Advancing Clinicopathologic Diagnosis of High-risk Neuroblastoma Using Computerized Image Analysis and Proteomic Profiling. (September 2017)

• Record Type: Journal Article
• Authors: Niazi, M Khalid Khan; Chung, Jonathan H; Heaton-Johnson, Katherine J; Martinez, Daniel; Castellanos, Raquel; Irwin, Meredith S; Master, Stephen R.; Pawel, Bruce R; Gurcan, Metin N; Weiser, Daniel A
• Is Part Of: Pediatric and developmental pathology. Volume 20:Number 5(2017)
• Page Start: 394
• Page End: 402
• Publication Date: 2017-09
• Subjects: image analysis -- neuroblastoma -- prognostic biomarker -- proteomics -- tumor heterogeneity; Pediatric pathology -- Periodicals; Children -- Diseases -- Periodicals; Diagnosis, Laboratory -- Periodicals; Abnormalities, Human -- Periodicals; Child development -- Periodicals; Pediatrics -- Periodicals; 616.07
• DOI: 10.1177/1093526617698603
• Languages: English
• ISSNs: 1093-5266

Abstract

A subset of patients with neuroblastoma are at extremely high risk for treatment failure, though they are not identifiable at diagnosis and therefore have the highest mortality with conventional treatment approaches. Despite tremendous understanding of clinical and biological features that correlate with prognosis, neuroblastoma at ultra-high risk for treatment failure remains a diagnostic challenge. As a first step towards improving prognostic risk stratification within the high-risk group of patients, we determined the feasibility of using computerized image analysis and proteomic profiling on single slides from diagnostic tissue specimens. After expert pathologist review of tumor sections to ensure quality and representative material input, we evaluated multiple regions of single slides as well as multiple sections from different patients' tumors using computational histologic analysis and semiquantitative proteomic profiling. We found that both approaches determined that intertumor heterogeneity was greater than intratumor heterogeneity. Unbiased clustering of samples was greatest within a tumor, suggesting a single section can be representative of the tumor as a whole. There is expected heterogeneity between tumor samples from different individuals with a high degree of similarity among specimens derived from the same patient. Both techniques are novel to supplement pathologist review of neuroblastoma for refined risk stratification, particularly since we demonstrate these results using only a single slide derived from what is usually a scarce tissue resource. Due to limitations of traditional approaches for upfront stratification, integration of new modalities with data derived from one section of tumor hold promise as tools to improve outcomes.