Multimodal integration of single cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations

Accurately deciphering tumor cellular heterogeneity is crucial for developing effective treatments. While single-cell epigenomics assays offer powerful insights into studying tumor heterogeneity beyond the transcriptional level, their application remains a significant challenge. To address this, we introduce Multimodal-based Analysis of scATAC-Seq data (MAAS), a method designed to identify functional tumor cell subpopulations and reconstruct their lineages from single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq). MAAS uniquely integrates multimodal information, encompassing chromatin accessibility, copy number variations (CNVs), and single-nucleotide variants, through self-expressive multimodal matrix factorization. MAAS outperforms existing methods in accuracy and robustness on both simulated and real datasets. When applied to multiple glioma scATAC-seq datasets, MAAS uncovered a previously hidden tumor cell subpopulation resistant to temozolomide treatment. Furthermore, MAAS effectively detected clinically relevant subpopulations with low CNVs, such as those found in pediatric ependymoma and B-cell lymphoma. Additionally, we developed a novel MAAS-derived clinical signature that provides superior prognostic prediction than traditional clinicopathologic features across multiple cancer types. In summary, MAAS significantly advances the identification of clinically relevant tumor cell subpopulations, thereby accelerating the discovery of potential therapeutic targets.