Graphical abstract graphic file with name fx1.jpg [82]Open in a new tab Highlights * • Integrated multi-omics identifies core differences in EBV(+) PTLD and EBV(−) PTLD * • EBV(+) B lymphoma cells shape the TME and promote enrichment of CD163+ monocytes * • CD300a is required for maximal survival of EBV(+) B cells in PTLD __________________________________________________________________ Toh et al. use computational and multi-omics approaches to define the tumor-intrinsic and tumor microenvironment characteristics that distinguish EBV(+) and EBV(−) PTLD. This strategy has identified candidate molecules that can be explored further to understand what controls tumor growth and to develop new therapies. Introduction Epstein-Barr virus (EBV) is a broadly disseminated gamma-1 herpesvirus estimated to infect 90%–95% of adults globally.[83]^1^,[84]^2 EBV infection is mostly asymptomatic, with the virus proceeding through a complex life cycle including a productive lytic phase but ultimately establishing a latent infection in a subset of memory B cells.[85]^3 In this manner, EBV persists for the lifetime of the host, though periodic reactivation of EBV drives further lytic replication and viral transmission. Host immunity mediated by vigorous natural killer cell and antiviral T cell responses is vital in controlling EBV infection.[86]^4^,[87]^5^,[88]^6 EBV has potent oncogenic function and is linked to epithelial malignancies including nasopharyngeal carcinoma (NPC) as well as lymphoid malignancies such as Burkitt and Hodgkin lymphoma. Individuals with immune perturbations associated with aging, HIV infection, genetic immunodeficiency, or iatrogenic immunosuppression are at increased risk for the development of EBV(+) B cell lymphoma. In the setting of bone marrow or organ transplantation, the immunosuppression required to prevent graft rejection can impair the development of protective immunity following primary EBV infection and attenuate the immune response during reactivation of latent EBV infection, thereby predisposing the uncontrolled expansion of EBV-transformed B cells.[89]^7 Thus, EBV(+) B cell lymphomas constitute a life-threatening manifestation of atypical post-transplant lymphoproliferations,[90]^8 collectively termed post-transplant lymphoproliferative disorder (PTLD). The incidence of PTLD varies depending on the organ transplanted with reported incidences of 2%–20%[91]^8^,[92]^9^,[93]^10 and significant mortality rates.[94]^11 Although most B cell PTLDs are associated with EBV, a significant portion of cases are EBV(−) and similarly carry considerable risk.[95]^8^,[96]^12^,[97]^13 The underlying tumor-intrinsic features, as well as the characteristics of the tumor microenvironment (TME), of EBV(+) versus EBV(−) PTLD are poorly understood. Consequently, current therapeutic strategies are generally similar for EBV(+) and EBV(−) PTLD, including reduction of immunosuppression, chemotherapy, and the anti-CD20 monoclonal antibody rituximab. A better understanding of the molecular characteristics distinguishing EBV(+) and EBV(−) PTLD would advance more precise therapeutic strategies for the treatment of these B cell malignancies. Here, we integrated computational and multi-omics approaches to identify the molecular signatures distinguishing EBV(+) and EBV(−) B cell PTLD, as well as their respective TME. We identified tumor-derived chemokines that shape the TME of EBV(+) B cell PTLD characterized by an enrichment of CD163^+ anti-inflammatory monocytes and a reduction in T cells. We also establish that CD300a expression on B lymphoma cells is specifically associated with EBV(+) B cell PTLD. We use gene editing approaches and pre-clinical models to demonstrate that CD300a is required for maximal survival of EBV(+) B cell lymphomas. Together, these data demonstrate the utility of implementing multi-modal systems analysis approaches to reveal molecular insights into human cancers and facilitate target discovery for the development of more effective therapies. Results Integrated multi-cohort analysis of PTLD transcriptomic profiles identifies differentially expressed genes between EBV(+) and EBV(−) PTLD We applied an integrated multi-modal approach in order to determine the distinguishing characteristics of the B lymphoma cells and the TME in EBV(+) and EBV(−) PTLD. To define the tumor and TME transcriptional landscape in B cell PTLD, we collected three existing datasets that contained whole transcriptome profiles from 60 fresh-frozen samples from patients with EBV(+) (n = 42) and EBV(−) (n = 18) PTLD lesions from three institutions[98]^14^,[99]^15^,[100]^16 ([101]Table S1). While these samples reflect the heterogeneity of B cell lymphoproliferations in PTLD, the majority are monomorphic diffuse large B cell lymphoma (DLBCL), the most common clinical subtype of PTLD.[102]^17^,[103]^18 An integrated multi-cohort analysis of these three datasets using MetaIntegrator[104]^19 identified 189 significantly differentially expressed genes (DEGs) (false discovery rate [FDR] < 20%, log[2]|effect size| (ES) > 0.59, measured in ≥2 out of 3 datasets) between EBV(+) PTLD and EBV(−) PTLD ([105]Figure 1A), of which 113 were overexpressed and 76 underexpressed in EBV(+) PTLD patients compared to those with EBV(−) PTLD ([106]Table S2). These 189 genes had concordant expression patterns across all three datasets analyzed for either EBV(+) or EBV(−) PTLD ([107]Figure 1B). Collectively, we refer to these as the EBV-associated PTLD gene signature. Figure 1. [108]Figure 1 [109]Open in a new tab Integrated multi-cohort analysis identifies a 189-gene signature distinguishing EBV(+) PTLD from EBV(−) PTLD (A) Overview of multi-cohort gene expression analysis comparing samples from EBV(+) and EBV(−) PTLD lesions. (B) Heatmap of log2-transformed effect size values for each gene in the EBV-associated PTLD signature. Gray cells denote genes not measured in the dataset. Red: overexpressed in EBV(+) PTLD; blue: overexpressed in EBV(−) PTLD. (C) Volcano plot of gene expression data from PTLD tumor microarray datasets. Red: overexpressed in EBV(+) PTLD; blue: overexpressed in EBV(−) PTLD. (D) Pathway overrepresentation analysis of genes in the EBV-associated PTLD signature. The top 15 Gene Ontology terms as ranked by adjusted p values are shown. See also [110]Tables S1 and [111]S2. Overexpressed DEGs in EBV(+) PTLD included cytokines and chemokines (CCL3, CCL4, CCL8, IL15, IFNG), immune cell surface markers (CD38, CD163, CD300A), interferon-stimulated genes (SIGLEC1, OAS1, OAS2, OASL, ISG15, IFI44L, IFIT3, IFIT5), cytotoxic