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Introduction: To evaluate the survival of Glioblastoma Multiforme (GBM). Material and Methods: Patients with a pathological diagnosis of Glioblastoma Multiforme (GBM) between 1 January 1994 and 30 November 2013, were retrospectively reviewed. Inclusion criteria: 1) GBM patients with confirmed pathology, 2) GBM patients were treated by multimodality therapy. Exclusion criteria: 1) GBM patients with unconfirmed pathology, 2) GBM patients with spinal involvement, 3) GBM patients with incomplete data records. Seventy-seven patients were treated by multimodality therapy such as surgery plus post-operative radiotherapy (PORT), post-operative Temozolomide (TMZ) concurrent with radiotherapy (CCRT), post-operative CCRT with adjuvant TMZ. The overall survival was calculated by the Kaplan-Meier method and the log-rank test was used to compare the survival curves. A p-value of ≤ 0.05 was considered to be statistically significant. Results: Seventy-seven patients with a median age of 53 years (range 4-76 years) showed a median survival time (MST) of 12 months. In subgroup analyses, the PORT patients revealed a MST of 11 months and 2 year overall survival (OS) rates were 17.2%, the patients with post-operative CCRT with or without adjuvant TMZ revealed a MST of 23 months and 2 year OS rates were 38.2%. The MST of patients by Recursive Partitioning Analysis (RPA), classifications III, IV, V, VI were 26.8 months, 14.2 months, 9.9 months, and 4.0 months, (p <0.001). Conclusions: The MST of the patients who had post-operative CCRT with or without adjuvant TMZ was better than the PORT group. The RPA classification can be used to predict survival. Multimodality therapy demonstrated the most effective treatment outcome. Temozolomide might be beneficial for GBM patients in order to increase survival time.
Health information systems contain extensive amounts of patient data. Information relevant to public health and individuals' medical histories are both available. In clinical research, the prediction of patient survival rates and identification of prognosis factors are major challenges. To alleviate the difficulties related to these factors, Metadata Utilities was developed to help researchers manage column definitions and information such as import/query/generator Metadata files. These utilities also include an automatic update mechanism to ensure consistency between the data and parameters of the batch produced in the conversion procedure. Survival Metadata Analysis Responsive Tool (SMART) provides a comprehensive set of statistical tests that are easy to understand, including support for analyzing nominal variables, ordinal variables, interval variables or ratio variables as means, standard deviations, maximum values, minimum values, and percentages. In this article, the development of a raw data source and transfer mechanism, Extract-Transform-Load (ETL), is described for data cleansing, extraction, transformation and loading. We also built a handy method for data presentation, which can be customized to the trial design. As demonstrated here, SMART is useful for risk-adjusted baseline cohort and randomized controlled trials.
The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/ .
Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types. RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan-Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing. Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR 4.26, p < 1E-16), RUNX1 (sustaining proliferative signaling, HR 2.96, p = 3.1E-10) and SERPINE1 (inducing angiogenesis, HR 3.36, p = 1.5E-12) in low grade glioma, CDK1 (cell death resistance, HR = 5.67, p = 2.1E-10) in kidney papillary carcinoma, E2F1 (tumor suppressor, HR 0.38, p = 2.4E-05) and EREG (enabling replicative immortality, HR 3.23, p = 2.1E-07) in cervical cancer, FBP1 (deregulation of cellular energetics, HR 0.45, p = 2.8E-07) in kidney renal clear cell carcinoma and MYC (invasion and metastasis, HR 1.81, p = 5.8E-05) in bladder cancer. We observed unexpected heterogeneity and tissue specificity when correlating cancer hallmark genes and survival. These results will help to prioritize future targeted therapy development in different types of solid tumors.
Despite the continuous improvement of dialysis technology and pharmacological treatment, mortality rates for dialysis patients are still high. A 2-year prospective study was conducted at a tertiary care hospital to determine the factors influencing survival among patients on maintenance hemodialysis. 96 patients with end-stage renal disease surviving more than 3 months on hemodialysis (8-12 h/week) were studied. Follow-up was censored at the time of death or at the end of 2-year study period, whichever occurred first. Of the 96 patients studied (mean age 49.74 ± 14.55 years, 75% male and 44.7% diabetics), 19 died with an estimated mortality rate of 19.8%. On an age-adjusted multivariate analysis, female gender and hypokalemia independently predicted mortality. In Cox analyses, patient survival was associated with delivered dialysis dose (single pool Kt/V, hazard ratio [HR] =0.01, P = 0.016), frequency of hemodialysis (HR = 3.81, P = 0.05) and serum albumin (HR = 0.24, P = 0.005). There was no significant difference between diabetes and non-diabetes in relation to death (Relative Risk = 1.109; 95% CI = 0.49-2.48, P = 0.803). This study revealed that mortality among hemodialysis patients remained high, mostly due to sepsis and ischemic heart disease. Patient survival was better with higher dialysis dose, increased frequency of dialysis and adequate serum albumin level. Efforts at minimizing infectious complications, preventing cardiovascular events and improving nutrition should increase survival among hemodialysis patients.
