scholarly journals Assessing reproducibility of matrix factorization methods in independent transcriptomes

2019 ◽  
Vol 35 (21) ◽  
pp. 4307-4313 ◽  
Author(s):  
Laura Cantini ◽  
Ulykbek Kairov ◽  
Aurélien de Reyniès ◽  
Emmanuel Barillot ◽  
François Radvanyi ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Meta Analysis ◽  
Supplementary Information ◽  
Cancer Type ◽  
Tumor Type ◽  
Clinical Biomarkers ◽  
Technological Artifacts ◽  
Stabilization Procedure ◽  
Mechanistic Basis ◽  
User Friendly

Abstract Motivation Matrix factorization (MF) methods are widely used in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). MF algorithms have never been compared based on the between-datasets reproducibility of their outputs in similar independent datasets. Lack of this knowledge might have a crucial impact when generalizing the predictions made in a study to others. Results We systematically test widely used MF methods on several transcriptomic datasets collected from the same cancer type (14 colorectal, 8 breast and 4 ovarian cancer transcriptomic datasets). Inspired by concepts of evolutionary bioinformatics, we design a novel framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the MF methods for their ability to produce generalizable components. We show that a particular protocol of application of independent component analysis (ICA), accompanied by a stabilization procedure, leads to a significant increase in the between-datasets reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other standard methods. We developed a user-friendly tool for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors associated to biological processes or to technological artifacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping. Availability and implementation The RBH construction tool is available from http://goo.gl/DzpwYp Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Stabilized Independent Component Analysis outperforms other methods in finding reproducible signals in tumoral transcriptomes

2018 ◽  
Author(s):  
Laura Cantini ◽  
Ulykbek Kairov ◽  
Aurélien de Reyniès ◽  
Emmanuel Barillot ◽  
François Radvanyi ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Matrix Factorization ◽  
Component Analysis ◽  
Independent Component ◽  
Supplementary Information ◽  
Cancer Type ◽  
Tumor Type ◽  
Clinical Biomarkers ◽  
Art Methods ◽  
State Of Art

AbstractMotivationMatrix factorization methods are widely exploited in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). Applying such methods to similar independent datasets should yield reproducible inter-series outputs, though it was never demonstrated yet.ResultsWe systematically test state-of-art methods of matrix factorization on several transcriptomic datasets of the same cancer type. Inspired by concepts of evolutionary bioinformatics, we design a new framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the method’s reproducibility. We show that a particular protocol of application of Independent Component Analysis (ICA), accompanied by a stabilisation procedure, leads to a significant increase in the inter-series output reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other state-of-art methods. We developed a user-friendly tool BIODICA for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent publicly available transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors that can be associated to biological processes or to technological artefacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping.AvailabilityThe BIODICA tool is available from https://github.com/LabBandSB/[email protected] and [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Mutational signature learning with supervised negative binomial non-negative matrix factorization

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i154-i160 ◽  
Author(s):  
Xinrui Lyu ◽  
Jean Garret ◽  
Gunnar Rätsch ◽  
Kjong-Van Lehmann
Keyword(s):  
Matrix Factorization ◽  
Negative Binomial ◽  
Extraction Methods ◽  
Supplementary Information ◽  
Cancer Type ◽  
Mutational Signatures ◽  
Signature Extraction ◽  
Mutational Signature ◽  
Mutational Processes ◽  
Non Negative Matrix Factorization

Abstract Motivation Understanding the underlying mutational processes of cancer patients has been a long-standing goal in the community and promises to provide new insights that could improve cancer diagnoses and treatments. Mutational signatures are summaries of the mutational processes, and improving the derivation of mutational signatures can yield new discoveries previously obscured by technical and biological confounders. Results from existing mutational signature extraction methods depend on the size of available patient cohort and solely focus on the analysis of mutation count data without considering the exploitation of metadata. Results Here we present a supervised method that utilizes cancer type as metadata to extract more distinctive signatures. More specifically, we use a negative binomial non-negative matrix factorization and add a support vector machine loss. We show that mutational signatures extracted by our proposed method have a lower reconstruction error and are designed to be more predictive of cancer type than those generated by unsupervised methods. This design reduces the need for elaborate post-processing strategies in order to recover most of the known signatures unlike the existing unsupervised signature extraction methods. Signatures extracted by a supervised model used in conjunction with cancer-type labels are also more robust, especially when using small and potentially cancer-type limited patient cohorts. Finally, we adapted our model such that molecular features can be utilized to derive an according mutational signature. We used APOBEC expression and MUTYH mutation status to demonstrate the possibilities that arise from this ability. We conclude that our method, which exploits available metadata, improves the quality of mutational signatures as well as helps derive more interpretable representations. Availability and implementation https://github.com/ratschlab/SNBNMF-mutsig-public. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Meta-Analysis of the Correlation between Apparent Diffusion Coefficient and Standardized Uptake Value in Malignant Disease

