Better prediction by use of co-data: Adaptive group-regularized ridge regression

For many high-dimensional studies, additional information on the variables, like (genomic) annotation or external p-values, is available. In the context of binary and continuous prediction, we develop a method for adaptive group-regularized (logistic) ridge regression, which makes structural use of such 'co-data'. Here, 'groups' refer to a partition of the variables according to the co-data. We derive empirical Bayes estimates of group-specific penalties, which possess several nice properties: i) they are analytical; ii) they adapt to the informativeness of the co-data for the data at hand; iii) only one global penalty parameter requires tuning by cross-validation. In addition, the method allows use of multiple types of co-data at little extra computational effort. We show that the group-specific penalties may lead to a larger distinction between `near-zero' and relatively large regression parameters, which facilitates post-hoc variable selection. The method, termed GRridge, is implemented in an easy-to-use R-package. It is demonstrated on two cancer genomics studies, which both concern the discrimination of precancerous cervical lesions from normal cervix tissues using methylation microarray data. For both examples, GRridge clearly improves the predictive performances of ordinary logistic ridge regression and the group lasso. In addition, we show that for the second study the relatively good predictive performance is maintained when selecting only 42 variables.Comment: 15 pages, 2 figures. Supplementary Information available on first author's web sit

Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing

Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform

Genome-wide microRNA analysis of HPV-positive self-samples yields novel triage markers for early detection of cervical cancer

Contains fulltext : 201045.pdf (publisher's version ) (Open Access

Detection of hypermethylated genes as markers for cervical screening in women living with HIV

INTRODUCTION : To evaluate the performance of hypermethylation analysis of ASCL1, LHX8 and ST6GALNAC5 in physician-taken cervical scrapes for detection of cervical cancer and cervical intraepithelial neoplasia (CIN) grade 3 in women living with HIV (WLHIV) in South Africa. METHODS : Samples from a prospective observational cohort study were used for these analyses. Two cohorts were included: a cohort of WLHIV who were invited for cervical screening (n = 321) and a gynaecologic outpatient cohort of women referred for evaluation of abnormal cytology or biopsy proven cervical cancer (n = 108, 60% HIV seropositive). Cervical scrapes collected from all subjects were analysed for hypermethylation of ASCL1, LHX8 and ST6GALNAC5 by multiplex quantitative methylation specific PCR (qMSP). Histology endpoints were available for all study subjects. RESULTS : Hypermethylation levels of ASCL1, LHX8 and ST6GALNAC5 increased with severity of cervical disease. The performance for detection of CIN3 or worse (CIN3+) as assessed by the area under the receiver operating characteristic (ROC) curves (AUC) was good for ASCL1 and LHX8 (AUC 0.79 and 0.81 respectively), and moderate for ST6GALNAC5 (AUC 0.71). At a threshold corresponding to 75% specificity, CIN3+ sensitivity was 72.1% for ASCL1 and 73.8% for LHX8 and all samples from women with cervical cancer scored positive for these two markers. CONCLUSIONS : Hypermethylation analysis of ASCL1 or LHX8 in cervical scrape material of WLHIV detects all cervical carcinomas with an acceptable sensitivity and good specificity for CIN3+, warranting further exploration of these methylation markers as a stand-alone test for cervical screening in low-resource settings.The VU University Research Fellowship (URF) programme (Amsterdam, The Netherlands), the European Research Council (ERC advanced 2012-AdG; 322986) to C.J.L.M. Meijer, the 1st For Women Foundation (Pretoria, South Africa) and the Carl & Emily Fuchs Foundation (Pretoria, South Africa).https://onlinelibrary.wiley.com/journal/17582652am2019Medical VirologyObstetrics and Gynaecolog

Identification and Validation of a 3-Gene Methylation Classifier for HPV-Based Cervical Screening on Self-Samples

Contains fulltext : 194428.pdf (publisher's version ) (Closed access

Cervical cancer detection by DNA methylation analysis in urine

Abstract Urine samples provide a potential alternative to physician-taken or self-collected cervical samples for cervical screening. Screening by primary hrHPV testing requires additional risk assessment (so-called triage) of hrHPV-positive women. Molecular markers, such as DNA methylation, have proven most valuable for triage when applied to cervical specimens. This study was set out to compare hrHPV and DNA methylation results in paired urine and cervical scrapes, and to evaluate the feasibility of DNA methylation analysis in urine to detect cervical cancer. Urine samples (n = 41; native and sediment) and paired cervical scrapes (n = 38) from cervical cancer patients, and urine from 44 female controls, were tested for hrHPV and 6 methylation markers. Results on native urine and sediment were highly comparable. A strong agreement was found between hrHPV testing on urine and scrapes (kappa = 0.79). Also, methylation levels in urine were moderately to strongly correlated to those detected in scrapes (r = 0.508–0.717). All markers were significantly increased in urine from cervical cancer patients compared to controls and showed a good discriminatory power for cervical cancer (AUC = 0.744–0.887). Our results show a good agreement of urine-based molecular analysis with reference cervical samples, and suggest that urine-based DNA methylation testing may provide a promising strategy for cervical cancer detection

