Variant Selection and Interpretation: An Example of Modified VarSome Classifier of ACMG Guidelines in the Diagnostic Setting

Variant interpretation is challenging as it involves combining different levels of evidence in order to evaluate the role of a specific variant in the context of a patient’s disease. Many in-depth refinements followed the original 2015 American College of Medical Genetics (ACMG) guidelines to overcome subjective interpretation of criteria and classification inconsistencies. Here, we developed an ACMG-based classifier that retrieves information for variant interpretation from the VarSome Stable-API environment and allows molecular geneticists involved in clinical reporting to introduce the necessary changes to criterion strength and to add or exclude criteria assigned automatically, ultimately leading to the final variant classification. We also developed a modified ACMG checklist to assist molecular geneticists in adjusting criterion strength and in adding literature-retrieved or patient-specific information, when available. The proposed classifier is an example of integration of automation and human expertise in variant curation, while maintaining the laboratory analytical workflow and the established bioinformatics pipeline.

Secondary findings from whole-exome sequencing data in families with familial combined hyperlipidemia (FCHL)

Background During the interpretation of genome sequencing data, some types of secondary findings are identified that are located in genes that do not appear to be related to the causes of the primary disease. Although these are not the primary targets for evaluation, they have a high risk for some diseases different from the primary disease. Therefore, they can be vital for preventing and intervention from such disease. Results Here, we analyzed secondary findings obtained from WES in 6 families with FCHL disease who had an autosomal-dominant pattern based on their pedigrees. These finding are found in CDKAL1, ITGA2, FAM111A, WNK4, PTGIS, SCN10, TBX20, DCHS1, ANK2 and ABCA1 genes. Conclusions Secondary findings are very important and must be considered different variants from sequencing results in a diagnostic setting. Although we have considered these variants as secondary findings, some of them may be related to the primary disease.

Prognostic role of Ki-67 in glioblastomas excluding contribution from non-neoplastic cells

Survival of glioblastoma patients varies and prognostic markers are important in the clinical setting. With digital pathology and improved immunohistochemical multiplexing becoming a part of daily diagnostics, we investigated the prognostic value of the Ki-67 labelling index (LI) in glioblastomas more precisely than previously by excluding proliferation in non-tumor cells from the analysis. We investigated the Ki-67 LI in a well-annotated population-based glioblastoma patient cohort (178 IDH-wildtype, 3 IDH-mutated). Ki-67 was identified in full tumor sections with automated digital image analysis and the contribution from non-tumor cells was excluded using quantitative double-immunohistochemistry. For comparison of the Ki-67 LI between WHO grades (II-IV), 9 IDH-mutated diffuse astrocytomas and 9 IDH-mutated anaplastic astrocytomas were stained. Median Ki-67 LI increased with increasing WHO grade (median 2.7%, 6.4% and 27.5%). There was no difference in median Ki-67 LI between IDH-mutated and IDH-wildtype glioblastomas (p = 0.9) and Ki-67 LI was not associated with survival in glioblastomas in neither univariate (p = 0.9) nor multivariate analysis including MGMT promoter methylation status and excluding IDH-mutated glioblastomas (p = 0.2). Ki-67 may be of value in the differential diagnostic setting, but it must not be over-interpreted in the clinico-pathological context.

Current Status of Contrast Enhanced Mammography: A Comprehensive Review

Objectives: The purpose of this article is to provide a detailed and updated review of the physics, techniques, indications, limitations, reporting, implementation and management of contrast enhanced mammography. Background: Contrast enhanced mammography (CEM), is an emerging iodine-based modified dual energy mammography technique. In addition to having the same advantages as standard full-field digital mammography (FFDM), CEM provides information regarding tumor enhancement, relying on tumor angiogenesis, similar to dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). This article reviews current literature on CEM and highlights considerations that are critical to the successful use of this modality. Conclusion: Multiple studies point to the advantage of using CEM in the diagnostic setting of breast imaging, which approaches that of DCE-MRI.

Clinical Utility of Plasma-Based Comprehensive Molecular Profiling in Advanced Non–Small-Cell Lung Cancer

PURPOSE Comprehensive molecular profiling (CMP) plays an essential role in clinical decision making in metastatic non–small-cell lung cancer (mNSCLC). Circulating tumor DNA (ctDNA) analysis provides possibilities for molecular tumor profiling. In this study, we aim to explore the additional value of centralized ctDNA profiling next to current standard-of-care protocolled tissue-based molecular profiling (SoC-TMP) in the primary diagnostic setting of mNSCLC in the Netherlands. METHODS Pretreatment plasma samples from 209 patients with confirmed mNSCLC were analyzed retrospectively using the NGS AVENIO ctDNA Targeted Kit (Roche Diagnostics, Basel, Switzerland) and compared with paired prospective pretreatment tissue-based molecular profiling from patient records. The AVENIO panel is designed to detect single-nucleotide variants, copy-number variations, insertions or deletions, and tyrosine kinase fusion in 17 genes. RESULTS Potentially targetable drivers were detected with SoC-TMP alone in 34.4% of patients. Addition of clonal hematopoiesis of indeterminate potential–corrected, plasma-based CMP increased this to 39.7% ( P < .001). Concordance between SoC-TMP and plasma-CMP was 86.6% for potentially targetable drivers. Clinical sensitivity of plasma-CMP was 75.2% for any oncogenic driver. Specificity and positive predictive value were more than 90% for all oncogenic drivers. CONCLUSION Plasma-CMP is a reliable tool in the primary diagnostic setting, although it cannot fully replace SoC-TMP. Complementary profiling by combined SoC-TMP and plasma-CMP increased the proportion of patients who are eligible for targeted treatment.

