The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Risk Prediction for Late-Stage Ovarian Cancer by Meta-analysis of 1525 Patient Samples
Open accessAbstract
BACKGROUND: Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients. METHODS: We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided. RESULTS: The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93). CONCLUSIONS: Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.
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Thomas, A. M., Manghi, P., Asnicar, F., Pasolli, E., Armanini, F., Zolfo, M., Beghini, F., Manara, S., Karcher, N., Pozzi, C., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0405-7 · Cited by 1,030
Self-citationMachine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004977 · Cited by 659
Self-citationGLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling
Xu, X., Liu, X., Zhang, H., Wang, W., Nepal, S., Sefidgar, Y., Seo, W., Kuehn, K. S., Huckins, J. F., Morris, M. E., et al. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3569485 · Cited by 96
Metadata
- Authors
- Riester, M., Wei, W., Waldron, L., Culhane, A. C., Trippa, L., Oliva, E., Kim, S.-H., Michor, F., Huttenhower, C., Parmigiani, G., & Birrer, M. J.
- Venue
- JNCI: Journal of the National Cancer Institute
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-04-03
- Volume
- 106
- Issue
- 5
- ISSN
- 0027-8874 (ISSN-L)
- 1460-2105
- OpenAlex
- W2127807352
- Cited by
- 195
- References
- 7
- FWCI
- 4.83
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Riester, M., Wei, W., Waldron, L., Culhane, A. C., Trippa, L., Oliva, E., Kim, S.-H., Michor, F., Huttenhower, C., Parmigiani, G., & Birrer, M. J. (2014). Risk Prediction for Late-Stage Ovarian Cancer by Meta-analysis of 1525 Patient Samples. JNCI: Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/dju048
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