Spatial transcriptomics reveals profound subclonal heterogeneity and T-cell dysfunction in extramedullary myeloma.

Extramedullary disease (EMD) is a high-risk feature of multiple myeloma (MM) and remains a poor prognostic factor even in the era of novel immunotherapies. Here we applied spatial transcriptomics (tomo-seq [n=2] and 10X Visium [n=12]), and single-cell RNA sequencing (scRNAseq [n=3]) to a set of 14 EMD biopsies to dissect the three-dimensional architecture of tumor cells and their microenvironment. Overall, the infiltrating immune and stromal cells showed both intra- and inter-patient variation with no uniform distribution over the lesion. We observed substantial heterogeneity at the copy number level within plasma cells, including the emergence of new subclones in circumscribed areas of the tumor, consistent with genomic instability. We further identified spatial expression differences of GPRC5D and TNFRSF17, two important antigens for bispecific antibody therapy. EMD masses were infiltrated by various immune cells, including T-cells. Notably, exhausted TIM3+/PD-1+ T-cells diffusely co-localized with MM cells, whereas functional and activated CD8+ T-cells showed a focal infiltration pattern along with M1 macrophages in otherwise tumor-free regions. This segregation of fit and exhausted T-cells was resolved in the case of response to T-cell engaging bispecific antibodies. MM cells and microenvironment cells were embedded in a complex network that influenced immune activation and angiogenesis, and oxidative phosphorylation represented the major metabolic program within EMD lesions. In summary, spatial transcriptomics has revealed a multicellular ecosystem in EMD with checkpoint inhibition and dual targeting as potential new therapeutic avenues.

Insight into the mechanism of CD34+ cell mobilisation impairment in multiple myeloma patients treated with anti-CD38 therapy.

Induction therapy followed by CD34+ cell mobilisation and autologous transplantation represents standard of care for multiple myeloma (MM). However, the anti-CD38 monoclonal antibodies daratumumab and isatuximab have been associated with mobilisation impairment, yet the mechanism remains unclear. In this study, we investigated the effect of three different regimens (dara-VCd, isa-KRd and VTd) on CD34+ cells using flow cytometry and transcriptomics. Decreased CD34+ cell peak concentration and yields, longer collection and delayed engraftment were reproduced after dara-VCd/isa-KRd versus VTd induction in 34 patients in total. Using flow cytometry, we detected major changes in the proportion of apheresis product and bone marrow CD34+ subsets in patients treated with regimens containing anti-CD38 therapy; however, without any decrease in CD38high B-lymphoid progenitors in both materials. RNA-seq of mobilised CD34+ cells from 21 patients showed that adhesion genes are overexpressed in CD34+ cells after dara-VCd/isa-KRd and JCAD, NRP2, MDK, ITGA3 and CLEC3B were identified as potential target genes. Finally, direct in vitro effect of isatuximab in upregulating JCAD and CLEC3B was confirmed by quantitative PCR. These findings suggest that upregulated adhesion-related interactions, rather than killing of CD34+ cells by effector mechanisms, could be leading causes of decreased mobilisation efficacy in MM patients treated with anti-CD38 therapy.

Transcriptome Analysis of Diffuse Large B-Cell Lymphoma Cells Inducibly Expressing MyD88 L265P Mutation Identifies Upregulated CD44, LGALS3, NFKBIZ, and BATF as Downstream Targets of Oncogenic NF-κB Signaling.

During innate immune responses, myeloid differentiation primary response 88 (MyD88) functions as a critical signaling adaptor protein integrating stimuli from toll-like receptors (TLR) and the interleukin-1 receptor (IL-1R) family and translates them into specific cellular outcomes. In B cells, somatic mutations in MyD88 trigger oncogenic NF-κB signaling independent of receptor stimulation, which leads to the development of B-cell malignancies. However, the exact molecular mechanisms and downstream signaling targets remain unresolved. We established an inducible system to introduce MyD88 to lymphoma cell lines and performed transcriptomic analysis (RNA-seq) to identify genes differentially expressed by MyD88 bearing the L265P oncogenic mutation. We show that MyD88L265P activates NF-κB signaling and upregulates genes that might contribute to lymphomagenesis, including CD44, LGALS3 (coding Galectin-3), NFKBIZ (coding IkBƺ), and BATF. Moreover, we demonstrate that CD44 can serve as a marker of the activated B-cell (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) and that CD44 expression is correlated with overall survival in DLBCL patients. Our results shed new light on the downstream outcomes of MyD88L265P oncogenic signaling that might be involved in cellular transformation and provide novel therapeutical targets.

More Than 2% of Circulating Tumor Plasma Cells Defines Plasma Cell Leukemia-Like Multiple Myeloma.

PURPOSE: Primary plasma cell leukemia (PCL) is the most aggressive monoclonal gammopathy. It was formerly characterized by ≥ 20% circulating plasma cells (CTCs) until 2021, when this threshold was decreased to ≥ 5%. We hypothesized that primary PCL is not a separate clinical entity, but rather that it represents ultra-high-risk multiple myeloma (MM) characterized by elevated CTC levels. METHODS: We assessed the levels of CTCs by multiparameter flow cytometry in 395 patients with newly diagnosed transplant-ineligible MM to establish a cutoff for CTCs that identifies the patients with ultra-high-risk PCL-like MM. We tested the cutoff on 185 transplant-eligible patients with MM and further validated on an independent cohort of 280 transplant-ineligible patients treated in the GEM-CLARIDEX trial. The largest published real-world cohort of patients with primary PCL was used for comparison of survival. Finally, we challenged the current 5% threshold for primary PCL diagnosis. RESULTS: Newly diagnosed transplant-ineligible patients with MM with 2%-20% CTCs had significantly shorter progression-free survival (3.1 v 15.6 months; P < .001) and overall survival (14.6 v 33.6 months; P = .023) than patients with < 2%. The 2% cutoff proved to be applicable also in transplant-eligible patients with MM and was successfully validated on an independent cohort of patients from the GEM-CLARIDEX trial. Most importantly, patients with 2%-20% CTCs had comparable dismal outcomes with primary PCL. Moreover, after revealing a low mean difference between flow cytometric and morphologic evaluation of CTCs, we showed that patients with 2%-5% CTCs have similar outcomes as those with 5%-20% CTCs. CONCLUSION: Our study uncovers that ≥ 2% CTCs is a biomarker of hidden primary PCL and supports the assessment of CTCs by flow cytometry during the diagnostic workup of MM.

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system. It can analyze whole-genome and transcriptome data and is characterized by a user-friendly GUI that offers several advantages, including minimal execution steps and eliminating the need for complex command-line arguments. Notably, we have integrated algorithms developed in-house to predict pathogenicity among cancer-causing mutations and differentiate between tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM-GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r = 0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines.