A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma.

Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM. Using a well-characterized panel of human myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM, we created an in vitro chemosensitivity profile in response to treatment with the four PIs bortezomib, carfilzomib, ixazomib and oprozomib as single agents. Gene expression profiling was performed using next-generation high-throughput RNA-sequencing. Applying machine learning-based computational approaches including the supervised ensemble learning methods Random forest and Random survival forest, we identified a 42-gene expression signature that could not only distinguish good and poor PI response in the HMCL panel, but could also be successfully applied to four different clinical data sets on MM patients undergoing PI-based chemotherapy to distinguish between extraordinary (good and poor) outcomes. Our results demonstrate the use of in vitro modeling and machine learning-based approaches to establish predictive biomarkers of response and resistance to drugs that may serve to better direct myeloma patient treatment options.

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

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.

Electroporation of salmon sperm for gene transfer: efficiency, reliability, and fate of transgene.

Uptake of exogenous DNA by electroporated salmon sperm for gene transfer is being investigated. Our studies show that electroporated salmon sperm cells were more efficient and more reliable than untreated sperm in picking up exogenous DNA and subsequently transferring the DNA into salmon embryos. Indirect evidence suggest that some of the exogenous DNA was internalized in the sperm nuclei. The taken up DNA retained its integrity as demonstrated by PCR. The foreign DNA was detected in 15-month-old fish, and had a mosaic pattern of distribution. Integration of the foreign DNA occurred infrequently, and the expression of the foreign genes was poor. The potential of sperm-mediated gene transfer as a routine protocol for mass gene transfer in salmon will be dependent on the improvement of integration and expression of the foreign gene.