ArticleBMC medical genomics2016
Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.
Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identifying MicroRNAs Suitable for Detection of Breast Cancer: A Systematic Review of Discovery Phases Studies on MicroRNA Expression Profiles.International journal of molecular sciences · 2023Pooled it
- Article
- BatMan: Mitigating Batch Effects Via Stratification for Survival Outcome Prediction.JCO clinical cancer informatics · 2023Article
- On data normalization and batch-effect correction for tumor subtyping with microRNA data.NAR genomics and bioinformatics · 2023Article
- Depth normalization of small RNA sequencing: using data and biology to select a suitable method.Nucleic acids research · 2022Article
- Performance evaluation of transcriptomics data normalization for survival risk prediction.Briefings in bioinformatics · 2021Article
- Making External Validation Valid for Molecular Classifier Development.JCO precision oncology · 2021Article
- Statistical Assessment of Depth Normalization for Small RNA Sequencing.JCO clinical cancer informatics · 2020Article
- CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data.PLoS computational biology · 2020Article
- Study Design Considerations for Cancer Biomarker Discoveries.The journal of applied laboratory medicine · 2018Article
- A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples.Scientific data · 2018Article
- OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes.Microarrays (Basel, Switzerland) · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundAccurate discovery of molecular biomarkers that are prognostic of a clinical outcome is an important yet challenging task, partly due to the combination of the typically weak genomic signal for a clinical outcome and the frequently strong noise due to microarray handling effects. Effective strategies to resolve this challenge are in dire need.
methodsWe set out to assess the use of careful study design and data normalization for the discovery of prognostic molecular biomarkers. Taking progression free survival in advanced serous ovarian cancer as an example, we conducted empirical analysis on two sets of microRNA arrays for the same set of tumor samples: arrays in one set were collected using careful study design (that is, uniform handling and randomized array-to-sample assignment) and arrays in the other set were not.
resultsWe found that (1) handling effects can confound the clinical outcome under study as a result of chance even with randomization, (2) the level of confounding handling effects can be reduced by data normalization, and (3) good study design cannot be replaced by post-hoc normalization. In addition, we provided a practical approach to define positive and negative control markers for detecting handling effects and assessing the performance of a normalization method.
conclusionsOur work showcased the difficulty of finding prognostic biomarkers for a clinical outcome of weak genomic signals, illustrated the benefits of careful study design and data normalization, and provided a practical approach to identify handling effects and select a beneficial normalization method. Our work calls for careful study design and data analysis for the discovery of robust and translatable molecular biomarkers.
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