Search

Computation of exact p values of multiple comparisons rank statistics is often a very time-consuming task. In some cases approximations are available, but they are often unsatisfactory when samples are small or the number of ties is large. Some existing tables of exact critical values contain errors and are limited to small samples without ties and to Wilcoxon scores. Therefore, this article proposes a new algorithm to compute exact p values of generalized Steel statistics for one-way classification. This recursive algorithm builds up a generating function that represents the null distribution of the statistic. Several techniques are used to reduce computing time considerably. An improvement of an existing algorithm is given for computing exact p values, which is valid for a smaller class of statistics than the recursion. However, for this class of statistics, this algorithm outperforms the recursion. How to compute tight bounds on the p value when exact computation is too time-consuming is discussed. Also discussed is when to use which method (exact, bounds, normal approximation, or simulation) for computation of p values or critical values. [ABSTRACT FROM AUTHOR]

For high-dimensional linear regression models, we review and compare several estimators of variances τ 2 and σ 2 of the random slopes and errors, respectively. These variances relate directly to ridge regression penalty λ and heritability index h2, often used in genetics. Several estimators of these, either based on cross-validation (CV) or maximum marginal likelihood (MML), are also discussed. The comparisons include several cases of the high-dimensional covariate matrix such as multi-collinear covariates and data-derived ones. Moreover, we study robustness against model misspecifications such as sparse instead of dense effects and non-Gaussian errors. An example on weight gain data with genomic covariates confirms the good performance of MML compared to CV. Several extensions are presented. First, to the high-dimensional linear mixed effects model, with REML as an alternative to MML. Second, to the conjugate Bayesian setting, shown to be a good alternative. Third, and most prominently, to generalized linear models for which we derive a computationally efficient MML estimator by re-writing the marginal likelihood as an n-dimensional integral. For Poisson and Binomial ridge regression, we demonstrate the superior accuracy of the resulting MML estimator of λ as compared to CV. Software is provided to enable reproduction of all results. [ABSTRACT FROM AUTHOR]

In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) $p$-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical-variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced. [ABSTRACT FROM AUTHOR]

Summary: DNA copy number and mRNA expression are commonly used data types in cancer studies. Available software for integrative analysis arbitrarily fixes the parametric form of the association between the two molecular levels and hence offers no opportunities for modelling it. We present a new tool for flexible modelling of this association. PLRS uses a wide class of interpretable models including popular ones and incorporates prior biological knowledge. It is capable to identify the gene-specific type of relationship between gene copy number and mRNA expression. Moreover, it tests the strength of the association and provides confidence intervals. We illustrate PLRS using glioblastoma data from The Cancer Genome Atlas.Availability and implementation: PLRS is implemented as an R package and available from Bioconductor (as of version 2.12; http://bioconductor.org). Additional code for parallel computations is available as Supplementary Material.Contact: g.g.r.leday@vu.nlSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Objectives: To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance. Methods: Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity. Results: Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60). Conclusion: The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models. Key Points: • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols. [ABSTRACT FROM AUTHOR]

Patients with inflammatory bowel disease (IBD) produce enhanced immunoglobulin A (IgA) against the microbiota compared to healthy individuals, which has been correlated with disease severity. Since IgA complexes can potently activate myeloid cells via the IgA receptor FcαRI (CD89), excessive IgA production may contribute to IBD pathology. However, the cellular mechanisms that contribute to dysregulated IgA production in IBD are poorly understood. Here, we demonstrate that intestinal FcαRI-expressing myeloid cells (i.e., monocytes and neutrophils) are in close contact with B lymphocytes in the lamina propria of IBD patients. Furthermore, stimulation of FcαRI-on monocytes triggered production of cytokines and chemokines that regulate B-cell differentiation and migration, including interleukin-6 (IL6), interleukin-10 (IL10), tumour necrosis factor-α (TNFα), a proliferation-inducing ligand (APRIL), and chemokine ligand-20 (CCL20). In vitro, these cytokines promoted IgA isotype switching in human B cells. Moreover, when naïve B lymphocytes were cultured in vitro in the presence of FcαRI-stimulated monocytes, enhanced IgA isotype switching was observed compared to B cells that were cultured with non-stimulated monocytes. Taken together, FcαRI-activated monocytes produced a cocktail of cytokines, as well as chemokines, that stimulated IgA switching in B cells, and close contact between B cells and myeloid cells was observed in the colons of IBD patients. As such, we hypothesize that, in IBD, IgA complexes activate myeloid cells, which in turn can result in excessive IgA production, likely contributing to disease pathology. Interrupting this loop may, therefore, represent a novel therapeutic strategy. [ABSTRACT FROM AUTHOR]

