Ellen Chen is a rising senior at Rye Country Day School in New York. She is passionate about data science, art, and literature to accelerate precision medicine for cancer and rare disease. At Sema4, she designed and developed a web application, CNAEL, to enable clinical lab curators, scientists, and oncologists to analyze and interpret various types of Copy Number Alteration (CNA) for driver identification, genomic alteration visualization, and CNA edit for clinical reporting of WES/WGS. Her study proposed a standard operating procedure to standardize clinical CNA analysis, reduce the complexity of the tumor CNA analysis, shorten the analysis time, and make the tool publicly available. In 2023, she received Excellence in Medical Research Awards by Westchester Academy of Medicine at The Regeneron Westchester Science & Engineering Fair, 2nd place in the Tri County Science and Technology Fair, and advanced to New York State Science Congress Competition as one of the top 5 high school students in June. She is a dedicated leader who raises awareness for under-served communities, environmental issues, and diversity through visual art and literature. She received awards from The New York Times, Tidal Swift: Portland Museum of Art, and New York Congressional Art. She is one of the artists for Beyond the Diagnosis to unite art and science to inspire research and innovation of treatments for people with rare diseases. She has participated in various public art exhibitions to raise funds and scholarships to support education in the community. Her personal website is https://ellenchengallery.com.
Ellen Chena, Jinlian Wangb, Robert Kueffnerb, Hussam Al-Katebb, Antonina Silkovb, Andrew Uzilovb, Lucas Lochovskyb, Hui Lib, Scott Newmanb
aRye Country Day, Rye, NY, United States; bGeneDx, Stamford, CT, United States
Clinical analysis and reporting of somatically acquired copy number abnormalities (CNAs) detected through genome-wide next-generation sequencing (NGS) is time consuming and requires significant expertise. Recent guidelines for the clinical assessment of tumor CNAs harmonized and simplified the reporting criteria but did not directly address NGS-specific concerns or the need for a standardized and scalable analysis protocol. Therefore, a Standard Operating Procedure (SOP) for CNA analysis is needed to address these challenges and provide opportunities for efficient clinical review and reporting.
We developed a scalable 5-step NGS-derived CNA analysis SOP paired with a novel interactive web application, CNA Explorer and anaLyzer (CNAEL), to facilitate the rapid, scalable, and reproducible analysis and reporting of complex tumor-derived CNA profiles https://CNAEL.sema4.com. Novel features of CNAEL include on-the-fly data rescaling to account for tumor ploidy, purity, and modal chromosomal copy number; integration of gene expression, coding, and fusion variants into review and automated genome-wide summarization to enable rapid reporting. We randomly selected 20 de-identified cases to evaluate curation time and found a significant reduction when using CNAEL [median:7 mins, IQR = 4, 10.25] compared with our previous laboratory standard operating procedure [median: 61 mins, IQR = 23.75, 176,25] with p=4.631e-05.
Our analysis SOP coupled with CNAEL provides a comprehensive, efficient, and accurate clinical review and reporting of complex NGS-derived tumor copy number profiles by leveraging all genomic components. These steps can be used to optimize clinical sequencing operations and remove barriers to the adoption of precision oncology.