Purpose NCI’s Common Terminology Criteria for Adverse Events (CTCAE) is the universal framework for toxicity reporting in oncology trials. We sought to develop a CTCAE-compatible modified barium swallow (MBS) grade for the purpose of grading pharyngeal dysphagia as a toxicity endpoint in cooperative group organ preservation trials for head and neck cancer (HNC). We hypothesized that a 5-point CTCAE-compatible MBS grade (“DIGEST”) based on the interaction of pharyngeal residue and laryngeal penetration/aspiration ratings is feasible and psychometrically sound. Methods A modified Delphi exercise was conducted for content validation, expert consensus, and operationalization of DIGEST criteria. Two blinded raters scored 100 MBS conducted before or after surgical or non-surgical organ preservation. Intra- and inter-rater reliability were tested by weighted Kappa. Criterion validity against OPSE, MBSImP™©, MDADI, and PSS-HN was assessed with one-way ANOVA and post hoc pairwise comparisons between DIGEST grades. Results Intra-rater reliability was excellent (weighted Kappa=0.82–0.84) with substantial to almost perfect agreement between raters (weighted Kappa=0.67–0.81). DIGEST significantly discriminated levels of pharyngeal pathophysiology (MBSImP™©: r=0.77, p<0.0001), swallow efficiency (OPSE: r=−0.56, p<0.0001), perceived dysphagia (MDADI: r=−0.41, p<0.0001), and oral intake (PSS-HN diet: r=−0.49, p<0.0001). Conclusions With the development of DIGEST, we have adapted MBS rating to the CTCAE nomenclature of ordinal toxicity grading used in oncology trials. DIGEST offers a psychometrically sound measure for HNC clinical trials and investigations of toxicity profiles, dose-response, and predictive modeling.

Our results may inform research and prevention efforts. A large proportion of orofacial cleft risk is attributable to nonmodifiable factors; it is important to better understand the mechanisms involved for these factors.

Circulating microRNAs (miRNAs) could improve colorectal cancer (CRC) risk prediction. Here, we derive a blood-based miRNA panel and evaluate its ability to predict CRC occurrence in a population-based cohort of adults aged 50–75 years. Forty-one miRNAs are preselected from independent studies and measured by quantitative-real-time-polymerase-chain-reaction in serum collected at baseline of 198 participants who develop CRC during 14 years of follow-up and 178 randomly selected controls. A 7-miRNA score is derived by logistic regression. Its predictive ability, quantified by the optimism-corrected area-under-the-receiver-operating-characteristic-curve (AUC) using .632+ bootstrap is 0.794. Predictive ability is compared to that of an environmental risk score (ERS) based on known risk factors and a polygenic risk score (PRS) based on 140 previously identified single-nucleotide-polymorphisms. In participants with all scores available, optimism-corrected-AUC is 0.802 for the 7-miRNA score, while AUC (95% CI) is 0.557 (0.498–0.616) for the ERS and 0.622 (0.564–0.681) for the PRS.

Background: DNA methylation biomarkers in stool may have applications in early colorectal cancer (CRC) detection; however, their association with stages of CRC carcinogenesis or their performance in detecting various stages is unclear. We aimed to systematically review the evidence for DNA methylation markers in stool for risk stratification or detection of specific CRC stages, as well as precursors of CRC. Methods: We conducted a systematic search in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched PubMed and ISI Web of Knowledge to identify relevant studies published until 14th January 2020. Two reviewers independently extracted data on study population characteristics, candidate genes, methylation measurement methods, odds ratios (ORs), overall and stage-specific sensitivities, specificities, areas under the receiver operating characteristics curve, and p-values for statistical significance for OR and for association of methylation levels with stage. Results: Twenty-seven studies that reported stage-specific associations or performances of fecal DNA methylation markers for detecting colorectal neoplasms were identified. All studies used methylation-specific polymerase chain reaction for assessing methylation levels in the promoter or exon 1 regions of targeted genes. However, most studies were underpowered and limited by their case-control design. Furthermore, the stage-specific associations or sensitivities were validated for two markers (hypermethylation of GATA4 and VIM) only. Conclusion: Methylation markers in stool may be useful for detection of CRC precursors or CRC staging, but promising candidate markers need to be validated in longitudinal studies on large screening populations, performing epigenome-wide analyses. Identification of stage-specific DNA methylation biomarkers in stool could boost current strategies towards early detection and enable different approaches to precision medicine for CRC.

DNA methylation patterns in the blood, genetic risk scores (GRSs), and environmental risk factors can potentially improve breast cancer (BC) risk prediction. We assessed the individual and joint predictive performance of methylation, GRS, and environmental risk factors for BC incidence in a prospective cohort study. In a cohort of 5462 women aged 50-75 from Germany, 101 BC cases were identified during 14 years of follow-up and were compared to 263 BC-free controls in a nested case-control design. Three previously suggested methylation risk scores (MRSs) based on methylation of 423, 248, and 131 cytosine-phosphate-guanine (CpG) loci, and a GRS based on the risk alleles from 269 recently identified single nucleotide polymorphisms were constructed. Additionally, multiple previously proposed environmental risk scores (ERSs) were built based on environmental variables. Areas under the receiver operating characteristic curves (AUCs) were estimated for evaluating BC risk prediction performance. MRS and ERS showed limited accuracy in predicting BC incidence, with AUCs ranging from 0.52 to 0.56 and from 0.52 to 0.59, respectively. The GRS predicted BC incidence with a higher accuracy (AUC = 0.61). Adjusted odds ratios per standard deviation increase (95% confidence interval) were 1.07 (0.84-1.36) and 1.40 (1.09-1.80) for the best performing MRS and ERS, respectively, and 1.48 (1.16-1.90) for the GRS. A full risk model combining the MRS, GRS, and ERS predicted BC incidence with the highest accuracy (AUC = 0.64) and might be useful for identifying high-risk populations for BC screening.

Background: Risk stratification for lung cancer (LC) screening is so far mostly based on smoking history. This study aimed to assess if and to what extent such risk stratification could be enhanced by additional consideration of genetic risk scores (GRSs) and epigenetic risk scores defined by DNA methylation. Methods: We conducted a nested case-control study of 143 incident LC cases and 1460 LC-free controls within a prospective cohort of 9949 participants aged 50-75 years with 14-year follow-up. Lifetime smoking history was obtained in detail at recruitment. We built a GRS based on 31 previously identified LC-associated single-nucleotide polymorphisms (SNPs) and a DNA methylation score (MRS) based on methylation of 151 previously identified smoking-associated cytosine-phosphate-guanine (CpG) loci. We evaluated associations of GRS and MRS with LC incidence by logistic regression models, controlling for age, sex, smoking status, and pack-years. We compared the predictive performance of models based on pack-years alone with models additionally including GRS and/or MRS using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: GRS and MRS showed moderate and strong associations with LC risk even after comprehensive adjustment for smoking history (adjusted odds ratio [95% CI] comparing highest with lowest quartile 1.93 [1.05-3.71] and 5.64 [2.13-17.03], respectively). Similar associations were also observed within the risk groups of ever and heavy smokers. Addition of GRS and MRS furthermore strongly enhanced LC prediction beyond prediction by packyears (increase of optimism-corrected AUC among heavy smokers from 0.605 to 0.654, NRI 26.7%, p = 0.0106, IDI 3.35%, p = 0.0036), the increase being mostly attributable to the inclusion of MRS. Conclusions: Consideration of MRS, by itself or in combination with GRS, may strongly enhance LC risk stratification.

DNA methylation profiles within whole-blood samples have been reported to be associated with colorectal cancer (CRC) occurrence and might enable risk stratification for CRC. We systematically reviewed and summarized studies addressing the association of whole-blood DNA methylation markers and risk of developing CRC or its precursors. We searched PubMed and ISI Web of Knowledge to identify relevant studies published until 12th November 2018. Two reviewers independently extracted data on study population characteristics, candidate genes, methylation measurement methods, methylation levels of patients in comparison to healthy controls, p-values, and odds ratios of the markers. Overall, 19 studies reporting 102 methylation markers for risk assessment of colorectal neoplasms met our inclusion criteria. The studies mostly used Methylation Specific Polymerase Chain Reaction (MS-PCR) for assessing the methylation status of a defined set of genes. Only two studies applied array-based genome-wide assays to assess the methylation levels. Five studies incorporated panels consisting of 2–10 individual methylation markers to assess their potential for stratifying the risk of developing colorectal neoplasms. However, none of these associations was confirmed in an independent cohort. In conclusion, whole-blood DNA methylation markers may be useful as biomarkers for risk stratification in CRC screening, but reproducible risk prediction algorithms are yet to be established by large scale epigenome-wide studies with thorough validation of results in prospective study cohorts including large screening populations. The possibilities of enhancing predictive power by combining methylation data with polygenetic risk scores and environmental risk factors need to be explored.

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