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents. [ABSTRACT FROM AUTHOR]

Objectives: Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. Materials and Methods: Native T1-weighted images of four independent, retrospective (2005–2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). Results: In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). Conclusions: MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. Key Points: • MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models. [ABSTRACT FROM AUTHOR]

Self-perceived word-finding difficulties are common in aging individuals as well as in Alzheimer's Disease (AD). Language and speech deficits are difficult to objectify with neuropsychological assessments. We therefore aimed to investigate whether amyloid, an early AD pathological hallmark, is associated with speech-derived semantic complexity. We included 63 individuals with subjective cognitive decline (age 64 ± 8, MMSE 29 ± 1), with amyloid status (positron emission tomography [PET] scans n = 59, or Aβ 1-42 cerebrospinal fluid [CSF] n = 4). Spontaneous speech was recorded using three open-ended tasks (description of cookie theft picture, abstract painting and a regular Sunday), transcribed verbatim and subsequently, linguistic parameters were extracted using T-scan computational software, including specific words (content words, frequent, concrete and abstract nouns, and fillers), lexical complexity (lemma frequency, Type-Token-Ratio) and syntactic complexity (Developmental Level scale). Nineteen individuals (30%) had high levels of amyloid burden, and there were no differences between groups on conventional neuropsychological tests. Using multinomial regression with linguistic parameters (in tertiles), we found that high amyloid burden is associated with fewer concrete nouns (OR middle (95%CI): 7.6 (1.4–41.2), OR lowest : 6.7 (1.2–37.1)) and content words (OR lowest : 6.3 (1.0–38.1). In addition, we found an interaction for education between high amyloid burden and more abstract nouns. In conclusion, high amyloid burden was modestly associated with fewer specific words, but not with syntactic complexity, lexical complexity or conventional neuropsychological tests, suggesting that subtle spontaneous speech deficits might occur in preclinical AD. • High amyloid burden was associated with fewer specific words during spontaneous speech. • Amyloid burden was not associated with conversation fillers and type-token ratio. • Amyloid burden was not associated with fluency or confrontational naming. [ABSTRACT FROM AUTHOR]

• Predictive models based on MR-radiomic features were able to predict HPV status. • The study outcomes support the role of MR-radiomics as potential imaging biomarker. • Survival prediction improved by combining clinical features with MRI-based radiomics. Human papillomavirus- (HPV) positive oropharyngeal squamous cell carcinoma (OPSCC) differs biologically and clinically from HPV-negative OPSCC and has a better prognosis. This study aims to analyze the value of magnetic resonance imaging (MRI)-based radiomics in predicting HPV status in OPSCC and aims to develop a prognostic model in OPSCC including HPV status and MRI-based radiomics. Manual delineation of 249 primary OPSCCs (91 HPV-positive and 159 HPV-negative) on pretreatment native T1-weighted MRIs was performed and used to extract 498 radiomic features per delineation. A logistic regression (LR) and random forest (RF) model were developed using univariate feature selection. Additionally, factor analysis was performed, and the derived factors were combined with clinical data in a predictive model to assess the performance on predicting HPV status. Additionally, factors were combined with clinical parameters in a multivariable survival regression analysis. Both feature-based LR and RF models performed with an AUC of 0.79 in prediction of HPV status. Fourteen of the twenty most significant features were similar in both models, mainly concerning tumor sphericity, intensity variation, compactness, and tumor diameter. The model combining clinical data and radiomic factors (AUC = 0.89) outperformed the radiomics-only model in predicting OPSCC HPV status. Overall survival prediction was most accurate using the combination of clinical parameters and radiomic factors (C-index = 0.72). Predictive models based on MR-radiomic features were able to predict HPV status with sufficient performance, supporting the role of MRI-based radiomics as potential imaging biomarker. Survival prediction improved by combining clinical features with MRI-based radiomics. [ABSTRACT FROM AUTHOR]

