Revealing the immune cell subtype reconstitution profile in patients from the CLARITY study using deconvolution algorithms after cladribine tablets treatment

Transcriptomic observations of intra and extracellular immunotherapy targets for pediatric brain tumors

OBJECTIVES

Despite surgical resection, chemoradiation, and targeted therapy, brain tumors remain a leading cause of cancer-related death in children. Immunotherapy has shown some promise and is actively being investigated for treating childhood brain tumors. However, a critical step in advancing immunotherapy for these patients is to uncover targets that can be effectively translated into therapeutic interventions.

METHODS

In this study, our team performed a transcriptomic analysis across pediatric brain tumor types to identify potential targets for immunotherapy. Additionally, we assessed components that may impact patient response to immunotherapy, including the expression of genes essential for antigen processing and presentation, inhibitory ligands and receptors, interferon signature, and overall predicted T cell infiltration.

RESULTS

We observed distinct expression patterns across tumor types. These included elevated expression of antigen genes and antigen processing machinery in some tumor types while other tumors had elevated inhibitory checkpoint receptors, known to be associated with response to checkpoint inhibitor immunotherapy.

CONCLUSION

These findings suggest that pediatric brain tumors exhibit distinct potential for specific immunotherapies. We believe our findings can guide investigators in their assessment of appropriate immunotherapy classes and targets in pediatric brain tumors.

FCGR3A F158V alleles frequency differs in multiple myeloma patients from healthy population

FCGR3A presents a single nucleotide polymorphism at location 158 (V/F), which affects its binding to the fragment crystallizable (Fc) of antibodies (Abs). FcγRIIIa-158 V allotype has the highest affinity and is associated with a better clinical response to IgG1 monoclonal Abs (mAb) treatment. We compared the allele frequency of FCGR3A-F158V polymorphism in cohorts of patients with B-cell lymphoproliferative disorders, including multiple myeloma (MM), monoclonal gammopathy of undetermined significance (MGUS), non-Hodgkin lymphoma (NHL), and B-cell chronic leukemia (B-CLL). FCGR3A-158F homozygous were enriched and tended to be in MM and MGUS patients, respectively; but neither in B-CLL nor in NHL patients. We identified a significantly lower concentration of CD8 T-cells and resting memory CD4 T-cells in MM patients bone marrow with the F/F genotype, associated with an increase in the macrophage percentage. In contrast, natural killer cells increased in V/V homozygous patients. This suggests a deregulation of the immune microenvironment in FCGR3A-F/F homozygous patients. However, we did not observe difference in response following treatment combining chemotherapy associated or not with daratumumab, an IgG1 mAb direct against CD38. Our findings suggest that FCGR3A F158V polymorphism can regulate the immune environment and affect the development of tumor plasma cells.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we argue that RNA-seq should be considered a routine diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers vital insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 240 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that - combined with sequence alignments and pBLAST - they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.