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.

scholarly journals The impact of stroma on the discovery of molecular subtypes and prognostic gene signatures in serous ovarian cancer

2018 ◽  
Author(s):  
Matthew Schwede ◽  
Levi Waldron ◽  
Samuel C. Mok ◽  
Wei Wei ◽  
Azfar Basunia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Stromal Cells ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Gene Signatures ◽  
Prognostic Gene ◽  
Bulk Tissue

AbstractPurposeRecent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.Experimental DesignPublic gene expression data, two molecular subtype classifications, and 61 published gene signatures of ovarian cancer were examined. Using microdissected data, we developed gene signatures of ovarian tumor and stroma. Computational simulations of increasing stromal cell proportion were performed by mixing gene expression profiles of paired microdissected ovarian tumor and stroma.ResultsEstablished ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations, when the percentage of stromal cells increased. Stromal gene expression in bulk tumor was weakly prognostic, and in one dataset, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content alone.ConclusionsThe discovery that molecular subtypes of high grade serous ovarian cancer is influenced by cell admixture, and stromal cell gene expression is crucial for interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Single cell analysis may be required to refine the molecular subtypes of high grade serous ovarian cancer. Because stroma proportion was weakly prognostic, elucidating the role of the tumor microenvironment’s components will be important.Translational relevanceOvarian cancer is a leading cause of cancer death in women in the United States. Although the tumor responds to standard therapy for the majority of patients, it frequently recurs and becomes drug-resistant. Recent efforts have focused on identifying molecular subtypes and prognostic gene signatures of ovarian cancer in order to tailor therapy and improve outcomes. This study demonstrates that molecular subtype identification depends on the ratio of tumor to stroma within the specimen. We show that the specific anatomic location of the biopsy may influence the proportion of stromal involvement and potentially the resulting gene expression pattern. It will be crucial for these factors to be taken into consideration when interpreting and reproducing ovarian cancer molecular subtypes and gene signatures derived using bulk tissue and single cells. Furthermore, it will be important to define the relative proportions of stromal cells and model their prognostic importance in the tumor microenvironment.

scholarly journals Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1512
Author(s):  
Wanja Kassuhn ◽  
Oliver Klein ◽  
Silvia Darb-Esfahani ◽  
Hedwig Lammert ◽  
Sylwia Handzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Molecular Subtypes ◽  
Learning Algorithms ◽  
Imaging Mass Spectrometry ◽  
Machine Learning Algorithms ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Tissue Samples ◽  
Maldi Ims

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. 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). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). 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 personalized treatment.

scholarly journals Development of a Genomic Signatures-Based Predictor of Initial Platinum-Resistance in Advanced High-Grade Serous Ovarian Cancer Patients

2021 ◽  
Vol 10 ◽  
Author(s):  
Yuan Li ◽  
Xiaolan Zhang ◽  
Yan Gao ◽  
Chunliang Shang ◽  
Bo Yu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Platinum Resistance ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Single Nucleotide Variants ◽  
Tissue Samples ◽  
Platinum Sensitive ◽  
Platinum Based Chemotherapy

BackgroundHigh grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer. Although platinum-based chemotherapy has been the cornerstone for HGSOC treatment, nearly 25% of patients would have less than 6 months of interval since the last platinum chemotherapy, referred to as platinum-resistance. Currently, no precise tools to predict platinum resistance have been developed yet.MethodsNinety-nine HGSOC patients, who have finished cytoreductive surgery and platinum-based chemotherapy in Peking University Third Hospital from 2018 to 2019, were enrolled. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) were performed on the collected tumor tissue samples to establish a platinum-resistance predictor in a discovery cohort of 57 patients, and further validated in another 42 HGSOC patients.ResultsA high prevalence of alterations in DNA damage repair (DDR) pathway, including BRCA1/2, was identified both in the platinum-sensitive and resistant HGSOC patients. Compared with the resistant subgroup, there was a trend of higher prevalence of homologous recombination deficiency (HRD) in the platinum-sensitive subgroup (78.95% vs. 47.37%, p=0.0646). Based on the HRD score, microhomology insertions and deletions (MHID), copy number changes load, duplication load of 1–100 kb, single nucleotide variants load, and eight other mutational signatures, a combined predictor of platinum-resistance, named as DRDscore, was established. DRDscore outperformed in predicting the platinum-sensitivity than the previously reported biomarkers with a predictive accuracy of 0.860 at a threshold of 0.7584. The predictive performance of DRDscore was validated in an independent cohort of 42 HGSOC patients with a sensitivity of 90.9%.ConclusionsA multi-genomic signature-based analysis enabled the prediction of initial platinum resistance in advanced HGSOC patients, which may serve as a novel assessment of platinum resistance, provide therapeutic guidance, and merit further validation.

