Assessment of evaluation criteria for survival prediction from genomic data

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Characterization of Tumor Blood Vasculature Expression of Human Invasive Bladder Cancer by Laser Capture Microdissection and Transcriptional Profiling

Tumor-associated blood vessels differ from normal vessels and play key roles in tumor progression. We aimed to identify biomolecules that are expressed differentially in human bladder cancer-associated blood vessels to find novel biomarkers and mechanisms involved in tumor-associated angiogenesis. The transcriptome of tumor blood vasculature from human invasive bladder carcinoma (I-BLCA) and normal bladder tissue vasculature was compared using differential expression and unsupervised hierarchical clustering analyses. Pathway analysis identified up-regulation of genes involved in the proliferation, cell cycle, angiogenesis, inflammation, and transforming growth factor-β signaling in tumor blood vasculature. A common consensus gene expression signature was identified between bladder cancer tumor blood vasculature with tumor blood vasculature of other solid cancers, which correlated with the overall survival of patients with several of the solid cancers investigated in The Cancer Genome Atlas data set. In bladder tumor blood vasculature, the secreted factor angiopoietin-like protein 2 (ANGPTL2), was confirmed to be up-regulated by quantitative RT-PCR and immunohistochemical staining. The up-regulation of ANGPTL2 in plasma was also observed in non-invasive bladder carcinoma and I-BLCA. We semiquantitatively analyzed expression of ANGPTL2 in tissue microarrays from I-BLCA and surprisingly found an opposite correlation between staining intensity and progression-free survival. Our results indicate that ANGPTL2 might serve as a potential biomarker to predict progression-free survival in I-BLCA.

GradientScanSurv-An exhaustive association test method for gene expression data with censored survival outcome

Accurate assessment of the association between continuous variables such as gene expression and survival is a critical aspect of precision medicine. In this report, we provide a review of some of the available survival analysis and validation tools by referencing published studies that have utilized these tools. We have identified pitfalls associated with the assumptions inherent in those applications that have the potential to impact scientific research through their potential bias. In order to overcome these pitfalls, we have developed a novel method that enables the logrank test method to handle continuous variables that comprehensively evaluates survival association with derived aggregate statistics. This is accomplished by exhaustively considering all the cutpoints across the full expression gradient. Direct side-by-side comparisons, global ROC analysis, and evaluation of the ability to capture relevant biological themes based on current understanding of RAS biology all demonstrated that the new method shows better consistency between multiple datasets of the same disease, better reproducibility and robustness, and better detection power to uncover biological relevance within the selected datasets over the available survival analysis methods on univariate gene expression and penalized linear model-based methods.

Hyper expression of MTBP may be an adverse signal for the survival of some malignant tumors: A data-based analysis and clinical observation

To explore the relationship between mouse double minute 2 binding protein (MTBP) and the prognosis of cancer patients, a databank-based reanalysis was conducted and a clinical observation about lung adenocarcinoma was taken to verify the result of data analysis.We reanalyzed all the downloaded data in order to make a conclusion about the relationship between MTBP and the prognosis of cancer patients. At last, we collected 112 lung cancer patients with MTBP information to verify the results of data analysis (GSE30219).The overall Kaplan-Meier curve results of 6 eligible data groups were shown in Fig. 1. The Kaplan-Meier curve result of GSE16011 was shown in Fig. 1A (concordance index = 59.48, Log-Rank Equal Curves [P = 5.942e-05], R = 0.045/1, risk groups hazard ratio = 1.69 [conf. int. 1.3-2.9], P = 7.344e-05), while the stratification results were displayed independently in Figs. 2 and 3. The similar results could be seen in other 5 data groups. The tissue sections of 112 patients with lung adenocarcinoma were collected and immunohistochemically stained. The hyper expression rate of MTBP in adenocarcinoma was 23.21% (26/112). The results showed that patients with hyper expression of MTBP had significantly worse prognosis than the control group, and the survival curves were clearly separated from each other (Fig. 4B, P = .000).Hyper expression of MTBP maybe an adverse event for the survival of some cancer patients, especially in glioblastoma, kidney cancer, and lung cancer patients, which has been verified in 112 lung cancer patients with MTBP status.

ALK1Fc Suppresses the Human Prostate Cancer Growth in in Vitro and in Vivo Preclinical Models

Prostate cancer is the second most common cancer in men and lethality is normally associated with the consequences of metastasis rather than the primary tumor. Therefore, targeting the molecular pathways that underlie dissemination of primary tumor cells and the formation of metastases has a great clinical value. Bone morphogenetic proteins (BMPs) play a critical role in tumor progression and this study focuses on the role of BMP9- Activin receptor-Like Kinase 1 and 2 (ALK1 and ALK2) axis in prostate cancer. In order to study the effect of BMP9 in vitro and in vivo on cancer cells and tumor growth, we used a soluble chimeric protein consisting of the ALK1 extracellular domain (ECD) fused to human Fc (ALK1Fc) that prevents binding of BMP9 to its cell surface receptors and thereby blocks its ability to activate downstream signaling. ALK1Fc sequesters BMP9 and the closely related BMP10 while preserving the activation of ALK1 and ALK2 through other ligands. We show that ALK1Fc acts in vitro to decrease BMP9-mediated signaling and proliferation of prostate cancer cells with tumor initiating and metastatic potential. In line with these observations, we demonstrate that ALK1Fc also reduces tumor cell proliferation and tumor growth in vivo in an orthotopic transplantation model, as well as in the human patient derived xenograft BM18. Furthermore, we also provide evidence for crosstalk between BMP9 and NOTCH and find that ALK1Fc inhibits NOTCH signaling in human prostate cancer cells and blocks the induction of the NOTCH target Aldehyde dehydrogenase member ALDH1A1, which is a clinically relevant marker associated with poor survival and advanced-stage prostate cancer. Our study provides the first demonstration that ALK1Fc inhibits prostate cancer progression, identifying BMP9 as a putative therapeutic target and ALK1Fc as a potential therapy. Altogether, these findings support the validity of ongoing clinical development of drugs blocking ALK1 and ALK2 receptor activity.

