Interferon regulatory factor 1 is an independent predictor of platinum resistance and survival in high-grade serous ovarian carcinoma.

OBJECTIVE: High-grade serous ovarian cancer (HGSOC) that is resistant to platinum-based chemotherapy has a particularly poor prognosis. Response to platinum has both prognostic survival value and dictates secondary treatment strategies. Using transcriptome analysis, we sought to identify differentially expressed genes/pathways based on a tumor's platinum response for discovering novel predictive biomarkers. METHODS: Seven primary HGSOC tumor samples, representing two extremes of platinum sensitivity/timing of disease recurrence, were analyzed by RNA-Seq, Ingenuity Pathways Analysis (IPA) and Upstream Regulator Analysis (URA), and used to explore differentially expressed genes and prevalent molecular and cellular processes. Progression-free and overall survival (PFS, OS) was estimated using the Kaplan-Meier method in two different sample sets including GEO and TCGA data sets. RESULTS: IPA and URA highlighted an IRF1-driven transcriptional program (P=0.0017; z-score of 3.091) in the platinum sensitive improved PFS group. QRT-PCR analysis of 31 HGSOC samples demonstrated a significant difference in PFS between low and high IRF1 expression groups (P=0.048) and between groups that were platinum sensitive versus not (P=0.016). In a larger validation data set, increased levels of IRF1 were associated with both increased PFS (P=0.043) and OS (P=0.019) and the effect on OS was independent of debulking status (optimal debulking, P=0.025; suboptimal, P=0.041). CONCLUSION: Transcriptome analysis identifies IRF1, a transcription factor that functions both in immune regulation and as a tumor suppressor, as being associated with platinum sensitivity and an independent predictor of both PFS and OS in HGSOC.

AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data.

Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry.

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.

Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We develop opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Leibler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically improves computation time and enables high-quality visualization of large cytometry and transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters that often produce poorly resolved or misleading maps of fluorescent and mass cytometry data. In summary, opt-SNE enables superior data resolution in t-SNE space and thereby more accurate data interpretation.

Inhibition of the Nuclear Export Receptor XPO1 as a Therapeutic Target for Platinum-Resistant Ovarian Cancer.

Purpose: The high fatality-to-case ratio of ovarian cancer is directly related to platinum resistance. Exportin-1 (XPO1) is a nuclear exporter that mediates nuclear export of multiple tumor suppressors. We investigated possible clinicopathologic correlations of XPO1 expression levels and evaluated the efficacy of XPO1 inhibition as a therapeutic strategy in platinum-sensitive and -resistant ovarian cancer.Experimental Design: XPO1 expression levels were analyzed to define clinicopathologic correlates using both TCGA/GEO datasets and tissue microarrays (TMA). The effect of XPO1 inhibition, using the small-molecule inhibitors KPT-185 and KPT-330 (selinexor) alone or in combination with a platinum agent on cell viability, apoptosis, and the transcriptome was tested in immortalized and patient-derived ovarian cancer cell lines (PDCL) and platinum-resistant mice (PDX). Seven patients with late-stage, recurrent, and heavily pretreated ovarian cancer were treated with an oral XPO1 inhibitor.Results: XPO1 RNA overexpression and protein nuclear localization were correlated with decreased survival and platinum resistance in ovarian cancer. Targeted XPO1 inhibition decreased cell viability and synergistically restored platinum sensitivity in both immortalized ovarian cancer cells and PDCL. The XPO1 inhibitor-mediated apoptosis occurred through both p53-dependent and p53-independent signaling pathways. Selinexor treatment, alone and in combination with platinum, markedly decreased tumor growth and prolonged survival in platinum-resistant PDX and mice. In selinexor-treated patients, tumor growth was halted in 3 of 5 patients, including one with a partial response, and was safely tolerated by all.Conclusions: Taken together, these results provide evidence that XPO1 inhibition represents a new therapeutic strategy for overcoming platinum resistance in women with ovarian cancer. Clin Cancer Res; 23(6); 1552-63. ©2016 AACR.

Longitudinal Transcriptome Analysis Reveals a Sustained Differential Gene Expression Signature in Patients Treated for Acute Lyme Disease.

UNLABELLED: Lyme disease is a tick-borne illness caused by the bacterium Borrelia burgdorferi, and approximately 10 to 20% of patients report persistent symptoms lasting months to years despite appropriate treatment with antibiotics. To gain insights into the molecular basis of acute Lyme disease and the ensuing development of post-treatment symptoms, we conducted a longitudinal transcriptome study of 29 Lyme disease patients (and 13 matched controls) enrolled at the time of diagnosis and followed for up to 6 months. The differential gene expression signature of Lyme disease following the acute phase of infection persisted for at least 3 weeks and had fewer than 44% differentially expressed genes (DEGs) in common with other infectious or noninfectious syndromes. Early Lyme disease prior to antibiotic therapy was characterized by marked upregulation of Toll-like receptor signaling but lack of activation of the inflammatory T-cell apoptotic and B-cell developmental pathways seen in other acute infectious syndromes. Six months after completion of therapy, Lyme disease patients were found to have 31 to 60% of their pathways in common with three different immune-mediated chronic diseases. No differential gene expression signature was observed between Lyme disease patients with resolved illness to those with persistent symptoms at 6 months post-treatment. The identification of a sustained differential gene expression signature in Lyme disease suggests that a panel of selected human host-based biomarkers may address the need for sensitive clinical diagnostics during the "window period" of infection prior to the appearance of a detectable antibody response and may also inform the development of new therapeutic targets. IMPORTANCE: Lyme disease is the most common tick-borne infection in the United States, and some patients report lingering symptoms lasting months to years despite antibiotic treatment. To better understand the role of the human host response in acute Lyme disease and the development of post-treatment symptoms, we conducted the first longitudinal gene expression (transcriptome) study of patients enrolled at the time of diagnosis and followed up for up to 6 months after treatment. Importantly, we found that the gene expression signature of early Lyme disease is distinct from that of other acute infectious diseases and persists for at least 3 weeks following infection. This study also uncovered multiple previously undescribed pathways and genes that may be useful in the future as human host biomarkers for diagnosis and that constitute potential targets for the development of new therapies.