Dataset Information

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer.

ABSTRACT: Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.

SUBMITTER: Kasimir-Bauer S

PROVIDER: S-EPMC7564837 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer.

Kasimir-Bauer Sabine S Roder Joanna J Obermayr Eva E Mahner Sven S Vergote Ignace I Loverix Liselore L Braicu Elena E Sehouli Jalid J Concin Nicole N Kimmig Rainer R Net Lelia L Roder Heinrich H Zeillinger Robert R Aust Stefanie S On Behalf Of The Ovcad Ovarian Cancer Diagnosis Initiative

Cancers 20200904 9

Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: <i>n</i> = 199, validation cohort: <i>n</i> = 135) a blood-based p ...[more]

PMID: 32899818

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Project description:Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 and GSE51088) investigating OC gene expression were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in samples from patients with non?recurrent and recurrent OC were revealed via a homogeneity test and quality control analysis. A protein?protein interaction (PPI) network was subsequently established for the DEGs using data from Biological General Repository for Interaction Datasets, Human Protein Reference Database and Database of Interacting Proteins. Degrees of interaction and betweenness centrality (BC) scores were calculated for each node in the PPI network. The top 100 genes ranked by BC scores were selected to identify feature genes via recursive feature elimination using the GSE17260 dataset. Following this, a SVM classifier was constructed and further validated using the GSE44104 and GSE51088 datasets and independent gene expression data obtained from the Cancer Genome Atlas (TCGA). A total of 639 DEGs were identified from the three gene expression datasets, and a PPI network including 249 nodes and 354 edges was constructed. A SVM classifier consisting of 39 feature genes (including cullin 3, mouse double minute 2 homolog, aurora kinase A, WW domain containing oxidoreducatase, large tumor suppressor kinase 2, sirtuin 6, staphylococcal nuclease and tudor domain containing 1, leucine rich repeats and immunoglobulin like domains 1 and aurora kinase 1 interacting protein 1) was subsequently constructed. The prediction accuracies of the SVM classifier for GSE17260, GSE44104 and GSE51088 datasets as well as data downloaded from TCGA were revealed to be 92.7, 93.3, 96.6 and 90.4%, respectively. Furthermore, the results of the present study revealed that patients with predicted non?recurrent OC survived significantly longer compared with the patients with predicted recurrent OC (P=6.598x10?6). A SVM classifier consisting of 39 feature genes was established for predicting the recurrence and prognosis of OC. Therefore, the results of the present study suggested that the 39 feature genes may serve important roles in the development of OC and may represent therapeutic biomarkers of OC.

Project description:ObjectiveTo independently validate a published risk-calculator for adverse perioperative outcomes in patients with epithelial ovarian cancer undergoing debulking surgery at a high-volume surgical center.MethodsUsing our institution's curated prospective ovarian cancer database, we identified patients with epithelial ovarian cancer who underwent a debulking procedure from 7/2015 to 5/2019, to be used as the validation cohort. Variables used in the published nomogram were collected. These included American Society of Anesthesiology classification, preoperative albumin, history of bleeding disorder, presence of ascites on preoperative imaging, designation of elective or emergent surgery, age of the patient, and a procedure score. Patients were included if they had information available for all the variables used in the nomogram, and 30-day follow-up within our institution. The primary outcome was Clavien-Dindo Class IV with specific conditions (postoperative sepsis, septic shock, cardiac arrest, myocardial infarction, pulmonary embolism, ventilation >48 h, or unplanned intubation) and 30-day mortality. The combination of these endpoints is called the combined complication rate.ResultsA total of 700 patients who underwent debulking surgery for epithelial ovarian cancer during the timeframe met inclusion criteria. The combined complication rate was 11.7%; 9.9% of patients were readmitted; 2.7% required reoperation. Sepsis was the most common primary endpoint complication (4.4%), followed by septic shock (1.4%). There was no 30-day mortality in our cohort. The nomogram performed well, with a c index of 0.715 (95% CI 0.66-0.768), which was comparable to the published nomogram.ConclusionsWe independently validated a complication nomogram at a high-volume surgical center. This nomogram performs well at predicting a lower likelihood of serious postoperative complications. An enhanced nomogram would help identify patients at higher risk for serious complications.

