TY - JOUR AB - Background: This study seeks to evaluate the impact of breast cancer (BRCA) gene status on tumor dissemination pattern, surgical outcome and survival in a multicenter cohort of paired primary ovarian cancer (pOC) and recurrent ovarian cancer (rOC). Patients and Methods: Medical records and follow-up data from 190 patients were gathered retrospectively. All patients had surgery at pOC and at least one further rOC surgery at four European high-volume centers. Patients were divided into one cohort with confirmed mutation for BRCA1 and/or BRCA2 (BRCAmut) and a second cohort with BRCA wild type or unknown (BRCAwt). Patterns of tumor presentation, surgical outcome and survival data were analyzed between the two groups. Results: Patients with BRCAmut disease were on average 4 years younger and had significantly more tumor involvement upon diagnosis. Patients with BRCAmut disease showed higher debulking rates at all stages. Multivariate analysis showed that only patient age had significant predictive value for complete tumor resection in pOC. At rOC, however, only BRCAmut status significantly correlated with optimal debulking. Patients with BRCAmut disease showed significantly prolonged overall survival (OS) by 24.3 months. Progression-free survival (PFS) was prolonged in the BRCAmut group at all stages as well, reaching statistical significance during recurrence. Conclusions: Patients with BRCAmut disease showed a more aggressive course of disease with earlier onset and more extensive tumor dissemination at pOC. However, surgical outcome and OS were significantly better in patients with BRCAmut disease compared with patients with BRCAwt disease. We therefore propose to consider BRCAmut status in regard to patient selection for cytoreductive surgery, especially in rOC. AU - Glajzer, Jacek AU - Castillo-Tong, Dan Cacsire AU - Richter, Rolf AU - Vergote, Ignace AU - Kulbe, Hagen AU - Vanderstichele, Adriaan AU - Ruscito, Ilary AU - Trillsch, Fabian AU - Mustea, Alexander AU - Kreuzinger, Caroline AU - Gourley, Charlie AU - Gabra, Hani AU - Taube, Eliane T. AU - Dorigo, Oliver AU - Horst, David AU - Keunecke, Carlotta AU - Baum, Joanna AU - Angelotti, Timothy AU - Sehouli, Jalid AU - Braicu, Elena Ioana ID - 12205 JF - Annals of Surgical Oncology KW - Oncology KW - Surgery SN - 1068-9265 TI - Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome and patient survival in primary and recurrent high-grade serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium VL - 30 ER - TY - JOUR AU - Glajzer, Jacek AU - Castillo-Tong, Dan Cacsire AU - Richter, Rolf AU - Vergote, Ignace AU - Kulbe, Hagen AU - Vanderstichele, Adriaan AU - Ruscito, Ilary AU - Trillsch, Fabian AU - Mustea, Alexander AU - Kreuzinger, Caroline AU - Gourley, Charlie AU - Gabra, Hani AU - Taube, Eliane T. AU - Dorigo, Oliver AU - Horst, David AU - Keunecke, Carlotta AU - Baum, Joanna AU - Angelotti, Timothy AU - Sehouli, Jalid AU - Braicu, Elena Ioana ID - 12115 JF - Annals of Surgical Oncology KW - Oncology KW - Surgery SN - 1068-9265 TI - ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome, and patient survival in primary and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS) consortium VL - 30 ER - TY - DATA AB - 3D-reconstruction of living brain tissue down to individual synapse level would create opportunities for decoding the dynamics and structure-function relationships of the brain’s complex and dense information processing network. However, it has been hindered by insufficient 3D-resolution, inadequate signal-to-noise-ratio, and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine learning technology, LIONESS (Live Information-Optimized Nanoscopy Enabling Saturated Segmentation). It leverages optical modifications to stimulated emission depletion (STED) microscopy in comprehensively, extracellularly labelled tissue and prior information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise-ratio, and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D-reconstruction at synapse level incorporating molecular, activity, and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue. AU - Danzl, Johann G ID - 12817 TI - Research data for the publication "Dense 4D nanoscale reconstruction of living brain tissue" ER - TY - JOUR AB - Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue. AU - Velicky, Philipp AU - Miguel Villalba, Eder AU - Michalska, Julia M AU - Lyudchik, Julia AU - Wei, Donglai AU - Lin, Zudi AU - Watson, Jake AU - Troidl, Jakob AU - Beyer, Johanna AU - Ben Simon, Yoav AU - Sommer, Christoph M AU - Jahr, Wiebke AU - Cenameri, Alban AU - Broichhagen, Johannes AU - Grant, Seth G.N. AU - Jonas, Peter M AU - Novarino, Gaia AU - Pfister, Hanspeter AU - Bickel, Bernd AU - Danzl, Johann G ID - 13267 JF - Nature Methods SN - 1548-7091 TI - Dense 4D nanoscale reconstruction of living brain tissue VL - 20 ER - TY - JOUR AB - We developed LIONESS, a technology that leverages improvements to optical super-resolution microscopy and prior information on sample structure via machine learning to overcome the limitations (in 3D-resolution, signal-to-noise ratio and light exposure) of optical microscopy of living biological specimens. LIONESS enables dense reconstruction of living brain tissue and morphodynamics visualization at the nanoscale. AU - Danzl, Johann G AU - Velicky, Philipp ID - 14770 IS - 8 JF - Nature Methods KW - Cell Biology KW - Molecular Biology KW - Biochemistry KW - Biotechnology SN - 1548-7091 TI - LIONESS enables 4D nanoscale reconstruction of living brain tissue VL - 20 ER -