Sequencing and protein expression analyses provide complementary information about a tumor suppressor gene, which can be used to guide cancer treatment decisions.
Tumor suppressors such as LKB1, p53, and PTEN are central in regulating cellular fate and behavior. Loss of function in these tumor suppressors is common in the etiology of many cancers. Analysis of tumor suppressor function is especially challenging because a variety of genetic alterations (insertion, deletions, point mutations, promoter hyper-methylation, gene copy loss) may lead to tumor suppressor inactivation, yet not all genetic alterations affect protein expression and function.
Liquid Biopsy, or blood-based detection of biomarkers, offers an easily obtainable, minimally invasive source of biologic material. Specifically, circulating tumor DNA (ctDNA) is inherently specific, highly sensitive, and more representative of the mutational heterogeneity in a tumor.1
Personalized cancer management for treating cancer patients with molecularly targeted therapy is still challenged by the following limitations:
Technological application for implementation of clinical Next-Generation Sequencing (NGS) testing in patients with cancer.
The advance in NGS technologies and the dramatic reduction in the cost of sequencing have fueled the utility of massive parallel sequencing in cancer research and clinical diagnosis. However, the complex and time consuming “post-sequencing” data analysis currently limits the clinical application of NGS.1 A data analysis pipeline is a combination of informatics tools used for processing the raw NGS sequence data, by aligning the raw reads and detecting variants. The data analysis pipeline established by a laboratory ultimately determines the types of variants that can be reliably called within the targeted genomic regions. Some pre-designed NGS cancer panels include a data analysis pipeline, but these rarely utilize the full range of tools that are available for NGS analysis. Other pre-designed NGS panels offer minimal analysis support. A poor-quality data analysis pipeline can substantially hinder the identification of clinically relevant mutations.
Clinical Application for chronic myeloid leukemia (CML) and Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL).
The t(9;22) (q34;q11) translocation results in the BCR-ABL1 fusion gene. BCR-ABL1 mutation analysis is recommended to facilitate selection of appropriate therapy for patients with chronic myeloid leukemia (CML) and Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) who have failed or have sub-optimal response to first line tyrosine kinase inhibitor (TKI) therapy. (NCCN 2012 guidelines)
Polyclonal mutations in BCR-ABL1 kinase domain are commonly identified in TKI resistant patients. Thus, detection of low-level mutations after development of resistance offers critical information to guide subsequent therapy selection. Inappropriate kinase inhibitor selection could highly increase the risk of treatment failure with clonal expansion of the resistant mutant. (Parker JClinOncol 2011; Parker Blood 2012)