SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis
TL;DR: This work 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 and shows the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer.
Abstract: 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.
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TL;DR: PD-L1 mRNA expression is identified in nearly 60% of breast tumors and it is associated with increased TILs and improved recurrence-free survival, which support the evaluation of PD-1/PD- L1–targeted therapies in breast cancer.
Abstract: Purpose: Blockade of the PD-1/PD-L1 axis emerged as a promising new therapeutic option for cancer that has resulted in lasting responses in metastatic renal, lung carcinomas, and melanomas. Tumor PD-L1 protein expression may predict response to drugs targeting this pathway. Measurement of PD-L1 protein is limited by the lack of standardized immunohistochemical methods and variable performance of antibodies. Our goal was to correlate PD-L1 mRNA expression with clinical variables in primary breast carcinomas. Experimental Design: The fluorescent RNAscope paired-primer assay was used to quantify in situ PD-L1 mRNA levels in 636 stage I–III breast carcinomas on two sets of tissue microarrays [YTMA128 ( n = 238) and YTMA201 ( n = 398)]. Tumor-infiltrating lymphocytes (TIL) were assessed by hematoxylin/eosin stain and quantitative fluorescence. Results: On YTMA128 and YTMA201, 55.7% and 59.5% of cases showed PD-L1 mRNA expression, respectively. Higher PD-L1 mRNA expression was significantly associated with increased TILs ( P = 0.04) but not with other clinical variables. Elevated TILs (scores 2 and 3+) occurred in 16.5% on YTMA128 and 14.8% on YTMA201 and was associated with estrogen receptor–negative status ( P = 0.01 on YTMA128 and 0.0001 on YTMA201). PD-L1 mRNA expression was associated with longer recurrence-free survival (log-rank P = 0.01), which remained significant in multivariate analysis including age, tumor size, histologic grade, nodal metastasis, hormone receptor, HER2 status, and the extent of TILs (HR, 0.268; CI, 0.099–0.721; P = 0.009). Conclusions: PD-L1 mRNA expression is identified in nearly 60% of breast tumors and it is associated with increased TILs and improved recurrence-free survival. These observations support the evaluation of PD-1/PD-L1–targeted therapies in breast cancer. Clin Cancer Res; 20(10); 2773–82. ©2014 AACR .
403 citations
TL;DR: The MethSurv tool is a valuable platform for the researchers without programming skills to perform the initial assessment of methylation-based cancer biomarkers.
Abstract: AIM To develop a web tool for survival analysis based on CpG methylation patterns. MATERIALS & METHODS We utilized methylome data from 'The Cancer Genome Atlas' and used the Cox proportional-hazards model to develop an interactive web interface for survival analysis. RESULTS MethSurv enables survival analysis for a CpG located in or around the proximity of a query gene. For further mining, cluster analysis for a query gene to associate methylation patterns with clinical characteristics and browsing of top biomarkers for each cancer type are provided. MethSurv includes 7358 methylomes from 25 different human cancers. CONCLUSION The MethSurv tool is a valuable platform for the researchers without programming skills to perform the initial assessment of methylation-based cancer biomarkers.
300 citations
TL;DR: It is proposed that reduced MPC activity is an important aspect of cancer metabolism, perhaps through altering the maintenance and fate of stem cells.
287 citations
Cites background from "SurvExpress: An Online Biomarker Va..."
...LowMPC1 expression correlates with poor survival in almost all cancers examined, including colon, kidney, lung, bladder, and brain (Figures 1B and S1B) (Aguirre-Gamboa et al., 2013)....
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TL;DR: It is shown that the convergence of dietary factors and dysregulated WNT signaling alters BA profiles to drive malignant transformations in Lgr5-expressing cancer stem cells and promote an adenoma-to-adenocarcinoma progression.
248 citations
TL;DR: Results indicate that stress-induced glucocorticoid surge and Tsc22d3 upregulation can subvert therapy-induced anticancer immunosurveillance.
