早期标志物及肿瘤预测模型

选取癌症基因组图谱数据库的肺鳞状细胞癌(Lung Squamous Cell Carcinoma,LUSC)样本作为数据集,在全基因组的水平上研究肺鳞状细胞癌病人从正常到发病I期基因表达的变化,寻找与LUSC发病密切相关的早期标志物,并建立一种基于早期标志基因的肿瘤预测模型。方法 采用模式识别分类法和基因通路和功能分析相结合的筛选方法,对LUSC的早期标志物进行识别,并运用Fisher判别建立肿瘤预测模型。得到12个LUSC的早期标志物,分别是CLDN18, CD34, ESAM, JAM2, CDH5, F11, F8, CFD, MRC1, MARCO, SFTPA2 和 SFTPA1,机器学习建模后对LUSC早期癌症样本和正常肺组织样本的分类精度达到了98%以上。由基因SFTPA1和ESAM建立的LUSC早期肿瘤预测模型,对正常肺组织和LUSC肿瘤Ⅰ期样本的分类敏感性和特异性分别为99.18%和100%,并且独立验证集的分类准确率也在90%以上。结论 筛选出的12个早期分子标志物有望成为LUSC诊断的标志分子,并且建立的肿瘤预测模型具有极高的准确性,可以为LUSC的发生机理研究以及早期肿瘤预测提供帮助。     

Protocols for disease classification from mass spectrometry data

We report our results in classifying protein matrix-assisted laser desorption/ionization-time of flight mass spectra obtained from serum samples into diseased and healthy groups. We discuss in detail five of the steps in preprocessing the mass spectral data for biomarker discovery, as well as our criterion for choosing a small set of peaks for classifying the samples. Cross-validation studies with four selected proteins yielded misclassification rates in the 10-15% range for all the classification methods. Three of these proteins or protein fragments are down-regulated and one up-regulated in lung cancer, the disease under consideration in this data set. When cross-validation studies are performed, care must be taken to ensure that the test set does not influence the choice of the peaks used in the classification. Misclassification rates are lower when both the training and test sets are used to select the peaks used in classification versus when only the training set is used. This expectation was validated for various statistical discrimination methods when thirteen peaks were used in cross-validation studies. One particular classification method, a linear support vector machine, exhibited especially robust performance when the number of peaks was varied from four to thirteen, and when the peaks were selected from the training set alone. Experiments with the samples randomly assigned to the two classes confirmed that misclassification rates were significantly higher in such cases than those observed with the true data. This indicates that our findings are indeed significant. We found closely matching masses in a database for protein expression in lung cancer for three of the four proteins we used to classify lung cancer. Data from additional samples, increased experience with the performance of various preprocessing techniques, and affirmation of the biological roles of the proteins that help in classification, will strengthen our conclusions in the future.     

Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme

One of the most common and aggressive malignant brain tumors is Glioblastoma multiforme. Despite the multimodality treatment such as radiation therapy and chemotherapy (temozolomide: TMZ), the median survival rate of glioblastoma patient is less than 15 months. In this study, we investigated the association between measures of spatial diversity derived from spatial point pattern analysis of multiparametric magnetic resonance imaging (MRI) data with molecular status as well as 12-month survival in glioblastoma. We obtained 27 measures of spatial proximity (diversity) via spatial point pattern analysis of multiparametric T1 post-contrast and T2 fluid-attenuated inversion recovery MRI data. These measures were used to predict 12-month survival status (≤12 or >12 months) in 74 glioblastoma patients. Kaplan-Meier with receiver operating characteristic analyses was used to assess the relationship between derived spatial features and 12-month survival status as well as molecular subtype status in patients with glioblastoma. Kaplan-Meier survival analysis revealed that 14 spatial features were capable of stratifying overall survival in a statistically significant manner. For prediction of 12-month survival status based on these diversity indices, sensitivity and specificity were 0.86 and 0.64, respectively. The area under the receiver operating characteristic curve and the accuracy were 0.76 and 0.75, respectively. For prediction of molecular subtype status, proneural subtype shows highest accuracy of 0.93 among all molecular subtypes based on receiver operating characteristic analysis. We find that measures of spatial diversity from point pattern analysis of intensity habitats from T1 post-contrast and T2 fluid-attenuated inversion recovery images are associated with both tumor subtype status and 12-month survival status and may therefore be useful indicators of patient prognosis, in addition to providing potential guidance for molecularly-targeted therapies in Glioblastoma multiforme.     

EZH2 codon 641 mutations are common in BCL2-rearranged germinal center B cell lymphomas

Mutations at codon 641 of EZH2 are recurrent in germinal center B cell lymphomas, and the most common variants lead to altered EZH2 enzymatic activity and enhanced tri-methylation of histone H3 at lysine 27, a repressive chromatin modification. As an initial step toward screening patients for cancer genotype-directed therapy, we developed a screening assay for EZH2 codon 641 mutations amenable for testing formalin-fixed clinical specimens, based on the sensitive SNaPshot single nucleotide extension technology. We detected EZH2 mutations in 12/55 (22%) follicular lymphomas (FL), 5/35 (14%) diffuse large B cell lymphomas with a germinal center immunophenotype (GCB-DLBCL), and 2/11 (18%) high grade B cell lymphomas with concurrent rearrangements of BCL2 and MYC. No EZH2 mutations were detected in cases of Burkitt lymphoma (0/23). EZH2 mutations were frequently associated with the presence of BCL2 rearrangement (BCL2-R) in both the FL (28% of BCL-R cases versus 0% of BCL2-WT cases, p<0.05) and GCB-DLBCL groups (33% of BCL2-R cases versus 4% of BCL2-WT cases, p<0.04), and across all lymphoma types excluding BL (27% of BCL2-R cases versus 3% of BCL2-WT cases, p<0.003). We confirmed gain-of-function activity for all previously reported EZH2 codon 641 mutation variants. Our findings suggest that EZH2 mutations constitute an additional genetic "hit" in many BCL2-rearranged germinal center B cell lymphomas. Our work may be helpful in the selection of lymphoma patients for future trials of pharmacologic agents targeting EZH2 and EZH2-regulated pathways.     