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Shengming Deng ◽  
Zhifang Wu ◽  
Yiwei Wu ◽  
Wei Zhang ◽  
Jihui Li ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Meta Analysis ◽  
Standardized Uptake Value ◽  
Standard Uptake Value ◽  
Cancer Type ◽  
Cervix Carcinoma ◽  
Tumor Type ◽  
Patients With Cancer ◽  
Apparent Diffusion

The objective of this meta-analysis is to explore the correlation between the apparent diffusion coefficient (ADC) on diffusion-weighted MR and the standard uptake value (SUV) of 18F-FDG on PET/CT in patients with cancer. Databases such as PubMed (MEDLINE included), EMBASE, and Cochrane Database of Systematic Review were searched for relevant original articles that explored the correlation between SUV and ADC in English. After applying Fisher’s r-to-z transformation, correlation coefficient (r) values were extracted from each study and 95% confidence intervals (CIs) were calculated. Sensitivity and subgroup analyses based on tumor type were performed to investigate the potential heterogeneity. Forty-nine studies were eligible for the meta-analysis, comprising 1927 patients. Pooled r for all studies was −0.35 (95% CI: −0.42–0.28) and exhibited a notable heterogeneity (I2 = 78.4%; P < 0.01). In terms of the cancer type subgroup analysis, combined correlation coefficients of ADC/SUV range from −0.12 (lymphoma, n = 5) to −0.59 (pancreatic cancer, n = 2). We concluded that there is an average negative correlation between ADC and SUV in patients with cancer. Higher correlations were found in the brain tumor, cervix carcinoma, and pancreas cancer. However, a larger, prospective study is warranted to validate these findings in different cancer types.

deMeta: Removing sub-studies from meta-analysis of genome wide association studies (GWAS)

2020 ◽  
Author(s):  
Jiangming Sun ◽  
Yunpeng Wang
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Computationally Efficient ◽  
Genome Wide ◽  
Quantile Plots ◽  
User Friendly ◽  
The Impact

ABSTRACTSummaryPost-GWAS studies using the results from large consortium meta-analysis often need to correctly take care of the overlapping sample issue. The gold standard approach for resolving this issue is to reperform the GWAS or meta-analysis excluding the overlapped participants. However, such approach is time-consuming and, sometimes, restricted by the available data. deMeta provides a user friendly and computationally efficient command-line implementation for removing the effect of a contributing sub-study to a consortium from the meta-analysis results. Only the summary statistics of the meta-analysis the sub-study to be removed are required. In addition, deMeta can generate contrasting Manhattan and quantile-quantile plots for users to visualize the impact of the sub-study on the meta-analysis results.Availability and ImplementationThe python source code, examples and documentations of deMeta are publicly available at https://github.com/Computational-NeuroGenetics/[email protected] (J. Sun); [email protected] (Y. Wang)Supplementary informationNone.

scholarly journals Prognostic and clinicopathological significance of GPRC5A in various cancers: A systematic review and meta-analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249040
Author(s):  
Lu Dai ◽  
Xiao Jin ◽  
Zheng Liu
Keyword(s):  
Subgroup Analysis ◽  
Meta Analysis ◽  
Disease Free Survival ◽  
Cancer Prognosis ◽  
Significant Heterogeneity ◽  
Cancer Type ◽  
Advanced Tumor Stage ◽  
Tumor Type ◽  
Free Survival ◽  
Advanced Tumor

