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Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics

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Statistical Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2629))

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Abstract

Pediatric cancer multi-omics is a uniquely rewarding and challenging domain of biomedical research. Public generosity bestows an abundance of resources for the study of extremely rare diseases; this unique dynamic creates a research environment in which problems with high-dimension and low sample size are commonplace. Here, we present a few statistical methods that we have developed for our research setting and believe will prove valuable in other biomedical research settings as well. The genomic random interval (GRIN) method evaluates the loci and frequency of genomic abnormalities in the DNA of tumors to identify genes that may drive the development of malignancies. The association of lesions with expression (ALEX) method evaluates the impact of genomic abnormalities on the RNA transcription of nearby genes to inform the formulation of biological hypotheses on molecular mechanisms. The projection onto the most interesting statistical evidence (PROMISE) method identifies omic features that consistently associate with better prognosis or consistently associate with worse prognosis across multiple measures of clinical outcome. We have shown that these methods are statistically robust and powerful in the statistical bioinformatic literature and successfully used these methods to make fundamental biological discoveries that have formed the scientific rationale for ongoing clinical trials. We describe these methods and illustrate their application on a publicly available T-cell acute lymphoblastic leukemia (T-ALL) data set. A companion github site (https://github.com/stjude/TALL-example) provides the R code and data necessary to recapitulate the example data analyses of this chapter.

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Author information

Authors and Affiliations

  1. College of Nursing, University of Tennessee Health Science Center, Memphis, TN, USA

    Xueyuan Cao

  2. Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Abdelrahman H. Elsayed & Stanley B. Pounds

  3. Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Abdelrahman H. Elsayed

Authors
  1. Xueyuan Cao
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  2. Abdelrahman H. Elsayed
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  3. Stanley B. Pounds
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Corresponding author

Correspondence to Stanley B. Pounds .

Editor information

Editors and Affiliations

  1. Moffitt Cancer Center, Tampa, FL, USA

    Brooke Fridley

  2. Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Xuefeng Wang

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Cao, X., Elsayed, A.H., Pounds, S.B. (2023). Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics. In: Fridley, B., Wang, X. (eds) Statistical Genomics. Methods in Molecular Biology, vol 2629. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2986-4_16

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  • DOI: https://doi.org/10.1007/978-1-0716-2986-4_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2985-7

  • Online ISBN: 978-1-0716-2986-4

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