Quantification and Profiling of Early and Late Differentiation Stage T Cells in Mantle Cell Lymphoma Reveals Immunotherapeutic Targets in Subsets of Patients
<p>Overview of the study: (<b>A</b>) Three biological questions are explored in the study. These include (1) comparison between the differences in T-cell abundance and molecular profiles in tumor-rich and tumor-sparse regions, (2) the identification of unique mRNA and proteins on early- and late-stage T-cell differentiation stages and (3) the association between cell frequencies and molecular profiles of tumor and T cells. (<b>B</b>) Proteomic and transcriptomic data were collected from phenotypically identified cell-specific AOIs in MCL tissue from replicate TMA sections. Data collection focused on T-cell subsets using phenotypic staining of CD3, CD8 and CD57, allowing T<sub>C,57−</sub> (CD57− CD3+ CD8+ T cytotoxic cells), T<sub>H,57−</sub> (CD57− CD3+ CD8− T helper cells), T<sub>C,57+</sub> (CD57+ CD3+ CD8+ T cytotoxic cells) and T<sub>H,57+</sub> (CD57+ CD3+ CD8− T helper cells) to be enriched and collected in separate AOIs. Proteomic data collection focused on CD20+ MCL cells was achieved by staining for CD20 and CD3 to allow the identification of the separate tumor-rich and tumor-sparse compartments, where the latter is often rich in T cells. Transcriptional profiling included CD20+ MCL cells, T<sub>H</sub> and T<sub>C</sub> cells. (<b>C</b>) Image analysis was performed on mIF images stained for Syto13, CD3, CD8 and CD57. Cellpose models for nuclei and cell segmentation were finetuned using Syto13 staining and cell membrane markers (CD3, CD8 and CD57), respectively, which generated four cell masks. Cells were classified into four cell types by overlapping the generated cell segmentation masks with the centroid of the nuclei mask. Image-derived cell metrics were extracted and used in conjunction with expression data. <a href="https://Biorender.com" target="_blank">https://Biorender.com</a> was used to create the illustrations.</p> "> Figure 2
<p>A retrained image segmentation/classification pipeline was used to classify cells. (<b>A</b>) Composite mIF image and the four channels separated with predicted segmentation masks. The segmentation masks generated from the fine-tuned Cellpose models were overlapped and projected to classify individual cells based on appearance of an individual marker. (<b>B</b>) Pre-trained vs. fine-tuned Cellpose nuclei model showing the output of the nuclei segmentation performance when the model weights are updated based on project-specific images. (<b>C</b>) Depiction of the improved performance in cell segmentation and classification of fine-tuned Cellpose cyto models (Panel 4) in comparison to pre-trained Cellpose models (Panel 3). A secondary model was developed using the masks from fine-tuned Cellpose nuclei model which was expanded to the cell boundary and a random forest classifier was trained to distribute the cells into the T-cell subtypes (Panel 2). (<b>D</b>) Quantitative comparison of F1 score, recall (sensitivity), precision and accuracy of the three models applied to a subset (<span class="html-italic">n</span> = 11) of cropped images. (<b>E</b>) Example of final segmentation and classification of cells into the four T-cell subtypes based on the defined workflow. Scalebar is 10 µm for all sub-figures but (<b>E</b>) that is 20 µm.</p> "> Figure 3
<p>Tumor-infiltrating CD57+ T cells of both T<sub>C</sub> and T<sub>H</sub> subsets are present in MCL. (<b>A</b>) Histograms of tumor-infiltrating T-cell frequencies showing that tumor infiltration (%) is mainly composed of T<sub>H,57−</sub> cells followed by T<sub>C,57−</sub> cells. CD57+ cells were found in both the T<sub>H</sub> and T<sub>C</sub> compartment but were more common among T<sub>C</sub>. In the heatmap (right panel), cell frequencies were ranked (1–186) and sorted based on CD3+ cell frequency, to highlight relative differences. The columns represent the core-IDs. The ranked heatmap emphasizes the dependencies between total CD3 frequency and the CD57− T-cell subsets. (<b>B</b>) Paired analysis (<span class="html-italic">n</span> = 39) investigating the differences of T-cell frequencies in tumor-rich and tumor-sparse regions showing high variation of the CD57− T-cell subsets, with more such T cells in the tumor-sparse region. CD57+ T cells were equally abundant in the two regions. Analysis of the relative proportion compared to T57− subtypes shows that both