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- Dareng, EO, et al.
(författare)
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Polygenic risk modeling for prediction of epithelial ovarian cancer risk
- 2022
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Ingår i: European journal of human genetics : EJHG. - : Springer Science and Business Media LLC. - 1476-5438 .- 1018-4813. ; 30:3, s. 349-362
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Tidskriftsartikel (refereegranskat)abstract
- Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.
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- Polewko-Klim, A, et al.
(författare)
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Identification of Candidate Therapeutic Genes for More Precise Treatment of Esophageal Squamous Cell Carcinoma and Adenocarcinoma
- 2022
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Ingår i: Frontiers in genetics. - : Frontiers Media SA. - 1664-8021. ; 13, s. 844542-
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Tidskriftsartikel (refereegranskat)abstract
- The standard therapy administered to patients with advanced esophageal cancer remains uniform, despite its two main histological subtypes, namely esophageal squamous cell carcinoma (SCC) and esophageal adenocarcinoma (AC), are being increasingly considered to be different. The identification of potential drug target genes between SCC and AC is crucial for more effective treatment of these diseases, given the high toxicity of chemotherapy and resistance to administered medications. Herein we attempted to identify and rank differentially expressed genes (DEGs) in SCC vs. AC using ensemble feature selection methods. RNA-seq data from The Cancer Genome Atlas and the Fudan-Taizhou Institute of Health Sciences (China). Six feature filters algorithms were used to identify DEGs. We built robust predictive models for histological subtypes with the random forest (RF) classification algorithm. Pathway analysis also be performed to investigate the functional role of genes. 294 informative DEGs (87 of them are newly discovered) have been identified. The areas under receiver operator curve (AUC) were higher than 99.5% for all feature selection (FS) methods. Nine genes (i.e., ERBB3, ATP7B, ABCC3, GALNT14, CLDN18, GUCY2C, FGFR4, KCNQ5, and CACNA1B) may play a key role in the development of more directed anticancer therapy for SCC and AC patients. The first four of them are drug targets for chemotherapy and immunotherapy of esophageal cancer and involved in pharmacokinetics and pharmacodynamics pathways. Research identified novel DEGs in SCC and AC, and detected four potential drug targeted genes (ERBB3, ATP7B, ABCC3, and GALNT14) and five drug-related genes.
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- Zhang, JR, et al.
(författare)
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Systematic bias between blinded independent central review and local assessment: literature review and analyses of 76 phase III randomised controlled trials in 45 688 patients with advanced solid tumour
- 2018
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 8:9, s. e017240-
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Tidskriftsartikel (refereegranskat)abstract
- Unbiased assessment of tumour response is crucial in randomised controlled trials (RCTs). Blinded independent central review is usually used as a supplemental or monitor to local assessment but is costly. The aim of this study is to investigate whether systematic bias existed in RCTs by comparing the treatment effects of efficacy endpoints between central and local assessments.DesignLiterature review, pooling analysis and correlation analysis.Data sourcesPubMed, from 1 January 2010 to 30 June 2017.Eligibility criteria for selecting studiesEligible articles are phase III RCTs comparing anticancer agents for advanced solid tumours. Additionally, the articles should report objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS) or time to progression (TTP); the treatment effect of these endpoints, OR or HR, should be based on central and local assessments.ResultsOf 76 included trials involving 45 688 patients, 17 (22%) trials reported their endpoints with statistically inconsistent inferences (p value lower/higher than the probability of type I error) between central and local assessments; among them, 9 (53%) trials had statistically significant inference based on central assessment. Pooling analysis presented no systematic bias when comparing treatment effects of both assessments (ORR: OR=1.02 (95% CI 0.97 to 1.07), p=0.42, I2=0%; DCR: OR=0.97 (95% CI 0.92 to 1.03), p=0.32, I2=0%); PFS: HR=1.01 (95% CI 0.99 to 1.02), p=0.32, I2=0%; TTP: HR=1.04 (95% CI 0.95 to 1.14), p=0.37, I2=0%), regardless of funding source, mask, region, tumour type, study design, number of enrolled patients, response assessment criteria, primary endpoint and trials with statistically consistent/inconsistent inferences. Correlation analysis also presented no sign of systematic bias between central and local assessments (ORR, DCR, PFS: r>0.90, p<0.01; TTP: r=0.90, p=0.29).ConclusionsNo systematic bias could be found between local and central assessments in phase III RCTs on solid tumours. However, statistically inconsistent inferences could be made in many trials between both assessments.
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