scholarly journals Impact of humoral immune response against p53 on clinical outcome of High-Grade Serous Ovarian Cancer (HGSOC) patients

2016 ◽  
Vol 27 ◽  
pp. viii8
Author(s):  
M. Garziera ◽  
E. Cecchin ◽  
M. Montico ◽  
R. Roncato ◽  
S. Gagno ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Immune Response ◽  
Clinical Outcome ◽  
Humoral Immune Response ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Humoral Immune

Combined Immunoscore of CD103 and CD3 Identifies Long-Term Survivors in High-Grade Serous Ovarian Cancer

2016 ◽  
Vol 26 (4) ◽  
pp. 671-679 ◽  
Author(s):  
Hans-Christian Bösmüller ◽  
Philipp Wagner ◽  
Janet Kerstin Peper ◽  
Heiko Schuster ◽  
Deborah Lam Pham ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Clinical Outcome ◽  
Prognostic Value ◽  
Intraepithelial Lymphocytes ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Term Survival ◽  
Cell Counts

ObjectiveIncreased numbers of tumor-infiltrating lymphocytes (TILs) in high-grade serous ovarian cancer (HGSC) are associated with improved clinical outcome. Intraepithelial localization of TILs might be regulated by specific homing receptors, such as CD103, which is widely expressed by intraepithelial lymphocytes. Given the emerging role of CD103+ TILs, we aimed to assess their contribution to the prognostic value of immunoscoring in HGSC.MethodsThe density of intratumoral CD3+ and CD103+ lymphocytes was examined by immunohistochemistry on a tissue microarray of a series of 135 patients with advanced HGSC and correlated with CD4+, CD8+, CD56+, FoxP3+, and TCRγ+ T-cell counts, as well as E-cadherin staining and conventional prognostic parameters and clinical outcome.ResultsBoth the presence of CD103+ cells, as well as high numbers of intraepithelial CD3+ lymphocytes (CD3E), showed a significant correlation with overall survival, in the complete series, as well as in patients with optimal debulking and/or platinum sensitivity. Combining CD3 and CD103 counts improved prognostication and identified 3 major subgroups with respect to overall survival. The most pronounced effect was demonstrated for patients with optimally resected and platinum-sensitive tumors. Patients with CD3high/CD103high tumors showed a 5-year survival rate at 90%, CD3low/CD103high at 63%, and CD3low/CD103low at 0% (P < 0.001).ConclusionsThese results suggest that combined assessment of CD103 and CD3 counts improves the prognostic value of TIL counts in HGSC and might identify patients with early relapse or long-term survival based on the type and extent of the immune response.

scholarly journals Mesothelin Expression in Patients with High-Grade Serous Ovarian Cancer Does Not Predict Clinical Outcome But Correlates with CD11c+ Expression in Tumor

2020 ◽  
Vol 37 (12) ◽  
pp. 5023-5031
Author(s):  
Isabelle Magalhaes ◽  
Josefin Fernebro ◽  
Sulaf Abd Own ◽  
Daria Glaessgen ◽  
Sara Corvigno ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Clinical Outcome ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Mesothelin Expression

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.

Abstract 5082: Determinants of clinical outcome in high grade serous ovarian cancer

2012 ◽  
Author(s):  
David D. L. Bowtell ◽  
Prue Cown ◽  
Dariush Etemadmoghadam ◽  
Kathryn Alsop ◽  
Joshy George ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Clinical Outcome ◽  
High Grade ◽  
Serous Ovarian Cancer
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