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Robust meta-analysis shows that glioma transcriptional subtyping complements traditional approaches

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Abstract

Background

Gliomas traditionally have been sub-classified based on histopathological observations. However, this approach is subject to inter-observer variability, and histopathological features may not reflect the biological mechanisms that drive tumor growth. High-throughput transcriptional profiling has shown promise in objectively and reproducibly identifying glioma subtypes. Most prior studies have typically used only modest sample sizes and have sometimes overlooked important data-processing steps to ensure sample quality and to evaluate the robustness of quantitative findings. The purpose of our study was to define robust glioma subtypes by applying rigorous preprocessing and validation steps to 1,952 microarray samples aggregated from 16 prior studies. This data set is the most comprehensive collection of glioma microarray samples compiled to date.

Methods and results

We evaluated each sample for quality-control issues, corrected for probe-composition biases, and adjusted for intra- and inter-study batch effects. Using a training/testing validation design that simulates a “bench-to-bedside process,” we identified six transcriptional subtypes that contained a heterogeneous mix of histopathological subtypes and tumor grades. Similar to prior studies, age, survival and treatment patterns differed significantly across the transcriptional subtypes. However, due to our large sample size, we also observed that within a given histopathological subtype, our transcriptional subtypes provided additional prognostic value. Lastly, we used a pathway-based approach to elucidate the biological mechanisms associated with each subtype.

Conclusions

Our findings provide clinical and biological insights that may not be apparent with alternative approaches or smaller data sets, and our approach serves as an example for meta-analyses that can be applied to other complex diseases.

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Acknowledgments

We thank the patients who donated biospecimens for the various microarray studies we evaluated. We acknowledge an allocation of computer time from the Center for High Performance Computing at the University of Utah.

Author contributions

SL, SRP, and KAB conceived the study design and computational approach. SL and SRP performed all computational and statistical analyses. SL, SRP and KAB interpreted the analysis results and wrote the manuscript.

Conflict of interest

The authors declare no conflict of interest.

Funding sources

SRP was funded in part by NIH Grant 5T32CA093247. The authors declare no other funding sources that specifically supported this research.

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Authors and Affiliations

  1. Department of Biochemistry, University of Utah, 15 North Medical Drive East, Rm 5800, Salt Lake City, UT, 84112, USA

    Sanghoon Lee

  2. Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, 84112, USA

    Stephen R. Piccolo

  3. Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, 02118, USA

    Stephen R. Piccolo

  4. Division of Genetic Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, 84108, USA

    Kristina Allen-Brady

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  1. Sanghoon Lee
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Correspondence to Sanghoon Lee.

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Lee, S., Piccolo, S.R. & Allen-Brady, K. Robust meta-analysis shows that glioma transcriptional subtyping complements traditional approaches. Cell Oncol. 37, 317–329 (2014). https://doi.org/10.1007/s13402-014-0190-8

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