Epistasis Test in Meta-Analysis: A Multi-Parameter Markov Chain Monte Carlo Model for Consistency of Evidence

Comparative effects of progestin-based combination therapy for endometrial cancer or atypical endometrial hyperplasia: a systematic review and network meta-analysis

Objectives

The objective of this network meta-analysis is to systematically compare the efficacy of diverse progestin-based combination regimens in treating patients diagnosed with endometrial cancer or atypical endometrial hyperplasia. The primary goal is to discern the optimal combination treatment regimen through a comprehensive examination of their respective effectiveness.

Methods

We systematically searched four prominent databases: PubMed, Web of Science, Embase, and Cochrane Central Register of Controlled Trials, for randomized controlled trials addressing the efficacy of progestins or progestin combinations in the treatment of patients with endometrial cancer or atypical endometrial hyperplasia. The search spanned from the inception of these databases to December 2023. Key outcome indicators encompassed survival indices, criteria for assessing efficacy, as well as pregnancy and relapse rate. This study was registered in PROSPERO (CRD42024496311).

Results

From the 1,558 articles initially retrieved, we included 27 studies involving a total of 5,323 subjects in our analysis. The results of the network meta-analysis revealed that the mTOR inhibitor+megestrol acetate (MA)+tamoxifen regimen secured the top rank in maintaining stable disease (SD) (SUCRA=73.4%) and extending progression-free survival (PFS) (SUCRA=72.4%). Additionally, the progestin combined with tamoxifen regimen claimed the leading position in enhancing the partial response (PR) (SUCRA=75.2%) and prolonging overall survival (OS) (SUCRA=80%). The LNG-IUS-based dual progestin regimen emerged as the frontrunner in improving the complete response (CR) (SUCRA=98.7%), objective response rate (ORR) (SUCRA=99.1%), pregnancy rate (SUCRA=83.7%), and mitigating progression (SUCRA=8.0%) and relapse rate (SUCRA=47.4%). In terms of safety, The LNG-IUS-based dual progestin regimen had the lowest likelihood of adverse events (SUCRA=4.2%), while the mTOR inhibitor regimen (SUCRA=89.2%) and mTOR inbitor+MA+tamoxifen regimen (SUCRA=88.4%) had the highest likelihood of adverse events.

Conclusions

Patients diagnosed with endometrial cancer or atypical endometrial hyperplasia exhibited the most favorable prognosis when undergoing progestin combination therapy that included tamoxifen, mTOR inhibitor, or LNG-IUS. Notably, among these options, the LNG-IUS-based dual progestin regimen emerged as particularly promising for potential application.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42024496311.

Genetic interactions effects for cancer disease identification using computational models: a review

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.

Protocol for Epistasis Detection with Machine Learning Using GenEpi Package

To develop medical treatments and prevention, the association between disease and genetic variants needs to be identified. The main goal of genome-wide association study (GWAS) is to discover the underlying reason for vulnerability to disease and utilize this knowledge for the development of prevention and treatment against these diseases. Given the methods available to address the scientific problems involved in the search for epistasis, there is not any standard for detecting epistasis, and this remains a problem due to limited statistical power. The GenEpi package is a Python package that uses a two-level workflow machine learning model to detect within-gene and cross-gene epistasis. This protocol chapter shows the usage of GenEpi with example data. The package uses a three-step procedure to reduce dimensionality, select the within-gene epistasis, and select the cross-gene epistasis. The package also provides a medium to build prediction models with the combination of genetic features and environmental influences.

Performance of epistasis detection methods in semi-simulated GWAS

BACKGROUND

Part of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.

RESULTS

GBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU.

CONCLUSION

This study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.

