The ErbB family of
receptor tyrosine kinases comprises four members:
epidermal growth factor receptor (EGFR/ErbB1), human EGFR 2 (HER2/ErbB2), ErbB3/HER3, and ErbB4/HER4. The first two members of this family, EGFR and HER2, have been implicated in
tumorigenesis and
cancer progression for several decades, and numerous drugs have now been approved that target these two
proteins. Less attention, however, has been paid to the role of this family in mediating
cancer cell survival and drug tolerance. To better understand the complex signal transduction network triggered by the
ErbB receptor family, we built a computational model that quantitatively captures the dynamics of ErbB signaling. Sensitivity analysis identified ErbB3 as the most critical activator of
phosphoinositide 3-kinase (PI3K) and Akt signaling, a key pro-survival pathway in
cancer cells. Based on this insight, we designed a fully human
monoclonal antibody,
seribantumab (MM-121), that binds to ErbB3 and blocks signaling induced by the extracellular
growth factors heregulin (
HRG) and
betacellulin (BTC). In this article, we present some of the key preclinical simulations and experimental data that formed the scientific foundation for three Phase 2 clinical trials in metastatic
cancer. These trials were designed to determine if patients with advanced
malignancies would derive benefit from the addition of
seribantumab to standard-of-care drugs in
platinum-resistant/refractory
ovarian cancer,
hormone receptor-positive HER2-negative
breast cancer, and EGFR wild-type
non-small cell lung cancer (NSCLC). From preclinical studies we learned that basal levels of ErbB3 phosphorylation correlate with response to
seribantumab monotherapy in mouse xenograft models. As ErbB3 is rapidly dephosphorylated and hence difficult to measure clinically, we used the computational model to identify a set of five surrogate
biomarkers that most directly affect the levels of p-ErbB3:
HRG, BTC, EGFR, HER2, and ErbB3. Preclinically, the combined information from these five markers was sufficient to accurately predict which xenograft models would respond to
seribantumab, and the single-most accurate predictor was
HRG. When tested clinically in ovarian, breast and
lung cancer,
HRG mRNA expression was found to be both potentially prognostic of insensitivity to standard
therapy and potentially predictive of benefit from the addition of
seribantumab to standard of care
therapy in all three indications. In addition, it was found that
seribantumab was most active in
cancers with low levels of HER2, consistent with preclinical predictions. Overall, our clinical studies and studies of others suggest that
HRG expression defines a
drug-tolerant
cancer cell phenotype that persists in most solid
tumor indications and may contribute to rapid
clinical progression. To our knowledge, this is the first example of a
drug designed and clinically tested using the principles of Systems Biology.