As cancer cases continue to rise, with a 2023 study from Zhejiang and Harvard predicting a 31 percent increase in cases and a 21 percent increase in deaths by 2030, the need to find more effective treatments for cancer is greater than ever before. Traditional approaches to treating cancer, such as chemotherapy, often kill healthy cells because of their lack of targetability. In contrast, personalized cancer vaccines can utilize neoepitopes - distinctive peptides on cancer cells that are often missed by the body's immune system - that have strong binding affinities to a patient's MHC to provide a more targeted treatment approach. The selection of optimal neoepitopes that elicit an immune response is a time-consuming and costly process due to the required inputs of modern predictive methods. This project aims to facilitate faster, cheaper, and more accurate neoepitope binding predictions using Feed Forward Neural Networks (FFNN) and Recurrent Neural Networks (RNN). To address this, NEO was created. NEO requires next-generation sequencing data and uses a stacking ensemble method by calculating scores from state-of-the-art models (MHCFlurry 1.6, NetMHCstabpan 1.0, and IEDB). The model's architecture includes an FFNN and an RNN with LSTM layers capable of analyzing both sequential and non-sequential data. The results from both models are aggregated to produce predictions. Using this model, personalized cancer vaccines can be produced with improved results (AUC = 0.9166, recall = 91.67 percent).

Neoantigens play a key role in the recognition of tumour cells by T cells; however, only a small proportion of neoantigens truly elicit T-cell responses, and few clues exist as to which neoantigens are recognized by which T-cell receptors (TCRs). Researchers built a transfer learning-based model named the pMHC–TCR binding prediction network (pMTnet) to predict TCR binding specificities of the neoantigens--and T cell antigens in general--presented by class I major histocompatibility complexes. Altered transcription is a cardinal feature of acute myeloid leukemia (AML); however, exactly how mutations synergize to remodel the epigenetic landscape and rewire three-dimensional DNA topology is unknown. Here, researchers apply an integrated genomic approach to a murine allelic series that models the two most common mutations in AML: Flt3-ITD and Npm1c. High-throughput CRISPR-Cas9 knockout screens are widely used to evaluate gene essentiality in cancer research.

Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.

DALLAS – Sept. 23, 2021 – Researchers and data scientists at UT Southwestern Medical Center and The University of Texas MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.

Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.

A personalised cancer vaccine designed to boost the body's own natural defences when used alongside chemotherapy shows'promising signs' after a clinical trial. The treatment is created by taking a biopsy of a tumour and then using artificial intelligence to identify certain proteins not recognised by the immune system. They use these proteins to create tailor-made vaccines for each individual cancer patients and then administer them alongside immunotherapy drug atezolizumab. So far researchers have only tested it on patients with advanced cancers and just 8 per cent saw their tumours shrink - with 49 per cent seeing no change. An international team of researchers found the treatment, known as RO7198457, was'well tolerated' by patients and the they experienced'low-to-moderate' side effects. This is early days in the development of the treatment as the clinical trials were only designed to test its safety, further testing is needed to see how effective it is.

NEC Corporation, a leader in IT and network technologies, and VAXIMM AG, a Swiss/German biotech company focused on developing oral T-cell immunotherapies, today announced that the companies have signed a strategic clinical trial collaboration agreement and an equity investment agreement to develop novel personalized neoantigen cancer vaccines. Under the terms of the collaboration agreement, which is non-exclusive to both parties, NEC will provide funding for a Phase I clinical trial. NEC and VAXIMM will co-develop personalized cancer vaccines using NEC's cutting-edge artificial intelligence (AI) technology, which is utilized in its Neoantigen Prediction System, and VAXIMM's proprietary T-cell immunotherapy technology. The vaccines are planned to be evaluated in a Phase I clinical trial in various solid tumors. VAXIMM will be responsible for conducting the clinical trial, which is expected to be initiated in 2020.