The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.

The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly diluted samples like those obtained with liquid biopsy, NGS invariably introduces a certain level of misclassification, even with improved technology. Recently, there has been a high demand to use mutation genotypes as biomarkers for predicting prognosis and treatment selection. Many methods have also been proposed to build classifiers based on multiple loci with machine learning algorithms as biomarkers. How the higher misclassification rate introduced by liquid biopsy will affect the performance of these biomarkers has not been thoroughly investigated. In this paper, we report the results from a simulation study focused on the clinical utility of biomarkers when misclassification is present due to the current technological limit of NGS in the liquid biopsy setting. The simulation covers a range of performance profiles for current NGS platforms with different machine learning algorithms and uses actual patient genotypes. Our results show that, at the high end of the performance spectrum, the misclassification introduced by NGS had very little effect on the clinical utility of the biomarker. However, in more challenging applications with lower accuracy, misclassification could have a notable effect on clinical utility. The pattern of this effect can be complex, especially for machine learning-based classifiers. Our results show that simulation can be an effective tool for assessing different scenarios of misclassification.

A targeted simulation-extrapolation method for evaluating biomarkers based on new technologies in precision medicine.

New technologies for novel biomarkers have transformed the field of precision medicine. However, in applications such as liquid biopsy for early tumor detection, the misclassification rates of next generation sequencing and other technologies have become an unavoidable feature of biomarker development. Because initial experiments are usually confined to specific technology choices and application settings, a statistical method that can project the performance metrics of other scenarios with different misclassification rates would be very helpful for planning further biomarker development and future trials. In this article, we describe an approach based on an extended version of simulation extrapolation (SIMEX) to project the performance of biomarkers measured with varying misclassification rates due to different technological or application settings when experimental results are only available from one specific setting. Through simulation studies for logistic regression and proportional hazards models, we show that our proposed method can be used to project the biomarker performance with good precision when switching from one to anther technology or application setting. Similar to the original SIMEX model, the proposed method can be implemented with existing software in a straightforward manner. A data analysis example is also presented using a lung cancer data set and performance metrics for two gene panel based biomarkers. Results demonstrate that it is feasible to infer the potential implications of using a range of technologies or application scenarios for biomarkers with limited human trial data.

In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases.

BACKGROUND: Near universal administration of vaccines mandates intense pharmacovigilance for vaccine safety and a stringently low tolerance for adverse events. Reports of autoimmune diseases (AID) following vaccination have been challenging to evaluate given the high rates of vaccination, background incidence of autoimmunity, and low incidence and variable times for onset of AID after vaccinations. In order to identify biologically plausible pathways to adverse autoimmune events of vaccine-related AID, we used a systems biology approach to create a matrix of innate and adaptive immune mechanisms active in specific diseases, responses to vaccine antigens, adjuvants, preservatives and stabilizers, for the most common vaccine-associated AID found in the Vaccine Adverse Event Reporting System. RESULTS: This report focuses on Guillain-Barre Syndrome (GBS), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Idiopathic (or immune) Thrombocytopenic Purpura (ITP). Multiple curated databases and automated text mining of PubMed literature identified 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. While all data sources provided valuable and unique gene associations, text mining using natural language processing (NLP) algorithms provided the most information but required curation to remove incorrect associations. Six genes were associated with all four AIDs. Thirty-three pathways were shared by the four AIDs. Classification of genes into twelve immune system related categories identified more "Th17 T-cell subtype" genes in RA than the other AIDs, and more "Chemokine plus Receptors" genes associated with RA than SLE. Gene networks were visualized and clustered into interconnected modules with specific gene clusters for each AID, including one in RA with ten C-X-C motif chemokines. The intersection of genes associated with GBS, GBS peptide auto-antigens, influenza A infection, and influenza vaccination created a subnetwork of genes that inferred a possible role for the MAPK signaling pathway in influenza vaccine related GBS. CONCLUSIONS: Results showing unique and common gene sets, pathways, immune system categories and functional clusters of genes in four autoimmune diseases suggest it is possible to develop molecular classifications of autoimmune and inflammatory events. Combining this information with cellular and other disease responses should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA.

