Deciphering the immunobilogical mechanism of solid tumor liver metastasis using artificial intelligence and multi-omics data (PRJCA031365)
Metastasis is a key factor leading to the death of cancer patients, with the liver being the primary metastatic site for solid tumors. However, the biological mechanisms involved in liver metastasis are complex, including changes in the tumor microenvironment and the activation of various signaling pathways. Traditional research paradigms face numerous challenges in analyzing liver metastasis of tumors. Our preliminary studies have revealed that once a tumor metastasizes to the liver, it not only significantly reduces the efficacy of immunotherapy on the primary tumor but also adversely affects the patient's clinical prognosis. The applicant also discovered that after the tumor metastasizes to the liver, the number of antigen-specific T cells in the primary tumor site decreases sharply, and the immune-inflammatory response is reduced. Based on this, the applicant plans to leverage collected integrated large-scale transcriptome, WES, epigenome, and other omics data to establish a cancer metastasis immune decoding model based on graph contrast learning and attention mechanisms. This model is interpretable and employs a multi-contrast learning strategy. Using this model, the applicant plans to compare and analyze paired data of primary and liver metastatic lesions, revealing the intrinsic biological characteristics of solid tumor liver metastasis, predicting the clinical probability of liver metastasis, and promoting new clinical treatment paradigms. This project not only advances artificial intelligence technology and integrates cross-scale immunity data, but it also establishes a panoramic understanding of liver metastasis in cancer.
Metastasis is a key factor leading to the death of cancer patients, with the liver being the primary metastatic site for solid tumors. However, the biological mechanisms involved in liver metastasis are complex, including changes in the tumor microenvironment and the activation of various signaling pathways. Traditional research paradigms face numerous challenges in analyzing liver metastasis of tumors. Our preliminary studies have revealed that once a tumor metastasizes to the liver, it not only significantly reduces the efficacy of immunotherapy on the primary tumor but also adversely affects the patient's clinical prognosis. The applicant also discovered that after the tumor metastasizes to the liver, the number of antigen-specific T cells in the primary tumor site decreases sharply, and the immune-inflammatory response is reduced. Based on this, the applicant plans to leverage collected integrated large-scale transcriptome, WES, epigenome, and other omics data to establish a cancer metastasis immune decoding model based on graph contrast learning and attention mechanisms. This model is interpretable and employs a multi-contrast learning strategy. Using this model, the applicant plans to compare and analyze paired data of primary and liver metastatic lesions, revealing the intrinsic biological characteristics of solid tumor liver metastasis, predicting the clinical probability of liver metastasis, and promoting new clinical treatment paradigms. This project not only advances artificial intelligence technology and integrates cross-scale immunity data, but it also establishes a panoramic understanding of liver metastasis in cancer.