Although recent developments in MS have enabled the identification and quantification of hundreds of phosphorylation sites from a given biological sample, phosphoproteome analysis by MS has been plagued by inconsistent reproducibility arising from automated selection of precursor ions for fragmentation, identification, and quantification. To address this challenge, we have developed a new MS-based strategy, based on multiple reaction monitoring of stable isotope-labeled peptides, that enables highly reproducible quantification of hundreds of nodes (phosphorylation sites) within a signaling network and across multiple conditions simultaneously. We have applied this strategy to quantify temporal phosphorylation profiles of 222 tyrosine phosphorylated peptides across seven time points following EGF treatment, including 31 tyrosine phosphorylation sites not previously known to be regulated by EGF stimulation. With this approach, 88% of the signaling nodes were reproducibly quantified in four analyses, as compared with only 34% by typical information-dependent analysis. As a result of the improved reproducibility, full temporal phosphorylation profiles were generated for an additional 104 signaling nodes with the multiple reaction monitoring strategy, an 88% increase in our coverage of the signaling network. This method is broadly applicable to multiple signaling networks and to a variety of samples, including quantitative analysis of signaling networks in clinical samples. Using this approach, it should now be possible to routinely monitor the phosphorylation status of hundreds of nodes across multiple biological conditions.

The Protein Interaction Network Analysis (PINA) platform is a comprehensive web resource, which includes a database of unified protein–protein interaction data integrated from six manually curated public databases, and a set of built-in tools for network construction, filtering, analysis and visualization. The second version of PINA enhances its utility for studies of protein interactions at a network level, by including multiple collections of interaction modules identified by different clustering approaches from the whole network of protein interactions (‘interactome’) for six model organisms. All identified modules are fully annotated by enriched Gene Ontology terms, KEGG pathways, Pfam domains and the chemical and genetic perturbations collection from MSigDB. Moreover, a new tool is provided for module enrichment analysis in addition to simple query function. The interactome data are also available on the web site for further bioinformatics analysis. PINA is freely accessible at http://cbg.garvan.unsw.edu.au/pina/.

Combined PARP and immune checkpoint inhibition has yielded encouraging results in ovarian cancer, but predictive biomarkers are lacking. We performed immunogenomic profiling and highly multiplexed single-cell imaging on tumor samples from patients enrolled in a Phase I/II trial of niraparib and pembrolizumab in ovarian cancer (NCT02657889). We identify two determinants of response; mutational signature 3 reflecting defective homologous recombination DNA repair, and positive immune score as a surrogate of interferon-primed exhausted CD8 + T-cells in the tumor microenvironment. Presence of one or both features associates with an improved outcome while concurrent absence yields no responses. Single-cell spatial analysis reveals prominent interactions of exhausted CD8 + T-cells and PD-L1 + macrophages and PD-L1 + tumor cells as mechanistic determinants of response. Furthermore, spatial analysis of two extreme responders shows differential clustering of exhausted CD8 + T-cells with PD-L1 + macrophages in the first, and exhausted CD8 + T-cells with cancer cells harboring genomic PD-L1 and PD-L2 amplification in the second.

Our knowledge on tissue- and disease-specific functions of human genes is rather limited and highly context-specific. Here, we have developed a method for the comparison of mRNA expression levels of most human genes across 9,783 Affymetrix gene expression array experiments representing 43 normal human tissue types, 68 cancer types, and 64 other diseases. This database of gene expression patterns in normal human tissues and pathological conditions covers 113 million datapoints and is available from the GeneSapiens website.