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  1. Oncology Center of Excellence

Development of neuroblastoma tissue diagnostic methods through deep learning-based image analytics and targeted multiplex proteomics

FDA Collaborators: Reena Phillip, PhD; Marc Theoret, MD; Diana Bradford, MD; Prakash Jha, MD; Fengmin Li, PhD; Arpita Roy, PhD

External Collaborators:

  • Stanford University (Awardee): Bruce Ling, PhD (PI); Zhi Han, PhD; Bill Chiu, MD; Hiroyuki Shimada, MD, PhD    
  • mProbe Inc.: James Schilling, PhD; Robert Heaton, MD; Sheeno Thyparambil, PhD

Project Start Date: September 2023

Regulatory Science Challenge

Neuroblastoma (NB), a common pediatric solid tumor, is associated with significant clinical variability based on age and biological factors. While treatment based on risk stratification leads to favorable outcomes for low- and intermediate-risk patients (over 90% 5-year survival), high-risk NB presents a persistent challenge, with survival rates remaining below 50% despite aggressive multimodal therapy.1,2

The International Neuroblastoma Pathology Classification System (the Shimada system) utilizes microscopic examination of tumor tissue specimens to identify specific morphologic characteristics for differentiation grading and evaluation of NB clinical outcomes. Currently, treatment decisions rely on clinical and molecular risk factors to categorize patients into different risk groups.

Project Description & Goals

The goal of this research is to improve NB diagnosis and resulting risk classification and treatment by developing a novel diagnostic tool. To achieve this goal, we will analyze a large collection of neuroblastoma tissue slides from across the US, Canada, Australia, and New Zealand in three ways. We will analyze whole slide images of neuroblastoma tissue samples using AI to identify key features that aid in pathology diagnosis and risk assessment. Next, a specialized FFPE proteomics technique, leveraging laser microdissection will be used to quantify both established and novel NB protein biomarkers. Finally, we plan to combine both the AI and proteomic approaches to develop improved methods for tissue grading and prognosis evaluation in neuroblastoma.

References:

  1. Whittle SB, Smith V, Doherty E, Zhao S, McCarty S, Zage PE (2017). Overview and recent advances in the treatment of neuroblastoma. Expert review of anticancer therapy, 17(4), 369-386.
  2. Irwin MS, Naranjo A, Zhang FF, Cohn SL, London WB, Gastier-Foster JM, et al. (2021). Revised neuroblastoma risk classification system: a report from the Children's Oncology Group. Journal of Clinical Oncology, 39(29), 3229-3241.

Further Information

 
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