Background GPRC5A is associated with various cancer initiation and progression. Controversial findings have been reported about GPRC5A prognostic characteristics, and no meta-analysis has been conducted to assess the relationship between GPRC5A and cancer prognosis. Therefore, the objective of this meta-analysis is to evaluate the overall prognostic effectiveness of GPRC5A. Methods We first conducted a systematic search in the PubMed, Embase, Web of Science, CNKI, Cochrane, and WangFang databases. The hazard ratio (HR) and odds ratios (OR) with 95% CI were then pooled to assess the associations between GPRC5A expression and overall survival (OS), disease-free survival (DFS), event-free survival (EFS), and clinicopathological characteristics. Chi-squared test and I2 statistics were completed to evaluate the heterogeneity in our study. A random‐effects model was used when significant heterogeneity existed (I2>50% and p<0.05); otherwise, we chose the fixed-effect model. Subgroup analysis was stratified by tumor type, region, HR obtained measurements, and sample capacity to explore the source of heterogeneity. Results In total, 15 studies with 624 patients met inclusion criteria of this study. Our results showed that higher expression of GPRC5A is associated with worse OS (HR:1.69 95%CI: 1.20–2.38 I2 = 75.6% p = 0.000), as well as worse EFS (HR:1.45 95%CI: 1.02–1.95 I2 = 0.0% p = 0.354). Subgroup analysis indicated that tumor type might be the source of high heterogeneity. Additionally, cancer patients with enhanced GPRC5A expression were more likely to lymph node metastasis (OR:1.95, 95%CI 1.33–2.86, I2 = 43.9%, p = 0.129) and advanced tumor stage (OR: 1.83, 95%CI 1.15–2.92, I2 = 61.3%, p = 0.035), but not associated with age, sex, differentiation, and distant metastasis. Conclusion GPRC5A can be a promising candidate for predicting medical outcomes and used for accurate diagnosis, prognosis prediction for patients with cancer; however, the predictive value of GPRC5A varies significantly according to cancer type. Further studies for this mechanism will be necessary to reveal novel insights into application of GPRC5A in cancers.

Evaluating clinical activity of MAPK targeted therapies (TT) in cancer patients (pts) with non-V600 BRAF mutations: A systematic scoping review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3089-3089
Author(s):  
Matthew Dankner ◽  
Yifan Wang ◽  
Rouhi Fazelzad ◽  
Anna Spreafico ◽  
David W. Cescon ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Scoping Review ◽  
Retrospective Studies ◽  
Meta Analysis ◽  
Clinical Activity ◽  
Cancer Type ◽  
Tumor Type ◽  
Inclusion Criteria ◽  
Braf Mutations ◽  
Tumor Boards

3089 Background: Oncogenic nonV600 BRAF mutations (muts) can be classified according to distinct molecular characteristics. TT strategies for class 2 and 3 BRAF muts have not been established. In recent years there have been numerous reports of clinical activity for various TT in pts with nonV600 muts. We performed a systematic scoping review and meta-analysis to assess treatment outcomes with MAPK TT according to BRAF class, cancer type and TT type. Methods: An extensive literature search was conducted from 2010-20. All studies were independently reviewed and extracted by 2 reviewers and in accordance with PRISMA guidelines. Individual patient level data were collected and analyzed from studies that met the following inclusion criteria: published reports of 1) advanced cancer pts with; 2) class 2 or class 3 nonV600 BRAF muts; 3) who received MAPK TT; 4) with treatment response (TR) data available. Primary outcome was overall TR rate (TRR). To assess differences between groups, odds ratios (OR) were calculated using a multi-level mixed-effects logistic regression model. Results: 15,171 studies were screened and 168 were included for data extraction. We identified 100 studies with a total of 396 pts that met inclusion criteria. There were 17 reports (161 pts) of prospective clinical trials and 83 retrospective studies (235 pts). RECIST criteria were used for TR assessment in 183 (46%) pts. The entire study included 280 pts with class 2 and 116 pts with class 3 BRAF muts. Overall, 111 (28%) pts achieved a TR. TRR according to primary tumor type, BRAF class, and TT type is indicated in Table. TRR was lower in reports of prospective studies compared to retrospective studies (OR 0.14, P = 0.002), and in studies that employed RECIST criteria vs. those that didn’t (OR 0.29, P = 0.044). TRR was higher among pts with class 2 muts vs. those with class 3 muts (OR 2.21, P = 0.042). Conclusions: These data establish that MAPK TT have demonstrated clinical activity in cancers with oncogenic nonV600 mutations, and that BRAF mutation class may dictate responsiveness to different TT strategies. TRR may be over-estimated in the retrospective literature. This analysis will be valuable for molecular tumor boards and to guide future clinical trial design. Prospective clinical trials of TT in this pt population are warranted.[Table: see text]

