CeDR Atlas: a knowledgebase of cellular drug response.

Drug response to many diseases varies dramatically due to the complex genomics and functional features and contexts. Cellular diversity of human tissues, especially tumors, is one of the major contributing factors to the different drug response in different samples. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is now possible to study the drug response to different treatments at the single cell resolution. Here, we present CeDR Atlas (available at https://ngdc.cncb.ac.cn/cedr), a knowledgebase reporting computational inference of cellular drug response for hundreds of cell types from various tissues. We took advantage of the high-throughput profiling of drug-induced gene expression available through the Connectivity Map resource (CMap) as well as hundreds of scRNA-seq data covering cells from a wide variety of organs/tissues, diseases, and conditions. Currently, CeDR maintains the results for more than 582 single cell data objects for human, mouse and cell lines, including about 140 phenotypes and 1250 tissue-cell combination types. All the results can be explored and searched by keywords for drugs, cell types, tissues, diseases, and signature genes. Overall, CeDR fine maps drug response at cellular resolution and sheds lights on the design of combinatorial treatments, drug resistance and even drug side effects.

DiseaseMeth version 3.0: a major expansion and update of the human disease methylation database.

DNA methylation has a growing potential for use as a biomarker because of its involvement in disease. DNA methylation data have also substantially grown in volume during the past 5 years. To facilitate access to these fragmented data, we proposed DiseaseMeth version 3.0 based on DiseaseMeth version 2.0, in which the number of diseases including increased from 88 to 162 and High-throughput profiles samples increased from 32 701 to 49 949. Experimentally confirmed associations added 448 pairs obtained by manual literature mining from 1472 papers in PubMed. The search, analyze and tools sections were updated to increase performance. In particular, the FunctionSearch now provides for the functional enrichment of genes from localized GO and KEGG annotation. We have also developed a unified analysis pipeline for identifying differentially DNA methylated genes (DMGs) from the original data stored in the database. 22 718 DMGs were found in 99 diseases. These DMGs offer application in disease evaluation using two self-developed online tools, Methylation Disease Correlation and Cancer Prognosis & Co-Methylation. All query results can be downloaded and can also be displayed through a box plot, heatmap or network module according to whichever search section is used. DiseaseMeth version 3.0 is freely available at http://diseasemeth.edbc.org/.

Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.

Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B-precursor acute lymphoblastic leukaemia.

Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B-precursor ALL patients by gene expression profiles, using non-negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse-free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3-PBX1 (AUC = 0·986 in the training set and 1·0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high-relapse-risk patients in three subgroups of the test set (Wilcoxon test p = 0·048, 0·036, and 0·016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B-precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy.

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discriminative feature genes, and existing methods fail to deal with this problem efficiently. This article proposes a novel metaheuristic approach for gene feature extraction, called variable neighborhood learning Harris Hawks optimizer (VNLHHO). First, the F-score is used for a primary selection of the genes in gene expression data to narrow down the selection range of the feature genes. Subsequently, a variable neighborhood learning strategy is constructed to balance the global exploration and local exploitation of the Harris Hawks optimization. Finally, mutation operations are employed to increase the diversity of the population, so as to prevent the algorithm from falling into a local optimum. In addition, a novel activation function is used to convert the continuous solution of the VNLHHO into binary values, and a naive Bayesian classifier is utilized as a fitness function to select feature genes that can help classify biological tissues of binary and multi-class cancers. An experiment is conducted on gene expression profile data of eight types of tumors. The results show that the classification accuracy of the VNLHHO is greater than 96.128% for tumors in the colon, nervous system and lungs and 100% for the rest. We compare seven other algorithms and demonstrate the superiority of the VNLHHO in terms of the classification accuracy, fitness value and AUC value in feature selection for gene expression data.

Usage trends and performance characteristics of a "gene expression classifier" in the management of thyroid nodules: An institutional experience.

