Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles
Open accessAbstract
BACKGROUND: Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ~100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). METHODOLOGY/PRINCIPAL FINDINGS: We implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72). CONCLUSIONS/SIGNIFICANCE: We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.
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Multivariate LASSO models have been studied.
“Similarly, multivariate LASSO models (Zou, 2006;Nicolai and Bühlmann Peter, 2010;Mankoo et al., 2011) have been investigated.”
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- Authors
- Mankoo, P. K., Shen, R., Schultz, N., Levine, D. A., & Sander, C.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Mankoo, Parminder K.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-11-03
- Volume
- 6
- Issue
- 11
- Pages
- e24709
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3207809
- OpenAlex
- W2005388951
- Cited by
- 104
- References
- 45
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 8 · 2023: 2 · 2022: 6 · 2021: 10 · 2020: 11 · 2019: 11 · 2018: 10 · 2017: 11 · 2016: 7 · 2015: 7 · 2014: 4 · 2013: 6 · 2012: 9
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Cancer Institute (U24CA143840)
- Topics
- Health Sciences › Medicine › Reproductive Medicine › Ovarian cancer diagnosis and treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- serous ovarian cancer, prognostic signature, time to recurrence, progression-free survival, overall survival, multi-omics integration, DNA methylation, DNA copy number alterations, microRNA, mRNA expression, risk stratification, TP53 mutation, pathway analysis, TCGA
- MeSH terms
- Female, Humans, Middle Aged, Mutation, Ovarian Neoplasms, Prognosis, Recurrence, Survival Analysis, Tumor Suppressor Protein p53, DNA Methylation, Gene Expression Profiling
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