Staff from CBIIT’s Computational Genomics and Bioinformatics Branch, NCI, NIH, and FDA, along with a worldwide scientific consortium joined forces to create reference samples and data call sets to help the cancer community further decipher cancer-related gene mutations.

Their findings, detailed in the article, “Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole genome sequencing,” were recently published in Nature Biotechnology.

In this study, the authors used data from multiple next generation sequencing (NGS) to detect and confirm germline and somatic variants in two distinct cell lines (HCC1395 and HCC1395BL) linked to breast cancer. They used these sample call sets to benchmark sequencing pipelines that can be applied to two types of DNA sequencing technologies (whole genome and whole exome), as well as single-cell RNA sequencing.

The authors note that diverse sequencing data are being generated from multiple platforms at multiple sequencing centers across the globe. By developing well-defined, well-validated somatic mutation and germline reference call sets, the scientific community will have a means for evaluating their own NGS analysis pipelines.

The results of this study also can help tool developers build artificial intelligence models to detect somatic mutations on a broad range of data types.

According to the authors, the purified, preserved genomic DNA and other master cells identified in this study are available for use as standard reference materials for future assay development, qualification, validation, and proficiency testing. They note that additional reference materials likely will need to be developed once these resources are depleted.

This work also paves the way for future studies. As noted by contributing author, Dr. Meerzaman, “The methodology established here should prove useful in developing similar reference materials and reference data sets for these and other data types, further promoting cancer research.”

NCI contributors to this work hail from CBIIT, the Division of Cancer Epidemiology and Genetics (DCEG), the Center for Cancer Research (CCR), the Center for Cancer Genomics (CCG), and the Frederick National Laboratory for Cancer Research (FNLCR):

• Dr. Daoud Meerzaman, CBIIT
• Mr. Cu Nguyen, CBIIT
• Dr. Xiaopeng Bian, CBIIT
• Dr. Qingrong Chen, CBIIT
• Dr. Yun-Ching Chen, CBIIT
• Dr. Fayaz Seifuddin, CBIIT
• Dr. Chunhua Yan, CBIIT
• Dr. Bin Zhu, DCEG
• Mr. Lei Song, DCEG
• Dr. Mingyi Wang, DCEG
• Dr. Margaret Cam, CCR
• Mr. Parthav Jailwala, CCR
• Dr. Louis M. Staudt, CCG
• Ms. Yongmei Zhao, FNLCR
• Mr. Keyur Talsania, FNLCR
• Dr. Justin Lack, FNLCR
• Dr. Tsai-Wei Shen, FNLCR
• Dr. Cristobal Juan Vera, FNLCR
• Ms. Ashley Walton, FNLCR
• Dr. Bao Tran, FNLCR
• Ms. Jyoti Shetty, FNLCR
• Ms. Yuliya Kriga, FNLCR
• Dr. Monika Mehta, FNLCR
• Ms. Arati Raziuddin, FNLCR

Other contributors to this research were part of the Somatic Mutation Working Group of Sequencing Quality Control Phase II Consortium.