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A novel amplification-based approach to enable gene expression profiling from small clinical tumor specimens

  • Laboratory Investigation
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Abstract

Glioblastoma is the most common and deadly type of brain cancer. Over the past decade, several divergent genetic pathways have been implicated in the initiation, progression and clinical outcome of this disease. As our understanding of GBM expands and identifies actionable targets specific to individual tumors, there will be a pressing need for the development of new tools that will maximize the use of limited clinical samples to enable the employment of personalized care paradigms. We used PrimePCR validated assays to generate a custom real-time PCR screening panel, containing 74 previously published mRNA targets showing gene expression changes in glioblastoma, and five house-keeping genes. A cohort of 19 frozen brain specimens were analyzed, including WHO Grade II oligodendroglioma (n = 3), WHO Grade II astrocytoma (n = 2), WHO Grade III astrocytoma (n = 1), and glioblastoma (n = 13). Four normal brain samples were also analyzed. We performed RNA extraction, followed by cDNA synthesis, multiplexed pre-amplification and SYBR-based qPCR, to generate expression profiles on all samples. We demonstrated that the workflow shows high tolerance to variation in RNA quality (RIN 8.5-4) and high sensitivity in detection. cDNA input that is equivalent to 3 ng of starting RNA was sufficient to conduct accurate semiquantitative analysis of the panel of 79 assays. Using principal component analysis, we were able to accurately separate glioblastoma from low-grade glioma. The two WHO Grade III tumors analyzed clustered with glioblastoma, but showed more similarity to Grade II gliomas. In this study, we have shown the feasibility of consolidating high-throughput data into a single functional panel capable of accurately classifying glioma specimens based solely on semiquantitative gene expression profiling.

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Acknowledgments

We thank Dr. Yan Wang and Dr. Steven Okino for critically reading the manuscript. We wish to acknowledge the Labatt Brain Tumour Research Centre Tumour and Tissue Repository which is supported by b.r.a.i.nchild and Meagan’s Walk. This work was supported by grants from b.r.a.i.n.child and the American College of Surgeons Martin Research Fellowship.

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    Authors and Affiliations

    1. Life Sciences Group, Bio-Rad Laboratories, Mississauga, ON, Canada

      Haya Sarras

    2. Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada

      Megan Wu, Angela Celebre & Sunit Das

    3. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada

      Angela Celebre & Sunit Das

    4. The Centre for Applied Genomics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada

      Daniele Merico

    5. McGill University Health Centre, Montreal Neurological Hospital, Montreal, QC, Canada

      Jason Karamchandani

    6. Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, ON, M5B 1W8, Canada

      Sunit Das

    7. Division of Neurosurgery and Department of Surgery, St. Michael’s Hospital, University of Toronto, Toronto, ON, Canada

      Sunit Das

    Authors
    1. Haya Sarras
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    2. Megan Wu
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    3. Angela Celebre
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    4. Daniele Merico
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    5. Jason Karamchandani
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    6. Sunit Das
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    Corresponding author

    Correspondence to Sunit Das.

    Additional information

    Haya Sarras and Megan Wu have contributed equally to the manuscript.

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    Sarras, H., Wu, M., Celebre, A. et al. A novel amplification-based approach to enable gene expression profiling from small clinical tumor specimens. J Neurooncol 126, 69–75 (2016). https://doi.org/10.1007/s11060-015-1953-4

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    • DOI: https://doi.org/10.1007/s11060-015-1953-4

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