This integrated experimental–computational proposal seeks to unravel the complexity of immune signaling
networks in healthy individuals and patients with estrogen receptor-positive breast cancer (ER+ BC). Our goal
is to identify and characterize, at a systems level, the dynamical flow of information through immune signaling
networks in healthy individuals and ER+ BC patients and to understand how immune signaling defects at
diagnosis reflect patients' immune responses, leading to different clinical outcomes.
This work and its approach are motivated by our experience in immune signaling in cancer and our existing
collaborations with oncologists and computational biologists at the City of Hope Cancer Center. We have shown
that approximately 40% of ER+ BC patients harbor defects in their immune signaling network, specifically in
signaling molecules called cytokines. Importantly, our preliminary data revealed major cytokine signaling
abnormalities within immune cells from the peripheral blood of ER+ BC patients, which reflect immune activity
within tumors and can predict cancer relapse years later. We believe that understanding how these cytokines
interact with each other and other critical elements of the immune signaling network can ultimately lead to
improved cancer treatments. Because the individual components of signaling networks interact in complicated
and difficult-to-predict ways, we propose to apply a systems biology computational modeling approach. First, we
propose to experimentally capture a rich data set of biological variables (molecular, genetic, and cellular data)
using state-of-the-art technologies in peripheral blood collected from healthy people (Aim 1) and patients with
ER+ BC (Aim 2). We will integrate these data into Bayesian networks, a way of modeling the data that will allow
us to mathematically and statistically describe the relationships between cancer and measured variables. We
will also perform high-dimensional histology and spatial image analysis of human ER+ breast tumors (Aim 3),
then apply Bayesian networks and dynamical mathematical models to identify common immune features
between tumor tissue (Aim 3) and peripheral blood (Aims 1 and 2), which we will also correlate with outcome.
Impact and deliverables. This proposal will begin unraveling the complexity of the immune signaling network
from a systems biology perspective. Significant outcomes of the proposed studies will include i) identification, at
the systems biology level, of a prognostic and clinically relevant immune phenotype that is characterized by
defects in signaling networks in ER+ BC, and ii) development of a data-driven, computational framework for the
study of immune signaling and its defects as a dynamical system in cancer patients with such signaling defects.
Our approach should be broadly applicable to other types of cancers and immunological diseases. Therefore,
an important deliverable will be a computational systems biology data analysis toolkit to construct, interrogate,
and dissect immune signaling networks that can be shared with other groups and applied to other diseases.