Total healthcare company SCL Science has recently signed a technology transfer agreement with KAIST for a 'Single-Cell Big Data Warehouse,' officially embarking on the establishment of a precision medicine-based new drug development platform. This marks the first instance of transferring a foundation for large-scale collection and analysis of molecular data at the individual cell level to the industry. The achievement is drawing attention for its potential to have a ripple effect across the entire field of precision medicine, including the discovery of diagnostic markers, exploration of new drug candidates, and development of cancer vaccines.
According to SCL Science on the 29th, this agreement is the first outcome of transferring a major research project from the SCL-KAIST Translational Medicine Research Institute, launched last November, to the industry. The technology transfer process was finalized on the 5th. A research team led by Professor Jeong Kyun Choi of the Department of Biological Sciences at KAIST spearheaded the technology development. They succeeded in collecting large-scale omics data obtained at the single-cell level, organizing it by each human organ, and building a standardized database (DB).
This achievement is closely linked to the importance of single-cell omics technology. Single-cell omics is a next-generation analytical technique that can precisely interpret cell-to-cell heterogeneity and the tumor microenvironment by analyzing various molecular information within cells—such as the genome, transcriptome, and proteome—at the individual cell level.
It can capture subtle differences overlooked by conventional tissue-level analysis, thereby enhancing the understanding of disease development processes. It can be utilized for discovering disease diagnostic markers, searching for new drug candidates, and establishing classification systems for patient groups.
In particular, SCL Science plans to use this technology transfer to accelerate the advancement of its new drug development platform by combining single-cell big data with artificial intelligence (AI)-based analytical capabilities. By integrating the large-scale health checkup and test data held by the SCL Group, it becomes possible to conduct integrated analysis of multi-layered data, from basic health indicators to molecular characteristics. An SCL official stated, "We expect to secure a distinct competitive edge in cancer precision medicine and cancer vaccine research as well."
The area SCL Science is particularly focusing on through this technology transfer is cancer vaccine development. A cancer vaccine is a therapeutic strategy that 'retrains' a patient's own immune system to attack cancer cells. It is gaining attention as a new paradigm because it utilizes the body's defense system, unlike existing anticancer drugs or radiation and chemotherapy.
The vaccine works by enabling the immune system to recognize specific antigens derived from cancer cell mutations, inducing a T-cell response that selectively eliminates cancer cells. It is attracting intensive global research and investment, especially for its potential to not only provide therapeutic effects but also suppress recurrence.
The challenge is that cancer vaccines face a structural dilemma that prevents them from being a 'one-size-fits-all' cure. Because genetic mutations and the tumor microenvironment are heterogeneous from patient to patient, the same vaccine is unlikely to produce the same effect in all patients.
To overcome this limitation, a data-driven approach that reflects individual patient characteristics is essential. Rather than replacing existing treatments, cancer vaccines are a next-generation personalized anticancer strategy optimized for the era of precision medicine. The key to clinical success ultimately lies in data and analytical capabilities. Single-cell omics data supports this process.
Genomic data helps artificial intelligence (AI) search for neoantigens from mutation sequences, while proteomic data predicts the binding potential between antigen-presenting proteins and the receptors of immune cells, or T-cells. Transcriptomic data provides evidence to confirm whether an actual immune response is triggered. In essence, single-cell omics can be described as a guidebook for accurately identifying cancer vaccine targets.
When SCL Science's proprietary health checkup data is added to this, the potential for clinical application increases significantly. Genomic information, Human Leukocyte Antigen (HLA) types, and lifestyle data collected during checkups provide the basis for selecting effective antigens in specific patient groups. This can evolve into companion diagnostics (CDx) technology to predict the response and side effects of cancer vaccines in advance. This is why SCL Science, having secured both data analysis capabilities and clinical data, is considered to have a distinct competitive advantage in cancer vaccine research.
This agreement is expected to have a ripple effect beyond the business performance of a single company, impacting the entire precision medicine industry. Health checkup data acts as a hub connecting genetic and clinical information, which is crucial for the early diagnosis of major diseases like cancer, cardiovascular disease, and metabolic disorders, as well as for establishing personalized prediction strategies. In particular, next-generation precision medicine technologies, including cancer vaccines, can only be realized when vast data and AI algorithms are combined.
The large-scale investments by global pharmaceutical companies in neoantigen-based cancer vaccines, Chimeric Antigen Receptor (CAR)-T cell therapies, and AI-driven drug development follow the same context. In the United States and Europe, clinical trials based on multi-omics and single-cell analysis are already expanding. In Korea, data-driven precision medicine research is also gaining momentum as amendments to the Advanced Regenerative Bio Act and deregulation accelerate.
SCL Science's recent move is seen as an example of how a domestic company can respond to the paradigm shift in future medicine amidst these industry trends. An industry official commented, "A new drug development platform based on single-cell big data has the potential to not just be a simple research collaboration but to reshape the competitive landscape of the industrial ecosystem."