Introduction Survival of patients after resection of colorectal cancer liver metastasis (CRCLM) is 36%–58%. Positron emission tomography (PET) tracers, imaging the expression of prognostic biomarkers, may contribute to assign appropriate management to individual patients. Aurora kinase A (AURKA) expression is associated with survival of patients after CRCLM resection. Methods We synthesized [ 3 H]alisertib and [ 11 C]alisertib, starting from [ 3 H]methyl nosylate and [ 11 C]methyl iodide, respectively. We measured in vitro uptake of [ 3 H]alisertib in cancer cells with high (Caco2), moderate (A431, HCT116, SW480) and low (MKN45) AURKA expression, before and after siRNA-mediated AURKA downmodulation, as well as after inhibition of P-glycoprotein (P-gp) activity. We measured in vivo uptake and biodistribution of [ 11 C]alisertib in nude mice, xenografted with A431, HCT116 or MKN45 cells, or P-gp knockout mice. Results [ 3 H]Alisertib was synthesized with an overall yield of 42% and [ 11 C]alisertib with an overall yield of 23% ± 9% (radiochemical purity ≥ 99%). Uptake of [ 3 H]alisertib in Caco2 cells was higher than in A431 cells ( P = .02) and higher than in SW480, HCT116 and MKN45 cells ( P < .01). Uptake in A431 cells was higher than in SW480, HCT116 and MKN45 cells ( P < .01). Downmodulation of AURKA expression reduced [ 3 H]alisertib uptake in Caco2 cells ( P < .01). P-gp inhibition increased [ 3 H]alisertib uptake in Caco2 ( P < .01) and MKN45 ( P < .01) cells. In vivo stability of [ 11 C]alisertib 90 min post-injection was 94.7% ± 1.3% and tumor-to-background ratios were 2.3 ± 0.8 (A431), 1.6 ± 0.5 (HCT116) and 1.9 ± 0.5 (MKN45). In brains of P-gp knockout mice [ 11 C]alisertib uptake was increased compared to uptake in wild-type mice ( P < .01) Conclusions Radiolabeled alisertib can be synthesized and may have potential for the imaging of AURKA, particularly when AURKA expression is high. However, the exact mechanisms underlying alisertib accumulation need further investigation. Advances in knowledge and implications for patient care Radiolabeled alisertib may be used for non-invasively measuring AURKA protein expression and to stratify patients for treatment accordingly. [ABSTRACT FROM AUTHOR]

Abstract: To characterize the promoterome of caudate and putamen regions (striatum), frontal and temporal cortices, and hippocampi from aged human brains, we used high-throughput cap analysis of gene expression to profile the transcription start sites and to quantify the differences in gene expression across the 5 brain regions. We also analyzed the extent to which methylation influenced the observed expression profiles. We sequenced more than 71 million cap analysis of gene expression tags corresponding to 70,202 promoter regions and 16,888 genes. More than 7000 transcripts were differentially expressed, mainly because of differential alternative promoter usage. Unexpectedly, 7% of differentially expressed genes were neurodevelopmental transcription factors. Functional pathway analysis on the differentially expressed genes revealed an overrepresentation of several signaling pathways (e.g., fibroblast growth factor and wnt signaling) in hippocampus and striatum. We also found that although 73% of methylation signals mapped within genes, the influence of methylation on the expression profile was small. Our study underscores alternative promoter usage as an important mechanism for determining the regional differences in gene expression at old age. [Copyright &y& Elsevier]

