Advancing Cancer Diagnostics
Improving Lives
Warning! You won't be able to use the quotation basket until you enable cookies in your Web browser.
Warning! Your Web browser is no longer supported. Please upgrade to a modern browser.

Practical Guide to Predictive Factor Testing in Lung Cancer

PACE credits are no longer available for webinars more than 6 months old.


Per World Health Organization(WHO), 2014 Fact Sheet, cancer is a leading cause of death worldwide, accounting for 8.2 million deaths in 2012 (Globocan 2012, IARC ). The most common causes of cancer death are cancers:  lung (1.59 million deaths), liver (745 000 deaths), stomach (723 000 deaths), colorectal (694 000 deaths), breast (521 000 deaths), oesophageal cancer (400 000 deaths) (1).  According to the survey, lung cancer is the leading cause of death worldwide.  This presentation is an overview and a guide to lung cancer testing and the factors involved in testing.  A review of the lung cancer drug therapies will be discuss as well as the specimen and tumor types associated with the drug therapy.  A guide to developing testing algorithms for mutation screening and confirmatory testing will be discussed.   A comparable practical advantage and disadvantage to lung testing will be highlighted.  Finally, an appreciation for the economic impact of the various testing algorithms and significance importance of false positive or negative will be discussed.

Learning Objectives:

  1. Understanding of which specimens and tumor types are appropriate for testing to determine eligibility for erlotinib, gefitinib, afatinib, crizotinib, or other emerging targeted therapies.
  2. Overview on how to develop testing algorithms for mutation screening and confirmatory testing for EGFR, ALK, ROS1, and other genes of importance in lung cancer.
  3. Review on how to compare practical advantages and disadvantages of immunohistochemistry, in-situ hybridization, and molecular approaches in testing for EGFR, ALK, ROS1, and other gene mutations.
  4. Possible appreciation of the economic impact of various testing algorithms and the significance of false positive or false negative testing results.