mediators (PRF1, GNLY, GZMH), and the antiviral apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) protein family member APOBEC3G, as well as CTC-338M12.4, an uncharacterized non-coding RNA gene, and PIK3R5, encoding the understudied p101 regulatory subunit of PI3Kγ, a crucial kinase in the PI3K/Akt/mTOR pathway that we established is constitutively active in EBV(+) PTLD[112]^20^,[113]^21^,[114]^22 ([115]Figure 1C; [116]Table S2). Conversely, among the DEGs overexpressed in EBV(−) PTLD were anti-apoptotic transcription factor genes (BCL6, BCL11A), a chemokine receptor gene (CXCR5), genes encoding proteins involved in ubiquitin-mediated protein degradation (UBE2A, CUL3, and CUL4B), oncogenes (BZW2, LMO2), and an uncharacterized gene (CXorf56, also known as STEEP1) ([117]Figure 1C; [118]Table S2). To gain insight into the biological processes associated with EBV(+) and EBV(−) PTLD, we performed Gene Ontology (GO) pathway overrepresentation analysis[119]^23^,[120]^24^,[121]^25 on our signature genes. DEGs that were upregulated or downregulated in EBV(+) PTLD (i.e., genes with higher expression in EBV(+) PTLD or EBV(−) PTLD, respectively) were analyzed separately. For the DEGs more highly expressed in EBV(+) PTLD, many of the top enriched pathways involve viral recognition and immune-related processes ([122]Figure 1D). These findings imply that despite post-transplant immunosuppression, host immunity can detect and respond to EBV, and/or EBV induces a distinct set of immune processes, thereby contributing to the transcriptomic differences in EBV(+) and EBV(−) PTLD. In contrast, DEGs with higher expression in EBV(−) PTLD were associated with lipid metabolism and DNA interaction or DNA repair pathways ([123]Figure 1D), indicative of the similarity of EBV(−) PTLD to B cell lymphomas arising in immunocompetent individuals, which have been characterized to have higher mutational burden and aberrant metabolism.[124]^26^,[125]^27^,[126]^28 Overall, these data provide further evidence that the disparate drivers of oncogenesis between EBV(+) and EBV(−) PTLD lead to distinct gene expression patterns and hence implicate different underlying biological processes in the development and persistence of EBV(+) and EBV(−) PTLD. EBV(+) PTLD B cells are transcriptomically distinct from EBV(−) B lymphoma cells Since the datasets in our multi-cohort analysis contained transcriptomic profiles of fresh-frozen PTLD tumor specimens, each sample included RNA transcripts from the transformed B cells, together with the background tissue, stromal cells, and immune infiltrate within the lesion. In order to elucidate the cell-intrinsic differences between B cells transformed with, or in the absence of, EBV, we next investigated the transcriptome of EBV(+) B cell lymphoma lines derived from patients with EBV(+) PTLD with morphology corresponding to DLBCL, and EBV(−) DLBCL cell lines, by microarray gene expression analysis. Unsupervised clustering of all seven B lymphoma lines by their transcriptome showed that they segregate by EBV status ([127]Figure 2A), again indicating that malignant EBV(+) and EBV(−) B cells are clearly distinct in their underlying biology. Figure 2. [128]Figure 2 [129]Open in a new tab Independent validation of gene signature on EBV(+) PTLD patient-derived and EBV(−) B cell lines (A) tSNE plot and unsupervised affinity propagation clustering of EBV(+) and EBV(−) B cell lines by their transcriptomic profiles. (B) Volcano plot of gene expression data from EBV(+) and EBV(−) B cell lines. Red: overexpressed in EBV(+) cell lines; blue: overexpressed in EBV(−) cell lines. (C) Heatmap of log2-transformed fold change values for each of 1,407 DEGs between EBV(+) and EBV(−) B cell lines. Red: higher gene expression; blue: lower gene expression. (D) Pathway overrepresentation analysis on DEGs in EBV(+) B cell lines. The top 15 Gene Ontology terms as ranked by adjusted p values are shown. (E) Correlation of effect size from EBV-associated PTLD signature with fold change observed between EBV(+) and EBV(−) B cell lines. (F) Strategy overview to select genes for further in vitro and in vivo investigation using EBV(+) B cell lines. See also [130]Table S2. We identified 1,407 DEGs ([131]Figures 2B and 2C), of which 613 were overexpressed and 794 were underexpressed in the EBV(+) versus the EBV(−) B cell lymphoma lines. Specifically, the upregulated DEGs included genes encoding a multitude of chemokines, cluster of differentiation (CD) antigens, interferon-induced genes, and antigen presentation molecules, while the downregulated DEGs included genes encoding proteins associated with DNA organization or interactions, such as zinc fingers, transcription factors, and centromere-associated proteins ([132]Table S2). GO pathway overrepresentation analysis revealed that the upregulated DEGs in the EBV(+) B cell lines were enriched for pathways involved in pathogenic infection and immune signaling, while the downregulated DEGs (i.e., DEGs with higher expression in EBV(−) B cell lines) were most associated with cell cycle and DNA-related pathways ([133]Figure 2D). We further filtered the DEGs to identify the genes in the B cell lymphomas that overlapped with, and had expression patterns consistent with, those observed in the EBV-associated PTLD gene signature from our multi-cohort analysis ([134]Figures 2E and 2F). The DEGs with concordantly higher transcriptomic expression in patients with EBV(+) PTLD and B cell lines were mainly associated with immune processes, including cytokine/chemokine genes (CCL3, CCL4, IL15), an antiviral gene (APOBEC3G), interferon-related genes (ISG15, IFI44L, IFIT3, OAS1, OAS2), and immune receptor genes (CD300A, BTN3A1). Meanwhile, the DEGs with similarly increased expression in EBV(−) PTLD and B cell lines instead encompassed genes with anti-apoptotic functions (BCL6, BCL11A, SYPL1, SYK) and genes linked to more aggressive tumor presentations (SWAP70, GDI2, RBBP7).[135]^29^,[136]^30^,[137]^31 These transcriptomic differences reflect the capability of EBV-transformed B cells to orchestrate changes in immune signaling pathways and within the TME, even in the absence of interactions with the immune infiltrate and background stromal tissue, and in the case of EBV(−) B cell malignancies, the potentiation of their development through dysregulation of homeostatic processes. EBV(+) PTLD B cell lines have increased expression and secretion of chemokines, which correlate with enhanced monocyte migration and enrichment of CD163+ monocytes in the TME of primary EBV(+) PTLD lesions. While little is known about the TME in EBV(+) PTLD, there is evidence of an immune inflammatory environment.