Cancer Res Treat.  2024 Oct;56(4):1027-1039. 10.4143/crt.2024.146.

Safe Utilization and Sharing of Genomic Data: Amendment to the Health and Medical Data Utilization Guidelines of South Korea

• Park H
• Park J
• Woo HG
• Yun H
• Lee M
• Hong D

• KMID: 2560237
• DOI: http://doi.org/10.4143/crt.2024.146

Abstract

Purpose
In 2024, medical researchers in the Republic of Korea were invited to amend the health and medical data utilization guidelines (Government Publications Registration Number: 11-1352000-0052828-14). This study aimed to show the overall impact of the guideline revision, with a focus on clinical genomic data.
Materials and Methods
This study amended the pseudonymization of genomic data defined in the previous version through a joint study led by the Ministry of Health and Welfare, the Korea Health Information Service, and the Korea Genome Organization. To develop the previous version, we held three conferences with four main medical research institutes and seven academic societies. We conducted two surveys targeting special genome experts in academia, industry, and institutes.
Results
We found that cases of pseudonymization in the application of genome data were rare and that there was ambiguity in the terminology used in the previous version of the guidelines. Most experts (>~90%) agreed that the ‘reserved’ condition should be eliminated to make genomic data available after pseudonymization. In this study, the scope of genomic data was defined as clinical next-generation sequencing data, including FASTQ, BAM/SAM, VCF, and medical records. Pseudonymization targets genomic sequences and metadata, embedding specific elements, such as germline mutations, short tandem repeats, single-nucleotide polymorphisms, and identifiable data (for example, ID or environmental values). Expression data generated from multi-omics can be used without pseudonymization.
Conclusion
This amendment will not only enhance the safe use of healthcare data but also promote advancements in disease prevention, diagnosis, and treatment.

Keyword

Health and medical data utilization guideline; Genomic data; Pseudonymization; Next-generation sequencing; Personal information protection act

Figure

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  Fig. 1. Workflow of Genomic Data Production in Medical Institutions for Diagnosis and Research Purposes. (A) Utilization of genomic data generated for medical purposes. Clinical data include next-generation sequencing (NGS)–based data such as FASTQ, BAM/SAM, and VCF collected through the pathology department and diagnostic laboratory of the hospital, as well as genomic data included in medical records. The institution responsible for storing clinical data performs pseudonymization in accordance with the principles of minimal information before providing data, as required by the Personal Information Protection Act (PIPA). For pseudonymized genomic data, the recipient (researcher) undergoes an evaluation of the suitability of the research through the institution’s DRB, after which the genomic data are utilized. EMR, electronic medical record. (B) Utilization of genomic data generated for research purposes. Research data involve data generated through using of samples (serum, plasma, chromosomes, DNA, and protein, etc.) collected for human-derived material research by researchers using techniques like NGS. To utilize human-derived material, samples are obtained through informed consent from the sample providers. The provided samples are subject to anonymization under the Bioethics Act through the Human-Derived Material Bank, ensuring that personally identifiable genetic information is concealed from anyone. Samples that have undergone anonymization are evaluated for the suitability of research by the recipient (researcher) through the IRB. Subsequently, the samples are used to produce the necessary genomic data, including NGS data, for research purposes.

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  Fig. 2. Pseudonymization makes it difficult to identify data while preserving personal identifiable information. Replacing a person’s actual name with a different code or identifier is an example of pseudonymization. The data can be analyzed, but it becomes challenging to identify specific individuals. Anonymization involves data being transformed to the extent that it is no longer associated with the original individuals. Therefore, it becomes nearly impossible to identify or track individuals through anonymized data. Anonymization is used to enhance personal information protection and data privacy. In simple terms, pseudonymization obscures personal information in a way that makes identification difficult, while anonymization completely removes personal information, making individual identification impossible.

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  Fig. 3. Identification of unique personally identifiable genetic information within genomic sequence. Genomic data (VCF and SAM) consists of metadata (A) and genomic sequence data (B) within the files, with pseudonymization elements included in the composition. (A) Metadata in VCF and SAM files typically include information such as patient identifiers (patient ID, pathology information, etc.), file names, next-generation sequencing (NGS) analysis institutions, data production affiliations, NGS device names, and more. (B) Genomic sequence data can potentially lead to individual identification through information such as germline mutations, short tandem repeats (STRs), and rare single nucleotide polymorphisms (SNPs). For example, within the VCF file, the “info” column contains details like SAO (Sequence Ontology) that indicate whether a germline mutation is present. SAO=0 signifies somatic mutation, SAO=1 indicates germline mutation, and SAO=2 represents unknown status. Unless the research specifically focuses on germline mutations, this information can lead to individual identification. Additionally, forensic STR databases provided by National Institute of Standards and Technology (NIST) under the U.S. Department of Commerce can enable individual identification. By extracting information from chromosome locations in VCF and BAM/SAM files, individual identification becomes feasible. In VCF files, rare SNPs specific to Asians or Koreans can be extracted using data from gnomAD. The combination of such rare SNPs can facilitate individual identification. (C) Other omics data such as transcriptomics, proteomics, and metabolomics in files containing expression values are less likely to lead to individual identification due to their nature. Please note that the potential for individual identification exists in genomic data, especially in certain circumstances, and it’s essential to handle such data with privacy and security precautions.

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  Fig. 4. Pseudonymization elements and practical application for each file type. (A) In the VCF file, metadata information was pseudonymized by replacing the original sample IDs, S2301217N_20231029 and S2301217T_20231029, with pseudonyms CMN001_00 and CMT001_00, respectively. Within the genomic sequence data of the VCF file, elements indicating germline status and SAO=1 were removed. (B) In the BAM/SAM file, metadata information was pseudonymized by replacing the original sample ID, HD753_S1, with the pseudonym CMT001_00. Additionally, information about the testing institution and equipment used by that institution was pseudonymized. PU: MISEQ was replaced with PU:NGS. Within the genomic sequence data of the BAM/SAM file, germline mutations (T) were either replaced with reference sequences (C) or removed if it was challenging to pseudonymize while ensuring data integrity. Any elements that could potentially lead to individual identification were removed when pseudonymization was not feasible. Pseudonymization elements are indicated in red font in the files.

Reference

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