Risk of Hypertension Associated with Antivascular Endothelial Growth Factor Monoclonal Antibodies: A Meta-Analysis From 51088 Patients with Cancer

2020 ◽  
Vol 22 (7) ◽  
Author(s):  
Weilan Wang ◽  
Le Cai ◽  
Bingkun Xiao ◽  
Rongqing Huang
Keyword(s):  
Monoclonal Antibodies ◽  
Growth Factor ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Cancer Type ◽  
Tumor Type ◽  
High Grade ◽  
Anti Vegf ◽  
Antivascular Endothelial Growth Factor ◽  
Endothelial Growth Factor

Context: Hypertension events are the dominant adverse events observed in patients receiving the antivascular endothelial growth factor (anti-VEGF) monoclonal antibodies bevacizumab and ramucirumab treatment, which severe hypertension, particularly hypertensive emergencies, may cause acute target organ injury and major cardiovascular events, that has limited the administration of anti-VEGF monoclonal antibodies. The current meta-analysis aimed to examine the relative risk (RR) of hypertension associated with anti-VEGF monoclonal antibodies. Evidence Acquisition: PubMed, EMBASE, ASCO Abstracts, ESMO Abstracts, Cochrane Library, and Clinical Trials.gov were searched until July 2019 for relevant phase II and III randomized controlled trials (RCTs). Statistical analyses were performed to examine the RR (with 95% confidence intervals (CIs)) of hypertension associated with the anti-VEGF monoclonal antibodies. Results: Ninety four RCTs and 51088 patients were included in the current meta-analysis. According to the results, compared with the control arms, anti-VEGF monoclonal antibodies increased the risk of all-grade (RR: 3.45, 95% CI: 2.98 - 4.00) and high-grade (RR: 5.63, 95% CI: 5.05 - 6.26) hypertension. In the subgroup analyses, the risk of high-grade hypertension varied significantly with cancer type, so that the highest RR was for patients with ovarian cancer (17.27, 95% CI: 8.50 - 35.08), whereas the risk of all-grade hypertension did not vary significantly. When stratified based on drug types and drug dose, no significant difference was discovered. Conclusions: Anti-VEGF monoclonal antibodies significantly increased the risk of hypertension. The risk may vary with tumor type. Clinicians should be aware of the adverse reaction and clinical monitoring as well as effective management of such situations, particularly for high-risk patients.

scholarly journals PARP1 rs1136410 Val762Ala contributes to an increased risk of overall cancer in the East Asian population: a meta-analysis

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199295
Author(s):  
Yijuan Xin ◽  
Liu Yang ◽  
Mingquan Su ◽  
Xiaoli Cheng ◽  
Lin Zhu ◽  
...  
Keyword(s):  
Cancer Risk ◽  
Odds Ratio ◽  
Chinese Population ◽  
Meta Analysis ◽  
Asian Population ◽  
Cancer Type ◽  
East Asians ◽  
Asian Populations ◽  
Increased Risk ◽  
Meta Analyses

Objectives To investigate the association between poly(ADP-ribose) polymerase 1 ( PARP1) rs1136410 Val762Ala and cancer risk in Asian populations, as published findings remain controversial. Methods The PubMed and EMBASE databases were searched, and references of identified studies and reviews were screened, to find relevant studies. Meta-analyses were performed to evaluate the association between PARP1 rs1136410 Val762Ala and cancer risk, reported as odds ratio (OR) and 95% confidence interval (CI). Results A total of 24 studies with 8 926 cases and 15 295 controls were included. Overall, a significant association was found between PARP1 rs1136410 Val762Ala and cancer risk in East Asians (homozygous: OR 1.19, 95% CI 1.06, 1.35; heterozygous: OR 1.10, 95% CI 1.04, 1.17; recessive: OR 1.13, 95% CI 1.02, 1.25; dominant: OR 1.13, 95% CI 1.06, 1.19; and allele comparison: OR 1.09, 95% CI 1.03, 1.15). Stratification analyses by race and cancer type revealed similar results for gastric cancer among the Chinese population. Conclusion The findings suggest that PARP1 rs1136410 Val762Ala may be significantly associated with an increased cancer risk in Asians, particularly the Chinese population.

