Abstract: The invention provides a molecular marker set that can be used for prognosis of colorectal cancer in a colorectal cancer patient. The invention also provides methods and computer systems for evaluating prognosis of colorectal cancer in a colorectal cancer patient based on the molecular marker set. The invention also provides methods and computer systems for determining chemotherapy for a colorectal cancer patient and for enrolling patients in clinical trials.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: Actively dividing tumors appear to progress to a life threatening condition more rapidly than slowly dividing tumors. Assessing actively dividing tumors currently involves a manual assessment of the number of mitotic cells in a histological slide prepared from the tumor and assessed by a trained pathologist. Disclosed is a method for using cumulative information from a series of expressed genes to determine tumor prognosis. This cumulative information can be used to categorize tumor samples into high mitotic states or low mitotic states using a mathematical algorithm and gene expression data derived from microarrays or quantitative-Polymerase Chain Reaction (Q-PCR) data. The specific mathematical description outlines how the algorithm assesses the most informative subset of genes from the full list of genes during the assessment of each sample.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: This invention is a model that simulates the complexity of biological signaling in a cell in response to radiation therapy. Using gene expression profiles and radiation survival assays in an algorithm, a systems model was generated of the radiosensitivity network. The network consists of ten highly interconnected genetic hubs with significant signal redundancy. The model was validated with in vitro tests perturbing network components, correctly predicting radiation sensitivity ? times. The model's clinical relevance was shown by linking clinical radiosensitivity targets to the model network. Clinical applications were confirmed by testing model predictions against clinical response to preoperative radiochemotherapy in patients with rectal or esophageal cancer.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: This invention is a model that simulates the complexity of biological signaling in a cell in response to radiation therapy. Using gene expression profiles and radiation survival assays in an algorithm, a systems model was generated of the radiosensitivity network. The network consists of ten highly interconnected genetic hubs with significant signal redundancy. The model was validated with in vitro tests perturbing network components, correctly predicting radiation sensitivity 2/3 times. The model's clinical relevance was shown by linking clinical radiosensitivity targets to the model network. Clinical applications were confirmed by testing model predictions against clinical response to preoperative radiochemotherapy in patients with rectal or esophageal cancer.

Abstract: A classifier to predict cellular radiation sensitivity based on gene expression profiles in thirty-five cell lines from the NCI panel of 60 cancer cell lines (NCI-60), using a novel approach to predictive gene analysis. Three novel genes are provided, retinoblastoma binding protein 4 (RbAp48), G-protein signaling regulator 19 (RGS19) and ribose-5-phosphate isomerase A (R5PIA) whose expression values were correlated with radiation sensitivity.

Abstract: Actively dividing tumors appear to progress to a life threatening condition more rapidly than slowly dividing tumors. Assessing actively dividing tumors currently involves a manual assessment of the number of mitotic cells in a histological slide prepared from the tumor and assessed by a trained pathologist. Disclosed is a method for using cumulative information from a series of expressed genes to determine tumor prognosis. This cumulative information can be used to categorize tumor samples into high mitotic states or low mitotic states using a mathematical algorithm and gene expression data derived from microarrays or quantitative-Polymerase Chain Reaction (Q-PCR) data. The specific mathematical description outlines how the algorithm assesses the most informative subset of genes from the full list of genes during the assessment of each sample.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: This invention is a model that simulates the complexity of biological signaling in a cell in response to radiation therapy. Using gene expression profiles and radiation survival assays in an algorithm, a systems model was generated of the radiosensitivity network. The network consists of ten highly interconnected genetic hubs with significant signal redundancy. The model was validated with in vitro tests perturbing network components, correctly predicting radiation sensitivity 2/3 times. The model's clinical relevance was shown by linking clinical radiosensitivity targets to the model network. Clinical applications were confirmed by testing model predictions against clinical response to preoperative radiochemotherapy in patients with rectal or esophageal cancer.

Abstract: The invention provides a molecular marker set that can be used for prognosis of colorectal cancer in a colorectal cancer patient. The invention also provides methods and computer systems for evaluating prognosis of colorectal cancer in a colorectal cancer patient based on the molecular marker set. The invention also provides methods and computer systems for determining chemotherapy for a colorectal cancer patient and for enrolling patients in clinical trials.

Abstract: A classifier to predict cellular radiation sensitivity based on gene expression profiles in thirty-five cell lines from the NCI panel of 60 cancer cell lines (NCI-60), using a novel approach to predictive gene analysis. Three novel genes are provided, retinoblastoma binding protein 4 (RbAp48), G-protein signaling regulator 19 (RGS19) and ribose-5-phosphate isomerase A (R5PIA) whose expression values were correlated with radiation sensitivity.