Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma

Initial identification of biomarkers predicting the exact prognosis of high-grade serous ovarian carcinoma (HGSOC) is important in precision cancer medicine. This study aimed to investigate prognostic biomarkers of HGSOC through proteomic analysis. We conducted label-free liquid chromatography-mass spectrometry using chemotherapy-naïve, fresh-frozen primary HGSOC specimens, and compared the results between a favorable prognosis group (progression-free survival (PFS) ≥ 18 months, n = 6) and a poor prognosis group (PFS < 18 months, n = 6). Among 658 differentially expressed proteins, 288 proteins were upregulated in the favorable prognosis group and 370 proteins were upregulated in the poor prognosis group. Using hierarchical clustering, we selected α1-antitrypsin (AAT), nuclear factor-κB (NFKB), phosphomevalonate kinase (PMVK), vascular adhesion protein 1 (VAP1), fatty acid-binding protein 4 (FABP4), platelet factor 4 (PF4), apolipoprotein A1 (APOA1), and α1-acid glycoprotein (AGP) for further validation via immunohistochemical (IHC) staining in an independent set of chemotherapy-naïve primary HGSOC samples (n = 107). Survival analyses revealed that high expression of AAT, NFKB, and PMVK were independent biomarkers for favorable PFS. Conversely, high expression of VAP1, FABP4, and PF4 were identified as independent biomarkers for poor PFS. Furthermore, we constructed models predicting the 18-month PFS by combining clinical variables and IHC results. Through leave-one-out cross-validation, the optimal model was based on initial serum CA-125, germline BRCA1/2 mutations, residual tumors after surgery, International Federation of Gynecology and Obstetrics (FIGO) stage, and expression levels of the six proteins. The present results elucidate the proteomic landscape of HGSOC and six protein biomarkers to predict the prognosis of HGSOC.

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Mechanisms of Chromosomal Instability in High-grade Serous Ovarian Carcinoma

10.1101/727537 ◽

2019 ◽

Cited By ~ 1

Author(s):

N. Tamura ◽

N. Shaikh ◽

D. Muliaditan ◽

J. McGuinness ◽

D. Moralli ◽

...

Keyword(s):

Ovarian Cancer ◽

Ovarian Carcinoma ◽

Single Molecule ◽

Chromosomal Instability ◽

Poor Prognosis ◽

Spindle Assembly Checkpoint ◽

Western World ◽

Cancer Type ◽

High Grade ◽

Serous Ovarian Carcinoma

AbstractChromosomal instability (CIN), the continual gain and loss of chromosomes or parts of chromosomes, occurs in the majority of cancers and confers poor prognosis. Mechanisms driving CIN remain unknown in most cancer types due to a scarcity of functional studies. High-grade serous ovarian carcinoma (HGSC), the most common subtype of ovarian cancer, is the major cause of death due to gynaecological malignancy in the Western world with chemotherapy resistance developing in almost all patients. HGSC exhibits high rates of chromosome aberrations and knowledge of causative mechanisms is likely to represent an important step towards combating the poor prognosis of this disease. However, very little is known about the nature of chromosomal instability exhibited by this cancer type in particular due to a historical lack of appropriate cell line models. Here we perform the first in-depth functional characterisation of mechanisms driving CIN in HGSC by analysing eight cell lines that accurately recapitulate HGSC genetics as defined by recent studies. We show, using a range of established functional CIN assays combined with live cell imaging and single molecule DNA fibre analysis, that multiple mechanisms co-exist to drive CIN in HGSC. These include supernumerary centrosomes, elevated microtubule dynamics and DNA replication stress. By contrast, the spindle assembly checkpoint was intact. These findings are relevant for developing therapeutic approaches to manipulating CIN in ovarian cancer, and suggests that such approaches may need to be multimodal to combat multiple co-existing CIN drivers.

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Aldehyde dehydrogenase 1/epidermal growth factor receptor coexpression is characteristic of a highly aggressive, poor-prognosis subgroup of high-grade serous ovarian carcinoma

Human Pathology ◽

10.1016/j.humpath.2012.12.016 ◽

2013 ◽

Vol 44 (8) ◽

pp. 1465-1471 ◽

Cited By ~ 23

Author(s):

Catarina Alisa Liebscher ◽

Judith Prinzler ◽

Bruno Valentin Sinn ◽

Jan Budczies ◽

Carsten Denkert ◽

...

