Application of Generalized Additive Models to the Evaluation of Continuous Markers for Classification Purposes

Background: Receiver operating characteristic (ROC) curve and derived measures as the Area Under the Curve (AUC) are often used for evaluating the discriminatory capability of a continuous biomarker in distinguishing between alternative states of health. However, if the marker shows an irregular distribution, with a dominance of diseased subjects in noncontiguous regions, classification using a single cutpoint is not appropriate, and it would lead to erroneous conclusions. This study sought to describe a procedure for improving the discriminatory capacity of a continuous biomarker, by using generalized additive models (GAMs) for binary data.Methods: A new classification rule is obtained by using logistic GAM regression models to transform the original biomarker, with the predicted probabilities being the new transformed continuous biomarker. We propose using this transformed biomarker to establish optimal cut-offs or intervals on which to base the classification. This methodology is applied to different controlled scenarios, and to real data from a prospective study of patients undergoing surgery at a University Teaching Hospital, for examining plasma glucose as postoperative infection biomarker.Results: Both, theoretical scenarios and real data results show that when the risk marker-disease relationship is not monotone, using the new transformed biomarker entails an improvement in discriminatory capacity. Moreover, in these situations, an optimal interval seems more reasonable than a single cutpoint to define lower and higher disease-risk categories.Conclusions: Using statistical tools which allow for greater flexibility (e.g., GAMs) can optimize the classificatory capacity of a potential marker using ROC analysis. So, it is important to question linearity in marker-outcome relationships, in order to avoid erroneous conclusions

Flexible geoadditive survival analysis of non-Hodgkin lymphoma in Peru

Knowledge of prognostic factors is an important task for the clinical management of Non Hodgkin Lymphoma (NHL). In this work, we study the variables affecting survival of NHL in Peru by means of geoadditive Cox-type structured hazard regression models while accounting for potential spatial correlations in the survival times. We identified eight covariates with significant effect for overall survival. Some of them are widely known such as age, performance status, clinical stage and lactic dehydrogenase, but we also identified hemoglobin, leukocytes and lymphocytes as covariates with a significant effect on the overall survival of patients with NHL. Besides, the effect of continuous covariates is clearly nonlinear and hence impossible to detect with the classical Cox method. Although the spatial component does not show a significant effect, the results show a trend of low risk in certain areas

Highly sensitive marker panel for guidance in lung cancer rapid diagnostic units

While evidence for lung cancer screening implementation in Europe is awaited, Rapid Diagnostic Units have been established in many hospitals to accelerate the early diagnosis of lung cancer. We seek to develop an algorithm to detect lung cancer in a symptomatic population attending such unit, based on a sensitive serum marker panel. Serum concentrations of Epidermal Growth Factor, sCD26, Calprotectin, Matrix Metalloproteinases −1, −7, −9, CEA and CYFRA 21.1 were determined in 140 patients with respiratory symptoms (lung cancer and controls with/without benign pathology). Logistic Lasso regression was performed to derive a lung cancer prediction model, and the resulting algorithm was tested in a validation set. A classification rule based on EGF, sCD26, Calprotectin and CEA was established, able to reasonably discriminate lung cancer with 97% sensitivity and 43% specificity in the training set, and 91.7% sensitivity and 45.4% specificity in the validation set. Overall, the panel identified with high sensitivity stage I non-small cell lung cancer (94.7%) and 100% small-cell lung cancers. Our study provides a sensitive 4-marker classification algorithm for lung cancer detection to aid in the management of suspicious lung cancer patients in the context of Rapid Diagnostic Units.Ministerio de Ciencia e Innovación | Ref. PS09-00405Xunta de Galicia | Ref. INBIOMED 2012-273Xunta de Galicia | Ref. GRC2014/019Ministerio de Ciencia e Innovación | Ref. MTM2011-2320

Serum methylation of GALNT9, UPF3A, WARS, and LDB2 as noninvasive biomarkers for the early detection of colorectal cancer and advanced adenomas