Updating of the Gaussian graphical model via shrinkage estimation is studied. This shrinkage is towards a nonzero parameter value representing prior quantitative information. Once new data become available, the previously estimated parameter needs updating. Shrinkage provides the means to this end, using the latter as a shrinkage target to acquire an updated estimate. The process of iteratively updating the Gaussian graphical model in shrinkage fashion is shown to yield an improved fit and an asymptotically unbiased and consistent estimator. The workings of updating via shrinkage are elucidated by linking it to Bayesian updating and through the inheritance by the update of eigen-properties of the previous estimate. The effect of shrinkage on the moments and loss of the estimator are pointed out. Practical issues possibly hampering updating are identified and solutions outlined. The presented updating procedure is illustrated through the reconstruction of a gene–gene interaction network using transcriptomic data. [ABSTRACT FROM AUTHOR]

Objectives: The addition of two-way interactions is a classic problem in statistics, and comes with the challenge of quadratically increasing dimension. We aim to a) devise an estimation method that can handle this challenge and b) to aid interpretation of the resulting model by developing computational tools for quantifying variable importance., Methods: Existing strategies typically overcome the dimensionality problem by only allowing interactions between relevant main effects. Building on this philosophy, and aiming for settings with moderate n to p ratio, we develop a local shrinkage model that links the shrinkage of interaction effects to the shrinkage of their corresponding main effects. In addition, we derive a new analytical formula for the Shapley value, which allows rapid assessment of individual-specific variable importance scores and their uncertainties., Results: We empirically demonstrate that our approach provides accurate estimates of the model parameters and very competitive predictive accuracy. In our Bayesian framework, estimation inherently comes with inference, which facilitates variable selection. Comparisons with key competitors are provided. Large-scale cohort data are used to provide realistic illustrations and evaluations. The implementation of our method in RStan is relatively straightforward and flexible, allowing for adaptation to specific needs., Conclusions: Our method is an attractive alternative for existing strategies to handle interactions in epidemiological and/or clinical studies, as its linked local shrinkage can improve parameter accuracy, prediction and variable selection. Moreover, it provides appropriate inference and interpretation, and may compete well with less interpretable machine learners in terms of prediction., Competing Interests: Competing interests: The authors state no conflict of interest., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA 1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value., Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA 1c , HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic., Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA 1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA 1c ) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance., Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification., Data Availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch ., (© 2024. The Author(s).)

Motivation: In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature effects, e.g. regression coefficients from previous studies., Results: We propose an approach for integrating multiple sources of such prior information into penalized regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application., Availability and Implementation: The proposed method is implemented in the R package transreg (https://github.com/lcsb-bds/transreg, https://cran.r-project.org/package=transreg)., (© The Author(s) 2023. Published by Oxford University Press.)

Purpose: Patients with vanishing white matter (VWM) experience unremitting chronic neurological decline and stress-provoked episodes of rapid, partially reversible decline. Cerebral white matter abnormalities are progressive, without improvement, and are therefore unlikely to be related to the episodes. We determined which radiological findings are related to episodic decline., Methods: MRI scans of VWM patients were retrospectively analyzed. Patients were grouped into A (never episodes) and B (episodes). Signal abnormalities outside the cerebral white matter were rated as absent, mild, or severe. A sum score was developed with abnormalities only seen in group B. The temporal relationship between signal abnormalities and episodes was determined by subdividing scans into those made before, less than 3 months after, and more than 3 months after onset of an episode., Results: Five hundred forty-three examinations of 298 patients were analyzed. Mild and severe signal abnormalities in the caudate nucleus, putamen, globus pallidus, thalamus, midbrain, medulla oblongata, and severe signal abnormalities in the pons were only seen in group B. The sum score, constructed with these abnormalities, depended on the timing of the scan (χ 2 (2, 400) = 22.8; p < .001): it was least often abnormal before, most often abnormal with the highest value shortly after, and lower longer than 3 months after an episode., Conclusion: In VWM, signal abnormalities in brainstem, thalamus, and basal ganglia are related to episodic decline and can improve. Knowledge of the natural MRI history in VWM is important for clinical interpretation of MRI findings and crucial in therapy trials., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Elastic net penalization is widely used in high-dimensional prediction and variable selection settings. Auxiliary information on the variables, for example, groups of variables, is often available. Group-adaptive elastic net penalization exploits this information to potentially improve performance by estimating group penalties, thereby penalizing important groups of variables less than other groups. Estimating these group penalties is, however, hard due to the high dimension of the data. Existing methods are computationally expensive or not generic in the type of response. Here we present a fast method for estimation of group-adaptive elastic net penalties for generalized linear models. We first derive a low-dimensional representation of the Taylor approximation of the marginal likelihood for group-adaptive ridge penalties, to efficiently estimate these penalties. Then we show by using asymptotic normality of the linear predictors that this marginal likelihood approximates that of elastic net models. The ridge group penalties are then transformed to elastic net group penalties by matching the ridge prior variance to the elastic net prior variance as function of the group penalties. The method allows for overlapping groups and unpenalized variables, and is easily extended to other penalties. For a model-based simulation study and two cancer genomics applications we demonstrate a substantially decreased computation time and improved or matching performance compared to other methods. Supplementary materials for this article are available online., Competing Interests: The authors report there are no competing interests to declare., (© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.)