Abstract: Psychological distress has long been suspected to influence cancer incidence and mortality. It remains largely unknown whether and how stress affects the efficacy of anticancer therapies. We observed that social defeat caused anxiety-like behaviors in mice and dampened therapeutic responses against carcinogen-induced neoplasias and transplantable tumors. Stress elevated plasma corticosterone and upregulated the expression of glucocorticoid-inducible factor Tsc22d3, which blocked type I interferon (IFN) responses in dendritic cell (DC) and IFN-γ+ T cell activation. Similarly, close correlations were discovered among plasma cortisol levels, TSC22D3 expression in circulating leukocytes and negative mood in patients with cancer. In murine models, exogenous glucocorticoid injection, or enforced expression of Tsc22d3 in DC was sufficient to abolish therapeutic control of tumors. Administration of a glucocorticoid receptor antagonist or DC-specific Tsc22d3 deletion reversed the negative impact of stress or glucocorticoid supplementation on therapeutic outcomes. Altogether, these results indicate that stress-induced glucocorticoid surge and Tsc22d3 upregulation can subvert therapy-induced anticancer immunosurveillance.
162 citations
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Daniel C. Koboldt1, Robert S. Fulton1, Michael D. McLellan1, Heather Schmidt1 +352 more•Institutions (35)
TL;DR: The ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity.
Abstract: We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.
9,355 citations
5,867 citations
TL;DR: The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor-positive breast cancer and could be used as a continuous function to predict distant recurrent in individual patients.
Abstract: background The likelihood of distant recurrence in patients with breast cancer who have no involved lymph nodes and estrogen-receptor–positive tumors is poorly defined by clinical and histopathological measures. methods We tested whether the results of a reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assay of 21 prospectively selected genes in paraffin-embedded tumor tissue would correlate with the likelihood of distant recurrence in patients with node-negative, tamoxifen-treated breast cancer who were enrolled in the National Surgical Adjuvant Breast and Bowel Project clinical trial B-14. The levels of expression of 16 cancerrelated genes and 5 reference genes were used in a prospectively defined algorithm to calculate a recurrence score and to determine a risk group (low, intermediate, or high) for each patient. results Adequate RT-PCR profiles were obtained in 668 of 675 tumor blocks. The proportions of patients categorized as having a low, intermediate, or high risk by the RT-PCR assay were 51, 22, and 27 percent, respectively. The Kaplan–Meier estimates of the rates of distant recurrence at 10 years in the low-risk, intermediate-risk, and high-risk groups were 6.8 percent (95 percent confidence interval, 4.0 to 9.6), 14.3 percent (95 percent confidence interval, 8.3 to 20.3), and 30.5 percent (95 percent confidence interval, 23.6 to 37.4). The rate in the low-risk group was significantly lower than that in the high-risk group (P<0.001). In a multivariate Cox model, the recurrence score provided significant predictive power that was independent of age and tumor size (P<0.001). The recurrence score was also predictive of overall survival (P<0.001) and could be used as a continuous function to predict distant recurrence in individual patients. conclusions The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor–positive breast cancer.
5,685 citations
"SurvExpress: An Online Biomarker Va..." refers methods in this paper
...As an example for testing one biomarker in several datasets, we used the 16 OncotypeDX genes [14]....
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...To estimate the score, OncotypeDX uses a weighting algorithm equivalent to a weight multiplied by corresponding gene expression normalized by a reference [14]....
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TL;DR: The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.
2,870 citations
Book•
01 Dec 1993
TL;DR: This paper discusses the design of clinical trials, use of computer software in survival analysis, and some non-parametric procedures for modelling survival data.
Abstract: Some non-parametric procedures. Modelling survival data. The Cox Regression Model. Design of clinical trials. Some other models for survival data. Model checking. Time dependent co-variates. Interval censored survival data. Multi-state survival models. Some additional topics. Use of computer software in survival analysis. Appendices: Example data sets. Maximum liklihood estimation score statistics and information. GLIM macros for survival analysis.
2,564 citations