Background Early detection has proven to be the most effective strategy to reduce the incidence and mortality of colorectal cancer (CRC). Nevertheless, most current screening programs suffer from low participation rates. A blood test may improve both the adherence to screening and the selection to colonoscopy. In this study, we conducted a serum-based discovery and validation of cfDNA methylation biomarkers for CRC screening in a multicenter cohort of 433 serum samples including healthy controls, benign pathologies, advanced adenomas (AA), and CRC.Results First, we performed an epigenome-wide methylation analysis with the MethylationEPIC array using a sample pooling approach, followed by a robust prioritization of candidate biomarkers for the detection of advanced neoplasia (AN: AA and CRC). Then, candidate biomarkers were validated by pyrosequencing in independent individual cfDNA samples. We report GALNT9, UPF3A, WARS, and LDB2 as new noninvasive biomarkers for the early detection of AN. The combination of GALNT9/UPF3A by logistic regression discriminated AN with 78.8% sensitivity and 100% specificity, outperforming the commonly used fecal immunochemical test and the methylated SEPT9 blood test.Conclusions Overall, this study highlights the utility of cfDNA methylation for CRC screening. Our results suggest that the combination methylated GALNT9/UPF3A has the potential to serve as a highly specific and sensitive blood-based test for screening and early detection of CRC

Genotype-phenotype correlations for pancreatic cancer risk in Dutch melanoma families with pathogenic CDKN2A variants

BACKGROUND: Pathogenic variants in the CDKN2A gene are generally associated with the development of melanoma and pancreatic ductal adenocarcinoma (PDAC), but specific genotype-phenotype correlations might exist and the extent of PDAC risk is not well established for many variants. METHODS: Using the Dutch national familial melanoma database, we identified all families with a pathogenic CDKN2A variant and investigated the occurrence of PDAC within these families. We also estimated the standardised incidence ratio and lifetime PDAC risk for carriers of a highly prevalent variant in these families. RESULTS: We identified 172 families in which 649 individuals carried 15 different pathogenic variants. The most prevalent variant was the founder mutation c.225_243del (p16-Leiden, 484 proven carriers). Second most prevalent was c.67G>C (55 proven carriers). PDAC developed in 95 of 163 families (58%, including 373 of 629 proven carriers) harbouring a variant with an effect on the p16INK4a protein, whereas PDAC did not occur in the 9 families (20 proven carriers) with a variant affecting only p14ARF. In the c.67G>C families, PDAC occurred in 12 of the 251 (5%) persons at risk. The standardised incidence ratio was 19.1 (95% CI 8.3 to 33.6) and the cumulative PDAC incidence at age 75 years (lifetime risk) was 19% (95% CI 7.5% to 30.1%). CONCLUSIONS: Our results support the notion that pathogenic CDKN2A variants affecting the p16INK4a protein, including c.67G>C, are associated with increased PDAC risk and carriers of such variants should be offered pancreatic cancer surveillance. There is no clinical evidence that impairment of only the p14ARF protein leads to an increased PDAC risk

Germline variant affecting p53β isoforms predisposes to familial cancer

Germline and somatic TP53 variants play a crucial role during tumorigenesis. However, genetic variations that solely affect the alternatively spliced p53 isoforms, p53β and p53γ, are not fully considered in the molecular diagnosis of Li-Fraumeni syndrome and cancer. In our search for additional cancer predisposing variants, we identify a heterozygous stop-lost variant affecting the p53β isoforms (p.*342Serext*17) in four families suspected of an autosomal dominant cancer syndrome with colorectal, breast and papillary thyroid cancers. The stop-lost variant leads to the 17 amino-acid extension of the p53β isoforms, which increases oligomerization to canonical p53α and dysregulates the expression of p53’s transcriptional targets. Our study reveals the capacity of p53β mutants to influence p53 signalling and contribute to the susceptibility of different cancer types. These findings underscore the significance of p53 isoforms and the necessity of comprehensive investigation into the entire TP53 gene in understanding cancer predisposition

Validation of the BOADICEA model and a 313-variant polygenic risk score for breast cancer risk prediction in a Dutch prospective cohort

