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Development of in silico tools for designing cancer immunotherapy or subunit vaccine

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dc.contributor.author Dhall, Anjali
dc.contributor.author Raghava, Gajendra Pal Singh (Advisor)
dc.date.accessioned 2023-05-26T07:00:40Z
dc.date.available 2023-05-26T07:00:40Z
dc.date.issued 2022-10
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/1253
dc.description.abstract One of the major challenges in designing the cancer vaccine or immunotherapy is to predict the cancer-specific peptides or neopeptides that can stimulate the immune system to fight against the cancer cells. Human leukocyte antigens (HLA) bind and present neopeptides on the cell surface, where these neopeptides are recognized by the T-cells. T-cells activate a wide range of cytokines to provide protection/defence against the cancer cells. Thus, it is important to investigate the role of cytokines and HLA molecules in order to design the cancer immunotherapy. Broadly, this study can be divided in the following four parts; i) Prognostic biomarkers, ii) HLA binders, iii) Cytokine inducing peptides, and iv) Inhibition of STAT3. Firstly, we have investigated the prognostic role of class-I HLA (HLA-I) alleles, HLA-I binders and cytokines with the overall survival of the cancer patients. It was observed that certain HLA-alleles have high impact on the survival of a patients suffering from a specific type of cancer. Based on this observation, a method SKCMhrp has been developed for predicting high-risk cutaneous melanoma patients using HLA-alleles. In the past, numerous methods have been developed for predicting binders of classical HLA alleles. Thus, second part of this thesis describe methods developed for predicting binders of non-classical HLA alleles (HLA-G and HLA-E). Our server HLAncPred allow users to predict promiscuous binders for non-classical HLA-alleles (HLA-G*01:01, HLA-G*01:03, HLA-G*01:04, HLA-E*01:01, and HLA-E*01:03). Thirdly, methods have been developed to predict peptides or epitopes that can induce following types of cytokine; IL6 (IL6Pred), TNF-α (TNFepitope), and IFN‐γ (IFNepitope2). It has been shown in number of studies that STAT3 is a promising therapeutic target for several diseases including cancer. Thus, fourthly, a method has been developed to predict STAT3 inhibitor that can inhibit the STAT3 signaling pathway. In summary, in this thesis a number of in silico tools have been developed, which may play vital role directly or indirectly in developing the cancer vaccine/immunotherapy. en_US
dc.language.iso en_US en_US
dc.publisher IIIT-Delhi en_US
dc.subject cancer vaccine en_US
dc.subject immunotherapy en_US
dc.subject Human leukocyte antigens (HLA) en_US
dc.subject STAT3 en_US
dc.title Development of in silico tools for designing cancer immunotherapy or subunit vaccine en_US
dc.type Thesis en_US

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