@article {602802, title = {Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients }, journal = {Plos Computational Biology}, volume = {14}, number = {1}, year = {2018}, abstract = {Human primary glioblastomas (GBM) often harbor mutations within the epidermal growth factor receptor (EGFR). Treatment of EGFR-mutant GBM cell lines with the EGFR/HER2 tyrosine kinase inhibitor lapatinib can effectively induce cell death in these models. How- ever, EGFR inhibitors have shown little efficacy in the clinic, partly because of inappropriate dosing. Here, we developed a computational approach to model the in vitro cellular dynam- ics of the EGFR-mutant cell line SF268 in response to different lapatinib concentrations and dosing schedules. We then used this approach to identify an effective treatment strategy within the clinical toxicity limits of lapatinib, and developed a partial differential equation modeling approach to study the in vivo GBM treatment response by taking into account the heterogeneous and diffusive nature of the disease. Despite the inability of lapatinib to induce tumor regressions with a continuous daily schedule, our modeling approach consistently predicts that continuous dosing remains the best clinically feasible strategy for slowing down tumor growth and lowering overall tumor burden, compared to pulsatile schedules cur- rently known to be tolerated, even when considering drug resistance, reduced lapatinib tumor concentrations due to the blood brain barrier, and the phenotypic switch from prolifer- ative to migratory cell phenotypes that occurs in hypoxic microenvironments. Our mathe- matical modeling and statistical analysis platform provides a rational method for comparing treatment schedules in search for optimal dosing strategies for glioblastoma and other can- cer types.\ }, url = {https://doi.org/10.1371/journal.pcbi.1005924}, author = {Shayna Stein and Zhao, Rui and Hiroshi Haeno and Igor Vivanco and Franziska Michor} } @article {602803, title = {Assessment of the cPAS-based BGISEQ-500 platform for metagenomic sequencing }, journal = {GigaScience}, year = {2017}, url = {https://doi.org/10.1093/gigascience/gix133}, author = {Fang, Chao and Zhong, Huanzi and Lin, Yuxiang and Bin Chen and Han, Mo and Ren, Huahui and Lu, Haorong and Luber, Jacob Mayne and Xia, Min and Li, Wangsheng and Shayna Stein and Xu, Xun and Zhang, Wenwei and Drmanac, Radoje and Wang, Jian and Yang, Huanming and Hammarstr{\"o}m, Lennart and Kostic, Aleksandar David and Kristiansen, Karsten and Li, Junhua} } @article {490146, title = {Discover hidden splicing variations by mapping personal transcriptomes to personal genomes}, journal = {Nucleic Acids Research}, volume = {43}, number = {22}, year = {2015}, month = {Dec 2015}, pages = {10612{\textendash}10622}, abstract = {RNA-seq has become a popular technology for studying genetic variation of pre-mRNA alternative splicing. Commonly used RNA-seq aligners rely on the consensus splice site dinucleotide motifs to map reads across splice junctions. Consequently, genomic variants that create novel splice site dinucleotides may produce splice junction RNA-seq reads that cannot be mapped to the reference genome. We developed and evaluated an approach to identify {\textquoteleft}hidden{\textquoteright} splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Computational analysis and experimental validation indicate that this approach identifies personal specific splice junctions at a low false positive rate. Applying this approach to an RNA-seq data set of 75 individuals, we identified 506 personal specific splice junctions, among which 437 were novel splice junctions not documented in current human transcript annotations. 94 splice junctions had splice site SNPs associated with GWAS signals of human traits and diseases. These involve genes whose splicing variations have been implicated in diseases (such as\ OAS1), as well as novel associations between alternative splicing and diseases (such as\ ICA1). Collectively, our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations.}, url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkv1099}, author = {Shayna Stein and Zhixiang Lu and Emad Bahrami-Samani and Juw Won Park and Xing, Yi} }