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration's (FDA's) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

US FDA public meeting: identification of concepts and terminology for multicomponent biomarkers.

The US FDA convened a virtual public workshop with the goals of obtaining feedback on the terminology needed for effective communication of multicomponent biomarkers and discussing the diverse use of biomarkers observed across the FDA and identifying common issues. The workshop included keynote and background presentations addressing the stated goals, followed by a series of case studies highlighting FDA-wide and external experience regarding the use of multicomponent biomarkers, which provided context for panel discussions focused on common themes, challenges and preferred terminology. The final panel discussion integrated the main concepts from the keynote, background presentations and case studies, laying a preliminary foundation to build consensus around the use and terminology of multicomponent biomarkers.

Predicting drug sensitivity and resistance: profiling ABC transporter genes in cancer cells.

For analysis of multidrug resistance, a major barrier to effective cancer chemotherapy, we profiled mRNA expression of the 48 known human ABC transporters in 60 diverse cancer cell lines (the NCI-60) used by the National Cancer Institute to screen for anticancer activity. The use of real-time RT-PCR avoided artifacts commonly encountered with microarray technologies. By correlating the results with the growth inhibitory profiles of 1,429 candidate anticancer drugs tested against the cells, we identified which transporters are more likely than others to confer resistance to which agents. Unexpectedly, we also found and validated compounds whose activity is potentiated, rather than antagonized, by the MDR1 multidrug transporter. Such compounds may serve as leads for development.

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing.

BACKGROUND: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. RESULTS: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. CONCLUSIONS: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

Author Correction: Best practices for benchmarking germline small-variant calls in human genomes.

In the version of this article initially published online, two pairs of headings were switched with each other in Table 4: "Recall (PCR free)" was switched with "Recall (with PCR)," and "Precision (PCR free)" was switched with "Precision (with PCR)." The error has been corrected in the print, PDF and HTML versions of this article.

Best practices for benchmarking germline small-variant calls in human genomes.

Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Although variant-calling tools and the metrics used to assess their performance continue to improve, important challenges remain. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling. We provide guidance on how to match variant calls with different representations, define standard performance metrics, and stratify performance by variant type and genome context. We describe limitations of high-confidence calls and regions that can be used as truth sets (for example, single-nucleotide variant concordance of two methods is 99.7% inside versus 76.5% outside high-confidence regions). Our web-based app enables comparison of variant calls against truth sets to obtain a standardized performance report. Our approach has been piloted in the PrecisionFDA variant-calling challenges to identify the best-in-class variant-calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and evaluating the results.

Challenges in DNA microarray studies from the regulatory perspective.

Genomic classifiers using DNA microarrays are becoming powerful tools in the medical community with the potential to revolutionize the diagnosis and treatment of disease. However, despite the tremendous interest in using these classifiers in diagnosis and the management of disease, few genomic classifiers have made it into clinical practice. Some of the major challenges for the development and validation of genomic classifiers will be discussed in this article together with some of their difficulties.

Detailed DNA methylation profiles of the E-cadherin promoter in the NCI-60 cancer cells.

E-cadherin (E-cad) is a transmembrane adhesion glycoprotein, the expression of which is often reduced in invasive or metastatic tumors. To assess E-cad's distribution among different types of cancer cells, we used bisulfite-sequencing for detailed, base-by-base measurement of CpG methylation in E-cad's promoter region in the NCI-60 cell lines. The mean methylation levels of the cell lines were distributed bimodally, with values pushed toward either the high or low end of the methylation scale. The 38 epithelial cell lines showed substantially lower (28%) mean methylation levels compared with the nonepithelial cell lines (58%). The CpG site at -143 with respect to the transcriptional start was commonly methylated at intermediate levels, even in cell lines with low overall DNA methylation. We also profiled the NCI-60 cell lines using Affymetrix U133 microarrays and found E-cad expression to be correlated with E-cad methylation at highly statistically significant levels. Above a threshold of approximately 20% to 30% mean methylation, the expression of E-cad was effectively silenced. Overall, this study provides a type of detailed analysis of methylation that can also be applied to other cancer-related genes. As has been shown in recent years, DNA methylation status can serve as a biomarker for use in choosing therapy.

Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel.

Chromosome rearrangement, a hallmark of cancer, has profound effects on carcinogenesis and tumor phenotype. We used a panel of 60 human cancer cell lines (the NCI-60) as a model system to identify relationships among DNA copy number, mRNA expression level, and drug sensitivity. For each of 64 cancer-relevant genes, we calculated all 4,096 possible Pearson's correlation coefficients relating DNA copy number (assessed by comparative genomic hybridization using bacterial artificial chromosome microarrays) and mRNA expression level (determined using both cDNA and Affymetrix oligonucleotide microarrays). The analysis identified an association of ERBB2 overexpression with 3p copy number, a finding supported by data from human tumors and a mouse model of ERBB2-induced carcinogenesis. When we examined the correlation between DNA copy number for all 353 unique loci on the bacterial artificial chromosome microarray and drug sensitivity for 118 drugs with putatively known mechanisms of action, we found a striking negative correlation (-0.983; 95% bootstrap confidence interval, -0.999 to -0.899) between activity of the enzyme drug L-asparaginase and DNA copy number of genes near asparagine synthetase in the ovarian cancer cells. Previous analysis of drug sensitivity and mRNA expression had suggested an inverse relationship between mRNA levels of asparagine synthetase and L-asparaginase sensitivity in the NCI-60. The concordance of pharmacogenomic findings at the DNA and mRNA levels strongly suggests further study of L-asparaginase for possible treatment of a low-synthetase subset of clinical ovarian cancers. The DNA copy number database presented here will enable other investigators to explore DNA transcript-drug relationships in their own domains of research focus.

Karyotypic complexity of the NCI-60 drug-screening panel.

We used spectral karyotyping to provide a detailed analysis of karyotypic aberrations in the diverse group of cancer cell lines established by the National Cancer Institute for the purpose of anticancer drug discovery. Along with the karyotypic description of these cell lines we defined and studied karyotypic complexity and heterogeneity (metaphase-to-metaphase variations) based on three separate components of genomic anatomy: (a) ploidy; (b) numerical changes; and (c) structural rearrangements. A wide variation in these parameters was evident in these cell lines, and different association patterns between them were revealed. Analysis of the breakpoints and other specific features of chromosomal changes across the entire set of cell lines or within particular lineages pointed to a striking lability of centromeric regions that distinguishes the epithelial tumor cell lines. We have also found that balanced translocations are as frequent in absolute number within the cell lines derived from solid as from hematopoietic tumors. Important similarities were noticed between karyotypic changes in cancer cell lines and that seen in primary tumors. This dataset offers insights into the causes and consequences of the destabilizing events and chromosomal instability that may occur during tumor development and progression. It also provides a foundation for investigating associations between structural genome anatomy and cancer molecular markers and targets, gene expression, gene dosage, and resistance or sensitivity to tens of thousands of molecular compounds.

Apoptotic susceptibility of cancer cells selected for camptothecin resistance: gene expression profiling, functional analysis, and molecular interaction mapping.

