Proinflammatory activity of VEGF-targeted treatment through reversal of tumor endothelial cell anergy.

PURPOSE: Ongoing angiogenesis renders the tumor endothelium unresponsive to inflammatory cytokines and interferes with adhesion of leukocytes, resulting in escape from immunity. This process is referred to as tumor endothelial cell anergy. We aimed to investigate whether anti-angiogenic agents can overcome endothelial cell anergy and provide pro-inflammatory conditions. EXPERIMENTAL DESIGN: Tissues of renal cell carcinoma (RCC) patients treated with VEGF pathway-targeted drugs and control tissues were subject to RNAseq and immunohistochemical profiling of the leukocyte infiltrate. Analysis of adhesion molecule regulation in cultured endothelial cells, in a preclinical model and in human tissues was performed and correlated to leukocyte infiltration. RESULTS: It is shown that treatment of RCC patients with the drugs sunitinib or bevacizumab overcomes tumor endothelial cell anergy. This treatment resulted in an augmented inflammatory state of the tumor, characterized by enhanced infiltration of all major leukocyte subsets, including T cells, regulatory T cells, macrophages of both M1- and M2-like phenotypes and activated dendritic cells. In vitro, exposure of angiogenic endothelial cells to anti-angiogenic drugs normalized ICAM-1 expression. In addition, a panel of tyrosine kinase inhibitors was shown to increase transendothelial migration of both non-adherent and monocytic leukocytes. In primary tumors of RCC patients, ICAM-1 expression was found to be significantly increased in both the sunitinib and bevacizumab-treated groups. Genomic analysis confirmed the correlation between increased immune cell infiltration and ICAM-1 expression upon VEGF-targeted treatment. CONCLUSION: The results support the emerging concept that anti-angiogenic therapy can boost immunity and show how immunotherapy approaches can benefit from combination with anti-angiogenic compounds.

MammaPrint and BluePrint comprehensively capture the cancer hallmarks in early-stage breast cancer patients.

MammaPrint® (MP) is a 70-gene signature that stratifies early-stage breast cancer patients into low- and high risk of distant relapse. Further stratification of MP risk results identifies four risk subgroups, ultra-low (UL), low, high 1, and high 2, with specific prognostic and predictive outcomes. BluePrint® (BP) is an 80-gene signature that classifies breast tumors as basal, luminal, or HER2 molecular subtype. To gain insight into their biological significance, we annotated the MP 70- and BP 80-genes with respect to the 10 hallmarks of cancer (HoC). Furthermore, we related gene expression profiles of the extreme ends of the MP low- and high-risk patients (here called, ultra-low (UL) and ultra-high (UH) or High2, respectively), to the 10 HoC per BP subtype by differential gene expression and pathway analysis. MP and BP gene functions reflected all 10 HoCs. Most MP and BP genes were associated with sustaining proliferative signaling, followed by genome instability and mutation categories. Based on the gene expression profiles, UL and UH subgroup pathways were down -or upregulated, respectively, reflecting proliferative and metastatic features, such as G2M checkpoint, DNA repair, oxidative phosphorylation, immune invasion, PI3K/AKT/mTOR signaling, and hypoxia pathways. Notably, the UH HER2-type was enriched in several immune signaling pathways, such as IL2/STAT5 signaling and TNFα signaling via NFκB. Our results show that MP and BP gene signatures represent and capture all 10 HoCs and highlight underlying biological processes of MP extreme samples, which might guide treatment decisions as the signature captures the full spectrum of early breast cancers.

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions.

MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here, we present a webserver that implements this method efficiently. RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than 10-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry. AVAILABILITY AND IMPLEMENTATION: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.