Presentation Title

Computational approaches to predict neoantigens for improving cancer immunotherapy

Presenter Information

Vi DamFollow

Advisor Information

Dario Ghersi

Location

Library Poster Number 105

Presentation Type

Poster

Start Date

1-3-2019 9:00 AM

End Date

1-3-2019 10:15 AM

Abstract

.The toxicity of conventional cancer treatments is not limited to cancer cells but it extends to healthy cells. Therefore, it is critically important to target cancer cells while minimizing damage to healthy tissues. Neoantigens are very promising targets in cancer immunotherapy because they are specific to cancer cells only. Targeting these tumor-specific neoantigens is ideal as the risk of adverse side effects and healthy cells death is minimized. However, due to the complexity of our immune system as well as the extreme diversity of neoantigens in cancer types and patients, only a fraction of patients is responsive to immunotherapy. Elucidating the mechanisms of tumor cell elimination by the immune system and identifying patient-specific neoantigens is therefore imperative for improving therapeutic outcomes. In this study, I use the computational methods to identify neoantigens in colorectal cancer and to predict the binding affinity of neoantigens to cell surface proteins. Further, this project aims to develop a computational framework for validating these predicted neoantigens, which is critical given the large number of false positive neoantigen candidates

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Mar 1st, 9:00 AM Mar 1st, 10:15 AM

Computational approaches to predict neoantigens for improving cancer immunotherapy

Library Poster Number 105

.The toxicity of conventional cancer treatments is not limited to cancer cells but it extends to healthy cells. Therefore, it is critically important to target cancer cells while minimizing damage to healthy tissues. Neoantigens are very promising targets in cancer immunotherapy because they are specific to cancer cells only. Targeting these tumor-specific neoantigens is ideal as the risk of adverse side effects and healthy cells death is minimized. However, due to the complexity of our immune system as well as the extreme diversity of neoantigens in cancer types and patients, only a fraction of patients is responsive to immunotherapy. Elucidating the mechanisms of tumor cell elimination by the immune system and identifying patient-specific neoantigens is therefore imperative for improving therapeutic outcomes. In this study, I use the computational methods to identify neoantigens in colorectal cancer and to predict the binding affinity of neoantigens to cell surface proteins. Further, this project aims to develop a computational framework for validating these predicted neoantigens, which is critical given the large number of false positive neoantigen candidates