To study the molecular mechanisms by which drug resistance develops, we compared DU145 humanprostate cancer cells with a subline selected for resistance to camptothecin. Differences in gene expression level were assessed by hybridizing the two cell types against each other using quadruplicate "Oncochip" cDNA microarrays that included 1648 cancer-related genes. Expression levels differing by a factor of >1.5 were detected for 181 of the genes. These differences were judged statistically reliable on the basis of a stratum-adjusted Kruskal-Wallis test, after taking into account a dye-dependent variable. The 181 expression-altered genes included a larger than expected number of the "apoptosis-related" genes (P = 0.04). To assess whether this observation reflected a generalized resistance of RCO.1 to apoptosis, we exposed the cells to a range of stresses (cisplatin, staurosporine, UV, ionizing radiation, and serum starvation) and found greatly reduced apoptotic responses for RC0.1 (relative to DU145) using flow cytometric Annexin V and terminal deoxynucleotidyl transferase-mediated nick end labeling assays. We next examined the apoptosis-related genes in the context of a molecular interaction map and found expression differences in the direction "expected" on the basis of the apoptosis-resistance of RC0.1 for BAD, caspase-6, and genes that signal via the Akt pathway. Exposure of the cells to wortmannin, an inhibitor of the Akt effector phosphatidylinositol 3-kinase, provided functional support for involvement of the Akt pathway. However, closer examination of the molecular interaction map revealed a paradox: many of the expression differences observed for apoptosis-related genes were in the direction "contrary" to that expected given the resistance of RC0.1. The map indicated that most of these unexpected expression differences were associated with genes involved in the nuclear factor kappa B and transforming growth factor beta pathways. Overall, the patterns that emerged suggested a two-step model for the selection process that led to resistance in RC0.1 cells. The first hypothesized step would involve a decrease in apoptotic susceptibility through changes in the apoptosis-control machinery associated with the Bcl-2 and caspase gene families, and also in antiapoptotic pathways operating through Akt/PKB. The second step would involve changes in multifunctional upstream genes (including some genes in the nuclear factor kappa B and transforming growth factor beta pathways) that can facilitate apoptosis but that would also tend to contribute to cell proliferation in the presence of drug. Thus, we propose that a downstream blockade of apoptosis was "permissive" for the selection of upstream pathway changes that would otherwise have induced apoptosis. This model is analogous to one suggested previously for the relationship between oncogene function and apoptosis in carcinogenesis.

Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling.

Colon and ovarian cancers can be difficult to distinguish in the abdomen, and the distinction is important because it determines which drugs will be used for therapy. To identify molecular markers for that differential diagnosis, we developed a multistep protocol starting with the 60 human cancer cell lines used by the National Cancer Institute to screen for new anticancer agents. The steps included: (a) identification of candidate markers using cDNA microarrays; (b) verification of clone identities by resequencing; (c) corroboration of transcript levels using Affymetrix oligonucleotide chips; (d) quantitation of protein expression by "reverse-phase" protein microarray; and (e) prospective validation of candidate markers on clinical tumor sections in tissue microarrays. The two best candidates identified were villin for colon cancer cells and moesin for ovarian cancer cells. Because moesin stained stromal elements in both types of cancer, it would probably not have been identified as a marker if we had started with mRNA or protein profiling of bulk tumors. Villin appears at least as useful as the currently used colon cancer marker cytokeratin 20, and moesin also appears to have utility. The multistep process introduced here has the potential to produce additional markers for cancer diagnosis, prognosis, and therapy.

Cytochrome p450 and glutathione transferase expression in human breast cancer.

