| Patent application number | Description | Published |
|---|
| 20090105167 | Predicting responsiveness to cancer therapeutics - The invention provides for compositions and methods for predicting an individual's responsitivity to cancer treatments and methods of treating cancer. In certain embodiments, the invention provides compositions and methods for predicting an individual's responsitivity to chemotherapeutics, including salvage agents, to treat cancers such as ovarian cancer. The invention also provides reagents, such as DNA microarrays, software and computer systems useful for personalizing cancer treatments, and provides methods of conducting a diagnostic business for personalizing cancer treatments. | 04-23-2009 |
| 20090186024 | Gene Expression Signatures for Oncogenic Pathway Deregulation - The disclosure relates to identifying deregulated pathways in cancer. In certain embodiments, the methods of the disclosure can be used to evaluate therapeutic agents for the treatment of cancer. | 07-23-2009 |
| 20090319244 | BINARY PREDICTION TREE MODELING WITH MANY PREDICTORS AND ITS USES IN CLINICAL AND GENOMIC APPLICATIONS - The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal. | 12-24-2009 |
| 20100009357 | PREDICTION OF LUNG CANCER TUMOR RECURRENCE - The invention provides methods of estimating the likelihood of lung cancer recurrence in a subject, including those afflicted with NSCLC. The methods of the invention are useful for developing a therapeutic treatment plan to prevent cancer recurrence for subjects deemed to be at high risk, and withholding treatments from those subjects deemed to be at low risk. The invention also provides methods of generating and using metagene-based prediction tree models for estimating the likelihood of lung cancer recurrence. The invention also provides reagents, such as DNA microarrays, software and computer systems useful for estimating cancer recurrence, and provides methods of conducting a diagnostic business for the prediction of cancer recurrence. | 01-14-2010 |
| 20100273711 | INDIVIDUALIZED CANCER TREATMENTS - Provided herein are methods for the use of gene expression profiling to determine whether an individual afflicted with cancer will respond to a therapy, and in particular to therapeutic agents such as platinum-based agents and antimetabolite agents. Methods for the treatment of individuals with the therapeutic agents are also provided. Methods of predicting the efficacy of cancer therapeutic agents such as platinum-based and antimetabolite therapeutic agents are also provided. Kits including gene chips and instructions for predicting responsiveness are also provided. | 10-28-2010 |
| 20100279957 | PREDICTING RESPONSIVENESS TO CANCER THERAPEUTICS - Provided herein are methods for predicting the responsiveness of a cancer to a chemotherapeutic agent using gene expression profiles. In particular, methods for predicting the responsiveness to 5-fluorouracil, adriamycin, cytotoxan, docetaxol, etoposide, taxol, topotecan, PB kinase inhibitors and Src inhibitors are provided. Methods for developing treatment plans for individuals with cancer are also provided. Kits including gene chips and instructions for predicting responsiveness and computer readable media comprising responsivity information are also provided. | 11-04-2010 |
| 20100305058 | INDIVIDUALIZED CANCER TREATMENTS - The invention provides for compositions and methods for predicting an individual's responsitivity to cancer treatments and methods of treating cancer. In certain embodiments, the invention provides compositions and methods for predicting an individual's responsitivity to chemotherapeutics, including platinum-based chemotherapeutics, to treat cancers such as ovarian cancer. Furthermore, the invention provides for compositions and methods for predicting an individual's responsivity to salvage therapeutic agents. By predicting if an individual will or will not respond to platinum-based chemotherapeutics, a physician can reduce side effects and toxicity by administering a particular additional salvage therapeutic agent. This type of personalized medical treatment for ovarian cancer allows for more efficient treatment of individuals suffering from ovarian cancer. The invention also provides reagents, such as DNA microarrays, software and computer systems useful for personalizing cancer treatments, and provides methods of conducting a diagnostic business for personalizing cancer treatments. | 12-02-2010 |
