Krull, Jordan E.

Abstract Differentiation of aseptic and septic causes of arthroplasty failure is essential due to their different treatment schemes. We hypothesized that predicted cellularity profiling of sonicate fluid using the RNASeq-based the cellular deconvolution tools CIBERSORTx and ABIS-seq can differentiate between PJI and non-infected arthroplasty failure (NIAF). Profiles were created for 93 sonicate fluid samples (40 from NIAF and 53 from PJI patients) that had been subjected to RNASeq analysis. CIBERSORTx provided 22 total predicted cell types; 12 predicted cellular abundances were differentially expressed in PJI versus NIAF, including increased neutrophils, activated mast cells, and eosinophils (p≤0.0004) and decreased M0 macrophages, M2 macrophages, and Treg cells (p≤0.0001). ABIS-seq provided 17 total predicted cell types; seven predicted cellular abundances were differentially expressed in PJI versus NIAF, including increased neutrophils, Vd2 γδ T cells, and basophils (p-values of <0.0001, 0.0003, and 0.0263, respectively) and decreased nonclassical/intermediate (NC+I) monocytes, mucosal-associated invariant T (MAIT) cells, and myeloid dendritic cells (mDC) (p-values ≤0.0001). Receiver operative characteristic area under the curve (AUC) analysis identified cells types most predictive of PJI (CIBERSORTx: neutrophils [AUC = 94], activated mast cells [AUC = 93]; ABIS-seq: neutrophils [AUC = 88]) and NIAF (ABIS-seq: NC+I monocytes [AUC = 85], MAIT cells [AUC = 80], mDCs [AUC = 81]). PJI and NIAF samples were differentially clustered by principal component analysis using both tools. Overall, cellularity profiling using RNASeq-based cellular deconvolution can differentiate between PJI and NIAF sonicate fluid samples. Work supported by NIAMS (NIH R01 AR056647). CF was supported by the Mayo Clinic Graduate School of Biomedical Sciences and the Ph.D. Training Grant in Basic Immunology (NIH R25 GM055252 24).

Abstract The tumor microenvironment (TME) plays a crucial role in mediating the tumor immune response, thereby affecting patient outcomes in follicular lymphoma (FL). Using CyTOF, we analyzed a cohort of 82 FL patients with biopsy specimens collected before the first treatment regimen was administered. Our results showed that there was a difference in the T-cell phenotype in patients with various TMEs. Patients with TMEs rich in monocyte/macrophages/NK cells had more CD8+ cells and tended to have fewer TN, but more TEMRA and TEXH cells. Patients enriched with T cells exhibited a T-cell phenotype skewed toward to TCM and patients with B cell dominance tended to have significant higher number of TFH cells. T-cell phenotypes with more terminally differentiated, senescent and exhausted cells were highly represented in patients with less T cells when compared to patients with more T cells. We observed that CD57+ TFH cells was significantly more abundant in patients who had disease progression than patients who had complete response to therapy, suggesting a role for CD57+ TFH cells in promoting malignant cell growth in FL. Consistent with this finding, we observed that increased numbers of CD57+ TFH cells correlated with an inferior survival in FL. Using CITE-seq technology, we found that CD57+ TFH cells exhibited a substantially different transcriptome from CD57− TFH cells. Genes that were differentially upregulated in CD57+ TFH cells when compared to CD57− TFH cells included genes involved in cell survival, compromised inflammatory response, and metabolic activation. Taken together, our results indicate different tumor microenvironments among patient groups that is associated with variable T-cell phenotypes.

Background: Although cellularity is traditionally assessed morphologically, deep sequencing approaches being used for microorganism detection may be able to provide information about cellularity. We hypothesized that cellularity predicted using CIBERSORTx (Stanford University), a transcriptomic-based cellular deconvolution tool, would differentiate between infectious and non-infectious arthroplasty failure. Methods: CIBERSORTx-derived cellularity profiles of 93 sonicate fluid samples, including 53 from subjects who underwent failed arthroplasties due to periprosthetic joint infection (PJI) (abbreviated for the purpose of this study as PJIF) and 40 from subjects who had undergone non-infectious arthroplasty failure (abbreviated NIAF) that had been subjected to bulk RNA sequencing were evaluated. Results: Samples from PJIF and NIAF subjects were differentially clustered by principal component analysis based on the cellularity profile. Twelve of the 22 individual predicted cellular fractions were differentially expressed in the PJIF cases compared with the NIAF cases, including increased predicted neutrophils (mean and standard error, 9.73% ± 1.06% and 0.81% ± 0.60%), activated mast cells (17.12% ± 1.51% and 4.11% ± 0.44%), and eosinophils (1.96% ± 0.37% and 0.42% ± 0.21%), and decreased predicted M0 macrophages (21.33% ± 1.51% and 39.75% ± 2.45%), M2 macrophages (3.56% ± 0.52% and 8.70% ± 1.08%), and regulatory T cells (1.57% ± 0.23% and 3.20% ± 0.34%). The predicted total granulocyte fraction was elevated in the PJIF cases (32.97% ± 2.13% and 11.76% ± 1.61%), and the samples from the NIAF cases had elevated predicted total macrophage and monocyte (34.71% ± 1.71% and 55.34% ± 2.37%) and total B cell fractions (5.89% ± 0.30% and 8.62% ± 0.86%). Receiver operating characteristic curve analysis identified predicted total granulocytes, neutrophils, and activated mast cells as highly able to differentiate between the PJIF cases and the NIAF cases. Within the PJIF cases, the total granulocyte, total macrophage and monocyte, M0 macrophage, and M2 macrophage fractions were differentially expressed in Staphylococcus aureus compared with Staphylococcus epidermidis -associated samples. Within the NIAF cases, the predicted total B cell, naïve B cell, plasma cell, and M2 macrophage fractions were differentially expressed among different causes of failure. Conclusions: CIBERSORTx can predict the cellularity of sonicate fluid using transcriptomic data, allowing for the evaluation of the underlying immune response during the PJIF and NIAF cases, without a need to phenotypically assess cell composition.

Abstract Purpose: IgM monoclonal gammopathy of undetermined significance (MGUS) and Waldenström macroglobulinemia (WM) represent a disease spectrum with highly varied therapeutic management, ranging from observation to chemoimmunotherapy. The current classification relies solely on clinical features and does not explain the heterogeneity that exists within each of these conditions. Further investigation is warranted to shed light on the biology that may account for the clinical differences. Experimental Design: We used bone marrow (BM) clonal CD19+ and/or CD138+ sorted cells, matched BM supernatant, and peripheral blood serum from 32 patients (7 MGUS, 25 WM) to perform the first multi-omics approach including whole-exome sequencing, RNA sequencing, proteomics, metabolomics, and mass cytometry. Results: We identified three clusters with distinct pathway activation, immune content, metabolomic, and clinical features. Cluster 1 included only patients with WM and was characterized by transcriptional silencing of genes involved in cell cycle and immune response, enrichment of mitochondrial metabolism, infiltration of senescent T effector memory cells, and aggressive clinical behavior. Genetic/structural alterations of TNFAIP3 were distinct events of this cluster. Cluster 2 comprised both MGUS and WM patients with upregulation of inflammatory response, senescence and glycolysis signatures, increased activated T follicular helper and T regulatory cells, and indolent clinical behavior. Cluster 3 also included both MGUS and WM patients and exhibited intermediate features, including proliferative and inflammatory signaling, as well as glycolysis and mitochondrial metabolism. Conclusions: We have identified three distinct molecular clusters, suggesting a potential biologic classification that may have therapeutic implications.

Abstract Purpose: Regulatory T-cells (Treg) are essential to Tregs homeostasis and modulate the antitumor immune response in patients with lymphoma. However, the biology and prognostic impact of Tregs in splenic marginal zone lymphoma (SMZL) have not been studied. Experimental Design: Biopsy specimens from 24 patients with SMZL and 12 reactive spleens (rSP) from individuals without lymphoma were analyzed by using CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing), CyTOF (mass cytometry) analysis, and flow cytometry to explore the phenotype, transcriptomic profile, and clinical significance of intratumoral Tregs and their subsets. The biological characteristics and cell signaling pathways of intratumoral Treg subsets were confirmed by in vitro functional assays. Results: We found that Tregs are more abundant in SMZL patients' spleens than rSP, and Tregs from patients with SMZL and rSP can be separated into CD161+Treg and CD26+Treg subsets. CD161+Tregs are increased in SMZL but have dysregulated immune function. We found that CD161+Treg and CD26+Tregs have unique gene expression and phenotypic profiles and are differentially correlated with patient outcomes. Specifically, increased CD161+Tregs are significantly associated with a favorable prognosis in patients with SMZL, whereas CD26+Tregs are associated with a poor prognosis. Furthermore, activation of the IL2/STAT5 pathway contributes to the induction of CD26+Tregs and can be reversed by STAT5 inhibition. Conclusions: IL2/STAT5-mediated expansion of CD26+Tregs contributes to a poor clinical outcome in SMZL and may represent a therapeutic opportunity in this disease.