Biomarkers and personalized medicine: current status and further perspectives with special focus on dermatology.

Biomarkers are of increasingly high importance in medicine, particularly in the realm of 'personalized medicine'. They are valuable for predicting prognosis and dose selection. Moreover, they may be helpful in detecting therapeutic and adverse responses and in patient stratification based on efficacy or safety prediction. Thus, biomarkers are essential tools for the selection of appropriate patients for treatment with certain drugs to and enable personalized medicine, that is 'providing the right treatment to the right patient, at the right dose at the right time'. Currently, there are six drugs approved for dermatological indications with recommended or mandatory biomarker testing. Most of them are used to treat melanoma and human immunodeficiency virus infection. In contrast to the few fully validated biomarkers, many exploratory biomarkers and biomarker candidates have potential applications. Prognostic biomarkers are of particular significance for malignant conditions. Similarly, diagnostic biomarkers are important in autoimmune diseases. Disease severity biomarkers are helpful tools in the treatment for inflammatory skin diseases. Identification, qualification and implementation of the different kinds of biomarkers are challenging and frequently necessitate collaborative efforts. This is particularly true for stratification biomarkers that require a companion diagnostic marker that is co-developed with a certain drug. In this article general definitions and requirements for biomarkers as well as for the impact of biomarkers in dermatology are reviewed and opportunities and challenges are discussed.

AKT1 (E17K) mutation profiling in breast cancer: prevalence, concurrent oncogenic alterations, and blood-based detection.

BACKGROUND: The single hotspot mutation AKT1 [G49A:E17K] has been described in several cancers, with the highest incidence observed in breast cancer. However, its precise role in disease etiology remains unknown. METHODS: We analyzed more than 600 breast cancer tumor samples and circulating tumor DNA for AKT1 (E17K) and alterations in other cancer-associated genes using Beads, Emulsions, Amplification, and Magnetics digital polymerase chain reaction technology and targeted exome sequencing. RESULTS: Overall AKT1 (E17K) mutation prevalence was 6.3 % and not correlated with age or menopausal stage. AKT1 (E17K) mutation frequency tended to be lower in patients with grade 3 disease (1.9 %) compared with those with grade 1 (11.1 %) or grade 2 (6 %) disease. In two cohorts of patients with advanced metastatic disease, 98.0 % (n = 50) and 97.1 % (n = 35) concordance was obtained between tissue and blood samples for the AKT1 (E17K) mutation, and mutation capture rates of 66.7 % (2/3) and 85.7 % (6/7) in blood versus tissue samples were observed. Although AKT1-mutant tumor specimens were often found to harbor concurrent alterations in other driver genes, a subset of specimens harboring AKT1 (E17K) as the only known driver alteration was also identified. Initial follow-up survival data suggest that AKT1 (E17K) could be associated with increased mortality. These findings warrant additional long-term follow-up. CONCLUSIONS: The data suggest that AKT1 (E17K) is the most likely disease driver in certain breast cancer patients. Blood-based mutation detection is achievable in advanced-stage disease. These findings underpin the need for a further enhanced-precision medicine paradigm in the treatment of breast cancer.

Towards a survival risk prediction model for metastatic NSCLC patients on durvalumab using whole-lung CT radiomics.

Background: Existing criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting 'less-than-median-survival risk' in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet. Methods: To ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features. Results: The classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively. Conclusion: Machine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.

Variability in detection and quantification of interferon ß-1b-induced neutralizing antibodies.

BACKGROUND: Interferon-beta (IFNB) therapy for multiple sclerosis can lead to the induction of neutralizing antibodies (NAbs) against IFNB. Various methods are used for detection and quantification of NAbs. METHODS: Blood samples from 125 IFNB-1b-treated patients, which were tested NAb negative or NAb positive after conclusion of a clinical study, were retested three years after first being assessed in four different laboratories that offer routine NAb testing to practicing neurologists. The myxovirus protein A (MxA) induction assay, the cytopathic effect (CPE) assay (two laboratories), or the luciferase assay were used. Intra- and inter-laboratory agreement between assays with respect to NAb detection and NAb titer quantification were evaluated. RESULTS: High agreement for NAb detection (kappa coefficient, 0.86) and for titer levels was observed for the intra-laboratory comparison in the laboratory using the MxA induction assay performed three years ago and now. A similarly high agreement for NAb detection (kappa coefficient, 0.87) and for titer quantification was noted for the MxA assay of this laboratory with one of two laboratories using the CPE assay. All other inter-laboratory comparisons showed kappa values between 0.57 and 0.68 and remarkable differences in individual titer levels. CONCLUSIONS: There are considerable differences in the detection and quantification of IFNB-induced NAbs among laboratories offering NAb testing for clinical practice using different assay methods. It is important that these differences are considered when interpreting NAb results for clinical decision-making and when developing general recommendations for potentially clinically meaningful NAb titer levels.

Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci.

OBJECTIVE: To perform a 1-stage meta-analysis of genome-wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and to explore functional consequences of new susceptibility loci. METHODS: We synthesized 7 MS GWAS. Each data set was imputed using HapMap phase II, and a per single nucleotide polymorphism (SNP) meta-analysis was performed across the 7 data sets. We explored RNA expression data using a quantitative trait analysis in peripheral blood mononuclear cells (PBMCs) of 228 subjects with demyelinating disease. RESULTS: We meta-analyzed 2,529,394 unique SNPs in 5,545 cases and 12,153 controls. We identified 3 novel susceptibility alleles: rs170934(T) at 3p24.1 (odds ratio [OR], 1.17; p = 1.6 × 10(-8)) near EOMES, rs2150702(G) in the second intron of MLANA on chromosome 9p24.1 (OR, 1.16; p = 3.3 × 10(-8)), and rs6718520(A) in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR, 1.17; p = 3.4 × 10(-8)). The 3 new loci do not have a strong cis effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive p < 1 × 10(-6) , some of these loci have evidence of association in other inflammatory diseases (ie, IL12B, TAGAP, PLEK, and ZMIZ1). INTERPRETATION: We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.

Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials.

Understanding factors that impact prognosis for cancer patients have high clinical relevance for treatment decisions and monitoring of the disease outcome. Advances in artificial intelligence (AI) and digital pathology offer an exciting opportunity to capitalize on the use of whole slide images (WSIs) of hematoxylin and eosin (H&E) stained tumor tissue for objective prognosis and prediction of response to targeted therapies. AI models often require hand-delineated annotations for effective training which may not be readily available for larger data sets. In this study, we investigated whether AI models can be trained without region-level annotations and solely on patient-level survival data. We present a weakly supervised survival convolutional neural network (WSS-CNN) approach equipped with a visual attention mechanism for predicting overall survival. The inclusion of visual attention provides insights into regions of the tumor microenvironment with the pathological interpretation which may improve our understanding of the disease pathomechanism. We performed this analysis on two independent, multi-center patient data sets of lung (which is publicly available data) and bladder urothelial carcinoma. We perform univariable and multivariable analysis and show that WSS-CNN features are prognostic of overall survival in both tumor indications. The presented results highlight the significance of computational pathology algorithms for predicting prognosis using H&E stained images alone and underpin the use of computational methods to improve the efficiency of clinical trial studies.

Separable transcriptional regulatory domains within Otd control photoreceptor terminal differentiation events.

Orthodenticle (Otd)-related transcription factors are essential for anterior patterning and brain morphogenesis from Cnidaria to Mammals, and genetically underlie several human retinal pathologies. Despite their key developmental functions, relatively little is known regarding the molecular basis of how these factors regulate downstream effectors in a cell- or tissue-specific manner. Many invertebrate and vertebrate species encode two to three Otd proteins, whereas Drosophila encodes a single Otd protein. In the fly retina, Otd controls rhabdomere morphogenesis of all photoreceptors and regulates distinct Rhodopsin-encoding genes in a photoreceptor subtype-specific manner. Here, we performed a structure-function analysis of Otd during Drosophila eye development using in vivo rescue experiments and in vitro transcriptional regulatory assays. Our findings indicate that Otd requires at least three distinct transcriptional regulatory domains to control photoreceptor-specific rhodopsin gene expression and photoreceptor morphogenesis. Our results also uncover a previously unknown role for Otd in preventing co-expression of sensory receptors in blue vs. green-sensitive R8 photoreceptors. Sequence analysis indicates that many of the transcriptional regulatory domains identified here are conserved in multiple Diptera Otd-related proteins. Thus, these studies provide a basis for identifying shared molecular pathways involved in a wide range of developmental processes.