BACKGROUND: The gene expression classifier (GEC; Afirma-Veracyte) has proven to be an effective triage modality in the management of thyroid nodules. We evaluate our institutional experience with GEC, specifically examining performance as a first line testing strategy versus in conjunction with repeat fine needle aspiration (FNA), usage trends based on clinical setting, and performance related to diagnostic categories of The Bethesda System for Reporting Thyroid Cytology (TBSRTC). METHODS: All nodules undergoing GEC analysis from 1/2011 to 12/2015 at the Hospital of the University of Pennsylvania were identified using electronic database search methods. Corresponding cytologic diagnoses, GEC results, origin of the sample (in-house vs. satellite site), number and diagnosis of prior FNA's, and clinical and histologic follow-up were collected. RESULTS: The cohort included 294 nodules. Of these, 145 (49%) were classified as benign, 136 (46%) as suspicious, and 13 (5%) as quantity insufficient by GEC. Surgical resection was performed in 130 (130/294-44%) cases (107, 82% "suspicious" by GEC); final histopathologic diagnosis was benign in 85 (65%) and malignant in 45 (35%) cases. Three false negative diagnoses were identified in the setting of GEC analysis as a first line testing strategy. Most cases with GEC as a first line testing strategy came from satellite clinical sites (112, 66%). CONCLUSIONS: The GEC showed improved performance characteristics when coupled with a repeat FNA. It continues to be of low specificity and positive predictive value in oncocytic follicular lesions. Diagn. Cytopathol. 2016;44:867-873. © 2016 Wiley Periodicals, Inc.

Independent Prognostic Significance of Gene Expression Profile Class and Largest Basal Diameter of Posterior Uveal Melanomas.

PURPOSE: To determine whether any conventional clinical prognostic factors for metastasis from uveal melanoma retain prognostic significance in multivariate models incorporating gene expression profile (GEP) class of the tumor cells. DESIGN: Prospective, interventional case series with a prognostic model. METHODS: Single-institution study of GEP testing and other conventional prognostic factors for metastasis and metastatic death in 299 patients with posterior uveal melanoma evaluated by fine-needle aspiration biopsy (FNAB) at the time of or shortly prior to initial treatment. Univariate prognostic significance of all evaluated potential prognostic variables (patient age, largest linear basal diameter of tumor [LBD], tumor thickness, intraocular location of tumor, melanoma cytomorphologic subtype, and GEP class) was performed by comparison of Kaplan-Meier event rate curves and univariate Cox proportional hazards modeling. Multivariate prognostic significance of combinations of significant prognostic factors identified by univariate analysis was performed using step-up and step-down Cox proportional hazards modeling. RESULTS: GEP class was the strongest prognostic factor for metastatic death in this series. However, tumor LBD, tumor thickness, and intraocular tumor location also proved to be significant individual prognostic factors in this study. On multivariate analysis, a 2-term model that incorporated GEP class and largest basal diameter was associated with strong independent significance of each of the factors. CONCLUSION: Although GEP test is the most robust prognostic indicator in uveal melanoma and early studies of mostly larger tumors found that no clinicopathologic factors had significant prognostic value independent of GEP, our single-center study, which included a substantial proportion of smaller tumors, showed that both GEP and LBD of the tumor are independent prognostic factors for metastasis and metastatic death in multivariate analysis.

Computational Tools for Parsimony Phylogenetic Analysis of Omics Data.

