bioRxivCancer BiologyPreprint
Molecular and cellular dynamics of measurable residual disease progression in myelodysplastic syndromes
Yu-Hsiang CheniD, Timothy BiiD, Ming qing XuiD, Jaehyun Lee, Wesley S. LuiD, Mengchuan Zhuang, Alejandro Marinos VelardeiD, Dustin McCurryiD, Hui YangiD, Gheath AlatrashiD, Gabriela RondóniD, Elizabeth J. ShpalliD, Fatima Zahra JellouliD, Frances Natalia Cervoni-Curet, Sassine GhanemiD, Céline KerrosiD, Priya KoppikariD, Jeffrey J. MolldremiD, Guillermo Garcia‐ManeroiD, Ankit PateliD, Pavan BachireddyiD
Abstract
Cancer relapse after treatment invariably proceeds from the persistence and progression of measurable residual disease (MRD), an ultrasmall malignant population. Despite the poor prognostic impact established by increasingly sensitive MRD detection methods, the molecular pathways defining MRD remain unknown. To identify the unique features of MRD and the molecular forces shaping its progression, we performed single-cell multi-omic profiling on DNA, RNA and protein layers for a longitudinal patient cohort with relapsed myelodysplastic syndromes (MDS) after stem cell transplantation (SCT), the only curative modality for MDS. We provide a comprehensive molecular portrait of MRD cells with novel markers, shared across genetically heterogeneous patients. MDS relapse after SCT manifested universally with marked phenotypic evolution. Genotype and phenotype analyses revealed MRD progression as a dynamic, evolutionary process rather than a static expansionary one, driven by both subclonal sweeping and cell state transitions. Malignant cells adapted to infiltrating T cells by rewiring IFN-{gamma} responses to activate a key immunoevasive pathway. Our study demonstrates the power of longitudinal, single-cell multi-omic analysis for identifying, tracking, and understanding MRD cells, opening new avenues to target MRD persistence and progression.
- organism: human
- assay: scRNA-seq
- assay: multiome
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: gene regulatory network inference
- approach: deep learning
- Citations
- 1
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