Keyword(s):

Epidermal Growth Factor Receptor ◽

Growth Factor ◽

Epidermal Growth Factor ◽

Ovarian Carcinoma ◽

Aldehyde Dehydrogenase ◽

Poor Prognosis ◽

Growth Factor Receptor ◽

High Grade ◽

Serous Ovarian Carcinoma ◽

Epidermal Growth

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MAL gene overexpression as a marker of high-grade serous ovarian carcinoma stem-like cells that predicts chemoresistance and poor prognosis

BMC Cancer ◽

10.1186/s12885-017-3334-1 ◽

2017 ◽

Vol 17 (1) ◽

Cited By ~ 4

Author(s):

Laura Zanotti ◽

Chiara Romani ◽

Laura Tassone ◽

Paola Todeschini ◽

Renata Alessandra Tassi ◽

...

Keyword(s):

Ovarian Carcinoma ◽

Poor Prognosis ◽

Gene Overexpression ◽

High Grade ◽

Serous Ovarian Carcinoma

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Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma

Cancers ◽

10.3390/cancers13081875 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1875

Author(s):

Suhyun Hwangbo ◽

Se Ik Kim ◽

Ju-Hyun Kim ◽

Kyung Jin Eoh ◽

Chanhee Lee ◽

...

Keyword(s):

Machine Learning ◽

Ovarian Carcinoma ◽

Model Performance ◽

Ca 125 ◽

Lymph Node Status ◽

High Grade ◽

Learning Models ◽

Platinum Sensitivity ◽

Serous Ovarian Carcinoma ◽

Machine Learning Models

To support the implementation of individualized disease management, we aimed to develop machine learning models predicting platinum sensitivity in patients with high-grade serous ovarian carcinoma (HGSOC). We reviewed the medical records of 1002 eligible patients. Patients’ clinicopathologic characteristics, surgical findings, details of chemotherapy, treatment response, and survival outcomes were collected. Using the stepwise selection method, based on the area under the receiver operating characteristic curve (AUC) values, six variables associated with platinum sensitivity were selected: age, initial serum CA-125 levels, neoadjuvant chemotherapy, pelvic lymph node status, involvement of pelvic tissue other than the uterus and tubes, and involvement of the small bowel and mesentery. Based on these variables, predictive models were constructed using four machine learning algorithms, logistic regression (LR), random forest, support vector machine, and deep neural network; the model performance was evaluated with the five-fold cross-validation method. The LR-based model performed best at identifying platinum-resistant cases with an AUC of 0.741. Adding the FIGO stage and residual tumor size after debulking surgery did not improve model performance. Based on the six-variable LR model, we also developed a web-based nomogram. The presented models may be useful in clinical practice and research.

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Overexpression of ICAM-1 Predicts Poor Survival in High-Grade Serous Ovarian Carcinoma: A Study Based on TCGA and GEO Databases and Tissue Microarray

BioMed Research International ◽

10.1155/2019/2867372 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Shasha Wang ◽

Can Yin ◽

Ying Zhang ◽

Lu Zhang ◽

Lin Tao ◽

...

Keyword(s):

Overall Survival ◽

Ovarian Carcinoma ◽

Mrna Expression ◽

Adhesion Molecule ◽

Poor Prognosis ◽

Prognostic Biomarker ◽

The Cancer Genome Atlas ◽

High Grade ◽

Poor Survival ◽

Serous Ovarian Carcinoma

Intercellular cell adhesion molecule-1 (ICAM-1), an important adhesion molecule in the immunoglobulin superfamily, is expressed on many cell types. Recent studies have identified ICAM-1 as a potential oncogene that promotes the development of epithelial ovarian cancer (EOC); it was also found to be associated with poor survival. However, the clinical significance of its expression in high-grade serous ovarian carcinoma (HGSOC) is unclear. Thus, this study aimed to investigate the significance of ICAM-1 expression in HGSOC. Data on ICAM1 expression and mutations in serous ovarian carcinoma (SOC) were obtained from the Cancer Genome Atlas (TCGA), and ICAM1 mRNA expression data in HGSOC were obtained from the Gene Expression Omnibus (GEO) database. ICAM-1 expression was evaluated by immunohistochemistry in HGSOC and normal fallopian tube tissues microarray. In TCGA data, amplification/mutation of ICAM1 was identified in 12% of serous ovarian carcinoma samples, and overexpression of ICAM1 mRNA predicted reduced overall survival in SOC. From TCGA and GEO data, SOC patients with ICAM1 mRNA overexpression treated with chemotherapeutic drugs that contained taxol or taxol and platin together had significantly reduced progression-free survival. According to GEO data, ICAM1 mRNA expression was found significantly higher in HGSOC than in control samples. In our study, ICAM-1 overexpression was observed in 63.1% (65/103) of HGSOCs. As a prognostic biomarker, overexpression of ICAM-1 predicted reduced recurrence-free and overall survival and is an independent risk factor for poor prognosis. These findings suggest that overexpression of ICAM-I is an independent indicator of poor prognosis for HGSOC and that it can serve as an effective clinical prognostic biomarker for this disease.

