<div>Abstract<p>Hodgkin lymphoma is characterized by an extensively dominant tumor microenvironment (TME) composed of different types of noncancerous immune cells with rare malignant cells. Characterization of the cellular components and their spatial relationship is crucial to understanding cross-talk and therapeutic targeting in the TME. We performed single-cell RNA sequencing of more than 127,000 cells from 22 Hodgkin lymphoma tissue specimens and 5 reactive lymph nodes, profiling for the first time the phenotype of the Hodgkin lymphoma–specific immune microenvironment at single-cell resolution. Single-cell expression profiling identified a novel Hodgkin lymphoma–associated subset of T cells with prominent expression of the inhibitory receptor LAG3, and functional analyses established this LAG3<sup>+</sup> T-cell population as a mediator of immunosuppression. Multiplexed spatial assessment of immune cells in the microenvironment also revealed increased LAG3<sup>+</sup> T cells in the direct vicinity of MHC class II–deficient tumor cells. Our findings provide novel insights into TME biology and suggest new approaches to immune-checkpoint targeting in Hodgkin lymphoma.</p>Significance:<p>We provide detailed functional and spatial characteristics of immune cells in classic Hodgkin lymphoma at single-cell resolution. Specifically, we identified a regulatory T-cell–like immunosuppressive subset of LAG3<sup>+</sup> T cells contributing to the immune-escape phenotype. Our insights aid in the development of novel biomarkers and combination treatment strategies targeting immune checkpoints.</p><p><i>See related commentary by Fisher and Oh, p. 342</i>.</p><p><i>This article is highlighted in the In This Issue feature, p. 327</i></p></div>
Mantle cell lymphoma (MCL) and small lymphocytic lymphoma (SLL) exhibit similar but distinct immunophenotypic profiles. Many cases can be diagnosed readily by flow cytometry (FCM) alone; however, ambiguous cases are frequently encountered and necessitate additional studies, including immunohistochemical staining for cyclin D1 and fluorescence in situ hybridization for IgH-CCND1 rearrangement. To determine if greater diagnostic accuracy could be achieved from FCM data alone, we developed an unbiased, machine-based algorithm to identify features that best distinguish between the 2 diseases. By applying conventional diagnostic criteria to the flow cytometry data, we were able to assign 28 of 44 (64%) MCL and 48 of 70 (69%) SLL cases correctly. In contrast, we were able to assign all 44 (100%) MCL and 68 of 70 (97%) SLL cases correctly using a novel set of criteria, as identified by our automated approach. The most discriminating feature was the CD20/CD23 mean fluorescence intensity ratio, and we found unexpectedly that inclusion of FMC7 expression in the diagnostic algorithm actually reduced its accuracy. This study demonstrates that computational methods can be used on existing clinical FCM data to improve diagnostic accuracy and suggests similar computational approaches could be used to identify novel prognostic markers and perhaps subdivide existing or define new diagnostic entities.
<p>Effect of CDK10 expression on transcript levels of CDKs. CDK2, CDK4, CDK5, CDK6 and CDK9 expression in (A) MIP101 and (B) RKO CDK10 WT and DN overexpressing cells. Results represents n = 3, means {plus minus} SD.</p>
<p>Effects of CDK10 expression on cell cycle. Flow cytometry analysis of RKO/NeoB, RKO/CDK10 DN and RKO/CDK10 WT cell cycles. (A) One representative experiment is shown. (B) Quantitative analysis of the different cell phase populations. Results represent three independent experiments.</p>
All data introduced in the publication "SpatialSort: A Bayesian Model for Clustering and Cell Population Annotation of Spatial Proteomics Data" are deposited here. Forward simulated datasets, spatial Gaussian mixture datasets, semi-real datasets, and MIBI datasets are included. Code to run SpatialSort can be found at https://github.com/Roth-Lab/SpatialSort.