PURPOSE: The cytochrome P-450 (CYP) and glutathione S-transferase (GST) enzyme systems may influence the biological effects of carcinogens, including estrogens. As such, these enzymes may predict the developmental risk of breast cancer, as well as be potential targets for chemoprevention. The purpose of this study was to compare the expression of GST-Pi and CYPs 1A1, 2B6, 2E1, and 3A4 in paired samples of normal and malignant breast tissue from patients with breast cancer and women undergoing reduction mammoplasty. EXPERIMENTAL DESIGN: Expression of CYPs 1A1, 2B6, 2E1, 3A4, and GST-Pi was quantified in breast tissue from 33 patients with breast cancer and in 17 women without history of cancer who underwent reduction mammoplasty. The expression of CYP 1A1, 2B6, 2E1, 3A4, and GST-Pi was quantified by immunoblotting. RESULTS: CYP 1A1, 2E1, and 3A4 expression was significantly lower (P < 0.05) in malignant tissue as compared with morphologically normal adjacent tissue. Conversely, GST-Pi expression was marginally lower in the normal tissue (P = 0.08). No significant difference in enzyme expression was seen between the tissue from reduction mammoplasty and normal tissue from breast cancer patients. There was a trend for higher expression of CYP 2B6 and GST-Pi in the estrogen receptor expressing tumors than those tumors without expression (P > 0.28). CONCLUSION: The expression of these enzymes was similar in morphologically normal breast tissue from patients with or without breast cancer. The expression of CYPs was down-regulated in the tumor tissue. The clinical significance of CYP alterations in breast cancer will need further characterization.

Overall conceptual framework for studying the genetics of autoimmune diseases following vaccination: a regulatory perspective.

The US Vaccine Adverse Event Reporting System contains case reports of autoimmune diseases (ADs) occurring following vaccinations. ADs are rare and occur in unvaccinated people, making the potential association between vaccines and ADs challenging to evaluate. Developing mechanistic pathways that link genes, immune mediators, vaccine components and ADs would be helpful for hypothesis generation, enhancing theories of biologic plausibility and grouping rare autoimmune adverse events to increase the ability to detect and evaluate safety signals. Here, we propose a conceptual framework for investigating the genetics of ADs as safety signals following vaccination, potentially contributing to the identification of relevant biomarkers. We also discuss a study design that incorporates genetic information into postmarket clinical evaluation of autoimmune adverse events following vaccination.

Identification, characterization, and targeting of IL-4 receptor by IL-4-Pseudomonas exotoxin in mouse models of anaplastic thyroid cancer.

Thyroid cancer is a rapidly increasing endocrine cancer. Since interleukin-4 receptor (IL-4R) is overexpressed in human solid cancer, we examined expression of IL-4R in 50 cases of anaplastic thyroid cancer (ATC), 37 well-differentiated papillary cancer (WDPC), 35 well-differentiated follicular cancer of thyroid (WDFC), and 37 normal thyroid specimens by immunohistochemistry (IHC) and in-situ hybridization (ISH) techniques. We demonstrated that IL-4Rα was overexpressed in 36/50 (72%) ATC, 20/35 (57%) WDFC, and 11/37 (30%) WDPC tumors. Other two subunits of IL-4R, interleukin-13 receptor α1 (IL-13Rα1) and interleukin-2 receptor gamma (IL-2RγC), were either weakly expressed or absent. As ATC is a highly aggressive cancer with higher incidence of IL-4Rα expression, we characterized IL-4R in 3 ATC cell lines. RT-qPCR and IFA results showed that IL-4Rα is overexpressed while IL-13Rα1 is weakly expressed. Control human umbilical vein endothelial cell line (HUVEC) showed weak expression of IL-4Rα. Binding and competition studies with 125I-IL-4 in ATC cell lines demonstrated that IL-4 specifically bound to IL-4Rα on cell surface. ATC cell lines were highly sensitive to a chimeric fusion cytotoxin consisting of circularly permuted IL-4 and truncated Pseudomonas exotoxin (IL-4-PE), which killed them in a concentration dependent manner. IL-4-PE also blocked colony formation of ATC cell lines in clonogenic assays. IL-4-PE mediated a significant antitumor activity in mouse models of ATC. Intratumoral administration of IL-4-PE caused significant regression of established tumors in a dose dependent manner and increased the overall survival without any visible toxicity. Thus, IL-4Rα in ATC may represent a novel therapeutic target and IL-4-PE may serve as an investigational therapeutic option for ATC.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

BACKGROUND: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. RESULTS: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. CONCLUSIONS: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.