The features in a high-dimensional biomedical prediction problem are often well described by low-dimensional latent variables (or factors). We use this to include unlabeled features and additional information on the features when building a prediction model. Such additional feature information is often available in biomedical applications. Examples are annotation of genes, metabolites, or p-values from a previous study. We employ a Bayesian factor regression model that jointly models the features and the outcome using Gaussian latent variables. We fit the model using a computationally efficient variational Bayes method, which scales to high dimensions. We use the extra information to set up a prior model for the features in terms of hyperparameters, which are then estimated through empirical Bayes. The method is demonstrated in simulations and two applications. One application considers influenza vaccine efficacy prediction based on microarray data. The second application predicts oral cancer metastasis from RNAseq data., (© 2022 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Consensus molecular subtypes (CMSs) can guide precision treatment of colorectal cancer (CRC). We aim to identify methylation markers to distinguish between CMS2 and CMS3 in patients with CRC, for which an easy test is currently lacking. To this aim, fresh-frozen tumor tissue of 239 patients with stage I-III CRC was analyzed. Methylation profiles were obtained using the Infinium HumanMethylation450 BeadChip. We performed adaptive group-regularized logistic ridge regression with post hoc group-weighted elastic net marker selection to build prediction models for classification of CMS2 and CMS3. The Cancer Genome Atlas (TCGA) data were used for validation. Group regularization of the probes was done based on their location either relative to a CpG island or relative to a gene present in the CMS classifier, resulting in two different prediction models and subsequently different marker panels. For both panels, even when using only five markers, accuracies were > 90% in our cohort and in the TCGA validation set. Our methylation marker panel accurately distinguishes between CMS2 and CMS3. This enables development of a targeted assay to provide a robust and clinically relevant classification tool for CRC patients., (© 2021 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue's complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data., (© 2021. The Author(s).)

Screening to detect colorectal cancer (CRC) in an early or premalignant state is an effective method to reduce CRC mortality rates. Current stool-based screening tests, e.g. fecal immunochemical test (FIT), have a suboptimal sensitivity for colorectal adenomas and difficulty distinguishing adenomas at high risk of progressing to cancer from those at lower risk. We aimed to identify stool protein biomarker panels that can be used for the early detection of high-risk adenomas and CRC. Proteomics data (LC-MS/MS) were collected on stool samples from adenoma (n = 71) and CRC patients (n = 81) as well as controls (n = 129). Colorectal adenoma tissue samples were characterized by low-coverage whole-genome sequencing to determine their risk of progression based on specific DNA copy number changes. Proteomics data were used for logistic regression modeling to establish protein biomarker panels. In total, 15 of the adenomas (15.8%) were defined as high risk of progressing to cancer. A protein panel, consisting of haptoglobin (Hp), LAMP1, SYNE2, and ANXA6, was identified for the detection of high-risk adenomas (sensitivity of 53% at specificity of 95%). Two panels, one consisting of Hp and LRG1 and one of Hp, LRG1, RBP4, and FN1, were identified for high-risk adenomas and CRCs detection (sensitivity of 66% and 62%, respectively, at specificity of 95%). Validation of Hp as a biomarker for high-risk adenomas and CRCs was performed using an antibody-based assay in FIT samples from a subset of individuals from the discovery series (n = 158) and an independent validation series (n = 795). Hp protein was significantly more abundant in high-risk adenoma FIT samples compared to controls in the discovery (p = 0.036) and the validation series (p = 9e-5). We conclude that Hp, LAMP1, SYNE2, LRG1, RBP4, FN1, and ANXA6 may be of value as stool biomarkers for early detection of high-risk adenomas and CRCs. © 2019 Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland., (© 2019 Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.)

