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Creation and validation of models to predict response to chemotherapy before treatment in serous ovarian cancer

ABSTRACT: Creation and validation of models to predict response to chemotherapy before treatment in serous ovarian cancer

PROVIDER: PRJNA658793 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12502901_1.fastq.gz	Fastqsanger.gz
	SRR12502901_2.fastq.gz	Fastqsanger.gz
	SRR12502902_1.fastq.gz	Fastqsanger.gz
	SRR12502902_2.fastq.gz	Fastqsanger.gz
	SRR12502903_1.fastq.gz	Fastqsanger.gz

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Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.

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Creation and validation of models to predict response to chemotherapy before treatment in serous ovarian cancer

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