T<sub>C,57+</sub> and T<sub>H,57+</sub> had a higher proportion in the tumor-rich compared to tumor-sparse area. (<b>C</b>) Shannon Diversity Index (SDI) is plotted (upper panel) in relation to the distribution of the four investigated T-cell subsets (middle panel) in tumor-rich regions. No correlation between SDI and total CD3 frequency (lower panel) is observed (also see (<b>D</b>)). The higher SDI scores are associated with presence of CD57+ subsets, particularly the T<sub>C,57+</sub> (also see (<b>E</b>)), and larger variation in relative abundance of the four T-cell subsets. Lower scores were associated with dominance of mostly T<sub>H,57−</sub> cells. (<b>D</b>) Spearman correlation for SDI vs. CD3 frequency and (<b>E</b>) SDI vs. T<sub>C,57+</sub> cell frequency, showing that the score is positively (R = 0.73) associated with increasing CD57+ T<sub>C</sub> cells. The other subtypes exhibited less pronounced correlation: T<sub>C,57−</sub> (R = 0.21, <span class="html-italic">p</span> = 0.0036), T<sub>H,57+</sub> (R = 0.23, <span class="html-italic">p</span> = 0.0015) and T<sub>H,57−</sub> (R = −0.45, <span class="html-italic">p</span> < 0.00001).</p> "> Figure 4
<p>Deconvolution analysis investigating the predicted cell subtypes from tumor and T-cell AOIs. Boxplots show pairwise Wilcoxon analysis of cell types identified by deconvolution on paired (<span class="html-italic">n</span> = 25) transcriptome data. Data from tumor cells, and T<sub>C</sub> and T<sub>H</sub> in both tumor-rich and tumor-sparse regions were included in the analysis. Pink boxes indicate data sampled in tumor-rich regions, and blue boxes indicate data sampled in tumor-sparse regions. (<b>A</b>) Endothelial cells and fibroblasts, (<b>B</b>) macrophages, mDCs, monocytes (NCI, non-classical), neutrophils, NK and pDCs, (<b>C</b>) CD4+ memory T cells, CD4+ naïve T cells, CD8+ memory T cells, CD8+ naïve T cells, regulatory T cells, (<b>D</b>) naïve B cells, memory B cells and plasma B cells. ns, non-statistical significance, * <span class="html-italic">p</span> < 0.05, ** <span class="html-italic">p</span> < 0.01, *** <span class="html-italic">p</span> < 0.001, **** <span class="html-italic">p</span> < 0.0001.</p> "> Figure 5
<p>Functional differences in MCL-infiltrating T-cytotoxic and T-helper cell populations compared to adjacent T-cell-rich regions. Transcriptional data from <span class="html-italic">n</span> = 25 patients and proteomic data from <span class="html-italic">n</span> = 39 patients were used as these patients had data collected in both tumor-rich and tumor-sparse regions. (<b>A</b>) Gene set enrichment analysis of 1482 mRNA transcripts, performed for T<sub>H</sub> and T<sub>C</sub> cells separately. The plot has been adjusted to show pathways of interest in immuno-oncology. Paired linear mixed model (LMM) analysis (Patient ID as a random effect) comparing the differential expression of (<b>B</b>) mRNA transcripts (m) in T<sub>H</sub>, (<b>C</b>) mRNA transcripts (m) in T<sub>C</sub> and (<b>D</b>) proteins (p) inT<sub>H,57−</sub>, and (<b>E</b>) proteins (p) in T<sub>C,57−</sub> cells in tumor-sparse and tumor-rich regions, are visualized. (<b>F</b>) Tile plot summarizing the overlap of differentially expressed proteins in T<sub>H,57−</sub> and T<sub>C,57−</sub> cells in relation to spatial localization, as identified by paired LMM analysis (panel <b>D</b>,<b>E</b>). The values represent the direction of enrichment in relation to the spatial compartment. Proteins with higher abundance in the tumor-rich area are shown in pink while proteins with lower abundance are shown in blue.</p> "> Figure 6
<p>Comparison of the functional and phenotypic variation in infiltrating T-cell subtypes. (<b>A</b>) Spearman correlation plot exploring co-regulation between differentially expressed transcripts, as identified by differential gene expression using LMM analysis comparing the infiltrating T<sub>H</sub> and T<sub>C</sub> cells (<span class="html-italic">n</span> = 63). Color legend indicates positive (red) or negative (blue) correlation value. (<b>B</b>) Tile plot highlighting the differentially expressed proteins between the infiltrating four T-cell subsets (<span class="html-italic">n</span> = 102), as identified by ANOVA followed by Tukey-HSD test (q-value cutoff: 0.05). The values show the difference in the mean value between groups. The reference and comparison group are given in the tables below. Color code indicates relative higher (red) or lower (blue) abundance (difference in group means). (<b>C</b>) PCA biplot using the analytes identified in (<b>B</b>) showing the group segregation of the four T-cells subsets based on the magnitude and direction of differential protein expression between the two components. (<b>D</b>) Boxplot and Wilcoxon <span class="html-italic">p</span>-value analysis of PD-L1, PD-L2 and PD-1 expression among the four infiltrating T-cell subsets. Tumor-rich associated mean value expressions were aggregated by patient ID.</p> "> Figure 7
