Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels.
Tobias RoiderJulian SeufertAlexey UvarovskiiFelix FrauhammerMarie BordasNima AbedpourMarta StolarczykJan Philipp MallmSophie A HerbstPeter-Martin BruchHyatt Balke-WantMichael HundemerKarsten RippeBenjamin GoeppertMartina SeiffertBenedikt BrorsGunhild MechtersheimerThorsten ZenzMartin PeiferBjörn ChapuyMatthias SchlesnerCarsten Muller-TidowStefan FröhlingWolfgang HuberSimon AndersSascha DietrichPublished in: Nature cell biology (2020)
Tumour heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is known to affect the efficacy of cancer therapy, most personalized treatment approaches do not account for intratumour heterogeneity. We addressed this issue by studying the heterogeneity of nodal B-cell lymphomas by single-cell RNA-sequencing and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subpopulations and compared their drug-response and genomic profiles. Malignant subpopulations from the same patient responded strikingly differently to anti-cancer drugs ex vivo, which recapitulated subpopulation-specific drug sensitivity during in vivo treatment. Infiltrating T cells represented the majority of non-malignant cells, whose gene-expression signatures were similar across all donors, whereas the frequencies of T-cell subsets varied significantly between the donors. Our data provide insights into the heterogeneity of nodal B-cell lymphomas and highlight the relevance of intratumour heterogeneity for personalized cancer therapy.
Keyphrases
- single cell
- rna seq
- gene expression
- cancer therapy
- high throughput
- induced apoptosis
- flow cytometry
- drug delivery
- lymph node
- neoadjuvant chemotherapy
- end stage renal disease
- dna methylation
- cell cycle arrest
- chronic kidney disease
- transcription factor
- newly diagnosed
- endoplasmic reticulum stress
- adverse drug
- electronic health record
- machine learning
- squamous cell carcinoma
- kidney transplantation
- cell death
- deep learning
- combination therapy
- ejection fraction
- peritoneal dialysis
- radiation therapy
- artificial intelligence
- peripheral blood
- locally advanced
- rectal cancer