Citation: Kemmer, S.; Berdiel-Acer, M.; Reinz, E.; Sonntag, J.; Tarade, N.; Bernhardt, S.; Fehling-Kaschek, M.; Hasmann, M.; Korf, U.; Wiemann, S.; et al. Disentangling ERBB Signaling in Breast Cancer Subtypes—A Model-Based Analysis. Cancers 2022, 14, 2379. https://doi.org/10.3390/ cancers14102379 Academic Editor: Carolien H. M. van Deurzen Received: 31 March 2022 Accepted: 10 May 2022 Published: 12 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). cancers Article Disentangling ERBB Signaling in Breast Cancer Subtypes—A Model-Based Analysis Svenja Kemmer 1,2 , Mireia Berdiel-Acer 3 , Eileen Reinz 3 , Johanna Sonntag 3 , Nooraldeen Tarade 3,4 , Stephan Bernhardt 3 , Mirjam Fehling-Kaschek 1,2 , Max Hasmann 5 , Ulrike Korf 3 , Stefan Wiemann 3, * and Jens Timmer 1,2,6, * 1 Institute of Physics, University of Freiburg, 79104 Freiburg, Germany; svenja.kemmer@fdm.uni-freiburg.de (S.K.); mirjam.fehling-kaschek@posteo.de (M.F.-K.) 2 FDM—Freiburg Center for Data Analysis and Modeling, University of Freiburg, 79104 Freiburg, Germany 3 Division of Molecular Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany; mireia.berdiel@gmail.com (M.B.-A.); eileen.reinz@gmail.com (E.R.); johanna.nelly.sonntag@gmail.com (J.S.); n.tarade@dkfz-heidelberg.de (N.T.); stephanbernhardtemail@gmail.com (S.B.); d.fischer@dkfz.de (U.K.) 4 Faculty of Biosciences, University of Heidelberg, 69117 Heidelberg, Germany 5 Roche Diagnostics, 82377 Penzberg, Germany; max.hasmann@web.de 6 Signalling Research Centres BIOSS and CIBSS, University of Freiburg, 79104 Freiburg, Germany * Correspondence: s.wiemann@dkfz-heidelberg.de (S.W.); jeti@fdm.uni-freiburg.de (J.T.) Simple Summary: Breast cancer subtypes are characterized by the expression and activity of estrogen-, progesterone- and HER2-receptors and differ by the treatment as well as patient prognosis. Tumors of the HER2-subtype overexpress this receptor and are successfully targeted with anti-HER2 therapies. We wanted to know if the HER2-receptor and the downstream signaling network act similarly also in the other subtypes and if this network could potentially be a therapeutic target beyond the HER2-positive subtype. To this end, we quantitatively assessed the wiring of signaling events in the individual subtypes to unravel the characteristics of HER-signaling. Our data along with a model-based analysis suggest that major parts of the intracellular signal transduction network are unchanged between the different breast cancer subtypes and that the clinical differences mostly come from the different levels at which these receptors are present in tumor cells as well as from the particular mutations that are present in individual tumors. Abstract: Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand–drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line- specific mutations in the MAPK and PI3K pathway components was identified via L 1 regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype. Keywords: ERBB signaling; targeted therapy; systems biology; mathematical modeling; breast cancer; signal transduction Cancers 2022, 14, 2379. https://doi.org/10.3390/cancers14102379 https://www.mdpi.com/journal/cancers