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Yonkers, NY, United States

Orlandi F.,Sloan Kettering Cancer Center | Orlandi F.,Aureon Biosciences Inc. | Guevara-Patino J.A.,Sloan Kettering Cancer Center | Guevara-Patino J.A.,University of Chicago | And 4 more authors.
Vaccine | Year: 2011

HER2/neu is an oncogene amplified and over-expressed in 20-30% of breast adenocarcinomas. Treatment with the humanized monoclonal antibody trastuzumab has shown efficacy in combination with cytotoxic agents, although resistance occurs over time. Novel approaches are needed to further increase antibody efficacy. In this study, we provide evidence in a mouse breast cancer therapeutic tumor model that the combination of active immunization with a modified HER2/neu DNA vaccine and passive infusion of an anti-HER2/neu monoclonal antibody leads to significant regression of established tumors. Our data indicate that combination therapy with a HER2/neu DNA vaccine and trastuzumab may have clinical activity in breast cancer patients. © 2011 Elsevier Ltd. Source


Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts whether a disease (e.g., prostate cancer) is likely to progress in a patient after radiation therapy. In some embodiments, the molecular and computer-generated morphometric information is obtained through computer analysis of tissue obtained from the patient via a needle biopsy at diagnosis and before treatment of the patent with radiation therapy.


Khan F.M.,Aureon Biosciences Inc. | Liu Q.,Aureon Biosciences Inc.
Proceedings - IEEE International Conference on Data Mining, ICDM | Year: 2011

A crucial challenge in predictive modeling for survival analysis applications such as medical prognosis is the accounting of censored observations in the data. While these time-to-event predictions inherently represent a regression problem, traditional regression approaches are challenged by the censored characteristics of the data. In such problems the true target times of a majority of instances are unknown, what is known is a censored target representing some indeterminate time before the true target time. While censored samples can be considered as semi-supervised targets, the current limited efforts in semi-supervised regression do not take into account the partial nature of unsupervised information; samples are treated as either fully labeled or unlabelled. In this work we present a novel approach towards modifying an existing stateof- the-art survival analysis method by incorporating semisupervised learning. The true target times are approximated from the censored times through transduction to improve predictive performance. Our proposed approach represents one of the first applications of semi-supervised regression to survival analysis and yields a significant improvement in performance over the state-of-the-art in prostate and breast cancer prognosis applications. © 2011 IEEE. Source


Trademark
Aureon Inc. and Aureon Biosciences Inc. | Date: 2011-08-05

Medical tests comprised primarily of medical diagnostic reagents for predicting the probability of clinical and biochemical failure at biopsy in the absence of other treatment.


Trademark
Aureon Inc. and Aureon Biosciences Inc. | Date: 2011-08-05

Medical tests comprised primarily of medical diagnostic reagents for predicting the probability of clinical and biochemical failure at biopsy in the absence of other treatment.

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