Rapid Deployment of Whole Slide Imaging for Primary Diagnosis in Surgical Pathology at Stanford Medicine: Responding to Challenges of the COVID-19 Pandemic

CONTEXT.—

Stanford Pathology began stepwise subspecialty implementation of whole slide imaging (WSI) in 2018 soon after the first US Food and Drug Administration approval. In 2020, during the COVID-19 pandemic, the Centers for Medicare & Medicaid Services waived the requirement for pathologists to perform diagnostic tests in Clinical Laboratory Improvement Amendments (CLIA)-licensed facilities. This encouraged rapid implementation of WSI across all surgical pathology subspecialties.

OBJECTIVE.—

To present our experience with validation and implementation of WSI at a large academic medical center encompassing a caseload of more than 50 000 cases per year.

DESIGN.—

Validation was performed independently for 3 subspecialty services with a diagnostic concordance threshold above 95%. Analysis of user experience, staffing, infrastructure, and information technology was performed after department-wide expansion.

RESULTS.—

Diagnostic concordance was achieved in 96% of neuropathology cases, 100% of gynecologic pathology cases, and 98% of immunohistochemistry cases. After full implementation, 8 high-capacity scanners were operational, with whole slide images generated on greater than 2000 slides per weekday, accounting for approximately 80% of histologic slides at Stanford Medicine. Multiple modifications in workflow and information technology were needed to improve performance. Within months of full implementation, most attending pathologists and trainees had adopted WSI for primary diagnosis.

CONCLUSIONS.—

WSI across all surgical subspecialities is achievable at scale at an academic medical center; however, adoption required flexibility to adjust workflows and develop tailored solutions. WSI at scale supported the health and safety of medical staff while facilitating high-quality patient care and education during COVID-19 restrictions.

HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data

Background

The Human Protein Atlas (HPA) aims to map human proteins via multiple technologies including imaging, proteomics and transcriptomics. Access of the HPA data is mainly via web-based interface allowing views of individual proteins, which may not be optimal for data analysis of a gene set, or automatic retrieval of original images.

Results

HPAanalyze is an R package for retrieving and performing exploratory analysis of data from HPA. HPAanalyze provides functionality for importing data tables and xml files from HPA, exporting and visualizing data, as well as downloading all staining images of interest. The package is free, open source, and available via Bioconductor and GitHub. We provide examples of the use of HPAanalyze to investigate proteins altered in the deadly brain tumor glioblastoma. For example, we confirm Epidermal Growth Factor Receptor elevation and Phosphatase and Tensin Homolog loss and suggest the importance of the GTP Cyclohydrolase I/Tetrahydrobiopterin pathway. Additionally, we provide an interactive website for non-programmers to explore and visualize data without the use of R.

Conclusions

HPAanalyze integrates into the R workflow with the tidyverse framework, and it can be used in combination with Bioconductor packages for easy analysis of HPA data.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3059-z) contains supplementary material, which is available to authorized users.

Kinomics toolbox-A web platform for analysis and viewing of kinomic peptide array data

Kinomics is an emerging field of science that involves the study of global kinase activity. As kinases are essential players in virtually all cellular activities, kinomic testing can directly examine protein function, distinguishing kinomics from more remote, upstream components of the central dogma, such as genomics and transcriptomics. While there exist several different approaches for kinomic research, peptide microarrays are the most widely used and involve kinase activity assessment through measurement of phosphorylation of peptide substrates on the array. Unfortunately, bioinformatic tools for analyzing kinomic data are quite limited necessitating the development of accessible open access software in order to facilitate standardization and dissemination of kinomic data for scientific use. Here, we examine and present tools for data analysis for the popular PamChip^® (PamGene International) kinomic peptide microarray. As a result, we propose (1) a procedural optimization of kinetic curve data capture, (2) new methods for background normalization, (3) guidelines for the detection of outliers during parameterization, and (4) a standardized data model to store array data at various analytical points. In order to utilize the new data model, we developed a series of tools to implement the new methods and to visualize the various data models. In the interest of accessibility, we developed this new toolbox as a series of JavaScript procedures that can be utilized as either server side resources (easily packaged as web services) or as client side scripts (web applications running in the browser). The aggregation of these tools within a Kinomics Toolbox provides an extensible web based analytic platform that researchers can engage directly and web programmers can extend. As a proof of concept, we developed three analytical tools, a technical reproducibility visualizer, an ANOVA based detector of differentially phosphorylated peptides, and a heatmap display with hierarchical clustering.

Hepatitis C Virus Genie: A Web 2.0 Interpretation and Analytics Platform for the Versant Hepatitis C Virus Genotype Line Probe Assay Version 2.0

Context:

Hepatitis C virus (HCV) genotyping at our institution is performed using the Versant HCV genotype 2.0 Line Probe Assay (LiPA). The last steps of this procedure are manual, laborious, and error-prone process that involves the comparison of the banding pattern on a test strip to a physical reference table.

Aim:

We developed a web-based HCV genotype interpretation platform that utilizes a scanned image to generate the genotypes, thus minimizing interpretation time and reducing error.

Subjects and Methods:

HCV Genie 2 utilizes a database of banding patterns in conjuncture with image analysis algorithms to determine the genotype for any number of scanned LiPA strips. HCV Genie 2 is built with client-side JavaScript; allowing the program to run in the user' browser rather than on an unknown server, essentially eliminating data and patient privacy concerns.

Results:

HCV Genie 2 was tested over 2 months and proved identical to human expert interpretation for 148 samples (>1000 bands identified). Manual intervention was required only for two faint bands and one false-positive band; this was done utilizing the built-in-user interface. Utilizing the original method, the trained laboratory technician interpretation time for 16 samples was 13.8 (±0.96) min as compared to 5.0 (±1.09) min with HCV Genie 2, a 63.8% decrease. In addition to the time savings, the new method provides an additional validation step, which decreases the potential for errors.

Conclusions:

Our institution has moved exclusively to utilize the new techniques and tools described here. Both experienced technicians and the molecular pathologists at our institution prefer the workflow using HCV Genie. It is easier for the technicians to prepare and document, and the pathologists are more rapidly able to review and confirm results. The use of this tool will lead to increase the quality of patient care delivered through this test methodology by decreasing the potential for error. The algorithms developed here can be ported to similar band identification platforms, most directly to other LiPAs.

The significance of nitrogen cost minimization in proteomes of marine microorganisms

Marine microorganisms thrive under low levels of nitrogen (N). N cost minimization is a major selective pressure imprinted on open-ocean microorganism genomes. Here we show that amino-acid sequences from the open ocean are reduced in N, but increased in average mass compared with coastal-ocean microorganisms. Nutrient limitation exerts significant pressure on organisms supporting the trade-off between N cost minimization and increased average mass of amino acids that is a function of increased A+T codon usage. N cost minimization, especially of highly expressed proteins, reduces the total cellular N budget by 2.7–10% this minimization in combination with reduction in genome size and cell size is an evolutionary adaptation to nutrient limitation. The biogeochemical and evolutionary precedent for these findings suggests that N limitation is a stronger selective force in the ocean than biosynthetic costs and is an important evolutionary strategy in resource-limited ecosystems.