High-throughput assays from genomics, proteomics, metabolomics, and next generation sequencing produce massive omics datasets that are challenging to analyze in biological or clinical contexts. Thus far, there is no publicly available program for converting quantitative omics data into input formats to be used in off-the-shelf robust phylogenetic programs. To the best of our knowledge, this is the first report on creation of two Windows-based programs, OmicsTract and SynpExtractor, to address this gap. We note, as a way of introduction and development of these programs, that one particularly useful bioinformatics inferential modeling is the phylogenetic cladogram. Cladograms are multidimensional tools that show the relatedness between subgroups of healthy and diseased individuals and the latter's shared aberrations; they also reveal some characteristics of a disease that would not otherwise be apparent by other analytical methods. The OmicsTract and SynpExtractor were written for the respective tasks of (1) accommodating advanced phylogenetic parsimony analysis (through standard programs of MIX [from PHYLIP] and TNT), and (2) extracting shared aberrations at the cladogram nodes. OmicsTract converts comma-delimited data tables through assigning each data point into a binary value ("0" for normal states and "1" for abnormal states) then outputs the converted data tables into the proper input file formats for MIX or with embedded commands for TNT. SynapExtractor uses outfiles from MIX and TNT to extract the shared aberrations of each node of the cladogram, matching them with identifying labels from the dataset and exporting them into a comma-delimited file. Labels may be gene identifiers in gene-expression datasets or m/z values in mass spectrometry datasets. By automating these steps, OmicsTract and SynpExtractor offer a veritable opportunity for rapid and standardized phylogenetic analyses of omics data; their model can also be extended to next generation sequencing (NGS) data. We make OmicsTract and SynpExtractor publicly and freely available for non-commercial use in order to strengthen and build capacity for the phylogenetic paradigm of omics analysis.

New feature selection for gene expression classification based on degree of class overlap in principal dimensions.

Micro-array data are typically characterized by high dimensional features with a small number of samples. Several problems in identifying genes causing diseases from micro-array data can be transformed into the problem of classifying the features extracted from gene expression in micro-array data. However, too many features can cause low prediction accuracy as well as high computational complexity. Dimensional reduction is a method to eliminate irrelevant features to improve the prediction accuracy. Typically, the eigenvalues or dimensional data variance from principal component analysis are used as criteria to select relevant features. This approach is simple but not efficient since it does not concern the degree of data overlap in each dimension in the feature space. A new method to select relevant features based on degree of dimensional data overlap with proper feature selection was introduced. Furthermore, our study concentrated on small sized data sets which usually occur in reality. The experimental results signified that this new approach can achieve substantially higher prediction accuracy when compared with other methods.

Frequency and implications of discordant gene expression profile class in posterior uveal melanomas sampled by fine needle aspiration biopsy.

PURPOSE: To determine the frequency of discordant gene expression profile (GEP) classification of posterior uveal melanomas sampled at 2 tumor sites by fine-needle aspiration biopsy (FNAB). DESIGN: Prospective single-institution longitudinal study performed in conjunction with a multicenter validation study of the prognostic value of GEP class of posterior uveal melanoma cells for metastasis and metastatic death. METHODS: FNAB aspirates of 80 clinically diagnosed primary choroidal and ciliochoroidal melanomas were obtained from 2 tumor sites prior to or at the time of initial ocular tumor treatment and submitted for independent GEP testing and classification. Frequency of discordant GEP classification of these specimens was determined. RESULTS: Using the support vector machine learning algorithm favored by the developer of the GEP test employed in this study, 9 of the 80 cases (11.3% [95% confidence interval: 9.0%-13.6%]) were clearly discordant. If cases with a failed classification at 1 site or a low confidence class assignment by the support vector machine algorithm at 1 or both sites are also regarded as discordant, then this frequency rises to 13 of the 80 cases (16.3% [95% confidence interval: 13.0%-19.6%]). CONCLUSION: Sampling of a clinically diagnosed posterior uveal melanoma at a single site for prognostic GEP testing is associated with a substantial probability of misclassification. Two-site sampling of such tumors with independent GEP testing of each specimen may be advisable to lessen the probability of underestimating an individual patient's prognostic risk of metastasis and metastatic death.

Clinical diagnostic gene expression thyroid testing.

Thyroid fine-needle aspiration biopsies are cytologically indeterminate in 15% to 30% of cases. When cytologically indeterminate thyroid nodules undergo diagnostic surgery, approximately three-quarters prove to be histologically benign. A negative predictive value of more than or equal to 94% for the Afirma Gene Expression Classifier (GEC) is achieved for indeterminate nodules. Most Afirma GEC benign nodules can be clinically observed, as suggested by the National Comprehensive Cancer Network Thyroid Carcinoma Guideline. More than half of the benign nodules with indeterminate cytology (Bethesda categories III/IV) can be identified as GEC benign and removed from the surgical pool to prevent unnecessary diagnostic surgery.

Gene expression profiling in breast cancer: a clinical perspective.