Cervical cancer detection by DNA methylation analysis in urine

Urine samples provide a potential alternative to physician-taken or self-collected cervical samples for cervical screening. Screening by primary hrHPV testing requires additional risk assessment (so-called triage) of hrHPV-positive women. Molecular markers, such as DNA methylation, have proven most valuable for triage when applied to cervical specimens. This study was set out to compare hrHPV and DNA methylation results in paired urine and cervical scrapes, and to evaluate the feasibility of DNA methylation analysis in urine to detect cervical cancer. Urine samples (n = 41; native and sediment) and paired cervical scrapes (n = 38) from cervical cancer patients, and urine from 44 female controls, were tested for hrHPV and 6 methylation markers. Results on native urine and sediment were highly comparable. A strong agreement was found between hrHPV testing on urine and scrapes (kappa = 0.79). Also, methylation levels in urine were moderately to strongly correlated to those detected in scrapes (r = 0.508-0.717). All markers were significantly increased in urine from cervical cancer patients compared to controls and showed a good discriminatory power for cervical cancer (AUC = 0.744-0.887). Our results show a good agreement of urine-based molecular analysis with reference cervical samples, and suggest that urine-based DNA methylation testing may provide a promising strategy for cervical cancer detectio

Genome-wide DNA Methylation Profiling Reveals Methylation Markers Associated with 3q Gain for Detection of Cervical Precancer and Cancer

Purpose: Epigenetic host cell changes involved in cervical cancer development following a persistent high-risk human papillomavirus (hrHPV) infection, provide promising markers for the management of hrHPV-positive women. In particular, markers based on DNA methylation of tumor suppressor gene promoters are valuable. These markers ideally identify hrHPV-positive women with precancer (CIN2/3) in need of treatment. Here, we set out to identify biologically relevant methylation markers by genome-wide methylation analysis of both hrHPV-transformed cell lines and cervical tissue specimens. Experimental Design and Results: Genome-wide discovery by next-generation sequencing (NGS) of methyl-binding domain-enriched DNA (MBD-Seq) yielded 20 candidate methylation target genes. Further verification and validation by multiplex-targeted bisulfite NGS and (quantitative) methylation-specific PCR (MSP) resulted in 3 genes (GHSR, SST, and ZIC1) that showed a significant increase in methylation with severity of disease in both tissue specimens and cervical scrapes (P <0.005). The area under the ROC curve for CIN3 or worse varied between 0.86 and 0.89. Within the group of CIN2/3, methylation levels of all 3 genes increased with duration of lesion existence (P <0.0005), characterized by duration of preceding hrHPV infection, and were significantly higher in the presence of a 3q gain (P <0.05) in the corresponding tissue biopsy. Conclusions: By unbiased genome-wide DNA methylation profiling and comprehensive stepwise verification and validation studies using in vitro and patient-derived samples, we identified 3 promising methylation markers (GHSR, SST, and ZIC1) associated with a 3q gain for the detection of cervical (pre)cancer. (C) 2017 AACR

Host-cell DNA methylation patterns during high-risk HPV-induced carcinogenesis reveal a heterogeneous nature of cervical pre-cancer

<p>Cervical cancer development following a persistent infection with high-risk human papillomavirus (hrHPV) is driven by additional host-cell changes, such as altered DNA methylation. In previous studies, we have identified 12 methylated host genes associated with cervical cancer and pre-cancer (CIN2/3). This study systematically analyzed the onset and DNA methylation pattern of these genes during hrHPV-induced carcinogenesis using a longitudinal <i>in vitro</i> model of hrHPV-transformed cell lines (n = 14) and hrHPV-positive cervical scrapings (n = 113) covering various stages of cervical carcinogenesis. DNA methylation analysis was performed by quantitative methylation-specific PCR (qMSP) and relative qMSP values were used to analyze the data. The majority of genes displayed a comparable DNA methylation pattern in both cell lines and clinical specimens. DNA methylation onset occurred at early or late immortal passage, and DNA methylation levels gradually increased towards tumorigenic cells. Subsequently, we defined a so-called cancer-like methylation-high pattern based on the DNA methylation levels observed in cervical scrapings from women with cervical cancer. This cancer-like methylation-high pattern was observed in 72% (38/53) of CIN3 and 55% (11/20) of CIN2, whereas it was virtually absent in hrHPV-positive controls (1/26). In conclusion, hrHPV-induced carcinogenesis is characterized by early onset of DNA methylation, typically occurring at the pre-tumorigenic stage and with highest DNA methylation levels at the cancer stage. Host-cell DNA methylation patterns in cervical scrapings from women with CIN2 and CIN3 are heterogeneous, with a subset displaying a cancer-like methylation-high pattern, suggestive for a higher cancer risk.</p