[138]^32 To better understand how EBV(+) B cell PTLD may impact tumor-host cell interactions, we initially focused on EBV(+) PTLD B cell DEGs that could contribute to TME modulation. The chemokine genes CCL3, CCL3L1, CCL3L3, and CCL4 were upregulated in the EBV(+) B cell lymphoma lines and the EBV(+) PTLD signature genes ([139]Figure 3A). We validated their concordant overexpression in the EBV(+) B lymphoma lines relative to the EBV(−) lymphoma lines at the transcript level ([140]Figure 3B), and the protein level for CCL3 and CCL4 ([141]Figure 3C). Figure 3. [142]Figure 3 [143]Open in a new tab EBV(+) B cell lines have increased expression and secretion of chemokines compared to EBV(−) B cell lines, which correlate with enhanced monocyte migration (A) Forest plots of CCL3, CCL3L1, CCL3L3, and CCL4 expression from PTLD tumor microarray datasets. The x axes represent the log2-transformed effect size between groups. Rectangle size is proportional to the standard error of mean (SEM) difference in the study. Whiskers represent the 95% confidence interval. Diamonds represent the overall, combined effect size for the specific gene between groups. Width of the diamonds represents the 95% confidence interval of the summary effect size. (B) Relative gene expression of CCL3, CCL3L1, CCL3L3, and CCL4 in EBV(+) and EBV(−) B cell lines, quantified by quantitative reverse-transcription PCR (RT-qPCR). Red: EBV(+) cell lines; blue: EBV(−) cell lines. Data in quadruplicate are represented as mean ± SEM. (C) Secretion of CCL3 and CCL4 by EBV(+) and EBV(−) B cell lines, quantified by ELISA. Red: EBV(+) cell lines; blue: EBV(−) cell lines. Data in triplicate are represented as mean ± SEM. (D) Overview of in silico deconvolution and multi-cohort analysis of PTLD tumor microarray datasets. (E) Forest plots of estimated monocyte proportions from PTLD tumor microarray datasets. (F) Experimental schematic for transwell migration assay performed with cell culture supernatants from EBV(+) and EBV(−) B cell lines. (G) THP-1 migration indices for cell culture supernatants from EBV(+) and EBV(−) cell lines. ∗, adjusted p < 0.05; ∗∗, adjusted p < 0.01 by Wilcoxon rank-sum test. Pooled data from 3 independent experiments of n = 2 replicates each are represented as mean ± SEM. (H) Heatmap of log10-transformed mean fluorescence intensity (MFI) values from Luminex assay of cell culture supernatants. ND: cytokine MFI values below the lowest point on the standard curve (not detected). (I) Correlation of cytokine MFI values with THP-1 migration index computed as Pearson’s R. See also [144]Figure S1, [145]Table S7. Given the established roles of these chemokines in immune cell recruitment, we reasoned that their secretion by EBV(+) B cell PTLD could influence the composition of the immune infiltrate. Therefore, we estimated the proportion of various immune cell subsets present within each sample using in silico cell-mixture deconvolution with the basis matrix immunoStates[146]^33^,[147]^34^,[148]^35^,[149]^36 on the original multi-cohort analysis datasets ([150]Table S1). We then conducted another multi-cohort analysis on the estimated immune cell proportions to compare changes between EBV(+) PTLD and EBV(−) PTLD primary lesions ([151]Figure 3D). We determined that the proportions of total monocytes were significantly higher in EBV(+) PTLD compared to EBV(−) PTLD lesions (ES = 0.707, FDR = 0.018), largely driven by the significant enrichment of CD14^+ monocytes in EBV(+) PTLD (ES = 0.716, FDR = 0.0166) ([152]Figure 3E). The increased monocyte proportions in EBV(+) PTLD dovetail with our previous finding that CCL3 and CCL4, known monocyte chemoattractants, are overexpressed in EBV(+) PTLD, suggesting that lymphoma-derived chemokines promote monocyte infiltration into EBV(+) PTLD lesions, thereby altering the TME. To address this possibility, we determined whether the CCL3 and CCL4 quantities secreted by the EBV(+) PTLD cell lines were sufficient to affect monocyte recruitment. We performed transwell migration assays with the monocytic cell line THP-1, using culture supernatants from EBV(+) and EBV(−) B lymphoma cell lines as chemoattractants ([153]Figure 3F). Culture supernatants from three EBV(+) PTLD cell lines (AB5, JB7, and JC62) induced significantly higher THP-1 migration than supernatants from both the EBV(−) B lymphoma cell lines ([154]Figure 3G). To determine if other secreted factors could also contribute to the differential monocyte migration observed, we next assessed EBV(+) and EBV(−) culture supernatants via Luminex assays. Unsupervised hierarchical clustering of all seven cell lines by their cytokine secretion profile showed clear segregation by their EBV status ([155]Figure 3H). In addition to CCL3 and CCL4, multiple cytokines and chemokines (FLT3L, GM-CSF, interleukin [IL]-27, CCL2, M-CSF, CXCL9, and CCL5), many with known roles in monocyte recruitment, were also secreted by the EBV(+) cell lines at consistently higher levels than the EBV(−) cell lines ([156]Figures 3H and [157]S1). At the transcript level, CCL5 and CSF1 (M-CSF) were among the DEGs identified to also be upregulated in EBV(+) B cell lines ([158]Figure 2B). Furthermore, the THP-1 migration index was positively correlated (Pearson R ≥ 0.4) with FLT3L, GM-CSF, IL-27, CCL2, and M-CSF concentrations ([159]Figure 3I). In particular, secreted CCL2 in the EBV(+) B cells was strongly positively correlated (Pearson R = 0.96, p = 0.00062) with the extent of monocyte migration. These data indicate that chemokines produced by EBV(+) B cell lymphoma cells can promote monocyte infiltration into the TME of EBV(+) PTLD. To directly assess the monocyte infiltrate within primary EBV(+) and EBV(−) PTLD tumor lesions, we performed co-detection by indexing (CODEX), immunohistochemistry (IHC) and in situ hybridization (ISH) on formalin-fixed paraffin-embedded (FFPE) tissue microarrays from human DLBCL PTLD archival specimens (n = 33). We found that the median intensity of the monocyte/macrophage marker CD14 was significantly increased in non-B/non-T cells within EBV(+) compared to EBV(−) PTLD lesions ([160]Figure 4A; p = 0.033, q = 0.066), in agreement with our multi-cohort analysis ([161]Figure 3E). Although the median expression of CD163 as determined by CODEX was not significantly elevated ([162]Figure 4A; p = 0.076, q = 0.101) in EBV(+) PTLD, the proportions of CD163+ cells tended to be higher in EBV(+) PTLD lesions ([163]Figures 4B and 4C; p = 0.017, q = 0.085). IHC analysis further corroborated the enrichment of CD163+ cells in the TME of EBV-encoded RNA (EBER)+, i.e., EBV(+), PTLD ([164]Figure 4D; [165]Table S3; q = 0.024). Latent membrane protein (LMP)1+ and Epstein-Barr nuclear antigen (EBNA)+ PTLD cases also had increased proportions of CD163+ cells ([166]Table S3; q = 0.024 and 0.027, respectively). Moreover, in our original multi-cohort analysis datasets, CD163 was also upregulated within the EBV-associated PTLD signature genes ([167]Table S2; ES = 0.927, FDR = 2.79 × 10^−4). Taken together, our data suggest that the predominance of M2-like monocytes in EBV(+) PTLD could create an immunosuppressive environment. Consistent with this, we observed in LMP1+ or EBNA+ PTLD cases significantly decreased frequencies of both CD3^+ T cells (LMP1: q = 0.046; EBNA: q = 0.024) and CD4^+ T cells (LMP1: q = 0.024; EBNA: q = 0.046)) ([168]Figures 4D; [169]Table S3). Furthermore, the median expression of the myeloid lineage-restricted myeloperoxidase (MPO), whose enzymatic activity has been linked to poorer outcomes in cancer,[170]^37^,[171]^38 was also significantly increased in EBV(+) PTLD ([172]Figure 4A; p = 0.008, q = 0.030). Within the EBV(+) PTLD cases, latency III cases were more likely to have lower CD3^+ T cell proportions ([173]Table S3; q = 0.024). Figure 4. [174]Figure 4 [175]Open in a new tab CD163+ monocytes are increased in the tumor microenvironment of primary EBV(+) PTLD lesions (A) Expression of CD14, CD163, and MPO in non-B/non-T cells within primary EBV(+) or EBV(−) PTLD lesions. MFIs for each marker within the membrane compartment are quantified at the single-cell level and aggregated by computing the median marker MFI from all cells within each sample. Orange: EBV(+) (n = 18); blue: EBV(−) (n = 7); points represent individual cases. Data are represented as mean ± SEM. q = adjusted p values by Wilcoxon rank-sum test. (B) Percentage of CD163+ cells within primary EBV(+) or EBV(−) PTLD lesions, quantified by CODEX. Orange: EBV(+) (n = 11); blue: EBV(−) PTLD (n = 5); points represent individual cases. Data are represented as mean ± SEM. (C) CODEX images of FFPE tissue from diagnostic biopsies of EBV(+) and EBV(−) monomorphic PTLD (DLBCL subtype). Yellow: EBNA2; red: CD3/CD4; blue: CD68/CD163. A representative case in each group is shown in parallel for comparison. Scale bar, 50 μm. (D) Histology of FFPE tissue from diagnostic biopsies of EBV(+) and EBV(−) monomorphic PTLD (DLBCL subtype). EBER in situ hybridization and IHC staining for CD163, CD3, and CD4 from a representative case per group are shown in parallel for comparison. Scale bar, 50 μm. See also [176]Tables S3 and [177]S8. Collectively, our data suggest that EBV(+) B lymphoma cells alter the TME through producing monocyte-attracting chemokines, leading to an immunosuppressive environment in EBV(+) PTLD driven by tumor-associated M2 monocytes and diminished T cell proportions. Single-cell surface proteomic analysis distinguishes EBV(+) PTLD B lymphoma cells from EBV(−) B lymphoma cells and healthy B cells We next focused on deciphering the differences in the B lymphoma cells in EBV(+) and EBV(−) PTLD by performing high-dimensional single-cell analysis using cytometry by time of flight (CyTOF) and a custom antibody panel recognizing B cell surface molecules ([178]Table S4). Both stimulated and unstimulated B cells from PBMCs of a healthy blood donor (control B cells) were included ([179]Figure S2A). Principal-component analysis (PCA) performed on the median expression of individual markers for each sample demonstrates that EBV(+) and EBV(−) B lymphoma cell lines clearly segregate from each other and from control B cells using the first two principal components ([180]Figure 5A). Moreover, uniform manifold approximation and projection (UMAP) plots mapping the EBV(+) and EBV(−) lymphoma lines to the same high-dimensional space also show similarly distinct segregation between both groups ([181]Figures 5B and [182]S2B). Within the UMAP space, four clusters could be identified. EBV(+) B cell lines constituted almost the entirety of clusters A and B, while clusters C and D were similarly overrepresented by the EBV(−) B cell lines ([183]Figure 5C). Figure 5. [184]Figure 5 [185]Open in a new tab EBV(+) B cell lines have a distinct surface phenotype from EBV(−) B cell lines and healthy B cells (A) PCA of EBV(+) and EBV(−) B cell lines and stimulated and unstimulated B cells from a healthy donor (control B cells), using 27 markers measured by CyTOF. Points represent individual samples, color-coded by group. Eigenvectors of individual markers are indicated by arrows. (B) UMAP plots using 3,000 cells per cell line, using all 27 chosen markers. Cells are color-coded by sample and aggregated by EBV status (top) or colored by relative signal intensity of indicated surface markers (bottom). (C) Clustering of EBV(+) and EBV(−) B cell lines. Based on the UMAP in (B), 4 distinct clusters were identified (left) and the proportion of each cluster by EBV status was calculated (right). (D) Histograms for the expression of indicated phenotypic markers, represented as arcsinh-transformed signal intensity values. Each histogram represents marker expression in individual samples for EBV(+) and EBV(−) B cell lines, as well as stimulated and unstimulated control B cells. (E) Comparison of CD300a median signal intensity for indicated phenotypic markers between EBV(+) cell lines (n = 5), EBV(−) cell lines (n = 2), and control B cells (n = 2). q = adjusted p values by Kruskal-Wallis test. Data are shown as mean ± SEM. (F) Heatmap of globally scaled B cell phenotypic marker expression in B cell lines and control B cell subsets. Median arcsinh-transformed signal intensity values of each marker were scaled between 0 and 1 across all samples. Unsupervised clustering was carried out on both individual cell lines/B cell subsets (rows) and phenotypic markers (columns). (G) Heatmap of scaled B cell phenotypic marker expression in B cell lines and control B cell subsets, with cell lines and control B cells scaled separately. Median arcsinh-transformed signal intensity values of each marker were scaled between 0 and 1 on all B cell lines, and separately on all control B cell subsets, to map B cell lines to the closest control B cell subset. Unsupervised clustering was performed on both individual cell lines/B cell subsets (rows) and markers (columns). See also [186]Figure S2, [187]Table S4. Notably, the EBV(+) B cell lines are characterized by significantly higher CD23, CD95 (Fas receptor), and CD300a surface expression when compared to EBV(−) B cell lines and control B cells ([188]Figures 5B, 5D, and 5E), as well as increased expression of the activation marker CD69 ([189]Figures S2C–S2E), congruent with their activated phenotype.[190]^39 In contrast, both EBV(−) cell lines exhibit lower CD22 expression and significantly higher CD24 expression, compared to all five EBV(+) cell lines and control B cells ([191]Figures 5B, 5D, and 5E). Additionally, CD95, CD300A, CD22, and CD24 are among the DEGs distinguishing the EBV(+) and EBV(−) B cell lines at the transcript level ([192]Figures 2B; [193]Table S2), concurring with their protein expression. Since B cells from healthy individuals consist of multiple functional subsets, we then classified the control B cell samples into six subpopulations ([194]Figures S2F–S2H) based on previously described B cell phenotypic characterization schemes.[195]^40^,[196]^41 Unsupervised hierarchical clustering of all cell lines and control B cell subsets by their median marker expression separated the samples into three clusters corresponding to their EBV and transformation status, with notably distinct patterns of phenotypic marker expression between all experimental groups ([197]Figure 5F). We accounted for the marked separation of the EBV(+) and EBV(−) cell lines from the untransformed B cells by scaling the median marker expression of all cell lines separately from that of the control B cell subsets. With the median expression values scaled to the same relative range, we then sought to relate each B cell line to its closest control B cell subset. Subsequently, we performed unsupervised hierarchical clustering. This analysis revealed that the EBV(+) cell line JB7 was most similar to naive B cells while all other EBV(+) cell lines more closely resembled various CD45RB + CD27^+ memory B cell subsets. Among the EBV(−) cell lines, Pfeiffer was most associated with the CD45RB + CD27^−memory B cell subset, while Toledo did not correlate with any of the control B cell subsets ([198]Figure 5G). CD300a is more highly expressed and is required for maximal proliferation of EBV(+) PTLD B lymphoma cells Having applied an integrated multi-omics approach to resolve the differences between EBV(+) and EBV(−) PTLD, we next evaluated whether this approach could pinpoint molecules with a functional impact. We noted CD300A transcripts were concordantly upregulated in both primary EBV(+) PTLD lesions and EBV(+) B lymphoma lines ([199]Figures 2B and [200]S3; [201]Table S2). At the protein level, CD300a is similarly overexpressed on EBV(+) versus EBV(−) cell lines, while healthy B cells have negligible CD300a surface expression ([202]Figures 5D–5F and [203]S3C). Since CD300A exhibited increased expression in primary EBV(+) PTLD lesions and EBV(+) B lymphoma cell lines, at both the transcript and protein levels, we chose CD300A for further investigation ([204]Figure 2F). We first evaluated whether EBV can elicit expression of CD300a in B cells, by assessing the levels of surface CD300a on peripheral blood B cells from healthy donors and autologous B cells following EBV-mediated transformation. Three of three EBV(+) lymphoblastoid cell lines (LCLs), each generated by EBV infection of peripheral B cells from a healthy donor, show increased CD300a expression relative to autologous uninfected B cells ([205]Figures 6A and 6B). These data indicate that EBV infection and transformation of human B cells induce CD300a expression. Figure 6. [206]Figure 6 [207]Open in a new tab CD300a is more highly expressed on, and is required for, optimal growth of EBV(+) PTLD B lymphoma cells (A and B) Surface expression of CD300a in peripheral blood B cells from healthy donors and autologous LCL transformed in vitro by EBV (n = 3). Biaxial plots (A, left), histograms (A, right), and comparison of CD300a median signal intensity (B) are shown. (C) Schematic for CRISPR-Cas9-mediated gene editing for CD300A in EBV(+) PTLD B cell lines. (D) In vitro growth of CD300A KO cells relative to mock-edited controls of EBV(+) PTLD B cell lines. Cell quantities in mock-edited controls after 48 h of culture are used as the reference for normalization. ∗, p < 0.05 by Wilcoxon rank-sum test. Data shown in quadruplicate with mean ± SEM. (E) Apoptosis assay of CD300A KO or mock-edited EBV(+) PTLD B cell lines. Apoptosis stages were determined by DAPI and annexin V staining. ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001; ∗∗∗∗, p < 0.0001 by unpaired t test. Data shown in triplicate with mean ± SEM. (F) Pathway overrepresentation analysis of genes downregulated in CD300A KO compared to mock control EBV(+) B cell lines using clusterProfiler. See also [208]Figures S3–S5, [209]Tables S4 and [210]S5. CD300a is a member of the CD300 multi-gene family of receptors, mainly expressed externally on immune cells. Earlier studies show that CD300a has inhibitory function,[211]^42^,[212]^43^,[213]^44^,[214]^45^,[215]^46^,[216]^47^,[2 17]^48^,[218]^49^,[219]^50^,[220]^51 mediated by the four immunoreceptor tyrosine inhibitory motifs (ITIMs) within its cytoplasmic tail. To elucidate CD300a’s role in B cell lymphoma, we generated CD300A knockout (KO) cell lines from three EBV(+) PTLD-derived B cell lines (AB5, JB7, and VB5) using CRISPR-Cas9 gene editing ([221]Figure 6C). KO efficiencies exceeding 85% were achieved with two CD300A KO cell lines (AB5 and JB7), while the third (VB5) exhibited a KO efficiency of 40%–50% ([222]Figure S4). We first evaluated the effect of CD300a deficiency on the spontaneous in vitro growth of these cells. All three CD300A KO EBV(+) B lymphoma cell lines had significantly decreased expansion relative to their corresponding mock-edited parental cell controls ([223]Figure 6D). Furthermore, all three CD300A KO EBV(+) cell lines had higher proportions of cells in late apoptosis (DAPI+, annexin V+ cells) vis-à-vis the mock-edited parental cells ([224]Figures 6E and [225]S5A). We did not observe any evidence of cell-cycle arrest, nor significant differences in the cellular distribution throughout the cell cycle ([226]Figure S5B). Given that annexin V binds to phosphatidylserine, one of the only two known ligands of CD300a,[227]^52 our observation of annexin V+ cells in the parental EBV(+) lymphoma lines ([228]Figure S5A) provides evidence for the presence of a physiological ligand capable of engaging CD300a. Together, our data suggest that CD300a may promote the growth and/or provide a signal to impede apoptosis of EBV-transformed B cells. To directly evaluate the impact of CD300A depletion on cytokine/chemokine secretion in EBV-transformed B cell lymphomas, we conducted Luminex assays of cell culture supernatants from the CD300A KO EBV(+) cell lines and their corresponding mock-edited controls. Unsupervised hierarchical clustering of all samples by their cytokine secretion profile showed clear segregation only by their parental cell line, but not their CD300A expression nor the culture duration post-gene KO ([229]Figure S5C). To further understand how CD300a expression can contribute toward tumor growth, we characterized the gene expression changes associated with CD300A depletion using single-cell RNA sequencing (scRNA-seq) of the CD300A KO EBV(+) B lymphoma lines and their corresponding mock-edited controls. DEGs were identified between CD300A KO and mock-edited cells as those with fold change >1.15 and adjusted p value <0.05. GO pathway overrepresentation analysis using all DEGs between CD300A KO and mock-edited cells from all three EBV(+) cell lines showed that the DEGs downregulated in CD300A KO cells enriched for pathways involving respiration and homeostatic metabolic processes ([230]Figures 6F; [231]Table S5). A second, independent evaluation of DEG and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the GSEApy package also implicated alterations in metabolic processes in CD300A KO cells ([232]Figures S5D and S5E), further indicating that CD300a may participate in regulating homeostatic signaling pertaining to general cellular respiration, which could contribute to the reduced growth observed with CD300A deletion. Loss of CD300A expression leads to reduced growth of EBV(+) B cell PTLD in vivo To evaluate if CD300a deficiency affected lymphoma growth in vivo, we utilized a xenograft murine model of EBV(+) PTLD.[233]^22^,[234]^53^,[235]^54 Immunodeficient non-obese diabetic-severe combined immunodeficiency (NOD-SCID) mice were injected subcutaneously with either CD300A KO or mock-edited cells from the EBV(+) B cell lines AB5 or JB7. Mice receiving CD300A KO cells from both EBV(+) cell lines had significantly inhibited tumor growth compared to those receiving parental mock-edited cells within 5–7 days after tumor detection ([236]Figures 7A and 7B). At sacrifice, tumor tissue was excised and harvested. The average tumor volume was significantly smaller in the CD300A KO group than the mock group for both AB5 (755 ± 217 mm^3 vs. 1,874 ± 793 mm^3; mean ± standard deviation, p < 0.01) and JB7 (1,407 ± 387 mm^3 vs. 2,176 ± 413 mm^3, p < 0.05) ([237]Figures 7C and 7D). Figure 7. [238]Figure 7 [239]Open in a new tab Loss of CD300a leads to reduced growth of EBV(+) B cell lymphomas in a xenograft mouse model of PTLD (A and B) Average tumor volume (mm^3) calculated from mice injected with CD300A KO or mock-edited AB5 (A) or JB7 (B) cells (n = 5 mice per group). Data are shown as mean tumor volume ± SD for each group. Differences in tumor volume across all time points compared using a 2-way repeated measures ANOVA. (AB5: F(6, 48) = 7.987; JB7: F(7, 56) = 4.559). ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001 with Benjamini-Hochberg FDR correction. (C and D) Tumor volume (mm^3) at the time of sacrifice from mice injected with CD300A KO or mock-edited AB5 (C) or JB7 (D) cells. Individual tumor volumes (n = 5) are shown with mean ± SD. ∗, p < 0.05; ∗∗, p < 0.01 by Wilcoxon rank-sum test. (E) Histology of tumors harvested at sacrifice from mice injected with CD300A KO or mock-edited AB5 or JB7 cells. H&E staining, EBER in situ hybridization, and IHC staining for CD20 and Ki67 of tumors from a representative mouse per group are shown in parallel for comparison. Scale bar, 50 μm. (F) Necrosis scores of tumors harvested at sacrifice from mice injected with CD300A KO or mock-edited AB5 (top) or JB7 (bottom) cells. Scores from representative tumors (n = 3) are shown with mean ± SEM. See also [240]Figure S6, [241]Table S6. Histological and immunohistochemical analyses were also performed on three representative tumors per group. Notably, the CD300A KO group had lower Ki67 labeling indices ([242]Figure 7E) and increased necrosis than the mock group for both AB5 (30% ± 11.55% vs. 20% ± 5.77%; mean ± standard error of mean) and JB7 (8.33% ± 2.89% vs. 5.00% ± 0.00%) ([243]Figure 7F; [244]Table S6). Strong membranous CD20 staining and nuclear EBER ISH signals were observed in all tumors, confirming their human B cell lineage and EBV positivity ([245]Figure 7E). While mouse monocytes could be detected in the human tumor xenografts ([246]Figure S6), there were no significant differences in the frequency of monocytes infiltrating tumors derived from CD300A KO or mock-edited AB5 or JB7 cells ([247]Table S6), consistent with the highly similar cytokine/chemokine secretion profiles observed between CD300A KO and mock-edited cells in vitro ([248]Figure S5C). Together, these data indicate that CD300A deletion impairs the in vivo development of solid tumors derived from EBV(+) B cell PTLD lines, thus providing clear evidence that CD300a mediates pro-tumor effects in EBV(+) B cell PTLD. Discussion The interplay between EBV and host immunity in EBV-driven malignancies is presently poorly characterized, as are the underlying molecular and immunological differences between EBV(+) and EBV(−) cancers of the same cell type.[249]^2 In the post-transplant context, this translates to similar therapeutic approaches for managing EBV(+) and EBV(−) B cell PTLD. These strategies generally fail to consider EBV-driven disease pathogenesis and only broadly modulate host immunity without specifically targeting the virus. They may also heighten the risk of organ rejection[250]^55 and/or opportunistic infection.[251]^56^,[252]^57 While adoptive transfer of autologous or allogeneic EBV-specific cytotoxic T cells has shown promise, this approach has not been broadly applicable.[253]^58 Consequently, there is an unmet need to understand the divergent biological mechanisms involved when EBV is present or absent as an oncogenic driver to then facilitate personalized treatment options. In this study, we employed an integrated multi-modal strategy combining computational analyses of gene expression data from EBV(+) and EBV(−) PTLD with high-dimensional multi-omics to identify the underlying tumor and immune characteristics of EBV(+) and EBV(−) B cell PTLD. We establish that viral-immune dominance is a tumor-intrinsic feature of EBV(+) B cell PTLD in marked contrast to the DNA-interacting proteins, DNA repair, and altered lipid metabolism landscape that characterize EBV(−) B cell lymphomas. Second, we demonstrate that EBV(+) B cell lymphomas directly shape the TME through the elaboration of an array of monocyte-attracting chemokines, and an enrichment of M2-linked monocytes in EBV(+) PTLD, which may impact host anti-tumor responses via reducing T cells in the TME. Finally, we show that EBV transformation induces CD300a upregulation, required for maximal cell survival. CD300a-deficient EBV(+) B lymphoma lines have significantly diminished in vitro/in vivo growth vis-à-vis parental EBV(+) CD300a-expressing lines. The concordance of our EBV(+) versus EBV(−) B lymphoma transcriptomic analyses (across previously published datasets and B cell lines) was robust and revealed associations of EBV(+) lymphomas with immune response genes, and EBV(−) lymphomas with genes encoding DNA-interacting proteins. These findings support the hypotheses that EBV(+) B cell PTLD is either sufficiently immunogenic to elicit antiviral and/or anti-tumor responses or driven by EBV subverting immune pathways as it transforms the host cell, or both, whereas EBV(−) PTLD exhibits closer similarity to immunocompetent B cell lymphomas, with altered lipid metabolism and DNA interaction/repair processes. Previous work from our group has demonstrated that EBV induces constitutive IL-10 production.[254]^59 IL-10 has dual roles as an autocrine growth factor for EBV(+) B cell PTLD, and an immunosuppressive cytokine dampening anti-tumor responses. Here, we found that EBV(+) B cell lines have enhanced secretion of several monocyte-chemotactic cytokines/chemokines including CCL2, CCL3, and CCL4. EBV’s main oncogene, LMP1, induces CCL3 and CCL4 expression in NPC cell lines via the c-Jun N-terminal kinase pathway,[255]^60 as well as CCL2 expression in an epithelial cell line via nuclear factor κB (NF-κB).[256]^61 CCL3 and CCL4 have also been described as autocrine growth factors within multiple myeloma cell lines[257]^62 and in vitro-generated B LCLs.[258]^60 These chemokines may similarly function in spontaneously arising EBV(+) B cell lymphomas as well, given the expression of their cognate receptor CCR5 on all EBV(+) lymphoma lines in this study. Monocytes are an important immune population in the TME, given their potential to differentiate into macrophages and the role for tumor-associated macrophages (TAMs) with their spectrum of polarization states as crucial orchestrators of many hallmarks of cancer, including angiogenesis, invasion, and creation of an immunosuppressive TME.[259]^63 More recently, the role of monocytes themselves within the TME has become increasingly recognized, notably their acquisition of immunosuppressive function and development into myeloid-derived suppressor cells.[260]^64^,[261]^65 Studies investigating the TME in PTLD remain uncommon, although macrophage enrichment has been reported to correlate with EBV presence.[262]^32^,[263]^66^,[264]^67 Altogether, our findings further establish EBV’s capacity to alter its transformed host cell’s secretome, thereby manipulating the TME via monocyte recruitment. Moreover, we demonstrate that the TME in primary EBV(+) PTLD specimens is characterized by an enrichment of M2-like monocytes and a corresponding decrease in T cells, which may, in tandem with the aforementioned production of IL-10 by EBV+ B lymphomas,[265]^20^,[266]^59 create an immunosuppressive environment that can dampen anti-EBV T effector function. While PTLD includes a spectrum of abnormal lymphoproliferations, many are of the monomorphic DLBCL type.[267]^17^,[268]^18 Whereas monomorphic DLBCL was the predominant subtype in our multi-cohort analysis, our EBV(+)/EBV(−) B cell lines were also of the DLBCL subtype. Thus, our combined transcriptomic analyses and proteomic characterization of EBV(+) and EBV(−) B cell lymphomas provide a vast resource for further studies to interrogate biologic functions of specific differentially expressed molecules and their potential as therapeutic targets. Along these lines, CyTOF analysis of EBV(+) and EBV(−) B cell lines revealed further surface phenotypic differences, with EBV(+) B cell lines characterized by CD23, CD95, and CD300a overexpression and EBV(−) B cell lines by their lower CD22 expression and higher CD24 expression. CD24 is an anti-phagocytosis “don’t-eat-me” signal; when expressed on tumor cells, it impairs macrophage phagocytosis by binding the inhibitory receptor Siglec-10 on macrophages.[269]^68 CD22 (Siglec-2) is predominantly expressed on B cells and is canonically considered an inhibitory receptor due to its four cytoplasmic ITIMs. CD22 negatively regulates B cell receptor signaling[270]^69; CD22-deficient mice exhibit augmented responses to B cell receptor (BCR) activation[271]^70 and autoantibody titers.[272]^71 Hence, EBV(−) B cell lymphomas could be co-opting CD24 as an immune escape mechanism, while downregulating CD22 to maintain a heightened activation state. CD95 is a death receptor capable of triggering apoptosis upon activation. Its upregulation by the host cell could represent a defense mechanism attempting to induce cell death. Our lab has previously reported high CD95 expression on the same EBV(+) PTLD B cell lines and demonstrated that LMP1 can protect EBV(+) B cell lymphomas from apoptosis by NF-κB-dependent induction of cellular FLICE (FADD-like IL-1β-converting enzyme)-inhibitory protein (c-FLIP), blocking signal propagation downstream of CD95.[273]^72^,[274]^73 CD23 is an immunoglobulin E (IgE) receptor (FcεRII) whose cleavage releases soluble CD23, which maintains B cell survival and growth[275]^74^,[276]^75 by inhibiting apoptosis.[277]^76 We show that infection and transformation of B cells by EBV also resulted in higher CD300a surface expression. Interestingly, the EBV gene EBNA2 has been described to enhance FCER2 (CD23) and CD300A transcript expression,[278]^77 raising the possibility of their upregulation being another mechanism utilized by EBV to drive tumor growth. In this report, we show that CD300a expression is essential to maximal survival of EBV(+) B cell lymphomas. Paradoxically, CD300a is a receptor containing three classical and one non-classical cytoplasmic ITIM motifs.[279]^78 Early studies in B cells described CD300a as an inhibitory molecule because CD300a engagement suppressed calcium mobilization and NFAT activity elicited by concurrent BCR ligation.[280]^45^,[281]^46 However, subsequent reports on CD300a′s function have been mixed: a multitude of studies, predominantly in primary cells, showed that CD300a exhibits inhibitory function,[282]^42^,[283]^43^,[284]^44^,[285]^47^,[286]^48^,[287]^49^,[2 88]^50^,[289]^51 while others described its pro-tumor functions.[290]^79^,[291]^80^,[292]^81 The latter implicate downstream activation of the PI3K/Akt pathway, which our group has demonstrated to be constitutively active in EBV(+) PTLD.[293]^20^,[294]^21^,[295]^22 CD300a mediates its inhibitory signaling via SHP-1[296]^48 after engaging its ligands phosphatidylserine or phosphatidylethanolamine,[297]^52 exposed on the outer membrane leaflet of stressed or apoptotic cells. These cells commonly arise in cell culture systems and in vivo tumor environments; here, we observe phosphatidylserine presence on our EBV(+) B cell lines. More intriguingly, it is unknown if CD300a has other yet-undiscovered ligands that can potentiate its pro-tumor effects. Additionally, the studies demonstrating CD300a inhibitory function generally used immune cells isolated from healthy individuals or cell lines provided with strong exogenous stimuli, whereas those implicating CD300a in pro-tumor roles were performed using cancer cell lines without external stimulation. Therefore, it is plausible that CD300a has pleiotropic functions, depending on the biological context. Using CRISPR-Cas9 to deplete CD300A expression in EBV(+) B cell lines, we demonstrate that CD300a supports both in vitro and in vivo growth of EBV(+) B cell lymphomas. Moreover, scRNA-seq reveals an association of CD300a depletion with decreased expression of genes enriched in energy, aerobic, and cellular respiration. Interestingly, these include cyclooxygenase (COX) family members and genes linked to the PI3K/Akt/mTOR pathway, known to be critically involved in cellular metabolism and, as we have previously shown, essential to EBV(+) PTLD B cell survival.[298]^20^,[299]^21^,[300]^22^,[301]^54 Coupled with our finding that EBV infection and transformation elicit CD300a expression in B cells, these data collectively imply that EBV may maintain the survival and proliferative potential of its transformed host B cells through upregulating CD300a expression. Notably, our findings implicate CD300a in transformed cells with a viral driver of oncogenesis. Prior studies examining CD300a’s role in tumors described its overexpression in malignant tissues versus normal tissues, without sufficient resolution on tumor EBV status and/or etiology. How exactly CD300a, with its inhibitory properties, potentiates EBV(+) B cell lymphoma growth remains to be more thoroughly determined mechanistically. However, it is intriguing to consider that CD300a may tune constitutive pro-activation and proliferation signals provided by EBV proteins, to avoid surpassing signaling thresholds that may elicit cell death in transformed cells.[302]^82 In support of this hypothesized role for CD300a in EBV-transformed cells, we show that higher frequencies of apoptotic cells accompany CD300A depletion without affecting cell-cycle arrest/progression, indicating that CD300a could provide anti-apoptotic signaling to EBV(+) B cell lymphomas in PTLD, thereby offering mechanistic insight into the observed reduced expansion of CD300A KO cells. Collectively, our results highlight that EBV(+) and EBV(−) PTLD and B cell lymphomas have distinct molecular pathways and immune interactions, providing further evidence supporting the predominance of immune pathways in EBV(+) PTLD versus cytogenetic abnormalities in EBV(−) PTLD. We demonstrate the TME-altering potential of EBV-driven malignancies: EBV-transformed B cells secrete larger quantities of monocyte-chemotactic cytokines/chemokines, possibly contributing to the increased monocyte presence in EBV(+) PTLD lesions. Additionally, our integrated multi-modal approach enabled our identification of CD300a overexpression in EBV(+) PTLD, crucial for optimal expansion of EBV-transformed cells. Overall, these data establish the differential involvement of specific processes in the pathogenesis of EBV(+) and EBV(−) PTLD, thereby providing a basis for exploiting these biological targets to further refine the therapeutic management of PTLD based on tumor’s association with EBV or lack thereof. Limitations of the study EBV(−) sample sizes in our analyses were limited. As with cell lines, the B cell lines we used to model PTLD in vitro may not fully capture the variety of physiological processes at play in PTLD development and persistence. To mitigate this, we focused on genes with concordantly differential expression between EBV(+) and EBV(−) samples from both primary PTLD tumors and cell lines. While we have addressed CD300a’s functional impact on the tumor, its exact mechanistic contributions toward tumor growth have yet to be comprehensively explored. Unfortunately, we could not experimentally demonstrate the therapeutic effects of targeting CD300a in PTLD due to the lack of commercially available small molecules/drugs or reliable agonistic/antagonistic monoclonal antibodies capable of specifically modulating CD300a’s downstream activity. Future studies should focus on validating the therapeutic potential of targeting CD300a in PTLD. Lastly, the molecular mechanism(s) behind the induction of these differential gene/protein expression patterns upon EBV infection and transformation remain to be determined. Resource availability Lead contact Further information and requests for resources, software, and data should be directed to and will be fulfilled by the lead contact, Olivia M. Martinez (omm@stanford.edu). Materials availability There are restrictions to the availability of the EBV(+) PTLD patient-derived spontaneous lymphoblastoid cell lines (SLCLs), AB5, JB7, JC62, MF4, VB5, and their gene-edited derivatives. The informed consent utilized for the establishment of these lines, or their derivatives, precluded transfer to other parties; therefore, they cannot be shared with other investigators. This study did not generate any other new unique reagents. Further information and requests for resources, software, and data should be directed to and will be fulfilled by the [303]lead contact, Olivia M. Martinez (omm@stanford.edu). Data and code availability B cell line microarray data and CD300A KO scRNA-seq data have been deposited at National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) as GEO: [304]GSE279598 and GEO: [305]GSE279989, respectively, within superseries GEO: [306]GSE279991 and are publicly available as of the date of publication. This study does not report original code. All software and algorithms used in this study are publicly available. Any datasets or additional information required to reanalyze the data reported in this paper is available from the [307]lead contact/corresponding authors upon request. Acknowledgments