Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels

Abstract

Center Heidelberg) for their excellent technical assistance. We also thank the DKFZ Single-Cell Open Lab (scOpenLab) for the experimental assistance in terms of scRNA-seq. Also, this study was supported by the Heidelberg Center for Personalized Oncology (DKFZ-HIPO). We thank the DKFZ Omics IT and Data Management Core Facility (ODCF) and the DKFZ Genomics and Proteomics Core Facility (GPCF) for their technical support.

Leveraging Single-Cell Approaches in Cancer Precision Medicine

“…This study suggested that another cell population besides the KRAS-mutants may be responsible for overall resistance to drug resistance and poor prognosis of lung adenocarcinomas. Similar to these observations, scRNA-seq analysis of B cell lymphomas revealed distinct classes coexisted within tumors and showed differential drug response in an ex vivo model [21]. These scRNA-seq studies revealed the diversity of malignant cell populations with prognostic implications, which could not be captured with bulk transcriptomics.…”

Single-cell technologies and analyses in hematopoiesis and hematological malignancies

“…Turning now to B-cell lymphomas, an integrative single-cell analysis of the phenotype, gene expression, and variable-region sequence of the immunoglobulin heavy-chain locus revealed that the synchrony of germinal center transcriptional programs was lost in single lymphoma B cells [119]. A study of nodal B-cell lymphomas using scRNA-seq and transcriptome-informed flow cytometry indicated that transcriptionally distinct malignant subpopulations within the same patient responded differently to anticancer drugs [120]. scDNA-seq performed in T-cell acute lymphoblastic leukemia (T-ALL) distinguished clones harboring single nucleotide variants and small indels whose composition changed between diagnostic and relapse samples [121].…”

Lymphoma Heterogeneity Unraveled by Single-Cell Transcriptomics

“…These original approaches have been extended thanks to an algorithm called NicheNet (47). One single example of the application of this computational approach in nine lymphoma patients has been published (48). This analysis suggested that cancer B cells could receive signals from all four major subsets of T cells (especially T follicular helper subset as the major source for IL-4, a putative resistance mechanism against ibrutinib).…”

“…Most personalized therapies do not take into account heterogeneity of nodal lymphomas. A previously discussed paper elegantly investigated both malignant and non-malignant lymphocytes in 12 donors (nine with lymphoma and three reactive lymph nodes) (48). Authors have found coexistence of up to 4 transcriptionally distinct subpopulations of lymphoma cells, responding differently to treatments in vitro and in vivo in an example assessed at the time of relapse.…”