Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis

Abstract

Multiple myeloma is a plasma cell malignancy almost always preceded by precursor conditions, but low tumor burden of these early stages has hindered the study of their molecular programs through bulk sequencing technologies. Here, we generate and analyze single cell RNA-sequencing of plasma cells from 26 patients at varying disease stages and 9 healthy donors. In silico dissection and comparison of normal and transformed plasma cells from the same bone marrow biopsy enables discovery of patient-specific transcriptional changes. Using Non-Negative Matrix Factorization, we discover 15 gene expression signatures which represent transcriptional modules relevant to myeloma biology, and identify a signature that is uniformly lost in abnormal cells across disease stages. Finally, we demonstrate that tumors contain heterogeneous subpopulations expressing distinct transcriptional patterns. Our findings characterize transcriptomic alterations present at the earliest stages of myeloma, providing insight into the molecular underpinnings of disease initiation.

Publication
Nature Communications
Rebecca (Peyser) Boiarsky
Rebecca (Peyser) Boiarsky
PhD student

Rebecca’s research interests include developing methods to learn disease progression models and discover new biological insights for precision medicine applications. She works on machine learning algorithms that can utilize clinical and genomic data for this purpose, with a particular focus on single cell RNA-sequencing data and cancer.

Ming-Chieh Shih
Ming-Chieh Shih
Postdoctoral Fellow

Assistant Professor

David Sontag
David Sontag
Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

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