Differentiation of lymphocytic-plasmacytic enteropathy and small cell lymphoma in cats using histology-guided mass spectrometry

Abstract

Background: Differentiation of lymphocytic-plasmacytic enteropathy (LPE) from small cell lymphoma (SCL) in cats can be challenging.

Hypothesis/objective: Histology-guided mass spectrometry (HGMS) is a suitable method for the differentiation of LPE from SCL in cats.

Animals: Forty-one cats with LPE and 52 cats with SCL.

Methods: This is a retrospective clinicopathologic study. Duodenal tissue samples of 17 cats with LPE and 22 cats with SCL were subjected to HGMS, and the acquired data were used to develop a linear discriminate analysis (LDA) machine learning algorithm. The algorithm was subsequently validated using a separate set of 24 cats with LPE and 30 cats with SCL. Cases were classified as LPE or SCL based on a consensus by an expert panel consisting of 5-7 board-certified veterinary specialists. Histopathology, immunohistochemistry, and clonality testing were available for all cats. The panel consensus classification served as a reference for the calculation of test performance parameters.

Results: Relative sensitivity, specificity, and accuracy of HGMS were 86.7% (95% confidence interval [CI]: 74.5%-98.8%), 91.7% (95% CI: 80.6%-100%), and 88.9% (95% CI: 80.5%-97.3%), respectively. Comparatively, the clonality testing had a sensitivity, specificity, and accuracy of 85.7% (95% CI: 72.8%-98.7%), 33.3% (95% CI: 14.5%-52.2%), and 61.5% (95% CI: 48.3%-74.8%) relative to the panel decision.

Conclusions and clinical importance: Histology-guided mass spectrometry was a reliable technique for the differentiation of LPE from SCL in duodenal formalin-fixed paraffin-embedded samples of cats and might have advantages over tests currently considered state of the art.

Keywords: HGMS; MALDI mass spectrometry; PARR; PCR for antigen receptor rearrangements; chronic enteropathy; clonality testing; feline; inflammatory bowel disease; inflammatory enteropathy; small cell lymphoma.

Figure 1

HGMS workflow: A, sample processing—FFPE embedding and cutting of 5‐μm serial tissue sections; B, sample preparation—paraffin removal, antigen retrieval, trypsin digestion, and MALDI matrix application; C, annotation—anatomic pathologist marks 50‐μm diameter annotations of lymphocyte cell subpopulations on H&E image; D, image overlay—digital images of prepped slide and annotated H&E image are merged to teach mass spectrometer locations for analysis; E, sample analysis—proteomic data acquired by HGMS in MALDI mass spectrometer; F, data analysis—for algorithm development, machine learning is used to generate classification algorithm; for unknown sample assessment, classification algorithm is used to classify sample as SCL or LPE. FFPE, formalin‐fixed paraffin‐embedded; H&E, hematoxylin and eosin; HGMS, histology‐guided mass spectrometry; LPE, lymphocytic‐plasmacytic enteropathy; MALDI, matrix‐assisted laser desorption ionization; SCL, small cell lymphoma

Figure 2

Intensity box plot comparisons of (A) m/z 1270.7 and (B) m/z 1402.7 from SCiLS software. Each dot represents the normalized peak intensity of a unique mass spectrum, corresponding to a single annotation on a tissue section. All data from the cases receiving LPE and SCL diagnoses from the review panel are plotted. The center line in the box indicates the median, and the lower and upper boundaries of the box indicate the second and third quartiles. The vertical lines protruding from the box designate the lower (0%) and upper quantile (99%)

Figure 3

Three‐dimensional PCA of training set data showing clustering of SCL (red) and LPE (blue) spectra. LPE, lymphocytic‐plasmacytic enteropathy; PCA, principal component analysis; SCL, small cell lymphoma

Figure 4

Positive (A) and negative (B) predictive values for the HGMS and PARR methods as function of different disease prevalence. HGMS, histology‐guided mass spectrometry; PARR, polymerase chain reaction for antigen receptor rearrangements