Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response.

BACKGROUND: Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist-trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies. METHODS: A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies. RESULTS: Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0%CD3 VE = 13.4; Type 1-3%CD3 VE = 41.9, p < 0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0-2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p < 0.0001), and Type 0-1 crypt/villous epithelial area ratio = 0.59; Type 2-3 crypt/villous epithelial area ratio = 1.64, p < 0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort. CONCLUSIONS: Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.

Targeted Quantitative Mass Spectrometry Analysis of Protein Biomarkers From Previously Stained Single Formalin-Fixed Paraffin-Embedded Tissue Sections.

Formalin-fixed, paraffin-embedded tissues represent a majority of all biopsy specimens commonly analyzed by histologic or immunohistochemical staining with adhesive coverslips attached. Mass spectrometry (MS) has recently been used to precisely quantify proteins in samples consisting of multiple unstained formalin-fixed, paraffin-embedded sections. Here, we report an MS method to analyze proteins from a single coverslipped 4-µm section previously stained with hematoxylin and eosin, Masson trichrome, or 3,3'-diaminobenzidine-based immunohistochemical staining. We analyzed serial unstained and stained sections from non-small cell lung cancer specimens for proteins of varying abundance (PD-L1, RB1, CD73, and HLA-DRA). Coverslips were removed by soaking in xylene, and after tryptic digestion, peptides were analyzed by targeted high-resolution liquid chromatography with tandem MS with stable isotope-labeled peptide standards. The low-abundance proteins RB1 and PD-L1 were quantified in 31 and 35 of 50 total sections analyzed, respectively, whereas higher abundance CD73 and HLA-DRA were quantified in 49 and 50 sections, respectively. The inclusion of targeted ß-actin measurement enabled normalization in samples where residual stain interfered with bulk protein quantitation by colorimetric assay. Measurement coefficient of variations for 5 replicate slides (hematoxylin and eosin stained vs unstained) from each block ranged from 3% to 18% for PD-L1, from 1% to 36% for RB1, 3% to 21% for CD73, and 4% to 29% for HLA-DRA. Collectively, these results demonstrate that targeted MS protein quantification can add a valuable data layer to clinical tissue specimens after assessment for standard pathology end points.

Accelerated instability testing reveals quantitative mass spectrometry overcomes specimen storage limitations associated with PD-L1 immunohistochemistry.

Immunohistochemistry (IHC) using formalin-fixed, paraffin embedded (FFPE) tissue is limited by epitope masking, posttranslational modification and immunoreactivity loss that occurs in stored tissue by poorly characterized mechanisms. Conformational epitopes recognized by many programmed-death-ligand-1 (PD-L1) IHC assays are particularly susceptible to degradation and provide an ideal model for understanding signal loss in stored FFPE tissue. Here we assessed 1206 tissue sections to evaluate environmental factors impacting immunoreactivity loss. PD-L1 IHC using four antibodies (22C3, 28-8, E1L3N, and SP142), raised against intracellular and extracellular epitopes, was assessed in stored FFPE tissue alongside quantitative mass spectrometry (MS). Global proteome analyses were used to assess proteome-wide oxidation across an inventory of 3041 protein groups (24,737 distinct peptides). PD-L1 quantitation correlated well with IHC expression on unaged sections (R2 = 0.744; P < 0.001), with MS demonstrating no loss of PD-L1 protein, even in sections with significant signal loss by IHC impacting diagnostic category. Clones 22C3 and 28-8 were most susceptible to signal loss, with E1L3N demonstrating the most robust signal (56%, 58%, and 33% reduction respectively; p < 0.05). Increased humidity and temperature resulted in significant acceleration of immunoreactivity loss, which was mitigated by storage with desiccant. MS demonstrated only modest oxidation of 274 methionine-containing peptides and aligned with IHC results suggesting peptide oxidation is not a major factor. These data imply immunoreactivity loss driven by humidity and temperature results in structural distortion of epitopes rendering them unsuitable for antibody binding following epitope retrieval. Limitations of IHC biomarker analysis from stored tissue sections may be mitigated by cost-effective use of desiccant when appropriate. In some scenarios, complementary MS is a preferred approach for retrospective analyses of archival FFPE tissue collections.