Home Events A Canadian Guideline On The Use Of Next Generation Sequencing In Oncology

A Canadian Guideline On The Use Of Next Generation Sequencing In Oncology


March 03 2021


1:00 pm - 2:30 pm


  • Dr. Alan Spatz
    Dr. Alan Spatz

    Dr. Alan Spatz is the Chief of the MUHC Department of Laboratory medicine and Medical director of the OPTILAB-MUHC network. He is Professor of Pathology and Oncology at McGill University, and principal investigator of the research Molecular Pathology Center at the Lady Davis Institute.
    Dr. Spatz serves on the board and steering committee of several international and Canadian research organizations. He is currently the president of the Cancer sub-committee of the Quebec Molecular Diagnostic Network and scientific director of the Karen Anthony Research Consortium on lung cancer.
    Dr. Spatz was chair of several clinical research organizations, including the Melanoma and Pathobiology groups of the EORTC and the Melanoma committee of the CCTG.
    His specialties include melanoma, biomarkers, cancer progression, and the role of the X chromosome in cancer. Dr. Spatz has published more than 220 peer reviewed articles and books in highly ranked journals.

  • Dr. Stephen Yip
    Dr. Stephen Yip

    Stephen completed his combined M.D-Ph.D. training followed by 4 years of neurosurgical training at UBC. He switched to neuropathology and obtained his Royal College certification in 2007. He completed fellowship training in molecular neuro-oncology at the Massachusetts General Hospital under the mentorship of Dr David Louis (RC Clinician Investigator Program) and molecular genetic pathology at MGH/Harvard Medical School under the supervision of Dr John Iafrate. He currently practices neuropathology at Vancouver General Hospital and is the directors of the Cancer Genetics & Genomics Laboratory and Centre for Clinical Genomics at BC Cancer. His research interests include dissecting the molecular pathology of brain and spine cancers, practical deployment of advanced diagnostic assays including panel- based and whole genome sequencing, and the application of deep learning as a practical diagnostic tool integrating glass- and genome- based pathology features. Relevant papers Levine AB, Schlosser C, Grewal J, Coope R, Jones SJM, Yip S. Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis. Trends in Cancer. Doi: 10.1016/j.trecan.2019.02.002. 2019. Wong D, Lounsbury K, Lum A, Song J, Chan S, LeBlanc V, et al. Transcriptomic analysis of CIC and ATXN1L reveal a functional relationship exploited by cancer. Oncogene. 2018. Wong D, Yip S. Machine learning classifies cancer. Nature. 2018;555(7697):446-7. Tarpey PS, Behjati S, Young MD, Martincorena I, Alexandrov LB, Farndon SJ, et al. The driver landscape of sporadic chordoma. Nat Commun. 2017;8(1):890. Yip S, Butterfield YS, Morozova O, Chittaranjan S, Blough MD, An J, et al. Concurrent CIC mutations, IDH mutations, and 1p/19q loss distinguish oligodendrogliomas from other cancers. J Pathol. 2012;226(1):7-16.


  1. Apply methods related to the effective use of NGS in oncology
  2. Describe the limitations of NGS-based methodology in oncology
  3. Access resources to aid in the appropriate application of NGS-based results
  4. Apply methods to aid in identifying the clinical application of NGS


The workshop will be used to disseminate the Canadian next generation sequencing guidelines which are written by pathologists for
oncologists. The abstract for the guidelines is attached below:
Rapid advancements in next generation sequencing (NGS) technology have created an unprecedented opportunity to decipher the
molecular profile of tumors to more effectively prevent, diagnose, and treat cancer. Oncologists now have the option to order a number
of molecular tests that can guide treatment decisions. However, to date, most oncologists have received limited training in genomics
and are now faced with the challenge of understanding how these tests and their interpretation align with patient management.
Guidance on how to effectively use this technology is therefore needed to aid oncologists in applying the results of genomic tests. The
following Canadian guideline presents best practices and unmet needs for the clinical application, assay and sample selection,
bioinformatics and interpretation of reports performed by laboratories, patient communication, and clinical trials related to NGS-based
testing for somatic variants in oncology.