Purpose: To investigate the pathobiological origin of local relapse after chemoradiotherapy, we studied genetic relationships of primary tumors (PT) and local relapses (LR) of patients treated with chemoradiotherapy., Experimental Design: First, low-coverage whole genome sequencing was performed on DNA from 44 biopsies of resected head and neck squamous cell carcinoma (HNSCC) specimens (median 3 biopsies/tumor) to assess suitability of copy number alterations (CNAs) as biomarker for genetic relationships. CNAs were compared within and between tumors and an algorithm was developed to assess genetic relationships with consideration of intratumor heterogeneity. Next, this CNA-based algorithm was combined with target enrichment sequencing of genes frequently mutated in HNSCC to assess the genetic relationships of paired tumors and LRs of patients treated with chemoradiotherapy., Results: Genetic relationship analysis using CNAs could accurately (96%) predict tumor biopsy pairs as patient-matched or independent. However, subsequent CNA analysis of PTs and LRs after chemoradiotherapy suggested genetic relationships in only 20% of cases, and absence in 80%. Target enrichment sequencing for mutations confirmed absence of any genetic relationship in half of the paired PTs and LRs., Conclusions: There are minor variations in CNA profiles within different areas of HNSCC tumors and many between independent tumors, suggesting that CNA profiles could be exploited as a marker of genetic relationship. Using CNA profiling and mutational analysis of cancer driver genes, relapses after chemoradiotherapy appear to be partially genetically related to the corresponding PTs, but seem often genetically unrelated. This remarkable observation warrants further studies and will impact therapeutic innovations and prognostic modeling when using index tumor characteristics., (©2019 American Association for Cancer Research.)

Empirical Bayes is a versatile approach to "learn from a lot" in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well-known model-based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss "formal" empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross-validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed "co-data". In particular, we present two novel examples that allow for co-data: first, a Bayesian spike-and-slab setting that facilitates inclusion of multiple co-data sources and types and, second, a hybrid empirical Bayes-full Bayes ridge regression approach for estimation of the posterior predictive interval.