Association between angiotensin II receptor type 1 A1166C polymorphism and chronic kidney disease

Studies of the association between angiotensin II receptor type 1 A1166C (AGTR1 A1166C) polymorphism and chronic kidney disease (CKD) risk have yielded conflicting results. We conducted a combined case-control study and meta-analysis to better define this association. The case-control study included 634 end-stage renal disease (ESRD) patients and 739 healthy controls. AGTR1 A1166C genotype was determined using polymerase chain reaction and iPLEX Gold SNP genotyping methods. The meta-analysis included 24 studies found in the PubMed and Cochrane Library databases. Together, the case-control study and meta-analysis included 36 populations (7,918 cases and 6,905 controls). We found no association between the C allele and ESRD (case-control study: OR: 1.02, 95% CI: 0.77–1.37; meta-analysis: OR: 1.07; 95% CI: 0.97–1.18). Co-dominant, dominant, and recessive model results were also not significant. No known environmental factors moderated the effect of AGTR1 A1166C on CKD in our gene-environment interaction analysis. Sensitivity analysis showed an AGTR1 A1166C-CKD association in Indian populations (OR: 1.46, 95% CI: 1.26–1.69), but not in East Asian or Caucasian populations. Additional South Asian studies will be required to confirm the potential role of this polymorphism in CKD.

Angiotensin II receptor type 1 A1166C modifies the association between angiotensinogen M235T and chronic kidney disease

Single nucleotide polymorphisms (SNPs) in renin-angiotensin system (RAS) genes are associated with RAS imbalance and chronic kidney disease (CKD). We performed a case-control study and meta-analysis to investigate the association between angiotensinogen (AGT) M235T polymorphism and CKD. A total of 634 patients with end-stage renal disease and 739 healthy controls were studied. We also searched PubMed and the Cochrane Library to identify prospective observational studies published before December 2015. We found that the TT and MT genotypes were associated with a higher risk of CKD than the MM genotype (odds ratio [OR]: 3.56; 95% confidence interval [CI]: 1.14-11.16 and OR: 2.93; 95% CI: 0.91-9.46, respectively). Thirty-eight study populations were included in the meta-analysis. The T allele was associated with a higher risk of CKD than the M allele in all populations (OR: 1.19; 95% CI: 1.08-1.32). The OR was 1.33 in Asians (95% CI: 1.06-1.67) and 1.10 in Caucasians (95% CI: 1.02-1.18). Evaluation of gene-gene and gene-environment interactions using epistasis analysis revealed an interaction between AGT M235T and angiotensin II receptor type 1 A1166C in CKD (OR: 0.767; 95% CI: 0.609-0.965). Genetic testing for CKD in high-risk individuals may be an effective strategy for CKD prevention.

Angiotensin-Converting Enzyme Insertion/Deletion Polymorphism and Susceptibility to Osteoarthritis of the Knee: A Case-Control Study and Meta-Analysis

Background

Studies of angiotensin-converting enzyme insertion/deletion (ACE I/D) polymorphisms and the risks of knee osteoarthritis (OA) have yielded conflicting results.

Objective

To determine the association between ACE I/D and knee OA, we conducted a combined case-control study and meta-analysis.

Methods

For the case-control study, 447 knee OA cases and 423 healthy controls were recruited between March 2010 and July 2011. Knee OA cases were defined using the Kellgren-Lawrence grading system, and the ACE I/D genotype was determined using a standard polymerase chain reaction. The association between ACE I/D and knee OA was detected using allele, genotype, dominant, and recessive models. For the meta-analysis, PubMed and Embase databases were systematically searched for prospective observational studies published up until August 2015. Studies of ACE I/D and knee OA with sufficient data were selected. Pooled results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CI) for the D versus I allele with regard to knee OA risk.

Results

We found no significant association between the D allele and knee OA [OR: 1.09 (95% CI: 0.76–1.89)] in the present case-control study, and the results of other genetic models were also nonsignificant. Five current studies were included, and there were a total of six study populations after including our case-control study (1165 cases and 1029 controls). In the meta-analysis, the allele model also yielded nonsignificant results [OR: 1.37 (95% CI: 0.95–1.99)] and a high heterogeneity (I²: 87.2%).

Conclusions

The association between ACE I/D and knee OA tended to yield negative results. High heterogeneity suggests a complex, multifactorial mechanism, and an epistasis analysis of ACE I/D and knee OA should therefore be conducted.