Send to

Choose Destination
Leukemia. 2016 May;30(5):1094-102. doi: 10.1038/leu.2015.361. Epub 2015 Dec 29.

Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors.

Author information

Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
School of Statistics, University of Minnesota, Minneapolis, MN, USA.
Medical Genome Facility Genome Analysis Core, Mayo Clinic, Rochester, MN, USA.
Department of Health Science Research, Mayo Clinic, Rochester, MN, USA.
Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.


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, 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.

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center