ASSOCIATIONS BETWEEN MOLECULAR SUBTYPES AND POSTOPERATIVE COMPLICATIONS AFTER PRIMARY CYTOREDUCTIVE SURGERY FOR ADVANCED STAGE HIGH-GRADE SEROUS OVARIAN CANCER

2017 ◽  
Author(s):  
Diogo Torres
Keyword(s):  
Ovarian Cancer ◽  
Postoperative Complications ◽  
Cytoreductive Surgery ◽  
Molecular Subtypes ◽  
High Grade ◽  
Advanced Stage ◽  
Serous Ovarian Cancer

scholarly journals Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score

2021 ◽  
Vol 11 (1) ◽  
pp. 87
Author(s):  
Alexandros Laios ◽  
Raissa Vanessa De Oliveira Silva ◽  
Daniel Lucas Dantas De Freitas ◽  
Yong Sheung Tan ◽  
Gwendolyn Saalmink ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Critical Care ◽  
Cytoreductive Surgery ◽  
Critical Care Unit ◽  
Predictive Score ◽  
High Grade ◽  
Advanced Stage ◽  
Serous Ovarian Cancer ◽  
Estimated Blood Loss

Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer (HGSOC) patients warrants an availability of Critical Care Unit (CCU) beds. Machine Learning (ML) could be helpful in monitoring CCU admissions to improve standards of care. We aimed to improve the accuracy of predicting CCU admission in HGSOC patients by ML algorithms and developed an ML-based predictive score. A cohort of 291 advanced stage HGSOC patients with fully curated data was selected. Several linear and non-linear distances, and quadratic discriminant ML methods, were employed to derive prediction information for CCU admission. When all the variables were included in the model, the prediction accuracies were higher for linear discriminant (0.90) and quadratic discriminant (0.93) methods compared with conventional logistic regression (0.84). Feature selection identified pre-treatment albumin, surgical complexity score, estimated blood loss, operative time, and bowel resection with stoma as the most significant prediction features. The real-time prediction accuracy of the Graphical User Interface CCU calculator reached 95%. Limited, potentially modifiable, mostly intra-operative factors contributing to CCU admission were identified and suggest areas for targeted interventions. The accurate quantification of CCU admission patterns is critical information when counseling patients about peri-operative risks related to their cytoreductive surgery.

scholarly journals SOX2 is a promising predictor of relapse and death in advanced stage high-grade serous ovarian cancer patients with residual disease after debulking surgery

2020 ◽  
Vol 7 (6) ◽  
pp. 1805094
Author(s):  
Maria Bååth ◽  
Sofia Westbom-Fremer ◽  
Laura Martin de la Fuente ◽  
Anna Ebbesson ◽  
Juliette Davis ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Residual Disease ◽  
High Grade ◽  
Advanced Stage ◽  
Serous Ovarian Cancer ◽  
Debulking Surgery

scholarly journals Molecular Profiling and Clinical Outcome of High-Grade Serous Ovarian Cancer Presenting with Low- versus High-Volume Ascites

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Tomer Feigenberg ◽  
Blaise Clarke ◽  
Carl Virtanen ◽  
Anna Plotkin ◽  
Michelle Letarte ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Immune Response ◽  
Clinical Outcome ◽  
Expression Profiles ◽  
High Volume ◽  
High Grade ◽  
Advanced Stage ◽  
Serous Ovarian Cancer ◽  
Immune Response Genes ◽  
Low Volume

