Recently, Li Guangyuan, an undergraduate alumnus of Wuhan University and a doctoral graduate of Cincinnati Children's Hospital in the United States, has developed a set of computational tools called SNAF (Splicing Neo Antigen Finder) for efficient identification of tumor-specific targets.
In addition, using SNAF, he and his colleagues have reported for the first time a series of previously unnoticed broad-spectrum cancer neoantigens, which are of great significance for current cancer immunity**.
Figure |Li Guangyuan (**Li Guangyuan).
Currently, there are a number of different cancer immunoregimens for the identified tumor-specific neoantigens**:
One is the injection of cancer vaccines, which can inject synthetic neoantigens into the patient's body, so as to train the T cells in the body to target and kill cancer cells with such neoantigens in a specific way, and then play a leading role.
As of now, there are many cancer vaccines that are in the early stage of clinical trials. The new targets identified in this study, as well as more neoantigens that may be discovered using SNAF tools in the future, could be used to prepare new cancer vaccines.
It is important to note that many patients receiving cancer vaccines are in a state of urgent need for neoantigens. "So time is of the essence to them, especially for cancers where there are currently no known neoantigens, and identifying new targets will give patients new hope. Li Guangyuan said.
Another category of strategies includes autologous T cell transplantation, engineered T cell receptor-T, and CAR-T
Although the specific methods are different, for these **, their first step is to discover suitable neoantigens, which highlights the role of SNAF as a target discovery tool.
It lays a theoretical foundation for the development of monoclonal antibodies and chimeric antigen receptors
For cancer immunity**, the key is to find that the specificity is expressed in the tumor, and there is no target in normal tissues.
In this way, well-designed immunity** can target this class of targets and can use its own immune system to kill cancer cells in a specific way.
There are two main difficulties in identifying this type of target:
The first difficulty is to ensure that the target is not expressed in normal tissues, that is, it is tumor-specific, otherwise it will cause extreme *** and lead to the failure of clinical trials.
The second difficulty is to find possible broad-spectrum antigens. Currently, many known neoantigens are derived from tumor-specific mutations. However, most mutations vary from person to person, so cancer immunity against this class of targets** must be tailored to the cost, resulting in a very high cost of construction.
It can be seen that the discovery of broad-spectrum and tumor-specific neoantigens is an unanswered problem in the field at present.
In this study, Li et al. applied SNAF to melanoma patients and identified two classes of possible targets, which are based on a tumor-specific post-transcriptional regulatory mechanism.
This type of post-transcriptional regulation is called alternative splicing, and Li et al. identified two mechanisms for the production of neoantigens due to tumor-specific splicing, one of which is called T cell targets, because the main effector cell is the T cell receptor.
In his research, he and his collaborators used proteomics and a series of biochemical techniques to demonstrate that these targets can be presented to cell membranes and activate T cell activation in vivo.
On the other hand, a new class of extracellular epitopes (exneoepitopes) has been discovered, which they call B-cell antigens because their main effector cells are B-cell receptors.
Similarly, they used the latest long-read techniques and in situ fluorescence experiments to demonstrate the existence and location of extracellular novel epitopes in cells, laying the theoretical foundation for the development of monoclonal antibodies and chimeric antigen receptors (CAR-T).
"Don't hide anything, even if it's a negative outcome".
It is understood that this project started in December 2019. Li Guangyuan's current research group is a computational biology laboratory. Initially, he and his colleagues simply set out to develop a computational tool to help cancer biology researchers discover possible neoantigens.
In the process of developing the tool, they encountered many difficulties, one of which is that many cancer targets can be expressed in normal tissues, which explains why many patients who participate in cancer clinical trials have appeared
However, the crux of the matter lies in how to better measure the tumor specificity of the target. In addition, since autoimmune cells are to be utilized, the identified tumor neoantigens must be immunogenic.
In order to solve the above two problems, they extended two other computational problems. As a result, in 2021, Lee Guangyuan developed a deep learning model based on convolutional neural networks**immunogenicity-deepimmuno, related** published in briefing in bioinformatics.
To his surprise, this ** received a lot of attention. New methods that emerged in the field were often compared to DeepImmuno, and the methods used by DeepImmuno were slowly accepted by the field.
"This gave me confidence and made me realize that a study must be explained clearly, that there should be no concealment, and that even negative results should be reported in their entirety, so that future generations can avoid detours," he said. ”
In 2023, he developed Bayests Score, based on a Bayesian model, to the question of how to quantify tumor specificity, and used it to rank all neoantigens. At present, this ** has been published on the preprint platform and has entered the peer review process.
After the completion of the above two research groups, the computational part of this series of work has finally come to an end. However, Li and his colleagues soon realized that the antigen had to be truly identified to prove the usability of the first two tools.
It was also the most difficult time because I was learning almost from scratch, looking for collaborators, explaining our topic to them, and hoping to be able to work with them and then do experiments. He said.
In the end, he was fortunate to have the help of three external research groups:
Professor Gloria Sheynkman of the University of Virginia is an expert in the field of proteomics and long-read, helping them to complete the validation of proteomics and long-read.
Professor Tamara Tilburgs and H. of Cincinnati Children's Hospital, USALeighton Grimes, HLeighton Grimes) and helped them understand the immunogenicity of T cell antigens, the co-localization of B cell antigens, and complete HLA affinity experiments, respectively.
These experimental verifications are crucial and take our findings one step further. At the beginning of the project, I could never have imagined that I would be able to go this far. Li Guangyuan said.
Eventually, the related ** was published in Science Translational Medicine under the title "Splicing Neoantigen Discovery with SNAF Reveals Shared Targets for Cancer Immunotherapy"[1].
Li Guangyuan is the first author and co-corresponding author, with Professors Tamara Tilburgs and Nathan Salomonis of Cincinnati Children's Hospital in the United States as co-corresponding authors.
Figure |Related** (science translational medicine).
"Red water and blue water".
Lee continued: "I would also like to share how to face the competition in scientific research. In academia there are sayings for red water and blue water. Red water is the most popular research direction, but the competition is fierce and can be snapped up at any time. Then blue water is a direction that is relatively less crowded and less competitive. ”
A lot of people want to go in the direction of Blue Water, and there's no doubt about that. However, as he became familiar with the subject he studied, Li Guangyuan gradually discovered that as a good scientific researcher, avoiding competition and challenges was inherently unworkable.
When you give yourself a step down the ladder the first time, you will definitely have a second time, which has a lot of similarities with competitive sports, that is, we have to keep ourselves at a high level of competition at all times, so don't be afraid of competition. No matter how crowded an area is, you just need to do better. Moreover, every study is not perfect, so stick to your original intention and go on bravely. He said.
On the other hand, Lee also hopes that everyone will be able to deal with the competition head-on. "Competition in academia, if not handled well, can turn into vicious competition," he said. And we can make the competition a positive driving force through communication. We just need to be open-minded, recognize what others are doing well, and even competitors can be good friends. ”
Figure |Li Guangyuan (**Li Guangyuan).
In addition, Li Guangyuan said that this research is only the first step in the development of immunity, and many challenges must be overcome in the future before it can be further advanced to clinical research, so that the new antigens he discovered can be used in practice.
References: 1li, g., mahajan, s., ma, s., jeffery, e. d., zhang, x., bhattacharjee, a., salomonis, n. (2024). splicing neoantigen discovery with snaf reveals shared targets for cancer immunotherapy.science translational medicine, 16(730), eade2886.
Operation Typesetting: He Chenlong.