Patent application number | Description | Published |
20100144554 | METHODS FOR ASSEMBLING PANELS OF CANCER CELL LINES FOR USE IN TESTING THE EFFICACY OF ONE OR MORE PHARMACEUTICAL COMPOSITIONS - The present invention relates to algorithms for use in defining genomic subgroups of tumors and cancer cell lines. The present invention also relates to methods for assembling panels of tumors and cancer cell lines according to genomic subgroups for use in testing the efficacy of one or more pharmaceutical compounds in the treatment of subjects suffering from at least one cancer. | 06-10-2010 |
20100145893 | GENOMIC CLASSIFICATION OF NON-SMALL CELL LUNG CARCINOMA BASED ON PATTERNS OF GENE COPY NUMBER ALTERATIONS - The invention is directed to methods and kits that allow for classification of non-small cell lung carcinoma tumors and cell lines according to genomic profiles, and methods of diagnosing, predicting clinical outcomes, and stratifying patient populations for clinical testing and treatment using the same. | 06-10-2010 |
20100145894 | GENOMIC CLASSIFICATION OF COLORECTAL CANCER BASED ON PATTERNS OF GENE COPY NUMBER ALTERATIONS - The invention is directed to methods and kits that allow for classification of colorectal cancer cells according to genomic profiles, and methods of diagnosing, predicting clinical outcomes, and stratifying patient populations for clinical testing and treatment using the same. | 06-10-2010 |
20110105341 | Diagnostic Methods For Determining Prognosis Of Non-Small Cell Lung Cancer - The present disclosure provides methods for identifying early stage non-small-cell lung cancer (NSCLC) patients who will have an unfavorable prognosis for the recurrence of lung cancer after surgical resection. The methods are based in part on the discovery of chromosomal copy number abnormalities that can be used for prognostic classification. The methods preferably use fluorescence in situ hybridization with fluorescently labeled nucleic acid probes to hybridize to patient samples to quantify the chromosomal copy number of these genetic loci. | 05-05-2011 |
20110130295 | Diagnostic Methods For Determining Prognosis Of Non-Small Cell Lung Cancer - Disclosed are methods for identifying early-stage non-small-cell lung cancer (NSCLC) patients who will have an unfavorable prognosis for the recurrence of lung cancer after surgical resection. The methods are based in part on the discovery that chromosomal copy number gains at Chr19, 34.7 Mb-35.6 Mb can be used for prognostic classification. The methods preferably use fluorescence in situ hybridization with fluorescently labeled nucleic acid probes to hybridize to patient samples to quantify the chromosomal copy number of this genetic locus. | 06-02-2011 |
20120115745 | METHODS FOR PREDICTING SENSITIVITY TO TREATMENT WITH A TARGETED TYROSINE KINASE INHIBITOR - The present disclosure relates generally to the evaluation and/or treatment of a subject having or suspected of having a neoplastic condition, and in particular to the use of biomarkers for identifying patients receptive to a certain drug therapy, and which permit monitoring of patient response to such therapy. | 05-10-2012 |
20120190563 | METHODS FOR PREDICTING SENSITIVITY TO TREATMENT WITH A TARGETED TYROSINE KINASE INHIBITOR - Methods and kits for predicting the sensitivity of a cancer to treatment with a targeted tyrosine kinase inhibitor are disclosed. | 07-26-2012 |
20140336950 | CLUSTERING COPY-NUMBER VALUES FOR SEGMENTS OF GENOMIC DATA - Clustering methods are disclosed including a hidden Markov model (HMM) based clustering algorithm having particular applicability for identifying tumor subtypes using array comparative genomic hybridization (aCGH) DNA copy number data. In one embodiment, clusters of tumor samples are modeled with a mixture of HMMs where each HMM fits a cluster of samples. With respect to this embodiment, a computationally efficient and fast clustering algorithm takes only a computational time of O(n), has less than half the error rate of non-negative matrix factorization (NMF) clustering, and can locate the optimal number of groups automatically (e.g., as applied to a data set including glioma aCGH data). | 11-13-2014 |