Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by or . This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/. Network-based computational models are attracting increasing attention in studying cancer genomics because molecular networks provide valuable information on the functional organizations of molecules in cells. Survival analysis mostly with the Cox proportional hazard model is widely used to predict or correlate gene expressions with time to an event of interest (outcome) in cancer genomics. Surprisingly, network-based survival analysis has not received enough attention. In this paper, we studied resistance to chemotherapy in ovarian cancer with a network-based Cox model, called Net-Cox. The experiments confirm that networks representing gene co-expression or functional relations can be used to improve the accuracy and the robustness of survival prediction of outcome in ovarian cancer treatment. The study also revealed subnetwork signatures that are enriched by extracellular matrix receptors and modulators and the downstream nuclear signaling components of extracellular signal-regulators, respectively. In particular, FBN1, which was detected as a signature gene of high confidence by Net-Cox with network information, was validated as a biomarker for predicting early recurrence in platinum-sensitive ovarian cancer patients in laboratory.

HtrA1 belongs to a family of serine proteases found in organisms ranging from bacteria to humans. Bacterial HtrA1 (DegP) is a heat shock-induced protein that behaves as a chaperone at low temperature and as a protease at high temperature to help remove unfolded proteins during heat shock. In contrast to bacterial HtrA1, little is known about the function of human HtrA1. Here, we report the first evidence that human HtrA1 is a microtubule-associated protein and modulates microtubule stability and cell motility. Intracellular HtrA1 is localized to microtubules in a PDZ (PSD95, Dlg, ZO1) domain-dependent, nocodazole-sensitive manner. During microtubule assembly, intracellular HtrA associates with centrosomes and newly polymerized microtubules. In vitro, purified HtrA1 promotes microtubule assembly. Moreover, HtrA1 cosediments and copurifies with microtubules. Purified HtrA1 associates with purified alpha- and beta-tubulins, and immunoprecipitation of endogenous HtrA1 results in coprecipitation of alpha-, beta-, and gamma-tubulins. Finally, downregulation of HtrA1 promotes cell motility, whereas enhanced expression of HtrA1 attenuates cell motility. These results offer an original identification of HtrA1 as a microtubule-associated protein and provide initial mechanistic insights into the role of HtrA1 in the regulation of cell motility by modulating microtubule stability.

We previously identified serine protease HtrA1 as a downregulated gene in epithelial ovarian cancer (EOC), but the functional consequence of loss of HtrA1 in EOC remains largely unclear. Here, we report that loss of HtrA1 attenuates anoikis—a critical physiologic barrier for tumor metastasis. In response to loss of anchorage, HtrA1 expression was upregulated in SKOV3 cells, resulting in autocatalytic activation of HtrA1. Stable knockdown of HtrA1 in SKOV3 and TOV21G cells resulted in resistance to anoikis due to enhanced activation of epidermal growth factor receptor (EGFR)/AKT pathway. In suspended SKOV3 cells, enhanced expression of HtrA1 inhibited EGFR/AKT pathway, leading to increased cell death, whereas protease-inactive mutant HtrA1 failed to result in either the inhibition of EGFR/AKT pathway or increased cell death, suggesting the requirement of HtrA1 protease activity in regulating anoikis. Immunoprecipitation and immunofluorescence assays revealed that HtrA1 interacted with EGFR not only on the cell membrane but also in the nucleus. Most importantly, downregulation of HtrA1 significantly enhanced the peritoneal dissemination of SKOV3ip1 cells in nonobese diabetic/severe combined immunodeficient mice, with increased phospho-EGFR level in corresponding tumor nodules compared with that in xenografts originated from the control cells. Taken together, these data reveal for the first time a novel function of HtrA1 in promoting anoikis by attenuating activation of EGFR/AKT pathway that may contribute to its metastasis suppression capacity, thus providing a possible explanation for the aggressive nature of human ovarian tumors with downregulated HtrA1. Cancer Res; 70(8); 3109–18. ©2010 AACR.

BACKGROUND: Precise stratification of patients with non–small cell lung cancer (NSCLC) is needed for appropriate application of PD-1/PD-L1 blockade therapy. METHODS: We measured soluble forms of the immune-checkpoint molecules PD-L1, PD-1, and CTLA-4 in plasma of patients with advanced NSCLC before PD-1/PD-L1 blockade. A prospective biomarker-finding trial (cohort A) included 50 previously treated patients who received nivolumab. A retrospective observational study was performed for patients treated with any PD-1/PD-L1 blockade therapy (cohorts B and C), cytotoxic chemotherapy (cohort D), or targeted therapy (cohort E). Plasma samples from all patients were assayed for soluble immune-checkpoint molecules with a highly sensitive chemiluminescence-based assay. RESULTS: Nonresponsiveness to PD-1/PD-L1 blockade therapy was associated with higher concentrations of these soluble immune factors among patients with immune-reactive (hot) tumors. Such an association was not apparent for patients treated with cytotoxic chemotherapy or targeted therapy. Integrative analysis of tumor size, PD-L1 expression in tumor tissue (tPD-L1), and gene expression in tumor tissue and peripheral CD8 ⁺ T cells revealed that high concentrations of the 3 soluble immune factors were associated with hyper or terminal exhaustion of antitumor immunity. The combination of soluble PD-L1 (sPD-L1) and sCTLA-4 efficiently discriminated responsiveness to PD-1/PD-L1 blockade among patients with immune-reactive tumors. CONCLUSION: Combinations of soluble immune factors might be able to identify patients unlikely to respond to PD-1/PD-L1 blockade as a result of terminal exhaustion of antitumor immunity. Our data suggest that such a combination better predicts, along with tPD-L1, for the response of patients with NSCLC. TRIAL REGISTRATION: UMIN000019674. FUNDING: This study was funded by Ono Pharmaceutical Co. Ltd. and Sysmex Corporation.