title: Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors.
abstract: Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that have a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Although genome profiling studies have demonstrated heterogeneity in subclonal architecture that may ultimately lead to relapse, a gene expression-based prediction program that can identify, distinguish and quantify drug response in sub-populations within a bulk population of myeloma cells is lacking. In this study, we performed targeted transcriptome analysis on 528 pre-treatment single cells from 11 myeloma cell lines and 418 single cells from 8 drug-naïve MM patients, followed by intensive bioinformatics and statistical analysis for prediction of proteasome inhibitor sensitivity in individual cells. Using our previously reported drug response gene expression profile signature at the single-cell level, we developed an R Statistical analysis package available at https://github.com/bvnlabSCATTome, SCATTome (single-cell analysis of targeted transcriptome), that restructures the data obtained from Fluidigm single-cell quantitative real-time-PCR analysis run, filters missing data, performs scaling of filtered data, builds classification models and predicts drug response of individual cells based on targeted transcriptome using an assortment of machine learning methods. Application of SCATT should contribute to clinically relevant analysis of intratumor heterogeneity, and better inform drug choices based on subclonal cellular responses.
authors.affiliation: Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
authors.forename: A K
authors.initials: AK
authors.lastname: Mitra
authors.affiliation: School of Statistics, University of Minnesota, Minneapolis, MN, USA.
authors.forename: U K
authors.initials: UK
authors.lastname: Mukherjee
authors.affiliation: Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
authors.forename: T
authors.initials: T
authors.lastname: Harding
authors.affiliation: Medical Genome Facility Genome Analysis Core, Mayo Clinic, Rochester, MN, USA.
authors.forename: J S
authors.initials: JS
authors.lastname: Jang
authors.affiliation: Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
authors.forename: H
authors.initials: H
authors.lastname: Stessman
authors.affiliation: Department of Health Science Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Health Science Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: A
authors.initials: A
authors.lastname: Abyzov
authors.affiliation: Medical Genome Facility Genome Analysis Core, Mayo Clinic, Rochester, MN, USA.
authors.forename: J
authors.initials: J
authors.lastname: Jen
authors.affiliation: Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: S
authors.identifier: 0000-0001-5392-9284
authors.initials: S
authors.lastname: Kumar
authors.affiliation: Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: V
authors.initials: V
authors.lastname: Rajkumar
authors.affiliation: Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
authors.forename: B
authors.initials: B
authors.lastname: Van Ness
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor; D019295:Computational Biology; D014644:Genetic Variation; D006801:Humans; D000069550:Machine Learning; D009101:Multiple Myeloma / Q000188:drug therapy / Q000235:genetics*; D061988:Proteasome Inhibitors / Q000494:pharmacology; D059010:Single-Cell Analysis / Q000379:methods*; D013223:Statistics as Topic; D059467:Transcriptome / Q000235:genetics*
chemical_list: D061988:Proteasome Inhibitors