AHRR and SFRP2 in primary versus recurrent high-grade serous ovarian carcinoma and their prognostic implication.

BACKGROUND: The aim of this study was to analyse transcriptomic differences between primary and recurrent high-grade serous ovarian carcinoma (HGSOC) to identify prognostic biomarkers. METHODS: We analysed 19 paired primary and recurrent HGSOC samples using targeted RNA sequencing. We selected the best candidates using in silico survival and pathway analysis and validated the biomarkers using immunohistochemistry on a cohort of 44 paired samples, an additional cohort of 504 primary HGSOCs and explored their function. RESULTS: We identified 233 differential expressed genes. Twenty-three showed a significant prognostic value for PFS and OS in silico. Seven markers (AHRR, COL5A2, FABP4, HMGCS2, ITGA5, SFRP2 and WNT9B) were chosen for validation at the protein level. AHRR expression was higher in primary tumours (p < 0.0001) and correlated with better patient survival (p < 0.05). Stromal SFRP2 expression was higher in recurrent samples (p = 0.009) and protein expression in primary tumours was associated with worse patient survival (p = 0.022). In multivariate analysis, tumour AHRR and SFRP2 remained independent prognostic markers. In vitro studies supported the anti-tumorigenic role of AHRR and the oncogenic function of SFRP2. CONCLUSIONS: Our results underline the relevance of AHRR and SFRP2 proteins in aryl-hydrocarbon receptor and Wnt-signalling, respectively, and might lead to establishing them as biomarkers in HGSOC.

LRP1B-a prognostic marker in tubo-ovarian high-grade serous carcinoma.

Low-density lipoprotein (LDL) receptor-related protein 1B (LRP1B) is a member of the LDL receptor family and has often been discussed as a tumor suppressor gene, as its down-regulation is correlated with a poor prognosis in multiple carcinoma entities. Due to the high metastasis rate into the fatty peritoneal cavity and current research findings showing a dysregulation of lipid metabolism in tubo-ovarian high-grade serous carcinoma (HGSC), we questioned the prognostic impact of the LRP1B protein expression. We examined a well-characterized large cohort of 571 patients with primary HGSC and analyzed the LRP1B protein expression via immunohistochemical staining (both in tumor and stroma cells separately), performed precise bioimage analysis with QuPath, and calculated the prognostic impact using SPSS. Our results demonstrate that LRP1B functions as a significant prognostic marker for overall survival (OS) and progression-free survival (PFS) in HGSC on the protein level. High cytoplasmic expression of LRP1B in tumor, stroma, and combined tumor and stroma cells has a significantly positive association with a mean prolongation of the OS by 42 months (P = .005), 29 months (P = .005), and 25 months (P = .001), respectively. Additionally, the mean PFS was 18 months longer in tumor (P = .002), 19 months in stroma (P = .004), and 19 months in both cell types combined (P = .01). Our results remained significant in multivariate analysis. We envision LRP1B as a potential prognostic tool that could help us understand the functional role of lipid metabolism in advanced HGSC, especially regarding liposomal medications.

Increased expression of IDO1 is associated with improved survival and increased number of TILs in patients with high-grade serous ovarian cancer.

BACKGROUND: The enzyme indoleamine 2,3-dioxygenase 1 (IDO1) plays a crucial role in regulating the immune system's response to tumors, but its exact role in cancer, especially in high-grade serous ovarian cancer (HGSOC), remains controversial. We aimed to investigate the prognostic impact of IDO1 expression and its correlation with tumor-infiltrating lymphocytes (TILs) in HGSOC. METHODS: Immunohistochemical (IHC) staining and bioimage analysis using the QuPath software were employed to assess IDO1 protein expression in a well-characterized cohort of 507 patients with primary HGSOC. Statistical evaluation was performed using SPSS, and in silico validation considering IDO1 mRNA expression in bulk and single-cell gene expression datasets was conducted. Additionally, IDO1 expression in interferon-gamma (IFNG) stimulated HGSOC cell lines was analyzed. RESULTS: Our findings revealed that IDO1 protein and mRNA expression serve as positive prognostic markers for overall survival (OS) and progression-free survival (PFS) in HGSOC. High IDO1 expression was associated with a significant improvement in OS by 21 months (p < 0.001) and PFS by 6 months (p = 0.016). Notably, elevated IDO1 expression correlated with an increased number of CD3+ (p < 0.001), CD4+ (p < 0.001), and CD8+ TILs (p < 0.001). Furthermore, high IDO1 mRNA expression and protein level were found to be associated with enhanced responsiveness to pro-inflammatory cytokines, particularly IFNG. CONCLUSIONS: Our study provides evidence that IDO1 expression serves as a positive prognostic marker in HGSOC and is associated with an increased number of CD3+, CD4+ and CD8+ TILs. Understanding the intricate relationship between IDO1, TILs, and the tumor microenvironment may hold the key to improving outcomes in HGSOC.

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.

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.

Predictive biomarker for surgical outcome in patients with advanced primary high-grade serous ovarian cancer. Are we there yet? An analysis of the prospective biobank for ovarian cancer.

BACKGROUND: High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer and is associated with high mortality rates. Surgical outcome is one of the most important prognostic factors. There are no valid biomarkers to identify which patients may benefit from a primary debulking approach. OBJECTIVE: Our study aimed to discover and validate a predictive panel for surgical outcome of residual tumor mass after first-line debulking surgery. STUDY DESIGN: Firstly, "In silico" analysis of publicly available datasets identified 200 genes as predictors for surgical outcome. The top selected genes were then validated using the novel Nanostring method, which was applied for the first time for this particular research objective. 225 primary ovarian cancer patients with well annotated clinical data and a complete debulking rate of 60% were compiled for a clinical cohort. The 14 best rated genes were then validated through the cohort, using immunohistochemistry testing. Lastly, we used our biomarker expression data to predict the presence of miliary carcinomatosis patterns. RESULTS: The Nanostring analysis identified 37 genes differentially expressed between optimal and suboptimal debulked patients (p < 0.05). The immunohistochemistry validated the top 14 genes, reaching an AUC Ø0.650. The analysis for the prediction of miliary carcinomatosis patterns reached an AUC of Ø0.797. CONCLUSION: The tissue-based biomarkers in our analysis could not reliably predict post-operative residual tumor. Patient and non-patient-associated co-factors, surgical skills, and center experience remain the main determining factors when considering the surgical outcome at primary debulking in high-grade serous ovarian cancer patients.

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

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.