<p>Multi-omics investigation of TIME with respect to infiltrating CD3 T-cell frequency using DIABLO. DIABLO prefers complete datasets, and to include relevant number of patients only five out of eight collected omics datasets were used resulting in data from <span class="html-italic">n</span> = 62 patients. Proteomic datasets included CD20 (pCD20), T<sub>H</sub> (pT<sub>H</sub>) and T<sub>C</sub> (pT<sub>C</sub>). Transcriptomic data included CD20 (mCD20) and T<sub>C</sub> (mT<sub>C</sub>). (<b>A</b>) Circos plot highlighting the identified analytes in each omics dataset for the optimally selected first component. The outer blue and red lines indicate the direction of the association between the individual parameter (gene or transcript) and the CD3 frequency group (high or low, cut-of 8.4%). The inner lines connect parameters with positive (red) or negative (blue) association-based correlation analogues to Pearson (R > ±0.6). The groups were determined based on optimal cut-off for high/low CD3 T-cell infiltration based on survival analysis in <a href="#app1-cancers-16-02289" class="html-app">Supplementary Materials Figure S4</a>. (<b>B</b>) Boxplot analysis with <span class="html-italic">t</span>-test significance per omics dataset of the analytes identified in (<b>A</b>). * <span class="html-italic">p</span> < 0.05, ** <span class="html-italic">p</span> < 0.01, *** <span class="html-italic">p</span> < 0.001. (<b>C</b>) Bar plot distributions of the high (<span class="html-italic">n</span> = 35) and low (<span class="html-italic">n</span> = 27) infiltration groups used for this analysis.</p> "> Figure 8
<p>Multi-omics investigation of TIME with respect to infiltrating T<sub>C,57+</sub> frequency using DIABLO. DIABLO prefers complete datasets, and to include a relevant number of patients only five out of eight collected omics datasets were used, resulting in data from <span class="html-italic">n</span> = 62 patients. Proteomic datasets included CD20 (pCd20), T<sub>H</sub> (pT<sub>H</sub>) and T<sub>C</sub> (pT<sub>C</sub>). Transcriptomic data included CD20 (mCD20) and T<sub>C</sub> (mT<sub>C</sub>). (<b>A</b>) Circos plot highlighting the identified analytes in each type of omics data for the optimally selected first component. The outer blue and red lines indicate the direction of the association between the individual parameter (gene or transcript) and the T<sub>C,57+</sub> frequency group (high or low, using median = 0.686% as cut-off). The inner lines connect parameters with positive (red) or negative (blue) association-based correlation analogues to Pearson (R > ±0.6). (<b>B</b>) Boxplot analysis with <span class="html-italic">t</span>-test significance per type of omics data of the analytes identified in (<b>A</b>). (<b>C</b>) Bar plot distribution of the high (<span class="html-italic">n</span> = 34) and low (<span class="html-italic">n</span> = 28) infiltration groups used for this analysis. ns, non-statistical significance, * <span class="html-italic">p</span> < 0.05, ** <span class="html-italic">p</span> < 0.01, *** <span class="html-italic">p</span> < 0.001, **** <span class="html-italic">p</span> < 0.0001.</p> "> Figure 9
<p>Summary of results. (<b>A</b>) Proteins (green box) and transcripts (purple box) upregulated in tumor-sparse regions (data shown over pink background) compared to tumor-rich regions. (<b>B</b>) Key proteins displayed on surface of the individual cells and mRNAs (purple boxes) on each T-cell subset. The left box refers to upregulated transcripts in T<sub>H</sub> subsets and the right box refers to upregulated transcripts in T<sub>C</sub> subsets. (<b>C</b>) Main proteins and mRNAs predictive of high or low CD3+ T-cell infiltration. Purple boxes indicate upregulated transcripts in each indicated cell type. (<b>D</b>) Main proteins and mRNAs predictive of high or low T<sub>C,57+</sub> infiltration. Purple boxes indicate differentially upregulated mRNAs in each indicated cell type. Biorender.com was used to create the illustrations.</p> ">
