Method and system for selecting individualized tumor neoantigen based on expected curative effect

Abstract

The invention discloses a method and a system for selecting an individualized tumor neoantigen based on an expected curative effect. The method comprises the following steps: establishing and optimizing a cell selectivity model; establishing and optimizing a treatment effect model, carrying out auxiliary selection on individualized tumor neoantigens and the like, and can maximize the utilization of biological experiments to carry out trial and error, establish the most accurate cell selectivity model and maximize the utilization of the biological experiments to improve the effect on individual patients under the condition of not involving the patients; the method further considers the correlation between the selectivity of new antigen cells and the immune state of patients, builds a complete curative effect prediction model, evaluates the applicability of a specific new antigen to a specific patient according to an actual curative effect, provides high-value reference information for a doctor to select an appropriate new antigen for immunotherapy, utilizes the actual effect feedback of doctor treatment to the maximum extent, and continuously improves the prediction precision of the overall effectiveness of new antigen immunotherapy.

Description

technical field [0001] The present invention relates to the field of tumor neoantigen selection, specifically, the present invention relates to a method and system for selecting individualized tumor neoantigens based on expected curative effect. This system can assist doctors in analyzing data, speed up the process of neoantigen immunotherapy, and improve the therapeutic effect. By analogy with the new drug discovery process, the system provided by the present invention corresponds to a candidate drug aided design system (software tool) in the new drug discovery process. Background technique [0002] At present, the conventional treatment of cancer patients mainly relies on non-individualized surgical resection, radiotherapy and chemotherapy, and targeted drug therapy. Transiently prolongs the survival of cancer patients. [0003] In recent years, the method of tumor immunotherapy by targeting the patient's tumor cells through the tumor patient's own immune system has ente...

AI Technical Summary

Problems solved by technology

The correlation between these characteristics and the actual treatment effect still has the disadvantages of unknown mechanism and less reference data.

In the absence of mechanistic understanding and insufficient reference data, modeling using these features will reduce the probability of correct selection of high-efficiency tumor neoantigens

In addition, most of the existing tumor neoantigen selection methods only score tumor neoantigens through the curative effect correlation theory proposed by the author, so as to obtain the most effective tumor neoantigens that the author believes

As the current mechanism of tumor neoantigens is still unclear, and the existing theories are not perfect, it is difficult to effectively select tumor neoantigens with high curative effect only based on the theoretical thinking of a certain author.

At the same time, these methods use non-artificial intelligence methods, which cannot find tumor neoantigens that are more immunogenic than normal proteins through existing theories, nor can they make full use of these data in the case of continuous accumulation of tumor neoantigen data to achieve Self-evolution of tumor neoantigen-assisted selection methods

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