Johns Hopkins Researchers Developed a Deep-Learning Technology Capable of Accurately Predicting Protein Fragments Linked to Cancer

Johns Hopkins Engineers and Cancer Researchers have collaboratively pioneered a breakthrough in personalized cancer therapy with their cutting-edge deep-learning technology. The innovation, named BigMHC, holds the potential to revolutionize the field by accurately forecasting cancer-related protein fragments that could stimulate an immune system response. This advancement, published in the Nature Machine Intelligence journal, is anticipated to alleviate a substantial hurdle in devising tailored immunotherapies and vaccines against cancer.

The team, comprised of engineers and cancer researchers from diverse departments within Johns Hopkins, has demonstrated that BigMHC possesses the capability to identify protein fragments present on cancer cells. These fragments can potentially activate an immune response aimed at eliminating cancer cells. This recognition process, facilitated by T cell binding to cancer-specific protein fragments on the cell surface, constitutes a pivotal phase in cancer immunotherapy. By harnessing the power of deep learning, this technology promises to accelerate the understanding of immunotherapy response and the development of customized cancer treatments.

The protein fragments that stimulate immune responses are often derived from genetic alterations within cancer cells, known as mutation-associated neoantigens. The unique set of these neoantigens within each patient’s tumor determines the degree of dissimilarity between the tumor and healthy cells. Identifying the most potent neoantigens that trigger immune responses is pivotal for tailoring effective cancer vaccines and immune therapies and guiding patient selection for these treatments. However, conventional techniques for identifying and validating such immune-response-triggering neoantigens are labor-intensive and costly, relying heavily on time-consuming wet laboratory experiments.

To address the scarcity of data available for training deep-learning models due to the resource-intensive nature of neoantigen validation, the researchers employed a two-stage transfer learning approach to train BigMHC. Initially, BigMHC learned to identify antigens presented on the cell surface, a phase of the immune response with abundant available data. Subsequently, it was fine-tuned to predict T-cell recognition, a later phase characterized by limited data availability. This strategy enabled the researchers to construct a comprehensive model of antigen presentation and refine it to forecast immunogenic antigens effectively.

Empirical tests of BigMHC on extensive independent datasets revealed its superior accuracy in predicting antigen presentation compared to other existing methods. Furthermore, when applied to data provided by the researchers, BigMHC significantly outperformed seven alternative techniques in identifying neoantigens responsible for triggering T-cell responses. This accomplishment not only demonstrates the remarkable predictive precision of BigMHC but also signifies its potential in addressing the pressing clinical need to personalize cancer immunotherapy.

As the team expands its investigation into BigMHC’s utility across multiple immunotherapy clinical trials, the technology’s potential to streamline the identification of promising neoantigens for immune responses becomes increasingly apparent. The ultimate goal is to employ BigMHC to guide the development of immunotherapies applicable to multiple patients or personalized vaccines tailored to enhance an individual’s immune response against cancer cells.

By embracing machine-learning-based tools like BigMHC, the researchers envision a future where clinicians and cancer investigators can efficiently sift through vast datasets, paving the way for more efficient, cost-effective, and personalized approaches to cancer treatment. As demonstrated by this pioneering work, the integration of deep learning into clinical cancer research and practice marks a significant step forward in the quest to conquer cancer through innovative technology and interdisciplinary collaboration.

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Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.

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