| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 105 === Immunotherapy is a treatment that activates immune responses to fight tumors. This therapeutic option has advanced rapidly in recent years and been applied to certain cancers. Previous researches have indicated that both tumor-infiltrating lymphocytes (TILs) and antibody-neoantigen specificity have a great impact on the immunotherapeutic efficacy and the prognosis of cancer patients. Therefore, TILs are confirmed as an important indicator to enhance the therapeutic efficacy. Investigating specific biomarkers for TILs then becomes the current research trend.
We devised two methods to integrate multi-omics data, including genomics, transcriptomics, epigenomics and immunogenomics. Through integration of multi-omics data, we can explore the association between TILs and tumor prognosis. In the first method, we employed gene set approach to identify TILs relevant to tumor prognosis and investigated the biological pathway associated with differentially expressed TILs through clustering analysis. With the combination of the tumor genomics and the immunogenomics, we utilized the random forest classification as the second method to identify the characteristics which affected the quantity of TILs. Thus, we can explore potential molecular mechanisms by ranking feature importance and functional annotation. We applied the methods mentioned above to the breast cancer cohorts from GEO and the CRC cohorts from TCGA and analyzed the association between TILs and the prognosis. The results indicated that 9 TILs subtypes significantly associated with ER+ breast cancer patient prognosis. Furthermore, we revealed the interaction of innate and adaptive immune cells demonstrating the co-regulation of TILs on the breast cancer patient prognosis. On the other hand, we successfully separated CRC patients with high expression TILs from low expression TILs in 21 subtypes by random forest algorithm. We also found the immunoglobulin-related pathways involved in regulation of two TILs subtypes in CRC patients. The results suggest the incorporation of multi-omics data could comprehensively identify the important biomarkers for TILs.
Overall, our study systematically investigated biomarkers associated with TILs and revealed the evidences that TILs were potential predictors for tumor prognosis. The findings are expected to facilitate the evaluation of immunotherapy response and enhance our understanding of tumor-immune interaction.
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