  In anti-cancer drug development, a major scientific challenge is
disentangling the complex relationships between high-dimensional genomics data
from patient tumor samples, the corresponding tumor's organ of origin, the drug
targets associated with given treatments and the resulting treatment response.
Furthermore, to realize the aspirations of precision medicine in identifying
and adjusting treatments for patients depending on the therapeutic response,
there is a need for building tumor dynamic models that can integrate both
longitudinal tumor size as well as multimodal, high-content data. In this work,
we take a step towards enhancing personalized tumor dynamic predictions by
proposing a heterogeneous graph encoder that utilizes a bipartite Graph
Convolutional Neural network (GCN) combined with Neural Ordinary Differential
Equations (Neural-ODEs). We applied the methodology to a large collection of
patient-derived xenograft (PDX) data, spanning a wide variety of treatments (as
well as their combinations) on tumors that originated from a number of
different organs. We first show that the methodology is able to discover a
tumor dynamic model that significantly improves upon an empirical model which
is in current use. Additionally, we show that the graph encoder is able to
effectively utilize multimodal data to enhance tumor predictions. Our findings
indicate that the methodology holds significant promise and offers potential
applications in pre-clinical settings.
