Cited by 97Gene expression and cancer classificationCancer Genomics and DiagnosticsBreast Cancer Treatment Studies

Patient-specific data fusion defines prognostic cancer subtypes

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Yuan, Yinyin, Savage, Richard S., Markowetz, Florian·2011·PLoS Computational Biology

Abstract

Different data types can offer complementary perspectives on the same biological phenomenon. In cancer studies, for example, data on copy number alterations indicate losses and amplifications of genomic regions in tumours, while transcriptomic data point to the impact of genomic and environmental events on the internal wiring of the cell. Fusing different data provides a more comprehensive model of the cancer cell than that offered by any single type. However, biological signals in different patients exhibit diverse degrees of concordance due to cancer heterogeneity and inherent noise in the measurements. This is a particularly important issue in cancer subtype discovery, where personalised strategies to guide therapy are of vital importance. We present a nonparametric Bayesian model for discovering prognostic cancer subtypes by integrating gene expression and copy number variation data. Our model is constructed from a hierarchy of Dirichlet Processes and addresses three key challenges in data fusion: (i) To separate concordant from discordant signals, (ii) to select informative features, (iii) to estimate the number of disease subtypes. Concordance of signals is assessed individually for each patient, giving us an additional level of insight into the underlying disease structure. We exemplify the power of our model in prostate cancer and breast cancer and show that it outperforms competing methods. In the prostate cancer data, we identify an entirely new subtype with extremely poor survival outcome and show how other analyses fail to detect it. In the breast cancer data, we find subtypes with superior prognostic value by using the concordant results. These discoveries were crucially dependent on our model's ability to distinguish concordant and discordant signals within each patient sample, and would otherwise have been missed. We therefore demonstrate the importance of taking a patient-specific approach, using highly-flexible nonparametric Bayesian methods.

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  • PSDF is a non-parametric Bayesian model for clustering prognostic cancer subtypes by combining gene expression and copy number variation data.

    “PSDF (Patient-Specific Data Fusion) (Yuan et al., 2011) is a non-parametric Bayesian model for clustering prognostic cancer subtypes by combining gene expression and copy number variation data.”

    From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Yuan et al., 2011

  • PSDF uses a two-step process and limits integration to only two datatypes.

    “It uses a two-step process and limits the integration to only two datatypes.”

    From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Yuan et al., 2011

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Authors
Yuan, Y., Savage, R. S., & Markowetz, F.
Affiliations
  • University of Cambridge (GB)
  • University of Warwick (GB)
Corresponding author
Yuan, Yinyin
Venue
PLoS Computational Biology
Published
2011-10-20
Volume
7
Issue
10
Pages
e1002227
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
OpenAlex
W2103923312
Cited by
97
References
50
Citations by year
2026: 3 · 2025: 5 · 2024: 9 · 2023: 6 · 2022: 5 · 2021: 11 · 2020: 12 · 2019: 9 · 2018: 12 · 2017: 3 · 2016: 7 · 2015: 6 · 2014: 5 · 2013: 3 · 2012: 1
FWCI
1.17
Type
journal article
Language
en
Open access
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Cancer Research UK
  • University of Cambridge
  • Hutchison Whampoa Limited
  • Medical Research Council
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
data fusion, gene expression, copy number variation, nonparametric Bayesian models, hierarchical Dirichlet process, patient-specific analysis, feature selection, prognostic subtypes, prostate cancer, breast cancer, tumor heterogeneity
MeSH terms
Bayes Theorem, Breast Neoplasms, Female, Humans, Male, Models, Biological, Prognosis, Prostatic Neoplasms, Models, Statistical, Signal Transduction, Survival Analysis, Statistics, Nonparametric, Gene Expression Profiling, DNA Copy Number Variations

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