Gene expression profiling tests are used in an attempt to determine the right treatment for the right person with early-stage breast cancer that may have spread to nearby lymph nodes but not to distant parts of the body. These new diagnostic approaches are designed to spare people who do not need additional treatment (adjuvant therapy) the side effects of unnecessary treatment, and allow people who may benefit from adjuvant therapy to receive it. In the present review we discuss in detail the major diagnostic tests available such as MammaPrint dx, Oncotype dx, PAM50, Mammostrat, IHC4, MapQuant DX, Theros-Breast Cancer Gene Expression Ratio Assay, and their potential clinical applications.

Does addition of BRAF V600E mutation testing modify sensitivity or specificity of the Afirma Gene Expression Classifier in cytologically indeterminate thyroid nodules?

OBJECTIVE: The purpose of this study was to determine the frequency of BRAF mutation in cytologically indeterminate thyroid nodules and to investigate whether adding the BRAF test improves diagnostic accuracy of the Afirma Gene Expression Classifier (GEC). DESIGN: BRAF V600E mutational status was determined for DNA extracted from cytologically benign (n = 40), indeterminate (n = 208), and malignant (n = 48) fine-needle aspiration specimens previously categorized by GEC as molecularly Benign or Suspicious. Analytical performance of the BRAF assay was assessed to establish reproducibility and limits of detection. Molecular testing results were correlated with blinded expert histopathological diagnoses. RESULTS: The BRAF assay detected mutations reproducibly to 2.5% mutant allele frequency. The prevalence of BRAF mutations in cytologically benign specimens was 2 of 40 (5.0%, 95% confidence interval [CI], 0-16) and in cytologically malignant specimens was 36 of 48 (75.0%, 95% CI, 60-86). In the cytologically indeterminate category, 10.1% of specimens were BRAF+: 2 of 95 were subcategorized as atypia of undetermined significance or follicular lesion of undetermined significance (2.1%, 95% CI, 0-7); 1 of 70 as follicular neoplasm or suspicious for follicular neoplasm (1.4%, 95% CI, 0-9); and 18 of 43 as suspicious for malignancy (41.9%, 95% CI, 27-58). All BRAF+ specimens were classified as Suspicious by the GEC. CONCLUSIONS: BRAF mutations are uncommon in nodules with atypia of undetermined significance or follicular lesion of undetermined significance or follicular neoplasm or suspicious for follicular neoplasm cytology. Most cytologically indeterminate nodules that proved to be malignant were also BRAF-, and all nodules that were false-negative by GEC were also BRAF-. Similarly, all BRAF+ specimens were also GEC Suspicious. Neither GEC test sensitivity nor specificity was improved by addition of BRAF mutation testing.

Differential gene expression profile of the calanoid copepod, Pseudodiaptomus annandalei, in response to nickel exposure.

To better understand the underlying mechanisms of reactions of copepods exposed to elevated level of nickel, the suppression subtractive hybridization (SSH) was used to elucidate the response of the copepod Pseudodiaptomus annandalei to nickel exposure at the gene level. P. annandale is one of a few copepod species that can be cultured relatively easy under laboratory condition, and it is considered to be a potential model species for toxicity study. In the present study, P. annandalei were exposed to nickel at a concentration of 8.86 mgL(-1) for 24h, after which the RNA was prepared for SSH using unexposed P. annandalei as drivers. A total of 474 clones on the middle scale in the SSH library were sequenced. Among these genes, 129 potential functional genes were recognized based on the BLAST searches in NCBI and Uniprot databases. These genes were then categorized into nine groups in association with different biological processes using AmiGO against the Gene Ontology database. Of the 129 genes, 127 translatable DNA sequences were predicted to be proteins, and the putative amino acid sequences were searched for conserved domains (CD) and proteins using the CD-Search service and BLASTp. Among 129 genes, 119 (92.2%) were annotated to be involved in different biological processes, while 10 genes (7.8%) were classified as an unknown-function gene group. To further confirm the up-regulation of differentially expressed genes, the quantitative real time PCR were performed to test eight randomly selected genes, in which five of them, i.e. α-tubulin, ribosomal protein L13, ferritin, separase and Myohemerythrin-1, exhibited clear up-regulation after nickel exposure. In addition, MnSOD was further studied for the differential expression pattern after nickel exposure and the results showed that MnSOD had a time- and dose-dependent expression pattern in the copepod after nickel exposure. To the best of our knowledge, this is the first attempt to investigate the toxicity effects of nickel on a copepod at molecular level.