Individualized quantification of brain ß-amyloid burden: results of a proof of mechanism phase 0 florbetaben PET trial in patients with Alzheimer's disease and healthy controls.

PURPOSE: Complementing clinical findings with those generated by biomarkers--such as ß-amyloid-targeted positron emission tomography (PET) imaging--has been proposed as a means of increasing overall accuracy in the diagnosis of Alzheimer's disease (AD). Florbetaben ([(18)F]BAY 94-9172) is a novel ß-amyloid PET tracer currently in global clinical development. We present the results of a proof of mechanism study in which the diagnostic efficacy, pharmacokinetics, safety and tolerability of florbetaben were assessed. The value of various quantitative parameters derived from the PET scans as potential surrogate markers of cognitive decline was also investigated. METHODS: Ten patients with mild-moderate probable AD (DSM-IV and NINCDS-ADRDA criteria) and ten age-matched (≥ 55 years) healthy controls (HCs) were administered a single dose of 300 MBq florbetaben, which contained a tracer mass dose of < 5 µg. The 70-90 min post-injection brain PET data were visually analysed by three blinded experts. Quantitative assessment was also performed via MRI-based, anatomical sampling of predefined volumes of interest (VOI) and subsequent calculation of standardized uptake value (SUV) ratios (SUVRs, cerebellar cortex as reference region). Furthermore, single-case, voxelwise analysis was used to calculate individual "whole brain ß-amyloid load". RESULTS: Visual analysis of the PET data revealed nine of the ten AD, but only one of the ten HC brains to be ß-amyloid positive (p = 0.001), with high inter-reader agreement (weighted kappa ≥ 0.88). When compared to HCs, the neocortical SUVRs were significantly higher in the ADs (with descending order of effect size) in frontal cortex, lateral temporal cortex, occipital cortex, anterior and posterior cingulate cortices, and parietal cortex (p = 0.003-0.010). Voxel-based group comparison confirmed these differences. Amongst the PET-derived parameters, the Statistical Parametric Mapping-based whole brain ß-amyloid load yielded the closest correlation with the Mini-Mental State Examination scores (r = -0.736, p < 0.001), following a nonlinear regression curve. No serious adverse events or other safety concerns were seen. CONCLUSION: These results indicate florbetaben to be a safe and efficacious ß-amyloid-targeted tracer with favourable brain kinetics. Subjects with AD could be easily differentiated from HCs by both visual and quantitative assessment of the PET data. The operator-independent, voxel-based analysis yielded whole brain ß-amyloid load which appeared valuable as a surrogate marker of disease severity.

Biological response genes after single dose administration of interferon beta-1b to healthy male volunteers.

Treatment with interferon beta-1b (IFNB-1b) is clinically effective in multiple sclerosis patients. However, the mechanism of action is only partially understood, and validated biological response markers are lacking. We assessed IFNB-1b-induced transcriptional changes by microarray technology. Healthy male volunteers received 250 mug IFNB-1b or placebo in a double-blind, randomized controlled trial (n=5 per group). Most transcripts demonstrated peak levels after 6-12 h and returned to baseline after 48 h. We identified 227 differentially regulated genes including novel and previously described markers. This panel may become a valuable tool for development of new IFNB-1b formulations and assessment of clinical drug effects.

Key factors for successful data integration in biomarker research.

Integrating a wide range of biomedical data such as that rapidly emerging from the use of next-generation sequencing is expected to have a key role in identifying and qualifying new biomarkers to support precision medicine. Here, we highlight some of the challenges for biomedical data integration and approaches to address them.

Recovery of microarray-quality RNA from frozen EDTA blood samples.

INTRODUCTION: The availability of blood collection systems with RNA stabilizing additives (e.g. PAXgene) has opened the field for gene expression profiling in large multi-center clinical trials. Here, we investigated whether the PAXgene system also offers a method for extraction of RNA from frozen EDTA blood samples, which do not yield RNA of high enough quality for RNA expression profiling, when extracted with standard protocols. METHODS: Whole blood was obtained from six healthy volunteers in conventional EDTA tubes and frozen. The thawed EDTA blood was transferred into PAXgene tubes, and the RNA was extracted using the PAXgene RNA extraction kit. Microarray analysis was performed to asses the effect of RNA quality on gene expression profiles. RESULTS: The RNA yield of the transferred samples was 1.76+/-0.88 microg/ml. This yield was clearly lower than the yield from a PAXgene reference group (2.84+/-0.62 microg/ml), but considerably higher than the yield resulting from a standard protocol usually applied to fresh EDTA blood samples (0.07+/-0.06 microg/ml). The RNA integrity number (RIN) of the transferred samples was 6.1+/-0.8 as compared to 9.8+/-0.1 for the PAXgene reference. Microarray analysis of the extracted RNA suggested that samples with RIN values above 5 produce data that fulfill the quality criteria defined by the manufacturer. DISCUSSION: The transfer of thawed EDTA blood into PAXgene blood collection tubes offers a method to recover sufficient RNA of acceptable quality for microarray experiments.

Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score.

BACKGROUND: Prediction of susceptibility to multiple sclerosis (MS) might have important clinical applications, either as part of a diagnostic algorithm or as a means to identify high-risk individuals for prospective studies. We investigated the usefulness of an aggregate measure of risk of MS that is based on genetic susceptibility loci. We also assessed the added effect of environmental risk factors that are associated with susceptibility for MS. METHODS: We created a weighted genetic risk score (wGRS) that includes 16 MS susceptibility loci. We tested our model with data from 2215 individuals with MS and 2189 controls (derivation samples), a validation set of 1340 individuals with MS and 1109 controls taken from several MS therapeutic trials (TT cohort), and a second validation set of 143 individuals with MS and 281 controls from the US Nurses' Health Studies I and II (NHS/NHS II), for whom we also have data on smoking and immune response to Epstein-Barr virus (EBV). FINDINGS: Individuals with a wGRS that was more than 1.25 SD from the mean had a significantly higher odds of MS in all datasets. In the derivation sample, the mean (SD) wGRS was 3.5 (0.7) for individuals with MS and 3.0 (0.6) for controls (p<0.0001); in the TT validation sample, the mean wGRS was 3.4 (0.7) for individuals with MS versus 3.1 (0.7) for controls (p<0.0001); and in the NHS/NHS II dataset, the mean wGRS was 3.4 (0.8) for individuals with MS versus 3.0 (0.7) for controls (p<0.0001). In the derivation cohort, the area under the receiver operating characteristic curve (C statistic; a measure of the ability of a model to discriminate between individuals with MS and controls) for the genetic-only model was 0.70 and for the genetics plus sex model was 0.74 (p<0.0001). In the TT and NHS cohorts, the C statistics for the genetic-only model were both 0.64; adding sex to the TT model increased the C statistic to 0.72 (p<0.0001), whereas adding smoking and immune response to EBV to the NHS model increased the C statistic to 0.68 (p=0.02). However, the wGRS does not seem to be correlated with the conversion of clinically isolated syndrome to MS. INTERPRETATION: The inclusion of 16 susceptibility alleles into a wGRS can modestly predict MS risk, shows consistent discriminatory ability in independent samples, and is enhanced by the inclusion of non-genetic risk factors into the algorithm. Future iterations of the wGRS might therefore make a contribution to algorithms that can predict a diagnosis of MS in a clinical or research setting.

Increased expression of Wnt5a in psoriatic plaques.

Psoriasis vulgaris is characterized by hyperproliferation and incomplete terminal differentiation of epidermal keratinocytes. Despite the established role of Wnt pathways in the regulation of stem cell proliferation and differentiation, they have not yet been associated with the pathophysiology of psoriasis. Here, we took biopsies from uninvolved and from lesional skin of 20 patients with plaque-type psoriasis. The biopsies were used for microarray RNA expression profiling. Based on paired samples from 13 patients, we defined 179 genes that were more than 2-fold differentially expressed in lesional skin. This list included 16 genes with known or possible association to the canonical Wnt/beta-catenin or the non-canonical Wnt/Ca2+ pathway. The expression of Wnt5a was 4-fold higher in lesional skin. Other Wnt molecules were largely unchanged (Wnt4 and Wnt16), or tended to be expressed at lower levels (Wnt7b). The mRNA expression levels of two inhibitory factors related to Wnt signaling, frizzled-related protein, and dickkopf homolog 2, were reduced in lesional skin, as was mRNA expression of cyclin D1. These findings were confirmed by quantitative reverse transcription-PCR experiments. We conclude that Wnt5a and other Wnt pathway genes are differentially expressed in psoriatic plaques. Their functional contribution to the pathophysiology of psoriasis needs to be elaborated.