Epithelial ovarian cancer consists of multiple histotypes differing in etiology and clinical course. The most prevalent histotype is high-grade serous ovarian cancer (HGSOC), which often presents at an advanced stage frequently accompanied with high-volume ascites. While some studies suggest that ascites is associated with poor clinical outcome, most reports have not differentiated between histological subtypes or tumor grade. We compared genome-wide gene expression profiles from a discovery cohort of ten patients diagnosed with stages III-IV HGSOC with high-volume ascites and nine patients with low-volume ascites. An upregulation of immune response genes was detected in tumors from patients presenting with low-volume ascites relative to those with high-volume ascites. Immunohistochemical studies performed on tissue microarrays confirmed higher expression of proteins encoded by immune response genes and increased tumorinfiltrating cells in tumors associated with low-volume ascites. Comparison of 149 advanced-stage HGSOC cases with differential ascites volume at time of primary surgery indicated low-volume ascites correlated with better surgical outcome and longer overall survival. These findings suggest that advanced stage HGSOC presenting with low-volume ascites reflects a unique subgroup of HGSOC, which is associated with upregulation of immune related genes, more abundant tumor infiltrating cells and better clinical outcomes.

scholarly journals Identification of FGFR4 as a Potential Therapeutic Target for Advanced-Stage, High-Grade Serous Ovarian Cancer

2013 ◽  
Vol 19 (4) ◽  
pp. 809-820 ◽  
Author(s):  
Tarrik M. Zaid ◽  
Tsz-Lun Yeung ◽  
Melissa S. Thompson ◽  
Cecilia S. Leung ◽  
Tom Harding ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Therapeutic Target ◽  
High Grade ◽  
Advanced Stage ◽  
Serous Ovarian Cancer ◽  
Potential Therapeutic Target

Dynamic of molecular subtypes of high‐grade serous ovarian cancer in paired primary and relapsed biopsies.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17091-e17091
Author(s):  
Elena Ioana Braicu ◽  
Hagen Kulbe ◽  
Felix Dreher ◽  
Eliane T Taube ◽  
Frauke Ringel ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Clinical Outcome ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Microarray Gene Expression ◽  
Tissue Samples ◽  
Paired Samples ◽  
Nanostring Technology

e17091 Background: Previously four molecular subtypes of high grade serous ovarian cancer (HGSOC) with distinct biological features and clinical outcome have been described: C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative). Using Nanostring technique and a minimal signature of 39 classifier genes could reproduce the subtypes identified by microarray gene expression profiling (Leong HS et al. Australian Ovarian Cancer Study. J Pathol. 2015). Methods: We characterized paraffin embedded tissue samples from 279 HGSOC patients for molecular subtypes, utilizing the 39 classifier signature and 9 control genes by Nanostring nCounter Analysis System. From 16 patients paired primary and relapsed samples were available. Only chemonaive primary HGSOC patients were included in the study. FFPEs and clinical data were provided by TOC ( www.toc-network.de ). For each sample, probability scores for the four molecular subtypes (C1, C2, C4, and C5) were calculated. The highest calculated score determined the most likely subtype of the tumor. Results: Of all analyzed primary tumor samples, 88 (31.5%) were classified as C1, 83 (29.8%), 53 (19.0%) and 55 (19.7%) as subtypes C2, C4 and C5, respectively. Our results confirmed data by the AOCS study, which described the distribution of HGSOC with 40.2% (C1), 22.5% (C2), 20.1% (C4) and 17.2% (C5), respectively. Within the paired samples, for 12 of the 16 patients dynamic changes in the molecular subtypes between primary and relapse occurred, while in the remaining 4 patients the phenotype was stable. Conclusions: Molecular subtypes of HGSOC using Nanostring technology with a small panel of classifier genes can be confirmed. Furthermore, the data showed that a change of the established molecular subtype might occur during the evolution of the disease, and therefore translate in a different clinical outcome.

scholarly journals Classification of molecular subtypes of high-grade serous ovarian cancer by MALDI-Imaging.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e17544-e17544
Author(s):  
Wanja Nikolai Kassuhn ◽  
Oliver Klein ◽  
Silvia Darb-Esfahani ◽  
Hedwig Lammert ◽  
Sylwia Handzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Molecular Subtypes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Clear Correlation ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Tissue Samples ◽  
Maldi Ims

