Methods, systems and products for predicting response of tumor cells to a therapeutic agent and treating a patient according to the predicted response

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.

1 . A method for a treating a patient having a neoplastic tumor, said method comprising: obtaining a sample of the tumor, determining a level of pErbB3 in the sample, and subsequently administering at least one anti-neoplastic therapeutic agent to the patient, wherein,
 if the level of pErbB3 determined in the sample is no lower than 50% of a level of pErbB3 measured in a culture of ACHN cells following culture for 20-24 hours in serum-free medium then the at least one anti-neoplastic therapeutic agent subsequently administered to the patient comprises an anti-ErbB3 antibody,   
       and
 if the level of pErbB3 determined in the sample is lower than 50% of the level of pErbB3 measured in the culture of ACHN cells then the at least one anti-neoplastic therapeutic agent subsequently administered to the patient does not comprise an anti-ErbB3 antibody. 
 
     
     
         2 . A method according to  claim 1 , wherein the level of pErbB3 in the sample is determined by:
 (a) measuring a level for each of at least two components of the ErbB3 signaling pathway in the sample;   (b) computing a Network Activation State that simulates the level of pErbB3 in the sample using the levels measured in (a) input into a computational model of the ErbB3 signaling pathway; and   (c) determining therefrom the level of pErbB3 in the sample.   
     
     
         3 . A method to according to  claim 2 , wherein at least one component of which a level is measured is selected from ErbB1, ErbB2, ErbB3, ErbB4, heregulin, betacellulin, epidermal growth factor, heparin-binding epidermal growth factor, transforming growth factor alpha, amphiregulin, epigen, and epiregulin. 
     
     
         4 - 10 . (canceled) 
     
     
         11 . The method according to  claim 1 , wherein the anti-ErbB3 antibody comprises at least one of:
 (i) an Ab #6 antibody having heavy chain variable region (VH) and light chain variable region (VL) sequences as shown in SEQ ID NOs: 1 and 2, respectively, or an antibody having VH and VL CDR sequences of Ab #6 as shown in SEQ ID NOs: 7-9 and 10-12, respectively;   (ii) Ab #3 having VH and VL sequences as shown in SEQ ID NOs: 3 and 4, respectively, or an antibody comprising VH and VL CDR sequences of Ab #3 shown in SEQ ID NOs: 13-15 and 16-18, respectively;   (iii) Ab #14 having VH and VL sequences as shown in SEQ ID NOs: 5 and 6, respectively, or an antibody comprising VH and VL CDR sequences of Ab #14 shown in SEQ ID NOs: 19-21 and 22-24, respectively;   (iv) Ab #17 having VH and VL sequences as shown in SEQ ID NOs: 25 and 26, respectively, or an antibody comprising VH and VL CDR sequences of Ab #17 shown in SEQ ID NOs: 27-29 and 30-32, respectively; and   (v) Ab #19 having VH and VL sequences as shown in SEQ ID NOs: 33 and 34, respectively, or an antibody comprising VH and VL CDR sequences of Ab #19 shown in SEQ ID NOs: 35-37 and 38-40, respectively.   
     
     
         12 . (canceled) 
     
     
         13 . A computerized method using a computing system that comprises at least one input device configured for receiving input and at least one output device configured for rendering output, said method being for predicting the response of cells comprising a cellular network to treatment with a therapeutic agent that targets a component within the cellular network, said method comprising:
 (a) receiving, through said computing system input device, input that identifies levels of one or more components in the cellular network measured in a sample of the cells;   (b) computing from the input, with the computing system, a Network Activation State or a Network Inhibition State for the cells using a mechanistic computational model of the cellular network; and   (c) generating with the computing system, and rendering at said output device, a predicted response of the cells to treatment with the therapeutic agent based at least in part on the Network Activation State or the Network Inhibition State computed in (b).   
     
     
         14 . A computerized method using a computing system that comprises at least one input device configured for receiving input and at least one output device configured for rendering output, said method being for predicting the response of cells comprising a cellular network to treatment with a therapeutic agent that targets a component within the cellular network, said method comprising:
 (a) receiving, through said computing system input device, input that identifies levels of one or more components in the cellular network measured in a sample of the cells;   (b) computing from the input, with the computing system, a Network Activation State or a Network Inhibition State for the cells using a computational model of the cellular network comprising a statistical classification algorithm; and   (c) generating with the computing system, and rendering at said output device, a predicted response of the cells to treatment with the therapeutic agent based at least in part on the Network Activation State or the Network Inhibition State computed in (b).   
     
     
         15 . A method for predicting the response of cells to treatment with a therapeutic agent that targets a component within a cellular network, the method comprising:
 (a) measuring the level in a sample of the cells of one or more components of the cellular network; and   (b) applying a computer-implemented method comprising: computing a Network Activation State or a Network Inhibition State for the cells using a mechanistic computational model of the cellular network input with the levels measured in (a); and predicting the response of the cells to treatment with the therapeutic agent based at least in part on a statistical classification algorithm that utilizes output of the mechanistic computational model.   
     
     
         16 . A computerized method using a computing system that comprises at least one input device configured for receiving input and at least one output device configured for rendering output, said method being for predicting the response of cells to treatment with a therapeutic agent that targets a component within a cellular network, the method comprising:
 (a) receiving, through said computing system input device, input that identifies levels of one or more components in a cellular network measured in a sample of the cells;   (b) computing with the computing system a Network Activation State or a Network Inhibition State for the cells using a computational model of the cellular network; and   (c) generating with the computing system, and rendering at said output device, a predicted response of the cells to treatment with the therapeutic.   
     
     
         17 - 61 . (canceled) 
     
     
         62 . A method for predicting the response of cells to treatment with a therapeutic agent that targets a component of an ErbB signaling pathway, the method comprising:
 (a) measuring, in a sample of the cells, levels of (i) heregulin and (ii) at least one receptor selected from ErbB1, ErbB2 and ErbB3; and   (b) predicting, using a computer, the response of the cells to treatment with the therapeutic agent based on the levels measured in (a), wherein elevated levels of HRG and the at least one receptor, relative to a control, predict responsiveness to treatment with the therapeutic agent.   
     
     
         63 . A computerized method using a computing system that comprises at least one input device configured for receiving input and at least one output device configured for rendering output, said method being for predicting the response of cells to treatment with a therapeutic agent that targets a component of an ErbB signaling pathway, the method comprising:
 (a) receiving, through said computing system input device, input that identifies measured levels of (i) heregulin and (ii) at least one receptor selected from ErbB1, ErbB2 and ErbB3, which levels have been measured in a sample of the cells; and   (b) generating with the computing system, and thereafter rendering at said output device, a predicted response of the cells to treatment with the therapeutic agent based on the measured levels, wherein elevated levels of HRG and the at least one receptor, relative to a control, predict responsiveness to treatment with the therapeutic agent.   
     
     
         64 - 68 . (canceled) 
     
     
         69 . A method for predicting the response of cells to treatment with a therapeutic agent that targets a component of an ErbB signaling pathway, the method comprising:
 (a) measuring, in a sample of the cells, levels of one or more of ErbB1/ErbB3 heterodimer, ErbB2 monomers, ErbB2/ErbB2 homodimer, ErbB2/ErbB3 heterodimer, phosphorylated ErbB1/ErbB3 heterodimer and phosphorylated ErbB2/ErbB3 heterodimer; and   (b) predicting, using a computer, the response of the cells to treatment with the therapeutic agent based on the levels measured in (a), wherein a difference in the level of ErbB1/ErbB3 heterodimer, ErbB2 monomers, ErbB2/ErbB2 homodimer, ErbB2/ErbB3 heterodimer, phosphorylated ErbB1/ErbB3 heterodimer or phosphorylated ErbB2/ErbB3 heterodimer, relative to a control, predicts responsiveness to treatment with the therapeutic agent.   
     