Heterogeneous data fusion for brain tumor classification.

Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment history, patient outcomes as well as gene expression, DNA sequences and other information sources such as gene ontology. Analysis of these data sets could lead to better disease diagnosis, prognosis, treatment and drug discovery. In this report, we present a novel machine learning framework for brain tumor classification based on heterogeneous data fusion of metabolic and molecular datasets, including state-of-the-art high-resolution magic angle spinning (HRMAS) proton (1H) magnetic resonance spectroscopy and gene transcriptome profiling, obtained from intact brain tumor biopsies. Our experimental results show that our novel framework outperforms any analysis using individual dataset.

ColoGuideEx: a robust gene classifier specific for stage II colorectal cancer prognosis.

BACKGROUND AND AIMS: Several clinical factors have an impact on prognosis in stage II colorectal cancer (CRC), but as yet they are inadequate for risk assessment. The present study aimed to develop a gene expression classifier for improved risk stratification of patients with stage II CRC. METHODS: 315 CRC samples were included in the study. Gene expression measurements from 207 CRC samples (stage I-IV) from two independent Norwegian clinical series were obtained using Affymetrix exon-level microarrays. Differentially expressed genes between stage I and stage IV samples from the test series were identified and used as input for L1 (lasso) penalised Cox proportional hazards analyses of patients with stage II CRC from the same series. A second validation was performed in 108 stage II CRC samples from other populations (USA and Australia). RESULTS: An optimal 13-gene expression classifier (PIGR, CXCL13, MMP3, TUBA1B, SESN1, AZGP1, KLK6, EPHA7, SEMA3A, DSC3, CXCL10, ENPP3, BNIP3) for prediction of relapse among patients with stage II CRC was developed using a consecutive Norwegian test series from patients treated according to current standard protocols (n=44, p<0.001, HR=18.2), and its predictive value was successfully validated for patients with stage II CRC in a second Norwegian CRC series collected two decades previously (n=52, p=0.02, HR=3.6). Further validation of the classifier was obtained in a recent external dataset of patients with stage II CRC from other populations (n=108, p=0.001, HR=6.5). Multivariate Cox regression analyses, including all three sample series and various clinicopathological variables, confirmed the independent prognostic value of the classifier (p≤0.004). The classifier was shown to be specific to stage II CRC and does not provide prognostic stratification of patients with stage III CRC. CONCLUSION: This study presents the development and validation of a 13-gene expression classifier, ColoGuideEx, for prognosis prediction specific to patients with stage II CRC. The robustness was shown across patient series, populations and different microarray versions.

Sixty-five gene-based risk score classifier predicts overall survival in hepatocellular carcinoma.

UNLABELLED: Clinical application of the prognostic gene expression signature has been delayed due to the large number of genes and complexity of prediction algorithms. In the current study we aimed to develop an easy-to-use risk score with a limited number of genes that can robustly predict prognosis of patients with hepatocellular carcinoma (HCC). The risk score was developed using Cox coefficient values of 65 genes in the training set (n = 139) and its robustness was validated in test sets (n = 292). The risk score was a highly significant predictor of overall survival (OS) in the first test cohort (P = 5.6 × 10(-5), n = 100) and the second test cohort (P = 5.0 × 10(-5) , n = 192). In multivariate analysis, the risk score was a significant risk factor among clinical variables examined together (hazard ratio [HR], 1.36; 95% confidence interval [CI], 1.13-1.64; P = 0.001 for OS). CONCLUSION: The risk score classifier we have developed can identify two clinically distinct HCC subtypes at early and late stages of the disease in a simple and highly reproducible manner across multiple datasets.