Two methods for comparing genomic data across independent studies in cancer research: Meta-analysis and oncomine concepts map

Author(s):  
Wendy Lockwood Banka ◽  
Matthew J. Anstett ◽  
Daniel R. Rhodes
2021 ◽  
Author(s):  
Timo Sander ◽  
Joly Ghanawi ◽  
Emma Wilson ◽  
Sajjad Muhammad ◽  
Malcolm Robert Macleod ◽  
...  

Background: Heterogeneity of results of exact same research experiments oppose a significant socio-economic burden. In vitro research presents the early step of basic science and drug development projects. Insufficient methodological reporting is likely to be one of the contributors to results heterogeneity, however, little knowledge on reporting habits of in vitro cancer research and their effects on results reproducibility is available. Glioblastoma is a form of brain cancer with largely unmet clinical need. Methods: Here we use systematic review to describe reporting practices in in vitro glioblastoma research using the U87-MG cell line and perform multilevel random-effects meta-analysis followed by meta-regression to explore sources of heterogeneity within that literature, and any associations between reporting characteristics and reported findings. Results: In 137 identified articles, the overall methodological reporting is disappointing, e.g., the control type, mediums glucose level and cell density are reported in only 36.5, 21.2 and 16.8 percent of the articles, respectively. After adjustments for different drug concentrations and treatment durations, a three-level meta-analysis proves meaningful results heterogeneity across the studies (I2 = 70.1%). Conclusions: Our results further support the ongoing efforts of establishing consensus reporting practices to elevate durability of results. By doing so, we hope that this work will raise awareness of how stricter reporting may help to improve the frequency of successful translation of preclinical results into human application, not only in neuro-oncology. Funding: We received no specific funding for this project.


2009 ◽  
Author(s):  
Carolyn V. Ustach ◽  
Haiyong Han ◽  
Seungchan Kim ◽  
Galen Hostetter ◽  
Caroline H. Diep ◽  
...  

2016 ◽  
Vol 10 (3) ◽  
pp. 815-822 ◽  
Author(s):  
Shahjahan Khan ◽  
Suhail A R Doi ◽  
M Ashraf Memon

2020 ◽  
Vol 31 (1) ◽  
pp. 23-32
Author(s):  
M. A. Rueda Calderón ◽  
M. Balzarini ◽  
C. Bruno

Genomic selection (GS) is used to predict the merit of a genotype with respect to a quantitative trait from molecular or genomic data. Statistically, GS requires fitting a regression model with multiple predictors associated with the molecular markers (MM) states. The model is calibrated in a population with phenotypic and genomic data. The abundance and correlation of MM information make model estimation challenging. For that reason there are diverse strategies to adjust the model: based on best linear unbiased predictors (BLUP), Bayesian regressions and machine learning methods. The correlation between the observed phenotype and the predicted genetic merit by the fitted model provides a measure of the efficiency (predictive ability) of the GS. The objective of this work was to perform a metaanalysis on the efficiency of GS in cereals. A systematic review of related GS studies and a meta-analysis, in wheat and maize, was carried out to obtain a global measure of GS efficiency under different scenarios (MM quantity and statistical models used in GS). The meta-analysis indicated an average correlation coefficient of 0.61 between observed and predicted genetic merits. There were no significant differences in the efficiency of the GS based on BLUP (RR-BLUP and GBLUP), the most common statistical approach. The increase of MM data, make GS efficiency do not vary widely. Key words: Systematic review; Random effects model; Forest plot; Predictive accuracy.


Oncotarget ◽  
2016 ◽  
Vol 7 (50) ◽  
pp. 82741-82756 ◽  
Author(s):  
Nicoletta Staropoli ◽  
Domenico Ciliberto ◽  
Silvia Chiellino ◽  
Francesca Caglioti ◽  
Teresa Del Giudice ◽  
...  

Nutrients ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 607 ◽  
Author(s):  
Federica Turati ◽  
Michela Dalmartello ◽  
Francesca Bravi ◽  
Diego Serraino ◽  
Livia Augustin ◽  
...  

The World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) introduced in 2007, and updated in 2018, nutrition-related recommendations for cancer prevention. Previous studies generally reported inverse associations of breast cancer with the 2007 recommendations, while no study has yet evaluated the association with the 2018 guidelines. We investigated the association between adherence to the 2018 WCRF/AICR recommendations and breast cancer risk in a case–control study from Italy and Switzerland (1991–2008) including 3034 incident histologically-confirmed breast cancer cases and 3392 hospital controls. Adherence to the 2018 guidelines was summarized through a score incorporating eight recommendations (body fatness, physical activity, consumption of wholegrains/vegetables/fruit/beans, “fast foods” and other processed foods high in fat, starches, or sugars, red/processed meat, sugar-sweetened drinks, alcohol, breastfeeding), with higher scores indicating higher adherence. Odds ratios (OR) were estimated using multiple logistic regression models. We also conducted a meta-analysis including 15 additional studies using random-effects models. In our case–control study, adherence to the 2018 WCRF/AICR guidelines was inversely associated with breast cancer, with ORs of 0.60 (95% confidence interval (CI), 0.51–0.70) for a score ≥5.5 vs. ≤4.25, and of 0.83 (95% CI, 0.79–0.88) for a 1-point increment. In our study, 25% of breast cancers were attributable to low-to-moderate guideline adherence. In the meta-analysis, the pooled relative risks (RRs) were 0.73 (95% CI, 0.65–0.82, p heterogeneity among studies < 0.001) for the highest vs. the lowest WCRF/AICR score category, and 0.91 (95% CI, 0.88–0.94, p heterogeneity < 0.001) for a 1-point increment. This work provides quantitative evidence that higher adherence to the WCRF/AICR recommendations reduces the risk of breast cancer, thus opening perspectives for prevention.


2020 ◽  
pp. 109980042096989
Author(s):  
Asha Mathew ◽  
Ardith Z. Doorenbos ◽  
Hongjin Li ◽  
Min Kyeong Jang ◽  
Chang Gi Park ◽  
...  

Background: Individuals with cancer experience stress throughout the cancer trajectory. Allostatic load (AL), a cumulative multi-system measure, may have a greater value in stress assessment and the associated biological burden than individual biomarkers. A better understanding of the use of AL and its operationalization in cancer could aid in early detection and prevention or alleviation of AL in this population. Purpose: To consolidate findings on the operationalization, antecedents, and outcomes of AL in cancer. Methods: Seven databases (CINAHL, Ovid MEDLINE, Web of Science, APA PsycInfo, Scopus, Embase, and Cochrane CENTRAL) were searched for articles published through April 2020. The NIH tools were used to assess study quality. Results: Twelve studies met inclusion criteria for this review. Although variability existed in the estimation of AL, biomarkers of cardiovascular, metabolic, and immune systems were mostly used. Associations of AL with cancer-specific variables were examined mostly utilizing population-databases. Significant associations of AL with variables such as cancer-related stress, positive cancer history, post traumatic growth, resilience, tumor pathology, and cancer-specific mortality were found. Mini meta-analysis found that a one-unit increase in AL was associated with a 9% increased risk of cancer-specific mortality. Conclusion: This review reveals heterogeneity in operationalization of AL in cancer research and lack of clarity regarding causal direction between AL and cancer. Nevertheless, AL holds a significant promise in cancer research and practice. AL could be included as a screening tool for high-risk individuals or a health outcome in cancer. Optimal standardized approaches to measure AL would improve its clinical utility.


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