The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive characterization of gene expression in the tumor microenvironment (TME) across multiple cancer types. As an increasing number of datasets are generated in the public domain, in this update, TISCH2 has included 190 tumor scRNA-seq datasets covering 6 million cells in 50 cancer types, with 110 newly collected datasets and almost tripling the number of cells compared with the previous release. Furthermore, TISCH2 includes several new functions that allow users to better utilize the large-scale scRNA-seq datasets. First, in the Dataset module, TISCH2 provides the cell–cell communication results in each dataset, facilitating the analyses of interacted cell types and the discovery of significant ligand–receptor pairs between cell types. TISCH2 also includes the transcription factor analyses for each dataset and visualization of the top enriched transcription factors of each cell type. Second, in the Gene module, TISCH2 adds functions for identifying correlated genes and providing survival information for the input genes. In summary, TISCH2 is a user-friendly, up-to-date and well-maintained data resource for gene expression analyses in the TME. TISCH2 is freely available at http://tisch.comp-genomics.org/.

An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel. Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP). Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80 % accuracy for 10 drugs, ≥ 75 % accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively. These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

The epithelial-to-mesenchymal transition is an important mechanism in cancer metastasis. Although transcription factors including SNAIL, SLUG, and TWIST1 regulate the epithelial-to-mesenchymal transition, other unknown transcription factors could also be involved. Identification of the full complement of transcription factors is essential for a more complete understanding of gene regulation in this process. Chromatin immunoprecipitation-sequencing (ChIP-Seq) technologies have been used to detect genome-wide binding of transcription factors; here, we developed a systematic approach to integrate existing ChIP-Seq and transcriptome data. We scanned multiple transcription factors to investigate their functional impact on the epithelial-to-mesenchymal transition in the human A549 lung adenocarcinoma cell line.

Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients. The results provided the detection for the pre-resistance state or early signs of endocrine resistance. Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors are commonly used to treat non-small cell lung cancers with EGFR mutations, but drug resistance often emerges. Intratumor heterogeneity is a known cause of targeted therapy resistance and is considered a major factor in treatment failure. This study identifies clones of EGFR-mutant non-small cell lung tumors expressing low levels of both wild-type and mutant EGFR protein. These EGFR-low cells are intrinsically more tolerant to EGFR inhibitors, more invasive, and exhibit an epithelial-to-mesenchymal-like phenotype compared to their EGFR-high counterparts. The EGFR-low cells secrete Transforming growth factor beta (TGFβ) family cytokines, leading to increased recruitment of cancer-associated fibroblasts and immune suppression, thus contributing to the drug-tolerant tumor microenvironment. Notably, pharmacological induction of EGFR using epigenetic inhibitors sensitizes the resistant cells to EGFR inhibition. These findings suggest that intrinsic drug resistance can be prevented or reversed using combination therapies.

Significance: In this study, Kim et al. identify Drosophila CFTR ( dCFTR ) and show that knockdown of dCFTR in the adult intestine disrupts osmotic homeostasis and displays CF-like phenotypes that lead to intestinal stem cell hyperplasia. They show that expression of wild-type human CFTR , but not mutated forms of CFTR that affect protein production or transport, rescues the mutant phenotypes of dCFTR . Furthermore, global transcriptomic analysis of dCFTR fly intestine identified a mucin gene that is required for proper intestinal barrier protection. Altogether, their findings suggest that Drosophila can be a powerful model organism for studying CF pathophysiology.