Project description:Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non?recurrent OC samples) and GSE49997 (204 OC samples), were obtained from the Gene Expression Omnibus database. Differentially expressed genes were screened using the differential expression via distance synthesis algorithm, followed by gene ontology enrichment analysis and weighted gene coexpression network analysis (WGCNA). Furthermore, subnetwork analysis was conducted for the protein?protein interaction (PPI) network using the BioNet package. Finally, a random forest classifier was constructed based on the subnetwork nodes, and its reliability was validated using the GSE44104 and GSE49997 validation datasets. A total of 44 upregulated and 117 downregulated genes were identified in the recurrent samples. Enrichment analysis indicated that cytochrome P450 family 17 subfamily A member 1 (CYP17A1) was associated with 'positive regulation of steroid hormone biosynthetic processes'. WGCNA identified turquoise and grey modules that were significantly correlated with status and prognosis. A significant PPI subnetwork containing 16 nodes was also identified, including: Transcription factor GATA?4; fibroblast growth factor 9; aromatase; 3??hydroxysteroid dehydrogenase/?5?4?isomerase type 2; corticosteroid 11??dehydrogenase isozyme 1; CYP17A1; pituitary homeobox 2; left?right determination factor 1; homeobox protein ARX; estrogen receptor ?; steroidogenic factor 1; forkhead box protein L2; myocardin; steroidogenic acute regulatory protein mitochondrial; vesicular inhibitory amino acid transporter; and twist?related protein 1. A random forest classifier was constructed using the subnetwork nodes as feature genes, which exhibited a 92% true positive rate when classifying recurrent and non?recurrent OC samples. The classifying efficiency of the random forest classifier was validated using the two other independent datasets. Overall, 44 upregulated and 117 downregulated genes associated with OC recurrence were identified. Furthermore, the 16 subnetwork node genes that were identified may be important molecules in OC recurrence.

Project description:Background: Although most thyroid nodules with indeterminate cytology are benign, in most of the world, surgery remains as the most frequent diagnostic approach. We have previously reported a 10-gene thyroid genetic classifier, which accurately predicts benign thyroid nodules. The assay is a prototype diagnostic kit suitable for reference laboratory testing and could potentially avoid unnecessary diagnostic surgery in patients with indeterminate thyroid cytology. Methods: Classifier performance was tested in two independent, ethnically diverse, prospective multicenter trials (TGCT-1/Chile and TGCT-2/USA). A total of 4061 fine-needle aspirations were collected from 15 institutions, of which 897 (22%) were called indeterminate. The clinical site was blind to the classifier score and the clinical laboratory blind to the pathology report. A matched surgical pathology and valid classifier score was available for 270 samples. Results: Cohorts showed significant differences, including (i) clinical site patient source (academic, 43% and 97% for TGCT-1 and -2, respectively); (ii) ethnic diversity, with a greater proportion of the Hispanic population (40% vs. 3%) for TGCT-1 and a greater proportion of African American (11% vs. 0%) and Asian (10% vs. 1%) populations for TGCT-2; and (iii) tumor size (mean of 1.7 and 2.5?cm for TGCT-1 and -2, respectively). Overall, there were no differences in the histopathological profile between cohorts. Forty-one of 155 and 45 of 115 nodules were malignant (cancer prevalence of 26% and 39% for TGCT-1 and -2, respectively). The classifier predicted 37 of 41 and 41 of 45 malignant nodules, yielding a sensitivity of 90% [95% confidence interval; CI 77-97] and 91% [95% CI 79-98] for TGCT-1 and -2, respectively. One hundred one of 114 and 61 of 70 nodules were correctly predicted as benign, yielding a specificity of 89% [95% CI 82-94] and 87% [95% CI 77-94], respectively. The negative predictive values for TGCT-1 and TGCT-2 were 96% and 94%, respectively, whereas the positive predictive values were 74% and 82%, respectively. The overall accuracy for both cohorts was 89%. Conclusions: Clinical validation of the classifier demonstrates equivalent performance in two independent and ethnically diverse cohorts, accurately predicting benign thyroid nodules that can undergo surveillance as an alternative to diagnostic surgery.

Dataset Information

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer.

Publications

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer.

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