Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours.
Avishai GavishMichael TylerAlissa C GreenwaldRouven HoefflinDor SimkinRoi TschernichovskyNoam Galili DarnellEinav SomechChaya BarbolinTomer AntmanDaniel KovarskyThomas F BarrettL Nicolas Gonzalez CastroDebdatta HalderRony Chanoch-MyersJulie LaffyMichael MintsAdi WiderRotem TalAvishay SpitzerToshiro HaraMaria Raitses-GurevichChani StosselTalia GolanAmit TiroshMario L SuvàSidharth V PuramMichael MintsPublished in: Nature (2023)
Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics 1 . Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH 2 . Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell-cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH.
Keyphrases
- single cell
- rna seq
- high throughput
- cell cycle
- transcription factor
- public health
- induced apoptosis
- stem cells
- small molecule
- heat shock
- cell cycle arrest
- clinical practice
- machine learning
- cell therapy
- oxidative stress
- big data
- bone marrow
- young adults
- squamous cell carcinoma
- genome wide
- quality improvement
- stress induced
- deep learning
- dna methylation
- heat shock protein
- artificial intelligence
- lymph node metastasis