Methylomic predictors demonstrate the role of NF-κB in old-age mortality and are unrelated to the aging-associated epigenetic drift

Changes in the DNA methylation (DNAm) landscape have been implicated in aging and cellular senescence. To unravel the role of specific DNAm patterns in late-life survival, we performed genome-wide methylation profiling in nonagenarians (n=111) and determined the performance of the methylomic predictors and conventional risk markers in a longitudinal setting. The survival model containing only the methylomic markers was superior in terms of predictive accuracy compared with the model containing only the conventional predictors or the model containing conventional predictors combined with the methylomic markers. At the 2.55-year follow-up, we identified 19 mortality-associated (false-discovery rate <0.5) CpG sites that mapped to genes functionally clustering around the nuclear factor kappa B (NF-κB) complex. Interestingly, none of the mortality-associated CpG sites overlapped with the established aging-associated DNAm sites. Our results are in line with previous findings on the role of NF-κB in controlling animal life spans and demonstrate the role of this complex in human longevity.

NOD2 maybe a biomarker for the survival of kidney cancer patients

Background

Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) may play an important role in the outcome of kidney cancer patients. To explore the relationship between NOD2 and the prognosis of kidney cancer patients, a databank-based reanalysis was conducted.

Materials and Methods

Data related to kidney cancer patients at least with survival information, was obtained mainly from The Cancer Genome Atlas (TCGA). Some clinical data, not available online, was collected by personal email to the author. Then, we reanalyzed all the data in order to make a conclusion about the relationship between NOD2 gene and the prognosis of kidney cancer patients.

Results

A total of 1953 samples with NOD2 information from four databanks of The Cancer Genome Atlas (TCGA) were enrolled in this study. The results of KIPAN showed the Kaplan-Meier curve for risk groups, concordance index, and p-value of the log-rank testing equality of survival curves ( Concordance Index = 56.57, Log−Rank Equal Curves p=0.0009006, R^2 = 0.036/0.953, Risk Groups Hazard Ratio = 1.61 (conf. int. 1.21 ~ 2.13), p = 0.001005) , while a box plot across risk groups, including the p-value testing for difference using t-test (or f-test for more than two groups) was shown. There was a statistical significance for the p value of the result (p < 0.01 ). The similar results could be seen in KIRC and the fourth data (including 468 samples).

Conclusions

The status of NOD2 gene maybe a biomarker for the survival of kidney cancer patients.

A comparison of region-of-interest measures for extracting whole brain data using survival analysis in alcoholism as an example

BACKGROUND

Aggregation of functional magnetic resonance imaging (fMRI) data in regions-of-interest (ROIs) is required for complex statistical analyses not implemented in standard fMRI software. Different data-aggregation measures assess various aspects of neural activation, including spatial extent and intensity.

NEW METHOD

In this study, conducted within the framework of the PREDICT study, we compared different aggregation measures for voxel-wise fMRI activations to be used as prognostic factors for relapse in 49 abstinent alcohol-dependent individuals in an outpatient setting using a cue-reactivity task. We compared the importance of the data-aggregation measures as prognostic factors for treatment outcomes by calculating the proportion of explained variation.

RESULTS AND COMPARISON WITH EXISTING METHOD(S)

Relapse risk was associated with cue-induced brain activation during abstinence in the ventral striatum (VS) and in the orbitofrontal cortex (OFC). While various ROI measures proved appropriate for using fMRI cue-reactivity to predict relapse, on the descriptive level the most "important" prognostic factor was a measure defined as the sum of t-values exceeding an individually defined threshold. Data collected in the VS was superior to that from other regions.

CONCLUSIONS

In conclusion, it seems that fMRI cue-reactivity, especially in the VS, can be used as prognostic factor for relapse in abstinent alcohol-dependent patients. Our findings suggest that data-aggregation measures that take both spatial extent and intensity of cue-induced brain activation into account make better biomarkers for predicting relapse than measures that consider an activation's spatial extent or intensity alone.

SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

Survival analysis by penalized regression and matrix factorization

Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We tried L1- (lasso), L2- (ridge), and L1-L2 combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found that L1-L2 combined method predicts survival best with the smallest logrank P value. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrank P values. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully.

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence

BACKGROUND

Many studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours.

RESULTS

Biclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models.

CONCLUSIONS

The increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients.

Survival prediction based on compound covariate under Cox proportional hazard models

Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that the new proposal has a theoretical justification from a statistical large sample theory and is naturally interpreted as a shrinkage-type estimator, a popular class of estimators in statistical literature. Two datasets, the primary biliary cirrhosis of the liver data and the non-small-cell lung cancer data, are used for illustration. The proposed method is implemented in R package “compound.Cox” available in CRAN at http://cran.r-project.org/.

An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation.