Single-cell phenotypic profiling to identify a set of immune cell protein biomarkers for relapsed and refractory diffuse large B cell lymphoma: A single-center study

Abstract

Diffuse large B‐cell lymphoma (DLBCL) is the most common invasive type of non‐Hodgkin lymphoma. Cell‐of‐origin (COO) classification is related to patients’ prognoses. Primary drug resistance in treatment for DLBCL has been observed. The specific serum biomarkers in these patients who suffer from relapsed and refractory (R/R)‐DLBCL remains unclear. In the current study, using single‐cell RNA sequencing (scRNA‐seq) and mass cytometry (CyTOF), we determined and verified immune cell biomarkers at the mRNA and protein levels in single‐cell resolution from 18 diagnostic PBMC specimens collected from patients with R/R DLBCL. As controls, 5 PBMC specimens from healthy volunteers were obtained. We identified a panel of 35 surface marker genes for the features of R/R DLBCL unique cell cluster by scRNA‐seq of 8 R/R DLBCL patient samples and validated its efficiency in an external cohort consisting of 10 R/R DLBCL patients by CyTOF. The cell clustering and dimension reduction were compared among R/R DLBCL samples in CyTOF Space with COO as well as the C‐MYC expression designation. Immune cells from each patient occupied unique regions in the 32‐dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Significant heterogeneity observed in subgroups was mainly attributed to individual differences among samples and not to expression differences in a single, homogeneous immune cell subpopulation. The marker panel showed reliability in labeling R/R DLBCL without any influence from COO stratification and C‐MYC expression designation. Furthermore, we compared all the markers between R/R DLBCL and normal samples. A total of 12 biomarkers were significantly overexpressed in R/R DLBCL relative to the normal samples. Therefore, we further optimized the diagnostic biomarker panel of R/R DLBCL comprising CD82, CD55, CD36, CD63, CD59, IKZF1, CD69, CD163, CD14, CD226, CD84, and CD31. In summary, we developed a novel set of biomarkers for the diagnoses of patients with R/R DLBCL. Detections procedures at single‐cell resolution provide precise biomarkers, which may substantially overcome intertumoral and intratumoral heterogeneity among primary samples. The findings confirmed that each case was unique and may comprise multiple, genetically distinct subclones.

SLAM Family Receptors in B Cell Chronic Lymphoproliferative Disorders

“…Using single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF), Shi et al (2022) identified a diagnostic biomarker panel comprising 12 biomarkers, including SLAMF5/CD84, that were overexpressed in 18 patients with relapsed/refractory DLBCL versus 5 healthy volunteers [60].…”

Characterization of Human B Cell Hematological Malignancies Using Protein-Based Approaches

“…Assessment of the healthy B cell pool of patients to find disease mechanisms [128] DLBCL Evaluation of the intertumoral and intratumoral heterogeneity [129] Identification of clinically relevant interactions between tumor-associated macrophages and blood endothelial cells [130] Finding proteins overexpressed in relapsed and refractory patients [131] Comparison of major immune subsets in DLBCL and double-hit lymphoma [132] Identification of differences in immune cell compartments across various stages of MM and healthy individuals [133] Description of the immune tumor microenvironment in patients with MGUS and MM at diagnosis and post-initial therapies [134] Analysis of the immune checkpoint signature and regulation [135] Characterization of NK cells in newly diagnosed cases [136] Understanding of the molecular and cellular complexities underlying disease heterogeneity and prognosis [137] Provision of insights into the mechanism of action of daratumumab and the anti-PD-L1 monoclonal antibody atezolizumab [138] MM Employment of protein profiling as a tool for prognosis and treatment stratification [139] Other Tools RRPA CLL Prediction of survival outcomes based on the proteomic signature [140] Protein Microarrays FL Identification of antibodies that distinguish lymphoid follicles in FL and benign follicular hyperplasia [141] MCL Monitoring of patient serum proteomes to identify treatment-modulated proteins linked to the presence of minimal residual disease [142] Western Blot MCL Definition of the pathologic hallmark of MCL as a tool for the diagnosis [143] araCTP, Next, we describe the basis of the main protein-based strategies employed for studying B cell malignancies.…”

“…In other cases, mass cytometry is used as a validation strategy. For instance, Shi et al [131] performed a single-cell RNA-seq analysis on relapsed and refractory DLBCL patients using PB mononuclear cell samples, identifying 35 unique markers expressed in these patients vs. healthy donors. Based on this protein panel, mass cytometry was applied in an extra cohort of patients, revealing the importance of 12 of those proteins (CD14, CD31, CD36, CD55, CD59, CD63, CD69, CD82, CD84, CD163, CD226, and IKZF1) which were significantly overexpressed in the DLBCL samples.…”

A single-cell multi-omic and spatial atlas of B-cell lymphomas reveals differentiation drives intratumor heterogeneity

“…This association between DLBCL tumor maturation state and clinical outcomes has recently been extended beyond the ABC vs GCB dichotomy to across the maturation spectrum 17 . In parallel, intratumor subpopulations show differences in their treatment sensitivities 3,18,19 .…”