Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #38

serial: '38'
sid: '148254'
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uri: /nci/ccdisymposium/abstract
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langcode: en
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data:
  authors_:
    - add_author_degree: ''
      add_author_first_name: Allison
      add_author_last_name: Murray
      add_author_middle: A
      add_author_organization: 'University of North Carolina Chapel Hill'
    - add_author_degree: Ph.D.
      add_author_first_name: Breanna
      add_author_last_name: Mann
      add_author_middle: E
      add_author_organization: 'University of North Carolina Chapel Hill'
    - add_author_degree: Ph.D.
      add_author_first_name: Andrew
      add_author_last_name: Satterlee
      add_author_middle: B
      add_author_organization: 'University of North Carolina Chapel Hill'
    - add_author_degree: 'M.D., MCR'
      add_author_first_name: David
      add_author_last_name: Kram
      add_author_middle: ''
      add_author_organization: 'University of North Carolina Chapel Hill'
    - add_author_degree: Ph.D.
      add_author_first_name: Jeremy
      add_author_last_name: Wang
      add_author_middle: R
      add_author_organization: 'University of North Carolina Chapel Hill'
  abstract: "Central nervous system (CNS) tumors are the most common solid tumors diagnosed in children, making up about 17% of diagnosed childhood cancer cases. Most pediatric CNS tumor cases require a combination of imaging, histopathological, and molecular diagnoses to determine the tumor type/subtype. My project utilizes an emerging molecular diagnostic technology -- nanopore sequencing -- to quickly and accurately generate high depth whole genome DNA methylation profiles for a diverse cohort of 150 pediatric CNS tumors, and this data will be used to train and validate a pediatric-specific deep learning classifier for intraoperative use. As part of a clinical research collaboration, I tested the previously-developed machine learning classifier Sturgeon on high depth nanopore DNA methylation data for 8 matched pairs of standard patient tissue and tumor aspirate (16 total samples). For each sample, I demonstrated between 4-34x genomic coverage, classified the sample’s tumor type/subtype, validated key subtype-defining features, and generated a digital karyotype to visualize large tumor-specific copy number variation(s). Through this initial analysis, I was able to identify key shortcomings in the existing software, including inappropriate labeling for pediatric samples, insufficient adjustment for low tumor purity samples, and confident misclassification of rare/unique tumors. After making adjustments to the classifier's data processing, all 16 samples were classified correctly with high confidence. Together, I have used nanopore DNA methylation profiling to demonstrate fast and accurate classification of CNS tumor aspirate, and in the future, I will generate a similar machine learning classifier for pediatric-specific CNS tumor classification."
  abstract_title_: 'Nanopore Sequencing for Pediatric CNS Tumor Classification'
  email_address_: amurray1@unc.edu
  institution_: 'University of North Carolina Chapel Hill'
  presenting_author_: 'Allison A Murray'