HER2+ Cancer Cell Dependence on PI3K vs. MAPK Signaling Axes Is Determined by Expression of EGFR, ERBB3 and CDKN1B.

Understanding the molecular pathways by which oncogenes drive cancerous cell growth, and how dependence on such pathways varies between tumors could be highly valuable for the design of anti-cancer treatment strategies. In this work we study how dependence upon the canonical PI3K and MAPK cascades varies across HER2+ cancers, and define biomarkers predictive of pathway dependencies. A panel of 18 HER2+ (ERBB2-amplified) cell lines representing a variety of indications was used to characterize the functional and molecular diversity within this oncogene-defined cancer. PI3K and MAPK-pathway dependencies were quantified by measuring in vitro cell growth responses to combinations of AKT (MK2206) and MEK (GSK1120212; trametinib) inhibitors, in the presence and absence of the ERBB3 ligand heregulin (NRG1). A combination of three protein measurements comprising the receptors EGFR, ERBB3 (HER3), and the cyclin-dependent kinase inhibitor p27 (CDKN1B) was found to accurately predict dependence on PI3K/AKT vs. MAPK/ERK signaling axes. Notably, this multivariate classifier outperformed the more intuitive and clinically employed metrics, such as expression of phospho-AKT and phospho-ERK, and PI3K pathway mutations (PIK3CA, PTEN, and PIK3R1). In both cell lines and primary patient samples, we observed consistent expression patterns of these biomarkers varies by cancer indication, such that ERBB3 and CDKN1B expression are relatively high in breast tumors while EGFR expression is relatively high in other indications. The predictability of the three protein biomarkers for differentiating PI3K/AKT vs. MAPK dependence in HER2+ cancers was confirmed using external datasets (Project Achilles and GDSC), again out-performing clinically used genetic markers. Measurement of this minimal set of three protein biomarkers could thus inform treatment, and predict mechanisms of drug resistance in HER2+ cancers. More generally, our results show a single oncogenic transformation can have differing effects on cell signaling and growth, contingent upon the molecular and cellular context.

Single-cell quantitative HER2 measurement identifies heterogeneity and distinct subgroups within traditionally defined HER2-positive patients.

Human epidermal growth factor receptor 2 (HER2) is an important biomarker for breast and gastric cancer prognosis and patient treatment decisions. HER2 positivity, as defined by IHC or fluorescent in situ hybridization testing, remains an imprecise predictor of patient response to HER2-targeted therapies. Challenges to correct HER2 assessment and patient stratification include intratumoral heterogeneity, lack of quantitative and/or objective assays, and differences between measuring HER2 amplification at the protein versus gene level. We developed a novel immunofluorescence method for quantitation of HER2 protein expression at the single-cell level on FFPE patient samples. Our assay uses automated image analysis to identify and classify tumor versus non-tumor cells, as well as quantitate the HER2 staining for each tumor cell. The HER2 staining level is converted to HER2 protein expression using a standard cell pellet array stained in parallel with the tissue sample. This approach allows assessment of HER2 expression and heterogeneity within a tissue section at the single-cell level. By using this assay, we identified distinct subgroups of HER2 heterogeneity within traditional definitions of HER2 positivity in both breast and gastric cancers. Quantitative assessment of intratumoral HER2 heterogeneity may offer an opportunity to improve the identification of patients likely to respond to HER2-targeted therapies. The broad applicability of the assay was demonstrated by measuring HER2 expression profiles on multiple tumor types, and on normal and diseased heart tissues.

Computational modeling of ERBB2-amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors.

Crosstalk and compensatory circuits within cancer signaling networks limit the activity of most targeted therapies. For example, altered signaling in the networks activated by the ErbB family of receptors, particularly in ERBB2-amplified cancers, contributes to drug resistance. We developed a multiscale systems model of signaling networks in ERBB2-amplified breast cancer to quantitatively investigate relationships between biomarkers (markers of network activity) and combination drug efficacy. This model linked ErbB receptor family signaling to breast tumor growth through two kinase cascades: the PI3K/AKT survival pathway and the Ras/MEK/ERK growth and proliferation pathway. The model predicted molecular mechanisms of resistance to individual therapeutics. In particular, ERBB2-amplified breast cancer cells stimulated with the ErbB3 ligand heregulin were resistant to growth arrest induced by inhibitors of AKT and MEK or coapplication of two inhibitors of the receptor ErbB2 [Herceptin (trastuzumab) and Tykerb (lapatinib)]. We used model simulations to predict the response of ErbB2-positive breast cancer xenografts to combination therapies and verified these predictions in mice. Treatment with trastuzumab, lapatinib, and the ErbB3 inhibitor MM-111 was more effective in inhibiting tumor growth than the combination of AKT and MEK inhibitors and even induced tumor regression, indicating that targeting both ErbB3 and ErbB2 may be an improved therapeutic approach for ErbB2-positive breast cancer patients.

Calling heads from tails: the role of mathematical modeling in understanding cell polarization.

Theorists have long speculated on the mechanisms driving directed and spontaneous cell polarization. Recently, experimentalists have uncovered many of the mechanisms underlying polarization, enabling these models to be directly tested. In the process, they have demonstrated the explanatory and predictive value of these models and, at the same time, uncovered additional complexities not currently explained by them. In this review, we discuss some of main theories regarding cell polarization and highlight how the intersection of mathematical and experimental biology has yielded new insights into these mechanisms in the case of budding yeast and eukaryotic chemotaxis.