DeepHeme: A generalizable, bone marrow classifier with hematopathologist-level performance.

Morphology-based classification of cells in the bone marrow aspirate (BMA) is a key step in the diagnosis and management of hematologic malignancies. However, it is time-intensive and must be performed by expert hematopathologists and laboratory professionals. We curated a large, high-quality dataset of 41,595 hematopathologist consensus-annotated single-cell images extracted from BMA whole slide images (WSIs) containing 23 morphologic classes from the clinical archives of the University of California, San Francisco. We trained a convolutional neural network, DeepHeme, to classify images in this dataset, achieving a mean area under the curve (AUC) of 0.99. DeepHeme was then externally validated on WSIs from Memorial Sloan Kettering Cancer Center, with a similar AUC of 0.98, demonstrating robust generalization. When compared to individual hematopathologists from three different top academic medical centers, the algorithm outperformed all three. Finally, DeepHeme reliably identified cell states such as mitosis, paving the way for image-based quantification of mitotic index in a cell-specific manner, which may have important clinical applications.

A Guide to Using ClinTAD for Interpretation of DNA Copy Number Variants in the Context of Topologically Associated Domains.

DNA copy number variants (CNVs) are routinely evaluated as part of clinical diagnosis in both the prenatal and postnatal genetic settings. Current guidelines for interpreting the potential clinical significance of these CNVs, typically identified by chromosomal microarray, focus entirely on genes localized within the CNV region. However, recent work has suggested that some CNVs can actually produce clinical impacts by influencing transcription of genes outside the CNV region. These alterations of transcription appear to occur by disrupting the composition of DNA topologically associated domains (TADs), which strongly influence contacts between gene promoters and their associated enhancers. Here we present a set of detailed protocols for the use of the free software tool ClinTAD (https://www.clintad.com). This decision-support software allows for prediction as to whether a given CNV may potentially disrupt a TAD boundary, and offers phenotype matching to genes near, but not within the CNV region, whose expression could be influenced by altered TAD architecture and that have phenotypic impacts related to that reported in a given patient. Our protocols here provide specific examples of how to implement these tools. In addition, the software has the capability to impact genomic research by evaluating multiple cases in parallel. We propose that this decision-support tool can benefit and improve genetic diagnosis. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Evaluating a single case using ClinTAD Basic Protocol 2: Evaluating a single case with multiple variants using ClinTAD Basic Protocol 3: Evaluating multiple cases using ClinTAD Basic Protocol 4: Creating tracks with custom data.

ClinTAD: a tool for copy number variant interpretation in the context of topologically associated domains.

Standard clinical interpretation of DNA copy number variants (CNVs) identified by cytogenomic microarray involves examining protein-coding genes within the region and comparison to other CNVs. Emerging basic research suggests that CNVs can also exert a pathogenic effect through disruption of DNA structural elements such as topologically associated domains (TADs). To begin to integrate these discoveries with current practice, we developed ClinTAD, a free browser-based tool to assist with interpretation of CNVs in the context of TADs ( www.clintad.com ). We used ClinTAD to examine 209 randomly selected single-nucleotide polymorphism microarray cases with a total of 236 CNVs. We compared 118 CNVs classified as variants of uncertain clinical significance (VUS), where additional insight into pathogenicity of these CNVs would be of greatest utility, to 118 CNVs classified as benign. We found that a higher proportion of VUS had at least two genes in a nearby TAD related to a phenotype seen in the patient based on Human Phenotype Ontology (HPO) annotation. We present example cases demonstrating scenarios where ClinTAD may either increase or decrease clinical suspicion of pathogenicity for VUS, depending on disruption of TAD boundaries and HPO phenotype match. ClinTAD is an easy-to-use tool, based on emerging research in chromatin architecture, that can help inform CNV interpretation.