Survival analysis is a collection of statistical procedures employed on time-to-event data. The outcome variable of interest is time until an event occurs. Conventionally, it dealt with death as the event, but it can handle any event occurring in an individual like disease, relapse from remission, and recovery. Survival data describe the length of time from a time of origin to an endpoint of interest. By time, we mean years, months, weeks, or days from the beginning of being enrolled in the study. The major limitation of time-to-event data is the possibility of an event not occurring in all the subjects during a specific study period. In addition, some of the study subjects may leave the study prematurely. Such situations lead to what is called censored observations as complete information is not available for these subjects. Life table and Kaplan-Meier techniques are employed to obtain the descriptive measures of survival times. The main objectives of survival analysis include analysis of patterns of time-to-event data, evaluating reasons why data may be censored, comparing the survival curves, and assessing the relationship of explanatory variables to survival time. Survival analysis also offers different regression models that accommodate any number of covariates (categorical or continuous) and produces adjusted hazard ratios for individual factor.
So far, no randomized trial or meta-analysis has been conducted on overall survival (OS) and recurrence-free survival (RFS) factors in patients treated with radiofrequency ablation (RFA) alone. The purpose of this meta-analysis was to evaluate prognostic factors of OS and RFS in patients treated with RFA.
Clinical trials have reported conflicting results on whether oral clodronate therapy improves survival in breast cancer patients. This study was undertaken to evaluate further the effect of oral clodronate therapy on overall survival, bone metastasis-free survival and nonskeletal metastasis-free survival among breast cancer patients. An extensive literature search was undertaken for the period 1966 to July 2006 to identify clinical trials examining survival in breast cancer patients who received 2 or 3 years of oral clodronate therapy at 1600 mg day(-1) compared with those without therapy. Meta-analyses were carried out separately for patients diagnosed with advanced breast cancer and early breast cancer. Our meta-analysis found no evidence of any statistically significant difference in overall survival, bone metastasis-free survival or nonskeletal metastasis-free survival in advanced breast cancer patients receiving clodronate therapy or early breast cancer patients receiving adjuvant clodronate treatment compared with those who did not receive any active treatment.
Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions. An exponential increase in the number of candidate markers requires large correction factor in the multiple-testing correction and hide the significance.
Epithelial-myoepithelial carcinoma is an uncommon malignant neoplasm seen most frequently in the salivary glands, representing approximately 1 to 2% of salivary gland tumors. Less than 600 cases have been reported in the literature since its initial description in 1972. The aim of this study was to examine demographic, site, stage, and survival factors in patients with epithelial-myoepithelial carcinoma.
A total of 20031 ovarian cancer patients were included, with 291 (1.45%) patients who received radiotherapy. The median overall survival (OS) in patients who received radiotherapy was shorter than which in patients without radiotherapy (23 vs. 75 months, P < 0.001). The Elderly, nonepithelial pathology, advanced American Joint Committee on Cancer (AJCC) stage, elevated level of CA125, and receiving radiotherapy were risk predictors to survival in both multivariable analyses before and after PSM. Among 11872 patients with III/IV stage, the radiotherapy group also showed a significantly worse prognosis (median OS: 19 vs. 44 months in patients without radiotherapy, P < 0.001). Consistent results were observed in stratification analyses on pathology and stage among patients with III/IV stage.
More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate.
Recently, cancer immunotherapies have been life-savers, however, only a fraction of treated patients have durable responses. Consequently, statistical methods that enable the discovery of target genes for developing new treatments and predicting patient survival are of importance. This paper introduced a network-based survival analysis method and applied it to identify candidate genes as possible targets for developing new treatments. RNA-seq data from a mouse study was used to select differentially expressed genes, which were then translated to those in humans. We constructed a gene network and identified gene clusters using a training set of 310 human gliomas. Then we conducted gene set enrichment analysis to select the gene clusters with significant biological function. A penalized Cox model was built to identify a small set of candidate genes to predict survival. An independent set of 690 human glioma samples was used to evaluate predictive accuracy of the survival model. The areas under time-dependent ROC curves in both the training and validation sets are more than 90%, indicating strong association between selected genes and patient survival. Consequently, potential biomedical interventions targeting these genes might be able to alter their expressions and prolong patient survival.