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Patient Cohort
2.2. GeoMx™ Digital Spatial Profiling
2.2.1. Multicolor Immunofluorescence (mIF) Staining
2.2.2. Region of Interest (ROI) Selection
2.2.3. Retrieval of Probes for Proteomic and Transcriptional Analyses
2.2.4. Pre-Processing of GeoMx™ Data
2.3. Image Analysis, including Cell Segmentation and Classification
2.4. Statistical Analysis
3. Results
3.1. Image Analysis to Retrieve Information on T-Cell Subset Composition in MCL TIME
Fine-Tuning of Deep Learning-Based Image Analysis Models Is Required to Retrieve Accurate Measurements of Cell Frequencies in MCL
3.2. A proportion of Infiltrating T Cells in MCL Are CD57+
3.3. TC and Late-Stage Differentiated, CD57+ T-Cell Subsets Are Enriched among MCL-Infiltrating T Cells
3.4. Diversity among T-Cell Subtypes Is Not Associated with Total CD3 Infiltration
3.5. TC,57+ Cells Are Associated with Highly Proliferative MCL, while Total CD3+ T-Cell Infiltration Is Positively Associated with Favourable Prognosis
3.6. Molecular Comparison of TH and TC Subtypes in Tumor-Rich Versus Tumor-Sparse Regions of MCL Tissue
3.7. Deconvolution Analysis Supports Data on Well Differentiated T Cells of Memory Type among Infiltrating T Cells in MCL
3.8. T Cells in Tumor-Sparse Regions Show Increased Use of TNF-Related Pathways in TH Cells and Higher Levels of T-Cell Suppressive Proteins such as VISTA, TIM3, LAG3, and IDO1
3.9. Infiltrating T Cells in Tumor-Rich Regions: Identification of Unique Analytes among Early and Late TH and TC Subsets in MCL
Transcriptional Analysis Reveals Co-Regulation of Tregs Markers in TH Cells and Expression of Antigens Associated to Late Differentiation in TC Cells
3.10. CD47 Don’t Eat Me Signals Are Associated with High Total CD3 while CXCL9 Is Associated with High TC,57+ Frequency
3.10.1. High Level of Total CD3+ T-Cell Infiltration Is Associated with Increased Levels of CD47, IL7R and Key Components of Antigen Presentation
3.10.2. Differences in TC,57+ Infiltration Are Associated with Increased Proliferation, and Secretion of CXCL9 in Tumor Cells and Expression of CD45RO, TIGIT, PD-L1 and 4–1BB in TC Cells
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AOI | area of illumination |
APC | antigen presenting cells |
CNN | convolutional neural networks |
CP | Cellpose |
DSP | digital spatial profiler |
IA | image analysis |
IHC | immunohistochemistry |
LMM | linear mixed models |
MCL | mantle cell lymphoma |
mIF | multiplexed immunofluorescent images |
ns | nonsignificant |
ROI | region of interest |
SDI | Shannon-Wiener Diversity Index (SDI) |
T57+/T57− | ratio of CD3+ CD57+ T-cells over CD3+ CD57− T-cells |
TC | CD3+ CD8+ Cytotoxic T-cells |
TC,57− | CD3+ CD8+ CD4+ CD57− Cytotoxic T-cells |
TC,57+ | CD3+ CD8+ CD4+ CD57+ Cytotoxic T-cells |
TH | CD3+ CD4+ Helper T-cells |
TH,57− | CD3+ CD8− CD4+ CD57− Helper T-cells |
TH,57+ | CD3+ CD8− CD4+ CD57+ Helper T-cells |
TH/TC | Ratio of CD3+ CD4+ Helper T-cells over CD3+ CD8+ Cytotoxic T-cells |
TIME | tumor-immune microenvironment |
TMA | tissue microarray |
TME | tumor microenvironment |
TR | tumor-rich |
Tregs | regulatory T cells |
TS | tumor-sparse |
U-NET | U-type convolutional neural networks |
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Lokhande, L.; Nilsson, D.; de Matos Rodrigues, J.; Hassan, M.; Olsson, L.M.; Pyl, P.-T.; Vasquez, L.; Porwit, A.; Gerdtsson, A.S.; Jerkeman, M.; et al. Quantification and Profiling of Early and Late Differentiation Stage T Cells in Mantle Cell Lymphoma Reveals Immunotherapeutic Targets in Subsets of Patients. Cancers 2024, 16, 2289. https://doi.org/10.3390/cancers16132289
Lokhande L, Nilsson D, de Matos Rodrigues J, Hassan M, Olsson LM, Pyl P-T, Vasquez L, Porwit A, Gerdtsson AS, Jerkeman M, et al. Quantification and Profiling of Early and Late Differentiation Stage T Cells in Mantle Cell Lymphoma Reveals Immunotherapeutic Targets in Subsets of Patients. Cancers. 2024; 16(13):2289. https://doi.org/10.3390/cancers16132289
Chicago/Turabian StyleLokhande, Lavanya, Daniel Nilsson, Joana de Matos Rodrigues, May Hassan, Lina M. Olsson, Paul-Theodor Pyl, Louella Vasquez, Anna Porwit, Anna Sandström Gerdtsson, Mats Jerkeman, and et al. 2024. "Quantification and Profiling of Early and Late Differentiation Stage T Cells in Mantle Cell Lymphoma Reveals Immunotherapeutic Targets in Subsets of Patients" Cancers 16, no. 13: 2289. https://doi.org/10.3390/cancers16132289