Although chemotherapy is a conventional cancer treatment, it may induce a protumorigenic microenvironment by triggering the release of proinflammatory mediators. In this study, we demonstrate that ovarian tumor cell debris generated by first-line platinum- and taxane-based chemotherapy accelerates tumor progression by stimulating a macrophage-derived "surge" of proinflammatory cytokines and bioactive lipids. Thus, targeting a single inflammatory mediator or pathway is unlikely to prevent therapy-induced tumor progression. Here, we show that combined pharmacological abrogation of the cyclooxygenase-2 (COX-2) and soluble epoxide hydrolase (sEH) pathways prevented the debris-induced surge of both cytokines and lipid mediators by macrophages. In animal models, the dual COX-2/sEH inhibitor PTUPB delayed the onset of debris-stimulated ovarian tumor growth and ascites leading to sustained survival over 120 days postinjection. Therefore, dual inhibition of COX-2/sEH may be an approach to suppress debris-stimulated ovarian tumor growth by preventing the therapy-induced surge of cytokines and lipid mediators.

Cancer therapy is a double-edged sword, as surgery and chemotherapy can induce an inflammatory/immunosuppressive injury response that promotes dormancy escape and tumor recurrence. We hypothesized that these events could be altered by early blockade of the inflammatory cascade and/or by accelerating the resolution of inflammation. Preoperative, but not postoperative, administration of the nonsteroidal antiinflammatory drug ketorolac and/or resolvins, a family of specialized proresolving autacoid mediators, eliminated micrometastases in multiple tumor-resection models, resulting in long-term survival. Ketorolac unleashed anticancer T cell immunity that was augmented by immune checkpoint blockade, negated by adjuvant chemotherapy, and dependent on inhibition of the COX-1/thromboxane A2 (TXA2) pathway. Preoperative stimulation of inflammation resolution via resolvins (RvD2, RvD3, and RvD4) inhibited metastases and induced T cell responses. Ketorolac and resolvins exhibited synergistic antitumor activity and prevented surgery- or chemotherapy-induced dormancy escape. Thus, simultaneously blocking the ensuing proinflammatory response and activating endogenous resolution programs before surgery may eliminate micrometastases and reduce tumor recurrence.

Endostatin, a collagen XVIII fragment, is a potent anti-angiogenic protein. We sought to identify its endothelial cell surface receptor(s). Alkaline phosphatase- tagged endostatin bound endothelial cells revealing two binding affinities. Expression cloning identified glypican, a cell surface proteoglycan as the lower-affinity receptor. Biochemical and genetic studies indicated that glypicans' heparan sulfate glycosaminoglycans were critical for endostatin binding. Furthermore, endostatin selected a specific octasulfated hexasaccharide from a sequence in heparin. We have also demonstrated a role for endostatin in renal tubular cell branching morphogenesis and show that glypicans serve as low-affinity receptors for endostatin in these cells, as in endothelial cells. Finally, antisense experiments suggest the critical importance of glypicans in mediating endostatin activities.

Abstract Background First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum.