3D-Vac: 3D Convolutional Network based cancer vaccine candidate predictions

Start date: 01-02-2022
End date: 31-07-2022

Clinical Problem

Deeper understanding of the immune system’s intricacies has led to clinical breakthroughs of personalized cancer vaccines in eliminating tumors in advanced-stage cancer patients. Formulated with fragments from a patient’s tumor DNA, cancer vaccines train a patient’s own immune system to recognize a patient’s mutated cancer proteins as ‘foreign’ and wage a lethal attack against tumors. The major puzzle in this field is: which of a patient’s hundreds of tumor mutations can trigger the immune system to attack tumors? Complementary to costly and timeconsuming wet-lab screenings (e.g., Sipuleucel-T was priced at $93,0005), predictive algorithms that can quickly pinpoint neoantigens from a patient’s tumor DNA are urgently needed, if personalized cancer vaccines are to be applied on a large scale. We aim to predict cancer vaccine candidates in this project. Our overall aim is to improve the efficacy, safety and development time of existing T cell based cancer vaccine approaches.

People

Daniil Lepikhov

Daniil Lepikhov

 Li Xue

Li Xue