Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer patient, head and neck squamous cell carcinoma or colorectal cancer patient has likely developed a non-responsiveness to treatment with a drug targeting an epidermal growth factor receptor pathway. As the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of measuring when drugs targeting the EGFR pathway cease to be effective in certain patients. This discovery represents the first known example of true personalized selection of these types of cancer patients for treatment using these classes of drugs not only initially, but during the course of treatment.

Abstract: A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Abstract: A method for automatically identifying peaks in mass spectral data includes estimating m/z-dependent levels of background and noise, detecting all peaks with signal-to-noise ratio above a user-specified threshold, and compiling a list of all detected peaks including their m/z positions and intensities. The method can be extended to automatically detect monoisotopic peaks, to detect monoisotopic peaks in the presence of chemical noise, and to detect resolved isotopic clusters at high mass.

Abstract: A system for classification of a test object using a training set comprising a plurality of objects, each of which is assigned as a member of a class. Collectively, the objects in the training set are members of at least two classes. A computer system is configured as a probabilistic classifier. The classifier estimates the probability of the test object being a member of each of the classes in the training set. The probabilistic classifier estimates the probability with reference to the class assignments of the objects in the training set which are neighbors to the test object within a defined region within the training set. The probabilistic classifier takes into account the situation in which there is an imbalance in the number of objects in the different classes in the training set. Additionally, the probabilistic classifier does not require any knowledge of the probability distribution function of the classes in the training set.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a solid epithelial tumor cancer patient is likely to benefit from a therapeutic agent or a combination of therapeutic agents targeting agonists of the receptors, receptors or proteins involved in MAPK (mitogen-activated protein kinase) pathways or the PKC (protein kinase C) pathway upstream from or at Akt or ERK/JNK/p38 or PKC, such as therapeutic agents targeting EGFR and/or HER2. The methods also provide the ability to determine whether the cancer patient is likely to benefit from the combination of a therapeutic agent targeting EFGR and a therapeutic agent targeting COX2; or whether the cancer patient is likely to benefit from the treatment with an NF-?B inhibitor.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer patient, head and neck squamous cell carcinoma or colorectal cancer patient has likely developed a non-responsiveness to treatment with a drug targeting an epidermal growth factor receptor pathway. As the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of measuring when drugs targeting the EGFR pathway cease to be effective in certain patients. This discovery represents the first known example of true personalized selection of these types of cancer patients for treatment using these classes of drugs not only initially, but during the course of treatment.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer patient, head and neck squamous cell carcinoma or colorectal cancer patient has likely developed a non-responsiveness to treatment with a drug targeting an epidermal growth factor receptor pathway. As the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of measuring when drugs targeting the EGFR pathway cease to be effective in certain patients. This discovery represents the first known example of true personalized selection of these types of cancer patients for treatment using these classes of drugs not only initially, but during the course of treatment.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer patient, head and neck squamous cell carcinoma or colorectal cancer patient has likely developed a non-responsiveness to treatment with a drug targeting an epidermal growth factor receptor pathway. As the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of measuring when drugs targeting the EGFR pathway cease to be effective in certain patients. This discovery represents the first known example of true personalized selection of these types of cancer patients for treatment using these classes of drugs not only initially, but during the course of treatment.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer patient, head and neck squamous cell carcinoma or colorectal cancer patient has likely developed a non-responsiveness to treatment with a drug targeting an epidermal growth factor receptor pathway. As the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of measuring when drugs targeting the EGFR pathway cease to be effective in certain patients. This discovery represents the first known example of true personalized selection of these types of cancer patients for treatment using these classes of drugs not only initially, but during the course of treatment.

Abstract: A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a head and neck squamous cell carcinoma (HNSCC) patient is likely to benefit from a drug targeting an epidermal growth factor receptor pathway, including small molecule epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) and monoclonal antibody EGFR inhibitors.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer (NSCLC) patient is likely to benefit from a monoclonal antibody drug targeting an epidermal growth factor receptor pathway. A mass spectrum is obtained from a sample (e.g. blood sample) from the patient. One or more predefined pre-processing steps are performed on the mass spectrum. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained after the pre-processing steps have been performed. Such values are used in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other patients to identify the patient as being likely to benefit from treatment with the drug.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a colorectal cancer (CRC) patient is likely to benefit from a drug targeting an epidermal growth factor receptor pathway, such as monoclonal antibody EGFR inhibitors.

Abstract: A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Abstract: A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Abstract: A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Abstract: A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer patient, head and neck squamous cell carcinoma or colorectal cancer patient has likely developed a non-responsiveness to treatment with a drug targeting an epidermal growth factor receptor pathway. As the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of measuring when drugs targeting the EGFR pathway cease to be effective in certain patients. This discovery represents the first known example of true personalized selection of these types of cancer patients for treatment using these classes of drugs not only initially, but during the course of treatment.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a colorectal cancer (CRC) patient is likely to benefit from a drug targeting an epidermal growth factor receptor pathway, such as monoclonal antibody EGFR inhibitors.

Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer (NSCLC) patient is likely to benefit from a monoclonal antibody drug targeting an epidermal growth factor receptor pathway. A mass spectrum is obtained from a sample (e.g. blood sample) from the patient. One or more predefined pre-processing steps are performed on the mass spectrum. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained after the pre-processing steps have been performed. Such values are used in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other patients to identify the patient as being likely to benefit from treatment with the drug.