Offering self-sampling for HPV testing improves the effectiveness of current cervical screening programs by increasing population coverage. Molecular markers directly applicable on self-samples are needed to stratify HPV-positive women at risk of cervical cancer (so-called triage) and to avoid over-referral and overtreatment. Deregulated microRNAs (miRNAs) have been implicated in the development of cervical cancer, and represent potential triage markers. However, it is unknown whether deregulated miRNA expression is reflected in self-samples. Our study is the first to establish genome-wide miRNA profiles in HPV-positive self-samples to identify miRNAs that can predict the presence of CIN3 and cervical cancer in self-samples. Small RNA sequencing (sRNA-Seq) was conducted to determine genome-wide miRNA expression profiles in 74 HPV-positive self-samples of women with and without cervical precancer (CIN3). The optimal miRNA marker panel for CIN3 detection was determined by GRridge, a penalized method on logistic regression. Six miRNAs were validated by qPCR in 191 independent HPV-positive self-samples. Classification of sRNA-Seq data yielded a 9-miRNA marker panel with a combined area under the curve (AUC) of 0.89 for CIN3 detection. Validation by qPCR resulted in a combined AUC of 0.78 for CIN3+ detection. Our study shows that deregulated miRNA expression associated with CIN3 and cervical cancer development can be detected by sRNA-Seq in HPV-positive self-samples. Validation by qPCR indicates that miRNA expression analysis offers a promising novel molecular triage strategy for CIN3 and cervical cancer detection applicable to self-samples., (© 2018 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Introduction: Identification of blood-based metabolic changes might provide early and easy-to-obtain biomarkers., Methods: We included 127 Alzheimer's disease (AD) patients and 121 control subjects with cerebrospinal fluid biomarker-confirmed diagnosis (cutoff tau/amyloid β peptide 42: 0.52). Mass spectrometry platforms determined the concentrations of 53 amine compounds, 22 organic acid compounds, 120 lipid compounds, and 40 oxidative stress compounds. Multiple signatures were assessed: differential expression (nested linear models), classification (logistic regression), and regulatory (network extraction)., Results: Twenty-six metabolites were differentially expressed. Metabolites improved the classification performance of clinical variables from 74% to 79%. Network models identified five hubs of metabolic dysregulation: tyrosine, glycylglycine, glutamine, lysophosphatic acid C18:2, and platelet-activating factor C16:0. The metabolite network for apolipoprotein E ( APOE ) ε4 negative AD patients was less cohesive compared with the network for APOE ε4 positive AD patients., Discussion: Multiple signatures point to various promising peripheral markers for further validation. The network differences in AD patients according to APOE genotype may reflect different pathways to AD.

Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighbourhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference. We employ a simple Bayesian model with non-sparse, conjugate priors to facilitate the use of fast variational approximations to posteriors. We discuss empirical Bayes estimation of hyper-parameters of the priors, and propose a novel approach to rank-based posterior thresholding. Using extensive model- and data-based simulations, we demonstrate that the proposed inference strategy outperforms popular (sparse) methods, yields more stable edges, and is more reproducible. The proposed method, termed ShrinkNet, is then applied to Glioblastoma to investigate the interactions between genes associated with patient survival.

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., (Copyright © 2015 John Wiley & Sons, Ltd.)

Background: Prognosis of patients with colorectal cancer liver metastasis (CRCLM) is estimated based on clinicopathological models. Stratifying patients based on tumor biology may have additional value., Methods: Tissue micro-arrays (TMAs), containing resected CRCLM and corresponding primary tumors from a multi-institutional cohort of 507 patients, were immunohistochemically stained for 18 candidate biomarkers. Cross-validated hazard rate ratios (HRRs) for overall survival (OS) and the proportion of HRRs with opposite effect (P(HRR < 1) or P(HRR > 1)) were calculated. A classifier was constructed by classification and regression tree (CART) analysis and its prognostic value determined by permutation analysis. Correlations between protein expression in primary tumor-CRCLM pairs were calculated., Results: Based on their putative prognostic value, EGFR (P(HRR < 1) = .02), AURKA (P(HRR < 1) = .02), VEGFA (P(HRR < 1) = .02), PTGS2 (P(HRR < 1) = .01), SLC2A1 (P(HRR > 1) < 01), HIF1α (P(HRR > 1) = .06), KCNQ1 (P(HRR > 1) = .09), CEA (P (HRR > 1) = .05) and MMP9 (P(HRR < 1) = .07) were included in the CART analysis (n = 201). The resulting classifier was based on AURKA, PTGS2 and MMP9 expression and was associated with OS (HRR 2.79, p < .001), also after multivariate analysis (HRR 3.57, p < .001). The prognostic value of the biomarker-based classifier was superior to the clinicopathological model (p = .001). Prognostic value was highest for colon cancer patients (HRR 5.71, p < .001) and patients not treated with systemic therapy (HRR 3.48, p < .01). Classification based on protein expression in primary tumors could be based on AURKA expression only (HRR 2.59, p = .04)., Conclusion: A classifier was generated for patients with CRCLM with improved prognostic value compared to the standard clinicopathological prognostic parameters, which may aid selection of patients who may benefit from adjuvant systemic therapy.

