A Single-Cell Atlas of Classical Mantle Cell Lymphoma Reveals Paired Tumor-Immune Malignant States Correlating with Prognosis
Authors: Marouf Amira , Mitra Sneha , Isgor Irem , Joseph Ashlee , Rahman Jahan , Lin Ya-Hui , Takizawa Meril , Chaligne Ronan , Sethi Shenon , Dogan Ahmet , Roulland Sandrine , Salles Gilles , Leslie Christina , Kumar Anita , Vardhana Santosha A. 
Published: December 2024

Introduction: Mantle cell lymphoma (MCL) is an aggressive subtype of B-cell non-Hodgkin lymphoma that exhibits clinical, pathological, and genetic heterogeneity. Initial remission duration after first-line therapy is an important prognostic factor, and early progression (within two years, POD24) is associated with inferior survival outcomes. Clinically relevant molecular stratification of MCL has been challenging owing to the high degree of genomic instability that characterizes this disease. Furthermore, developing immune-based therapies for MCL underscores the importance of understanding how molecularly distinct MCL subtypes impact the tumor microenvironment. In the present study, we leveraged single-cell technologies to identify discrete MCL cell states and their impact on anti-tumor immune surveillance and clinical outcomes.

Methods: We analyzed 64 samples collected at diagnosis (n=41) and relapse (n=20) from 30 patients clinically annotated patients with MCL, including mononuclear cells from lymphoma-involved peripheral blood, lymph nodes, bone marrow, gastrointestinal tract, and skin (and 3 PB samples from healthy donors as control) using either Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) simultaneous with 5' TCR and BCR sequencing or single-nuclei sequencing using the single-cell Flex technology (10X Genomics). Malignant B-cells were identified based on co-expression of cyclin D1 (CCND1) and canonical B-cell markers (MS4A1, CD79A, CD79B, CD19). We inferred large-scale copy number variations (CNVs) using inferCNV to confirm the accurate distinction between malignant and non-malignant B-cells. To overcome inter-patient heterogeneity in gene expression, we performed sample-wise subclustering of both normal and malignant B-cells guided by expression of genes associated with a 'naïve-like' (SELL, IL4R, IGHD, IGHM, CD79A, PAX5), ‘memory-like’ (TNFRSF13C, CD79B, AIM2), or ‘proliferating’ (MKI67) state. Intratumoral T-cells were subclustered using an established reference mapping geneset (Roider, 2024).

Results: This cohort included 30 patients with a median age of 64 [36-78]. At diagnosis, Ki67 was ≥50% in 37% (n=11), TP53 alterations were detected in 33% (n=8) of the patients with available genomic data (n=24) and blastoid morphology was noticed in 16% of the cases (n=5). After quality control and filtering, we analyzed 106,650 cells, of which 44,364 were B-cells, including 39,212 malignant and 5,152 normal B-cells. We identified ‘naïve-like’ and ‘memory-like’ states within both malignant and normal B-cells as well as ‘proliferating’ states only within malignant B-cells. Malignant B-cells with a ‘naïve-like’ state were more clonally expanded compared to the “memory-like” state. Furthermore, ‘naïve-like’ cells exhibited dysregulation of gene modules known to be negatively prognostic in MCL, including an upregulation of genes associated with Myc activity and mitochondrial oxidative phosphorylation (MCM5, MCM7, MAD2L1, TYMS, COX8A, LDHA, SLC25A3) and downregulation of genes associated with NF-kB signaling (RELB, NFKB2, RELA, NFKB1). Analysis of malignant B-cell subtype composition by patient revealed that patients could be stratified based on the dominance of cells with a ‘naïve-like’ (M1) or ‘memory-like’ (M2) state, and we found that patients with an M1 profile exhibited a higher rate of POD24. Analysis of the T-cell compartment in MCL patients based on their malignant cell profile revealed a distinct T-cell composition in the peripheral blood of patients with an M1 or M2 profile. Patients with an M1 profile substantially reduced cytotoxic CD8+ T-cells, suggesting that a naïve-like MCL state might promote immune suppression. To explore this hypothesis further, we are currently performing single-cell spatial transcriptome analysis of these tumors using VisiumHD (10X Genomics). These data will allow us to understand malignant cell type distribution across disease sites and assess intercellular communication between malignant B-cells and tumor-infiltrating T-cells.

Conclusion: This work identifies distinct malignant B-cell states with potential biological and therapeutic relevance in MCL. These data could be useful in predicting response to novel therapeutic agents currently under clinical evaluation in MCL, including those known to leverage host immune responses to promote durable remission.