     
         70 . A computerized method using a computing system that comprises at least one input device configured for receiving input and at least one output device configured for rendering output, said method being for predicting the response of cells to treatment with a therapeutic agent that targets a component of an ErbB signaling pathway, the method comprising:
 (a) receiving, through said computing system input device, input that identifies measured levels of one or more of ErbB1/ErbB3 heterodimer, ErbB2 monomers, ErbB2/ErbB2 homodimer, ErbB2/ErbB3 heterodimer, phosphorylated ErbB1/ErbB3 heterodimer and phosphorylated ErbB2/ErbB3 heterodimer, which levels have been measured in a sample of the cells; and   (b) generating and rendering, with the computing system, a predicted response of the cells to treatment with the therapeutic agent based on the measured levels, wherein a difference in the level of ErbB1/ErbB3 heterodimers, ErbB2 monomers, ErbB2/ErbB2 homodimers, ErbB2/ErbB3 heterodimers, phosphorylated ErbB1/ErbB3 heterodimers or phosphorylated ErbB2/ErbB3 heterodimers, relative to a control, predicts responsiveness to treatment with the therapeutic agent.   
     
     
         71 - 87 . (canceled) 
     
     
         88 . A kit for predicting the response of cells to treatment with a therapeutic agent that targets a component within a cellular network, the kit comprising:
 (a) an assay or assays for detecting levels of one or more components of the cellular network; and   (b) instructions for computing a Network Activation State or a Network Inhibition State for the cells using a computational model of the cellular network.   
     
     
         89 - 100 . (canceled) 
     
     
         101 . A method for identifying a biomarker for predicting the response of cells to treatment with a therapeutic agent that targets a component within a cellular network, the method comprising:
 (a) measuring, in a sample of the cells, levels of one or more components of the cellular network;   (b) applying a computer-implemented method comprising:
 (i) computing levels of one or more additional components of the cellular network using a computational model of the cellular network; and 
 (ii) identifying a component of the cellular network whose computed level predicts response of the cells to treatment with a therapeutic agent to thereby identifying the component as a biomarker for predicting the response of the cells to treatment with the therapeutic agent. 
   
     
     
         102 . A computerized method using a computing system that comprises at least one input device configured for receiving input and at least one output device configured for rendering output, said method being for identifying a biomarker for predicting the response of cells to treatment with a therapeutic agent that targets a component within a cellular network, the method comprising:
 (a) receiving, through said computing system input device, input that identifies measured levels of one or more components of a cellular network measured in a sample of the cells;   (b) computing, with the computing system, levels of one or more additional components of the cellular network using a computational model of the cellular network; and   (c) identifying, with the computing system, a component of the cellular network whose computed level predicts response of the cells to treatment with a therapeutic agent, and thereby identifying the component as a biomarker for predicting a response of the cells to treatment with the therapeutic agent.   
     
     
         103 . (canceled) 
     
     
         104 . A method for a treating a patient having a neoplastic tumor, said method comprising: obtaining a sample of the tumor, determining a level of pErbB3 in the sample, and subsequently administering at least one anti-neoplastic therapeutic agent to the patient, wherein,
 if the level of pErbB3 determined in the sample is no lower than the minimum level, then if the at least one anti-neoplastic therapeutic agent subsequently administered to the patient comprises an anti-ErbB3 antibody,   
       and
 if the level of pErbB3 determined in the sample is lower than a minimum level that is 0.064 pg/μg total protein, 0.08 pg/μg total protein, 0.096 pg/μg total protein, 0.122 pg/μg total protein, 0.128 pg/μg total protein, 0.144 pg/μg total protein or 0.16 pg/μg total protein, then if the at least one anti-neoplastic therapeutic agent subsequently administered to the patient does not comprise an anti-ErbB3 antibody. 
 
