Abstract: The invention provides methods for treating patients which methods comprise methods for predicting responses of cells, such as tumor cells, to treatment with therapeutic agents. These methods involve measuring, in a sample of the cells, levels of one or more components of a cellular network and then computing a Network Activation State (NAS) or a Network Inhibition State (NIS) for the cells using a computational model of the cellular network. The response of the cells to treatment is then predicted based on the NAS or NIS value that has been computed. The invention also comprises predictive methods for cellular responsiveness in which computation of a NAS or NIS value for the cells (e.g., tumor cells) is combined with use of a statistical classification algorithm. Biomarkers for predicting responsiveness to treatment with a therapeutic agent that targets a component within the ErbB signaling pathway are also provided.

Abstract: Improved assays incorporating single-cell based image analysis that enable quantitation of expression of individual cellular proteins and heterogeneity in terms of individual cellular protein molecule numbers per cell at the single cell level and mapped across sections of clinical tissue samples are disclosed.

Abstract: Provided are methods for overcoming resistance to an ErbB pathway inhibitor, such as an EGFR inhibitor or a HER2 inhibitor. The resistance may be acquired resistance to an EGFR inhibitor, such as acquired resistance to gefitinib. In the methods provided, a subject exhibiting resistance to an ErbB pathway inhibitor is selected and both an ErbB 3 inhibitor and a second ErbB pathway inhibitor are administered to the subject, such as an EGFR inhibitor or a HER2 inhibitor. Also provided are methods for inhibiting the growth of a tumor comprising a T790M EGFR mutation by contacting the tumor with an ErbB3 inhibitor and an EGFR inhibitor. Compositions for overcoming resistance to an ErbB pathway inhibitor, comprising both an ErbB 3 inhibitor and a second ErbB pathway inhibitor, such as an EGFR inhibitor or a HER2 inhibitor, are also provided.

Abstract: Improved assays incorporating single-cell based image analyses that enable quantitation of expression of individual cellular proteins and heterogeneity in terms of individual cellular protein molecule numbers per cell at the single cell level and mapped across sections of clinical tissue samples are disclosed.

Abstract: Methods are disclosed for preventing toxic drug-drug interactions during combination cancer therapy with a drug that is an anti-ErbB3 agent, such as an anti-ErbB3 antibody, together with a drug that is a tyrosine kinase inhibitor and/or a drug that binds to alpha-1 acid glycoprotein (e.g., erlotinib). Health care practitioners obtaining any one of the drugs are warned that when co-administering the drug that is an anti-ErbB3 agent with either or both of a drug that is a tyrosine kinase inhibitor and a drug that binds to alpha-1 acid glycoprotein, at least one of the co-administered drugs should be administered using a reduced dosage to prevent toxicity. In a reduced dosage, the amount of drug administered per unit time is reduced as compared to a dose that would be administered if the drug was administered as monotherapy. The reduced dosage can be, for example, a reduced drug dose or a reduced drug dosing frequency, or both. Compositions useful in practicing the disclosed methods are also provided.

Abstract: The invention provides methods for treating patients which methods comprise methods for predicting responses of cells, such as tumor cells, to treatment with therapeutic agents. These methods involve measuring, in a sample of the cells, levels of one or more components of a cellular network and then computing a Network Activation State (NAS) or a Network Inhibition State (NIS) for the cells using a computational model of the cellular network. The response of the cells to treatment is then predicted based on the NAS or NIS value that has been computed. The invention also comprises predictive methods for cellular responsiveness in which computation of a NAS or NIS value for the cells (e.g., tumor cells) is combined with use of a statistical classification algorithm. Biomarkers for predicting responsiveness to treatment with a therapeutic agent that targets a component within the ErbB signaling pathway are also provided.

Abstract: The invention provides methods for treating patients which methods comprise methods for predicting responses of cells, such as tumor cells, to treatment with therapeutic agents. These methods involve measuring, in a sample of the cells, levels of one or more components of a cellular network and then computing a Network Activation State (NAS) or a Network Inhibition State (NIS) for the cells using a computational model of the cellular network. The response of the cells to treatment is then predicted124 based on the NAS or NIS value that has been computed. The invention also comprises predictive methods for cellular responsiveness in which computation of a NAS or NIS value for the cells (e.g., tumor cells) is combined with use of a statistical classification algorithm. Biomarkers for predicting responsiveness to treatment with a therapeutic agent that targets a component within the ErbB signaling pathway are also provided.

Abstract: The invention provides methods for treating patients which methods comprise methods for predicting responses of cells, such as tumor cells, to treatment with therapeutic agents. These methods involve measuring, in a sample of the cells, levels of one or more components of a cellular network and then computing a Network Activation State (NAS) or a Network Inhibition State (NIS) for the cells using a computational model of the cellular network. The response of the cells to treatment is then predicted based on the NAS or NIS value that has been computed. The invention also comprises predictive methods for cellular responsiveness in which computation of a NAS or NIS value for the cells (e.g., tumor cells) is combined with use of a statistical classification algorithm. Biomarkers for predicting responsiveness to treatment with a therapeutic agent that targets a component within the ErbB signaling pathway are also provided.

Abstract: The invention provides methods for treating patients which methods comprise methods for predicting responses of cells, such as tumor cells, to treatment with therapeutic agents. These methods involve measuring, in a sample of the cells, levels of one or more components of a cellular network and then computing a Network Activation State (NAS) or a Network Inhibition State (NIS) for the cells using a computational model of the cellular network. The response of the cells to treatment is then predicted based on the NAS or NIS value that has been computed. The invention also comprises predictive methods for cellular responsiveness in which computation of a NAS or NIS value for the cells (e.g., tumor cells) is combined with use of a statistical classification algorithm. Biomarkers for predicting responsiveness to treatment with a therapeutic agent that targets a component within the ErbB signaling pathway are also provided.