Successful Phase I Cancer Vaccine Trials Powered by Graph ML

Transgene and NEC Corporation published a press release on the successful Phase I trials of TG4050, neoantigen cancer vaccine, tested on ovarian cancer, head and neck cancer. The release outlines the NEC's Neoantigen Prediction System based on Graph ML algorithms.

We reached out to Mathias Niepert, Chief Research Scientist of NEC Labs, to shed a bit more light on the graph ml setup and he kindly provided a few interesting details! Mathias says:

The main graph ML method is derived from Embedding Propagation which is a GNN that’s trained in an unsupervised way and, crucially, is able to handle / impute missing data in embedding space. The most relevant papers are Learning Graph Representations with Embedding Propagation (NeurIPS 2017) and Learning Representations of Missing Data for Predicting Patient Outcomes

A major challenge is that for each neoantigen we have some measurements but not all. Obtaining some of these requires expensive tests and some have to be collected from previous biomedical studies. One ends up with several very different feature types (requiring different ML encoders) and, for each such feature type, we only sometimes have a value. The graph-based ML method helps to impute and learn a unifying embedding space.

The graph itself is created based on specific similarity measures between proteins and is not given a-priori. Having a general graph, the task is to rank peptide candidates which would be most efficient for a given patient.

From a probe of a patient's cancer and healthy cells, you get several tens of thousands of neoantigen candidates. To manufacture a personalized vaccine, you have to narrow this down to several dozen candidates. These candidates should have two properties (1) likelihood to elicit immune response (2) different from healthy cell antigen. You end up scoring the neoantigens with the ML method, take the top K, and based on these you synthesize the vaccine. Graph ML is one component of a pretty complex system.

Mathias would like to emphasize that this is based on the work of several people at NEC and most credit should go to the domain experts who have collected the data and adapted and applied the graph ML methods to this problem.