e17544 Background: High-grade serous ovarian cancer (HGSOC) can be separated by gene expression profiling into four molecular subtypes with clear correlation of the clinical outcome. However, these gene signatures have not been implemented in clinical practice to stratify patients for targeted therapy. This is mainly due to a lack of easy, cost-effective and reproducible methods, as well as the high heterogeneity of HGSOC. Hence, we aimed to examine the potential of unsupervised matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients, which might benefit from targeted therapeutic strategies. Methods: Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS, a novel technology to identify distinct mass profiles on the same paraffin-embedded tissue sections paired with machine learning algorithms to identify HGSOC subtypes by proteomic signature. Finally, we devised a novel strategy to annotate spectra of stromal origin. Results: We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma associated spectra provides tangible improvements to classification quality (AUC = 0.988). False discovery rates (FDR) were reduced from 10.2% to 8.0%. Finally, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999, FDR < 1.0%). Conclusions: Here, we present a concept integrating MALDI-IMS with machine learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for targeted therapies.

EP777 Survival of advanced stage high grade serous ovarian cancer patients in republic of north macedonia

2019 ◽  
Author(s):  
I Aluloski ◽  
M Tanturovski ◽  
S Kostadinova-Kunovska ◽  
R Jovanovic ◽  
G Petrusevska
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
High Grade ◽  
Advanced Stage ◽  
Serous Ovarian Cancer

A 2-Protein Signature Predicting Clinical Outcome in High-Grade Serous Ovarian Cancer

2018 ◽  
Vol 28 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Chengjuan Jin ◽  
Yingfeng Xue ◽  
Yingwei Li ◽  
Hualei Bu ◽  
Hongfeng Yu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Molecular Subtypes ◽  
Disease Free Survival ◽  
Tissue Microarrays ◽  
Multivariate Regression Analysis ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Free Survival ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded

ObjectiveHigh-grade serous ovarian cancer (HGSOC) accounts for approximately 70% deaths in ovarian cancer. The overall survival (OS) of HGSOC is poor and still remains a clinical challenge. High-grade serous ovarian cancer can be divided into 4 molecular subtypes. The prognosis of different molecular subtypes is still unclear. We aimed to investigate the prognostic values of immunohistochemistry-based different molecular subtypes in patients with HGSOC.MethodsWe analyzed the protein expression of representative biomarkers (CXCL11, HMGA2, and MUC16) of 3 different molecular subtypes in 110 formalin-fixed, paraffin-embedded HGSOC by tissue microarrays.ResultsHigh CXCL11 expression predicted worse OS, not disease-free survival (DFS; P = 0.028 for OS, P = 0.191 for DFS). High HMGA2 expression predicted worse OS and DFS (P = 0.037 for OS, P = 0.021 for DFS). MUC16 expression was not associated with OS or DFS (P = 0.919 for OS, P = 0.517 for DFS). Multivariate regression analysis showed that CXCL11 combined with HMGA2 signature was an independent predictor for OS and DFS in patients with HGSOC.ConclusionsCXCL11 combined with HMGA2 signature was a clinically applicable prognostic model that could precisely predict an HGSOC patient's OS and tumor recurrence. This model could serve as an important tool for risk assessment of HGSOC prognosis.

scholarly journals Effect of BRCA mutational status on survival outcome in advanced-stage high-grade serous ovarian cancer

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Se Ik Kim ◽  
Maria Lee ◽  
Hee Seung Kim ◽  
Hyun Hoon Chung ◽  
Jae-Weon Kim ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Survival Outcome ◽  
High Grade ◽  
Advanced Stage ◽  
Serous Ovarian Cancer ◽  
Mutational Status

scholarly journals The Influence of the Preoperative Status on the Risk of Postoperative Complications After Cytoreductive Surgery for Advanced-stage Ovarian Cancer

In Vivo ◽  
2020 ◽  
Vol 34 (2) ◽  
pp. 839-844 ◽  
Author(s):  
NICOLAE BACALBASA ◽  
IRINA BALESCU ◽  
MIHAI DIMITRIU ◽  
LAURA ILIESCU ◽  
CAMELIA DIACONU ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Postoperative Complications ◽  
Cytoreductive Surgery ◽  
Advanced Stage ◽  
Preoperative Status
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