Survival analyses of gene expression data has been a useful and widely used approach in clinical applications. But, in complex diseases, such as cancer, the identification of survival-associated cell processes - rather than single genes - provides more informative results because the efficacy of survival prediction increases when multiple prognostic features are combined to enlarge the possibility of having druggable targets. Moreover, genome-wide screening in molecular medicine has rapidly grown, providing not only gene expression but also multi-omic measurements such as DNA mutations, methylation, expression, and copy number data. In cancer, virtually all these aberrations can contribute in synergy to pathological processes, and their measurements can improve a patient's outcome and help in diagnosis and treatment decisions. Here, we present MOSClip, an R package implementing a new topological pathway analysis tool able to integrate multi-omic data and look for survival-associated gene modules. MOSClip tests the survival association of dimensionality-reduced multi-omic data using multivariate models, providing graphical devices for management, browsing and interpretation of results. Using simulated data we evaluated MOSClip performance in terms of false positives and false negatives in different settings, while the TCGA ovarian cancer dataset is used as a case study to highlight MOSClip's potential.
Net survival is commonly quantified as relative survival (observed survival among lung cancer patients versus expected survival among the general population) and cause-specific survival (lung cancer-specific survival among lung cancer patients). These approaches have drastically different assumptions; hence, failure to distinguish between them results in significant implications for study findings. We quantified the differences between relative and cause-specific survival when reporting net survival of patients with non-small cell lung cancer (NSCLC).
The data from immuno-oncology (IO) therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the proportional hazards (PH) assumption is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the PH assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered.
Kaplan-Meier (KM) survival analyses based on complex patient categorization due to the burgeoning volumes of genomic, molecular and phenotypic data, are an increasingly important aspect of the biomedical researcher's toolkit. Commercial statistics and graphing packages for such analyses are functionally limited, whereas open-source tools have a high barrier-to-entry in terms of understanding of methodologies and computational expertise. We developed surviveR to address this unmet need for a survival analysis tool that can enable users with limited computational expertise to conduct routine but complex analyses. surviveR is a cloud-based Shiny application, that addresses our identified unmet need for an easy-to-use web-based tool that can plot and analyse survival based datasets. Integrated customization options allows a user with limited computational expertise to easily filter patients to enable custom cohort generation, automatically calculate log-rank test and Cox hazard ratios. Continuous datasets can be integrated, such as RNA or protein expression measurements which can be then used as categories for survival plotting. We further demonstrate the utility through exemplifying its application to a clinically relevant colorectal cancer patient dataset. surviveR is a cloud-based web application available at https://generatr.qub.ac.uk/app/surviveR , that can be used by non-experts users to perform complex custom survival analysis.
Esophageal Adenocarcinoma (EAC) is one of the most common gastrointestinal tumors in the world. However, molecular prognostic systems are still lacking for EAC. Hence, we developed an Online consensus Survival analysis web server for Esophageal Adenocarcinoma (OSeac), to centralize published gene expression data and clinical follow up data of EAC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). OSeac includes 198 EAC cases with gene expression profiling and relevant clinical long-term follow-up data, and employs the Kaplan Meier (KM) survival plot with hazard ratio (HR) and log rank test to estimate the prognostic potency of genes of interests for EAC patients. Moreover, we have determined the reliability of OSeac by using previously reported prognostic biomarkers such as DKK3, CTO1, and TXNIP. OSeac is free and publicly accessible at http://bioinfo.henu.edu.cn/EAC/EACList.jsp.
Mucosal melanoma (MM) is a highly lethal variant of melanoma that carries a poor prognosis. Extremely low incidence and survival rates have led to few clinical trials, and a lack of protocols and guidelines. The present study performed a survival meta-analysis for the quantitative synthesis of available evidence to search for key patterns that would help clinicians tailor optimal therapeutic strategies in MM. PubMed, EMBASE, Cochrane, MEDLINE, Google Scholar and other databases were searched. Hazard ratios, in disease-specific and overall survival, were calculated for each of the survival-determining variables. MM was 2.25 times more lethal than cutaneous melanoma (CM). The most significant threats to survival were advanced Tumor-Node-Metastasis stage, sino-nasal location, and old age. Chemotherapy was the most effective form of adjuvant therapy. Disease-specific survival, the primary measure of the effect sizes, can fluctuate depending on the accuracy of the reported cause of mortality. In conclusion, MM is a peculiar type of melanoma, with clinical and molecular profile vastly different from the much-familiar CM. In the wake of the era of precision oncology, further studies on driver mutations and oncogenic pathways would likely lead to improved patient survival.
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