Abstract: Purpose: We evaluated the performance of the recently extended Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in a Dutch prospective cohort, using a polygenic risk score (PRS) based on 313 breast cancer (BC)–associated variants (PRS313) and other, nongenetic risk factors. Methods: Since 1989, 6522 women without BC aged 45 or older of European descent have been included in the Rotterdam Study. The PRS313 was calculated per 1 SD in controls from the Breast Cancer Association Consortium (BCAC). Cox regression analysis was performed to estimate the association between the PRS313 and incident BC risk. Cumulative 10-year risks were calculated with BOADICEA including different sets of variables (age, risk factors and PRS313). C-statistics were used to evaluate discriminative ability. Results: In total, 320 women developed BC. The PRS313 was significantly associated with BC (hazard ratio [HR] per SD of 1.56, 95% confidence interval [CI] [1.40–1.73]). Using 10-year risk estimates including age and the PRS313, other risk factors improved the discriminatory ability of the BOADICEA model marginally, from a C-statistic of 0.636 to 0.653. Conclusions: The effect size of the PRS313 is highly reproducible in the Dutch population. Our results validate the BOADICEA v5 model for BC risk assessment in the Dutch general population

The apparent genetic anticipation in PMS2-associated Lynch syndrome families is explained by birth cohort effect

BACKGROUND: PMS2-associated Lynch syndrome is characterized by a relatively low colorectal cancer penetrance compared with other Lynch syndromes. However, age at colorectal cancer diagnosis varies widely, and a strong genetic anticipation effect has been suggested for PMS2 families. In this study, we examined proposed genetic anticipation in a sample of 152 European PMS2 families. METHODS: The 152 families (637 family members) that were eligible for analysis were mainly clinically ascertained via clinical genetics centers. We used weighted Cox-type random effects model, adjusted by birth cohort and sex, to estimate the generational effect on the age of onset of colorectal cancer. Probands and young birth cohorts were excluded from the analyses. Weights represented mutation probabilities based on kinship coefficients, thus avoiding testing bias. RESULTS: Family data across three generations, including 123 colorectal cancers, were analyzed. When compared with the first generation, the crude HR for anticipation was 2.242 [95% confidence interval (CI), 1.162-4.328] for the second generation and 2.644 (95% CI, 1.082-6.464) for the third generation. However, after correction for birth cohort and sex, the effect vanished [HR = 1.302 (95% CI, 0.648-2.619) and HR = 1.074 (95% CI, 0.406-2.842) for second and third generations, respectively]. CONCLUSIONS: Our study did not confirm previous reports of genetic anticipation in PMS2-associated Lynch syndrome. Birth-cohort effect seems the most likely explanation for observed younger colorectal cancer diagnosis in subsequent generations, particularly because there is currently no commonly accepted biological mechanism that could explain genetic anticipation in Lynch syndrome. IMPACT: This new model for studying genetic anticipation provides a standard for rigorous analysis of families with dominantly inherited cancer predisposition

Validation of the BOADICEA model and a 313-variant polygenic risk score for breast cancer risk prediction in a Dutch prospective cohort

Purpose: We evaluated the performance of the recently extended Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in a Dutch prospective cohort, using a polygenic risk score (PRS) based on 313 breast cancer (BC)–associated variants (PRS313) and other, nongenetic risk factors. Methods: Since 1989, 6522 women without BC aged 45 or older of European descent have been included in the Rotterdam Study. The PRS313 was calculated per 1 SD in controls from the Breast Cancer Association Consortium (BCAC). Cox regression analysis was performed to estimate the association between the PRS313 and incident BC risk. Cumulative 10-year risks were calculated with BOADICEA including different sets of variables (age, risk factors and PRS313). C-statistics were used to evaluate discriminative ability. Results: In total, 320 women developed BC. The PRS313 was significantly associated with BC (hazard ratio [HR] per SD of 1.56, 95% confidence interval [CI] [1.40–1.73]). Using 10-year risk estimates including age and the PRS313, other risk factors improved the discriminatory ability of the BOADICEA model marginally, from a C-statistic of 0.636 to 0.653. Conclusions: The effect size of the PRS313 is highly reproducible in the Dutch population. Our results validate the BOADICEA v5 model for BC risk assessment in the Dutch general population