Neo-epitopes/antigens discovery for cancer immunotherapy based treatment with precision oncology using advanced Artificial Intelligence


Session type:

Jyothi Deva datta reddy1,Deva Reddy2,Dr.ram Reddy2,Samyukta Jyothi2
1Cancer moonshot @,2Cancermoonshot



Cancer has historically been treated with surgery, radiation therapy, chemotherapy and hormone therapy, which use nonspecific mechanisms to attempt to remove or to kill cancer cells. More recently, a new class of immunotherapy known as checkpoint inhibitors is also being applied. However, the majority of patients do not respond to checkpoint inhibitors. We believe that neoantigen-targeted therapies may precisely direct the immune system to improve patient outcomes across both checkpoint-responsive and unresponsive disease. Early evidence shows that neoantigen-based vaccination can elicit T-cell responses and that neo-antigen targeted cell-therapy can cause tumor regression.

To make this happen we need accurate, fast, efficient, affordable neoantigens and corresponding T-cells with high specificity.


Identifying true tumor-specific neoantigens which are rare and unique that on the cell surface is like finding a needle in the haystack. To address this great challenge, CancerMoonshot developed a proprietary artificial intelligence platform called NeoAintigen leveraging huge extensive data from real human tumor samples. NeoAintigen AI platform was trained on a huge dataset and enables us to use sequence data from a cancer patient tumor biopsy to predict/identify exact mutations that will generate tumor-specific neoantigens supposed to on the tumor cell surface.

  • Data-500+ patient samples, 1Million+ peptides, Various tumor types,HLA-Class1,CD4+ ,8-15 mer peptides
  • Technology – AI, Deep learning, Machine learning, Inference engine, In-silico



  • Identify at least  one T-cell recognized neoepitope
  • Average neo epitopes = 2
  • Better predictive performance : 10-15 %
  • Accuracy > 5x or 5 folds compared with publicly available approaches.
  • Better targetable/correlation with T-cells
  • Near real-time


·         Here we show that AI can be used to predict neoantigens better than current techniques and in vitro models.

·         Need to test for more data and cancer types ,and run clinical trials successfully.

·         Need to extend for other class types like HLS-II,Cd8+ and so on in the future.