Study of Integrated Heterogeneous Data Reveals Prognostic Power of Gene Expression for Breast Cancer Survival

BackgroundStudies show that thousands of genes are associated with prognosis of breast cancer. Towards utilizing available genetic data, efforts have been made to predict outcomes using gene expression data, and a number of commercial products have been developed. These products have the following shortcomings: 1) They use the Cox model for prediction. However, the RSF model has been shown to significantly outperform the Cox model. 2) Testing was not done to see if a complete set of clinical predictors could predict as well as the gene expression signatures.Methodology/FindingsWe address these shortcomings. The METABRIC data set concerns 1981 breast cancer tumors. Features include 21 clinical features, expression levels for 16,384 genes, and survival. We compare the survival prediction performance of the Cox model and the RSF model using the clinical data and the gene expression data to their performance using only the clinical data. We obtain significantly better results when we used both clinical data and gene expression data for 5 year, 10 year, and 15 year survival prediction. When we replace the gene expression data by PAM50 subtype, our results are significant only for 5 year and 15 year prediction. We obtain significantly better results using the RSF model over the Cox model. Finally, our results indicate that gene expression data alone may predict long-term survival.Conclusions/SignificanceOur results indicate that we can obtain improved survival prediction using clinical data and gene expression data compared to prediction using only clinical data. We further conclude that we can obtain improved survival prediction using the RSF model instead of the Cox model. These results are significant because by incorporating more gene expression data with clinical features and using the RSF model, we could develop decision support systems that better utilize heterogeneous information to improve outcome prediction and decision making.     

Feature selection for the prediction of translation initiation sites

Translation initiation sites （TISs） are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance： the nature of the cDNA sequence sets, the relevant features selected. and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS prediction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the sequence downstream ATG, the number of downstream stop codons. the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classification methods, including decision tree. naive Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful. while the experiments showed promising results.     

Prediction of protein modification sites of pyrrolidone carboxylic acid using mRMR feature selection and analysis

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations.     

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes.DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCAMethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance.ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.     

Identifying Prognostic Features by Bottom-Up Approach and Correlating to Drug Repositioning

Background

Traditionally top-down method was used to identify prognostic features in cancer research. That is to say, differentially expressed genes usually in cancer versus normal were identified to see if they possess survival prediction power. The problem is that prognostic features identified from one set of patient samples can rarely be transferred to other datasets. We apply bottom-up approach in this study: survival correlated or clinical stage correlated genes were selected first and prioritized by their network topology additionally, then a small set of features can be used as a prognostic signature.

Methods

Gene expression profiles of a cohort of 221 hepatocellular carcinoma (HCC) patients were used as a training set, ‘bottom-up’ approach was applied to discover gene-expression signatures associated with survival in both tumor and adjacent non-tumor tissues, and compared with ‘top-down’ approach. The results were validated in a second cohort of 82 patients which was used as a testing set.

Results

Two sets of gene signatures separately identified in tumor and adjacent non-tumor tissues by bottom-up approach were developed in the training cohort. These two signatures were associated with overall survival times of HCC patients and the robustness of each was validated in the testing set, and each predictive performance was better than gene expression signatures reported previously. Moreover, genes in these two prognosis signature gave some indications for drug-repositioning on HCC. Some approved drugs targeting these markers have the alternative indications on hepatocellular carcinoma.

Conclusion

Using the bottom-up approach, we have developed two prognostic gene signatures with a limited number of genes that associated with overall survival times of patients with HCC. Furthermore, prognostic markers in these two signatures have the potential to be therapeutic targets.     

Inference of patient‐specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient‐specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient‐specific subpathway activity profiles using a greedy search algorithm. A four‐subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high‐risk and low‐risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e‐13) and test set (median survival of 75 vs 101 months, P = 4.17e‐5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four‐subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four‐subpathway signature may be a useful biomarker for breast cancer prognosis.     

Prediction of tyrosine sulfation with mRMR feature selection and analysis

Protein tyrosine sulfation is a ubiquitous post-translational modification (PTM) of secreted and transmembrane proteins that pass through the Golgi apparatus. In this study, we developed a new method for protein tyrosine sulfation prediction based on a nearest neighbor algorithm with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). We incorporated features of sequence conservation, residual disorder, and amino acid factor, 229 features in total, to predict tyrosine sulfation sites. From these 229 features, 145 features were selected and deemed as the optimized features for the prediction. The prediction model achieved a prediction accuracy of 90.01% using the optimal 145-feature set. Feature analysis showed that conservation, disorder, and physicochemical/biochemical properties of amino acids all contributed to the sulfation process. Site-specific feature analysis showed that the features derived from its surrounding sites contributed profoundly to sulfation site determination in addition to features derived from the sulfation site itself. The detailed feature analysis in this paper might help understand more of the sulfation mechanism and guide the related experimental validation.