Detection of DNA copy number aberrations by shallow whole-genome sequencing (WGS) faces many challenges, including lack of completion and errors in the human reference genome, repetitive sequences, polymorphisms, variable sample quality, and biases in the sequencing procedures. Formalin-fixed paraffin-embedded (FFPE) archival material, the analysis of which is important for studies of cancer, presents particular analytical difficulties due to degradation of the DNA and frequent lack of matched reference samples. We present a robust, cost-effective WGS method for DNA copy number analysis that addresses these challenges more successfully than currently available procedures. In practice, very useful profiles can be obtained with ∼0.1× genome coverage. We improve on previous methods by first implementing a combined correction for sequence mappability and GC content, and second, by applying this procedure to sequence data from the 1000 Genomes Project in order to develop a blacklist of problematic genome regions. A small subset of these blacklisted regions was previously identified by ENCODE, but the vast majority are novel unappreciated problematic regions. Our procedures are implemented in a pipeline called QDNAseq. We have analyzed over 1000 samples, most of which were obtained from the fixed tissue archives of more than 25 institutions. We demonstrate that for most samples our sequencing and analysis procedures yield genome profiles with noise levels near the statistical limit imposed by read counting. The described procedures also provide better correction of artifacts introduced by low DNA quality than prior approaches and better copy number data than high-resolution microarrays at a substantially lower cost., (© 2014 Scheinin et al.; Published by Cold Spring Harbor Laboratory Press.)

Creatine transporter (SLC6A8) deficiency is the most common cause of cerebral creatine syndromes, and is characterized by depletion of creatine in the brain. Manifestations of this X-linked disorder include intellectual disability, speech/language impairment, behavior abnormalities, and seizures. At the moment, no effective treatment is available. In order to investigate the molecular pathophysiology of this disorder, we performed RNA sequencing on fibroblasts derived from patients. The transcriptomes of fibroblast cells from eight unrelated individuals with SLC6A8 deficiency and three wild-type controls were sequenced. SLC6A8 mutations with different effects on the protein product resulted in different gene expression profiles. Differential gene expression analysis followed by gene ontology term enrichment analysis revealed that especially the expression of genes encoding components of the extracellular matrix and cytoskeleton are altered in SLC6A8 deficiency, such as collagens, keratins, integrins, and cadherins. This suggests an important novel role for creatine in the structural development and maintenance of cells. It is likely that the (extracellular) structure of brain cells is also impaired in SLC6A8-deficient patients, and future studies are necessary to confirm this and to reveal the true functions of creatine in the brain., (© 2014 WILEY PERIODICALS, INC.)

Multiple sclerosis is a heterogeneous neurological disease with varying degrees of severity. The common hypothesis is that susceptibility to multiple sclerosis and its phenotype are caused by a combination of environmental and genetic factors. The genetic part exerts its effect through several genes, each having modest effects. We evaluated whether disease severity could be predicted by a model based on clinical data and data from a DNA chip. The DNA chip was designed containing several single nucleotide polymorphisms in 44 genes, previously described to be associated with multiple sclerosis. A total of 605 patients with multiple sclerosis were included in this analysis, using gender, onset type and age at onset as clinical covariates. We correlated 80 single nucleotide polymorphisms to the degree of disease severity using the following three outcome measures: linear Multiple Sclerosis Severity Score, dichotomous Multiple Sclerosis Severity Score (using a cut-off point of 2.5) and time to reach Expanded Disability Status Scale score 6. Sixty-nine single nucleotide polymorphisms were included in the analysis. No individual single nucleotide polymorphism showed a significant association; however, a combination of single nucleotide polymorphisms significantly improved the prediction of disease severity in addition to the clinical variables. In all three models the Interleukin 2 gene was included, confirming a previously reported modest effect on disease severity. The highest power was obtained using the dichotomized Multiple Sclerosis Severity Score as outcome. Several single nucleotide polymorphisms showed their added predictive value over the clinical data in the predictive models. These results support our hypothesis that disease severity is determined by clinical variables and genetic influences (through several genes with small effects) in concert.