     
     
         105 - 110 . (canceled) 
     
     
         111 . A method of treating breast cancer tumor in a patient by administering an effective amount of an ErbB3 antagonist to the patient in at least one dose, wherein the ErbB3 antagonist is only administered to the patient if the tumor has first been determined to express a level of EGFR and a level of heregulin calculated to be predictive of the tumor being responsive to the ErbB3 antagonist, wherein being responsive to the ErbB3 antagonist corresponds to being susceptible to an inhibition of growth of the tumor following treatment. 
     
     
         112 - 115 . (canceled) 
     
     
         116 . The A method according to  claim 111 , wherein the anti-ErbB3 antibody comprises at least one of:
 (i) MM-121 having heavy chain variable region (V H ) and light chain variable region (V L ) sequences as shown in SEQ ID NOs: 1 and 2, respectively, or an antibody having V H  and V L  CDR sequences of MM-121 as shown in SEQ ID NOs: 7-9 and 10-12, respectively;   (ii) Ab #3 having V H  and V L  sequences as shown in SEQ ID NOs: 3 and 4, respectively, or an antibody comprising V H  and V L  CDR sequences of Ab #3 shown in SEQ ID NOs: 13-15 and 16-18, respectively;   (iii) Ab #14 having V H  and V L  sequences as shown in SEQ ID NOs: 5 and 6, respectively, or an antibody comprising V H  and V L  CDR sequences of Ab #14 shown in SEQ ID NOs: 19-21 and 22-24, respectively;   (iv) Ab #17 having V H  and V L  sequences as shown in SEQ ID NOs: 25 and 26, respectively, or an antibody comprising V H  and V L  CDR sequences of Ab #17 shown in SEQ ID NOs: 27-29 and 30-32, respectively; and   (v) Ab #19 having V H  and V L  sequences as shown in SEQ ID NOs: 33 and 34, respectively, or an antibody comprising V H  and V L  CDR sequences of Ab #19 shown in SEQ ID NOs: 35-37 and 38-40, respectively.   
     
     
         117 . (canceled) 
     
     
         118 . An assay for selecting an agent for treating a breast cancer tumor in a patient, which assay comprises:
 (a) determining, in a biopsy sample of the tumor, the level of expression of EGFR, and   (b) determining, in the biopsy sample, the level of expression of heregulin, wherein if the tumor has been determined to express a level of EGFR and a level of heregulin calculated to be predictive of the tumor being responsive to treatment with an ErbB3 antagonist, the agent selected for treating the tumor is an ErbB3 antagonist.   
     
     
         119 - 122 . (canceled) 
     
     
         123 . An assay according to  claim 118 , wherein the anti-ErbB3 antibody comprises at least one of:
 (i) MM-121 having heavy chain variable region (V H ) and light chain variable region (V L ) sequences as shown in SEQ ID NOs: 1 and 2, respectively, or an antibody having V H  and V L  CDR sequences of MM-121 as shown in SEQ ID NOs: 7-9 and 10-12, respectively;   (ii) Ab #3 having V H  and V L  sequences as shown in SEQ ID NOs: 3 and 4, respectively, or an antibody comprising V H  and V L  CDR sequences of Ab #3 shown in SEQ ID NOs: 13-15 and 16-18, respectively;   (iii) Ab #14 having V H  and V L  sequences as shown in SEQ ID NOs: 5 and 6, respectively, or an antibody comprising V H  and V L  CDR sequences of Ab #14 shown in SEQ ID NOs: 19-21 and 22-24, respectively;   (iv) Ab #17 having V H  and V L  sequences as shown in SEQ ID NOs: 25 and 26, respectively, or an antibody comprising V H  and V L  CDR sequences of Ab #17 shown in SEQ ID NOs: 27-29 and 30-32, respectively; and   (vi) Ab #19 having V H  and V L  sequences as shown in SEQ ID NOs: 33 and 34, respectively, or an antibody comprising V H  and V L  CDR sequences of Ab #19 shown in SEQ ID NOs: 35-37 and 38-40, respectively.   
     
     
         124 . (canceled)