BloodCancer BiologyPeer-reviewed journal article
Enhancer heterogeneity in acute lymphoblastic leukemia drives differential gene expression between patients
Alastair SmithiD, Nicholas DennyiD, Catherine ChahrouriD, Kim Sharp, Marta ArachiiD, Ana M. Dopico-FernandeziD, Natalina ElliottiD, Joe R. HarmaniD, Thomas JacksoniD, Huimin GengiD, Owen Patrick SmithiD, Jonathan BondiD, Irene A.G. RobertsiD, Ronald W. StamiD, Nicholas T. CrumpiD, James O. J. DaviesiD, Anindita RoyiD, Thomas A. MilneiD
Abstract
Genetic alterations alone cannot account for the diverse phenotypes of cancer cells. Even cancers with the same driver mutation show significant transcriptional heterogeneity and varied responses to therapy. However, the mechanisms underpinning this heterogeneity remain under-explored. Here, we find that novel enhancer usage is a common feature in acute lymphoblastic leukemia (ALL). In particular, KMT2A::AFF1 ALL, an aggressive leukemia with a poor prognosis and a low mutational burden, exhibits substantial transcriptional heterogeneity between individuals. Using single cell multiome analysis and extensive chromatin profiling, we reveal that much transcriptional heterogeneity in KMT2A::AFF1 ALL is driven by novel enhancer usage. Using high resolution Micro-Capture-C in primary patient samples, we also identify patient-specific enhancer activity at key oncogenes such as MEIS1 and RUNX2, driving high levels of expression of both oncogenes in a patient-specific manner. Overall, our data show that enhancer heterogeneity is highly prevalent in KMT2A::AFF1 ALL and may also be a mechanism that drives transcriptional heterogeneity in cancer more generally. Key PointsO_LILeukemia patients with the same driver mutations often display gene expression differences C_LIO_LIUsing chromatin profiling and high resolution 3C methods we show that enhancer heterogeneity drives gene expression differences C_LI
- organism: human
- assay: scRNA-seq
- assay: ATAC-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 8
- FWCI
- 7.16
- Field percentile
- top 2%
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