Current and Emerging Biomarkers: Impact on Risk Stratification for Neuroblastoma.

Abstract

Neuroblastoma has heterogenous clinical presentations that are reflected by several well-defined clinical factors and biomarkers. Combinations of these clinical and biologic prognostic factors have been used for decades to generate classifiers to stratify patients into risk groups (low, intermediate, and high), which in turn are used to inform and tailor treatment as reported in the new NCCN Clinical Practice Guidelines in Oncology for Neuroblastoma. Risk classification uses clinical features, such as age and tumor stage, along with the most significant prognostic tumor biomarkers, including histologic features (differentiation and mitosis-karyorrhexis index), MYCN amplification status, chromosomal copy number alterations (segmental or numerical), and ploidy (DNA content). Recent next-generation sequencing approaches have identified additional tumor-specific genetic factors that have potential roles as prognostic and predictive biomarkers. These emerging biomarkers include telomerase maintenance mechanisms, such as telomerase reverse transcription (TERT) expression and alternative lengthening of telomeres (ALT) status. Somatic alterations of genes, including mutations in the anaplastic lymphoma kinase gene ALK, detected in >10% of patients with newly diagnosed disease, have both prognostic and predictive roles in determining eligibility for targeted therapies (eg, ALK tyrosine kinase inhibitors). In addition to diagnostic tumor-derived biomarkers, significant effort is being directed toward identification of markers to predict response to chemotherapy and immunotherapies. With the increasing use of GD2-containing immunotherapy regimens, efforts are aimed at identifying host or tumor microenvironment immune correlatives that can serve as predictive biomarkers. Understanding the potential role of liquid biopsies as biomarkers during and following treatment, including sequential circulating tumor DNA or tumor-specific mRNA transcripts, is expected to enhance the ability to predict recurrences and also inform understanding of tumor evolution and therapy resistance. These and other emerging biomarkers will lead to refinement and optimization of future neuroblastoma risk classification systems.