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Impact of CT-Determined Sarcopenia and Body Composition on Survival Outcome in Patients with Advanced-Stage High-Grade Serous Ovarian Carcinoma

Cancers ◽

10.3390/cancers12030559 ◽

2020 ◽

Vol 12 (3) ◽

pp. 559 ◽

Cited By ~ 1

Author(s):

Se Ik Kim ◽

Taek Min Kim ◽

Maria Lee ◽

Hee Seung Kim ◽

Hyun Hoon Chung ◽

...

Keyword(s):

Body Composition ◽

Ovarian Carcinoma ◽

Subgroup Analysis ◽

Ca 125 ◽

Survival Outcomes ◽

High Grade ◽

Advanced Stage ◽

Korean Patients ◽

Serous Ovarian Carcinoma ◽

The Impact

This study aimed to investigate the impact of sarcopenia and body composition on survival outcomes in Korean patients with advanced-stage high-grade serous ovarian carcinoma (HGSOC). We retrospectively identified patients diagnosed with and treated for International Federation of Gynecology and Obstetrics stage III-IV HGSOC. Skeletal muscle index (SMI) was measured using pre-treatment computed tomography scans at the third lumbar vertebra. Sarcopenia was defined as SMI <39.0 cm2/m2. Patients’ clinicopathologic characteristics and survival outcomes were compared according to sarcopenia presence. For subgroup analysis, we also measured the total fat area from the same image. In total, 76 and 103 patients were assigned to the sarcopenia and control groups, respectively. Comorbidities, stage, serum CA-125 levels, and size of residual tumor after surgery were similar between both groups. After a median follow up of 42.7 months, both groups showed similar progression-free survival (PFS) and overall survival (OS). In subgroup analysis confined to the sarcopenia group, patients with high fat-to-muscle ratio (FMR; ≥2.1, n = 38) showed significantly worse OS than those with low FMR (<2.1, n = 38) (5-year survival rate, 44.7% vs. 80.0%; p = 0.046), whereas PFS was not different (p = 0.365). Multivariate analyses identified high FMR as an independent poor prognostic factor for OS in this group (adjusted hazard ratio, 3.377; 95% confidence interval, 1.170–9.752; p = 0.024). In conclusion, sarcopenia did not influence recurrence rates and survival in Korean patients with advanced-stage HGSOC. However, among the patients with sarcopenia, high FMR was associated with decreased OS.

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Signal Transduction Pathway Activity in High-Grade, Serous Ovarian Carcinoma Reveals a More Favorable Prognosis in Tumors with Low PI3K and High NF-κB Pathway Activity: A Novel Approach to a Long-Standing Enigma

Cancers ◽

10.3390/cancers12092660 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2660

Author(s):

Laura van Lieshout ◽

Anja van de Stolpe ◽

Phyllis van der Ploeg ◽

David Bowtell ◽

Joanne de Hullu ◽

...

Keyword(s):

Signal Transduction ◽

Ovarian Carcinoma ◽

Transforming Growth Factor ◽

Signal Transduction Pathway ◽

Cancer Tissue ◽

High Grade ◽

Transduction Pathway ◽

Pathway Activity ◽

Favorable Prognosis ◽

Serous Ovarian Carcinoma

We investigated signal transduction pathway (STP) activity in high-grade serous ovarian carcinoma (HGSC) in relation to progression-free survival (PFS) and overall survival (OS). We made use of signal transduction pathway activity analysis (STA analysis), a novel method to quantify functional STP activity. Activity of the following pathways was measured: androgen receptor (AR), estrogen receptor (ER), phosphoinositide 3-kinase (PI3K), Hedgehog (Hh), Notch, nuclear factor-kappa B (NF-κB), transforming growth factor beta (TGF-β), and Wnt. We selected HGSC samples from publicly available datasets of ovarian cancer tissue, and used repeated k-means clustering to identify pathway activity clusters. PFS and OS of the clusters were analyzed. We used a subset of publicly available dataset GSE9891 (n = 140), where repeated k-means clustering based on PI3K and NF-κB pathway activity in HGSC samples resulted in two stable clusters. The cluster with low PI3K and high NF-κB pathway activity (n = 72) had a more favorable prognosis for both PFS (p = 0.004) and OS (p = 0.001) compared to the high-PI3K and low-NF-κB pathway activity cluster (n = 68). The low PI3K and high NF-κB pathway activity of the favorable prognosis cluster may indicate a more active immune response, while the high PI3K and low NF-κB pathway activity of the unfavorable prognosis cluster may indicate high cell division.

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