The aim of our project is to be able to
differentiate the responders from non-responders
in endometrial cancer patient treated with immunotherapy with
immune checkpoint inhibitor. The ultimate goal of our work is really to take advantage
of this reagent, using computational
science to be able to identify patient
with microsatellite instability and
hyper-mutation, using next-generation sequencing data.
And the plan is to compare this novel computational method
to identify this patient to the current
standard of care.
A major component of our work is the
development of novel computational
method based on machine learning and
artificial intelligence.
So the possibility to collaborate with a Microsoft Research expert is no brainer.
In my mind, it's going to really help
enormously the success of this endeavor.
We are really only at the beginning of
this type of marriage between medicine
and computational science. And our
project is to try to support this marriage
to demonstrate that artificial
intelligent machine-based learning
can really help physician for the selection of the best agent and the patient that are
most responsive to new treatment.
If we can show using artificial
intelligence computational science that
we can predict a responder from non-responders to immune checkpoint inhibitor,
the natural consequences of this is that
we're going to be able to treat the
right patient with the right agent, and
avoid, you know, in the other group of patient
that does not respond, the
potential toxicity related to
immunotherapy, immune checkpoint inhibitor. If we are able to accomplish this goal, and
demonstrate that, you know, these new
algorithm, these new computational method
can be better than the standard of care.
The goal is to change the standard of
care now to provide this patient with
this new approach to identify patient that
may respond the best to immunotherapy
treatment.