scholarly journals The association between XRCC3 rs1799794 polymorphism and cancer risk: a meta-analysis of 34 case–control studies

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Weiqing Liu ◽  
Shumin Ma ◽  
Lei Liang ◽  
Zhiyong Kou ◽  
Hongbin Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Thyroid Cancer ◽  
Cancer Risk ◽  
Large Scale ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Cancer Type ◽  
Increased Risk ◽  
Risk Of Cancer

Abstract Background Studies on the XRCC3 rs1799794 polymorphism show that this polymorphism is involved in a variety of cancers, but its specific relationships or effects are not consistent. The purpose of this meta-analysis was to investigate the association between rs1799794 polymorphism and susceptibility to cancer. Methods PubMed, Embase, the Cochrane Library, Web of Science, and Scopus were searched for eligible studies through June 11, 2019. All analyses were performed with Stata 14.0. Subgroup analyses were performed by cancer type, ethnicity, source of control, and detection method. A total of 37 studies with 23,537 cases and 30,649 controls were included in this meta-analysis. Results XRCC3 rs1799794 increased cancer risk in the dominant model and heterozygous model (GG + AG vs. AA: odds ratio [OR] = 1.04, 95% confidence interval [CI] = 1.00–1.08, P = 0.051; AG vs. AA: OR = 1.05, 95% CI = 1.00–1.01, P = 0.015). The existence of rs1799794 increased the risk of breast cancer and thyroid cancer, but reduced the risk of ovarian cancer. In addition, rs1799794 increased the risk of cancer in the Caucasian population. Conclusion This meta-analysis confirms that XRCC3 rs1799794 is related to cancer risk, especially increased risk for breast cancer and thyroid cancer and reduced risk for ovarian cancer. However, well-designed large-scale studies are required to further evaluate the results.

scholarly journals The association between immune-related adverse events and the prognosis of solid cancer patients treated with immunotherapy: a systematic review and meta-analysis

2020 ◽  
Vol 12 ◽  
pp. 175883592098054
Author(s):  
Huilin Xu ◽  
Ximing Xu ◽  
Wei Ge ◽  
Jinju Lei ◽  
Dedong Cao
Keyword(s):  
Adverse Events ◽  
Cancer Patients ◽  
Meta Analysis ◽  
Progression Free Survival ◽  
Cochrane Library ◽  
Good Survival ◽  
Solid Cancer ◽  
Cancer Type ◽  
Early Effect ◽  
Overall Response

Background: Immune-related adverse events (irAEs) are common during immune checkpoint inhibitor (ICI) treatment and reported to be associated with good survival. This study evaluated the association between onset timing of irAEs and survival of cancer patients treated with ICIs. Methods: Databases including PubMed, Embase, and the Cochrane library were systematically searched to retrieve clinical studies assessing the relationship between irAEs and survival in cancer patients with ICIs. The overall response rate for treatment response and hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS) were calculated using RevMan 5.3. Subgroup analysis in terms of cancer type, ICIs type, region, specific irAEs, accordingly. Results: A total of 34 studies were included. The HRs for OS and PFS in cancer patients with versus without irAEs were 0.57 [95% confidence interval (CI): 0.44, 0.74; p < 0.0001], and 0.50 (95% CI: 0.37, 0.67; p < 0.00001), respectively. The odds ratio for overall response in cancer patients with irAEs was 4.72 (95% CI: 3.48, 6.40; p < 0.00001) compared with those without irAEs. Subgroup analyses suggested that the prognostic role of irAEs was associated with cancer types and region, but not irAEs types. The landmark analysis of OS revealed that there is a non-proportional (early) effect of irAEs on OS in ICI-treated cancer patients (landmark >12 weeks, HROS = 1.08; 95% CI: 0.89, 1.30; p = 0.46). Conclusion: Our findings suggest that the occurrence of irAEs could be a prognostic factor for cancer patients who were treated with ICIs.

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