Diagnostic and Risk Stratification Molecular Markers of Prostate Cancer Beyond PSA

Prostate cancer still represents the most common urinary malignancies and the second most common cancer in adult men after skin cancer. Prostate specific antigen (PSA) represents a milestone in the diagnosis and screening of prostate cancer since its introduction even considering its limitations in term of sensitivity and specificity. The widespread use of PSA often led to unnecessary biopsies and to the diagnosis of indolent cancers that do not require treatment, therefore in the era of tailored personalized medicine there is a strong need for new markers that overcome PSA and that can help to identify the patients that have clinically significant disease that must be treated. To date different urinary and serum biomarkers have been proposed in the diagnostic setting with promising results in terms of sensitivity and specificity, however, none of them have been routinely introduced in clinical practice. In this review we reported the latest evidence for prostate cancer diagnosis in terms of urinary and blood biomarkers. Considering all the available markers, it is highly unlikely that one single assay could fit all the requirements and it seems appropriate to use the combination of different urinary and serum markers together with clinical parameters in order to guarantee a good diagnostic performance and to identify only clinically significant disease

OP0254 AN ARTIFICIAL NEURAL NETWORK FOR THE DETECTION OF DEFINITE RADIOGRAPHIC SACROILIITIS WITH HIGH SPECIFICITY IN THE DIAGNOSTIC SETTING

Background:Radiographs of the sacroiliac joints are commonly used as the first imaging method for the diagnosis of axial spondyloarthritis (axSpA), but the reliability of sacroiliitis detection is usually low. Recently we developed a deep convolutional neural network (CNN) that can detect radiographic sacroiliitis with expert-like accuracy in patients with axSpA, i.e., classification as radiographic or non-radiographic [1]. There is frequent criticism that many artificial intelligence algorithms for diagnostic analysis of medical images lack robust validation in real-world clinical applications.Objectives:The aim of this study was to evaluate the performance of our deep CNN in detecting definite radiographic sacroiliitis in the diagnostic setting.Methods:In this study, we included a total of 361 patients with chronic back pain who presented to a rheumatologist in a specialized SpA center with a suspicion of axSpA within the OptiRef project [2]. All patients received a structured rheumatologic diagnostic work-up that resulted in the final diagnosis of axial SpA/no axial SpA. Radiographs of sacroiliac joints were evaluated by a rheumatologist and radiologist according to the modified New York criteria; the consensus judgement of the presence of definite radiographic sacroiliitis (>=grade 2 bilaterally or >=grade 3 unilaterally) was used as a reference. The predictions of the deep CNN’s inference (with a balanced cutoff of 0.724 for the predictions of the model that was derived from the training and validation steps [1]) on all available pelvic radiographs was compared to this reference judgement.Results:Pelvic radiographs of 340 patients (110 with axSpA including 61 patient with radiographic and 49 with non-radiographic axSpA, and 230 without SpA) were available for the CNN evaluation. The deep CNN achieved a sensitivity of 79% for the diagnosis of radiographic axSpA. The specificity of radiographic sacroiliitis detection was 94% (Table 1). The area under the receiver operating characteristics curve for the prediction of the presence of definite radiographic sacroiliitis was 88%. Figure 1 shows an exemplary class activation map of our CNN.Table 1.Convolutional neural network predictions of the presence of radiographic sacroiliitis in patients with suspected axSpA according to the final diagnosis by rheumatologist in OptiRef (N=340).Clinical diagnosisCNN’s prediction on the presence of definite radiographic sacroiliitisPresentNot presentRadiographic axial SpA48/61 (78.7%)13/61 (21.3%)Non-radiographic axial SpA4/49 (8.2%)45/49 (91.8%)No SpA (other diagnosis)14/230 (6.1%)216/230 (93.9%)Conclusion:The artificial neural network showed good generalizability and a high specificity with acceptable sensitivity in the detection of radiographic sacroiliitis when applied in the diagnostic setting of patients with chronic back pain and suspicion of axSpA. This algorithm can therefore be used to aid the detection of radiographic sacroiliitis as a part of the diagnostic approach.References:[1]Bressem KK, et al. medRxiv. 2020:2020.05.19.20105304.[2]Proft F, et al. Semin Arthritis Rheum. 2020;50:1015-1021.Acknowledgements:The OptiRef study was supported by a research grant from Novartis.Disclosure of Interests:None declared

CNVxplorer: a web tool to assist clinical interpretation of CNVs in rare disease patients

Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ~12% diagnostic rate, with ~8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations; and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com