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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.


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.


Donovan M.J.,Aureon Biosciences Inc. | Khan F.M.,Aureon Biosciences Inc. | Bayer-Zubek V.,Aureon Biosciences Inc. | Powell D.,Aureon Biosciences Inc. | And 4 more authors.
BJU International | Year: 2012

OBJECTIVE To develop a systems-based model for predicting prostate cancer-specific survival (PCSS) using a conservatively managed cohort with clinically localized prostate cancer and long-term follow-up. PATIENTS AND METHODS Transurethral prostate (TURP) specimens in tissue microarray format and medical records from a 758 patient cohort were obtained. Slides were stained with haematoxylin and eosin (H&E), imaged and digitally outlined for invasive tumour. Additional sections were analysed with two multiplex quantitative immunofluorescence (IF) assays for cytokeratin-18 (epithelial cells), 4′-6-diamidino-2-phenylindole(nuclei), p63/high-molecular-weight keratin (basal cells), androgen receptor (AR) and α-methyl CoA-racemase, Ki67, phosphorylated AKT (pAKT)and CD34. Images were acquired with spectral imaging software. H&E and IF images were evaluated with image analysis algorithms; feature data were integrated with clinical variables to construct prognostic models for outcome. RESULTS Using a training set of 256 patients with 24% events, one clinical variable (Gleason score) and two tissue-specific characteristics (H&E morphometry and tumour-specific pAKT levels) were identified (concordance index [CoI] 0.79, sensitivity 76%, specificity 86%, hazard ratio [HR] 6.6) for predicting PCSS. Validation on an independent cohort of 269 patients with 29% events yielded a CoI of 0.76, sensitivity 59%, specificity 80% and HR of 3.6. Both H&E and IF features were selected in a multivariate setting and added incremental prognostic value to the Gleason score alone (CoI 0.77 to CoI 0.79). Furthermore, global Ki67 expression and AR levels in Gleason grade 3 tumours were both univariately associated with outcome; however, neither was selected in the final model. CONCLUSION A previously validated prostate needle-biopsy systems modelling approach that integrates clinical data with reproducible methods to assess H&E morphometry and biomarker expression, provided incremental benefit to the TURP Gleason score for predicting PCSS. Ki67 and AR, known to be associated with outcome in the prostate needle biopsy, were not associated with PCSS in multivariate models using TURP specimens. © 2011 BJU INTERNATIONAL.


Ajemba P.,Aureon Biosciences Inc. | Scott R.,Aureon Biosciences Inc. | Donovan M.,Aureon Biosciences Inc. | Fernandez G.,Aureon Biosciences Inc.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE | Year: 2011

Performance assessment of segmentation algorithms compares segmentation outputs to a handful of manually obtained ground-truth. This assumes that the ground-truth images are accurate, reliable and representative of the entire image set. In image cytometry, few ground-truth images are typically used because of the difficulty of manually segmenting images with large numbers of small objects. This violates the aforementioned assumptions. Automated methods of segmentation evaluation without ground-truth are needed. We describe a stable and reliable method for evaluating segmentation performance without ground-truth. Segmentation errors are either statistical or structural. Statistical errors reflect failure to account for random variations in pixel values while structural errors result from inadequate image description models. As statistical errors predominate image cytometry, our method focuses on statistical stability assessment. For any image-algorithm pair, we obtain multiple perturbed variants of the image by applying slight linear blur. We segment the image and its variants with the algorithm and determine the match between the output from the image and the output from its variants. We utilized 48 realistic phantom images with known ground-truth and four segmentation algorithms with large performance differences to assess the efficacy of the method. For each algorithm-image pair, we obtained a ground truth match score and four different statistical validation scores. Analyses show that statistical validation and ground-truth validation scores correlate in over 96% of cases. The statistical validation approach reduces segmentation review time and effort by over 99% and enables assessment of segmentation quality long after an algorithm has been deployed. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).


Ajemba P.,Aureon Biosciences Inc. | Al-Kofahi Y.,Aureon Biosciences Inc. | Scott R.,Aureon Biosciences Inc. | Donovan M.,Aureon Biosciences Inc. | Fernandez G.,Aureon Biosciences Inc.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE | Year: 2011

Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).


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.


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.


PubMed | Aureon Biosciences Inc.
Type: Journal Article | Journal: Journal of clinical oncology : official journal of the American Society of Clinical Oncology | Year: 2016

9597 Background: The association of EGFR mutations with bronchoalveolar carcinoma (BAC)/non-small cell lung cancer (NSCLC) and the therapeutic and prognostic variability reported for lung cancer has reinforced the need for more accurate NSCLC sub-classification. The present study utilizes clinical data, immunohistochemistry (IHC), molecular analyses, and quantified immunofluorescent multiplexing (QIFM) to develop a systems pathology model for NSCLC.73 NSCLC cases; 64 males/9 females were compiled (including whole sections) for Tissue Micro Array (TMA) generation: 34 adenocarcinoma (ACA), 27 squamous cell carcinoma (SCC), 3 adenosquamous carcinoma (ASC), 5 large cell undifferentiated carcinoma (LCC), and 1 BAC. Diagnostic assessment, for both whole sections and TMA, included histology and IHC with a panel of antibodies: EGFR (clones E30 and 31G7), cytokeratins (CK7, 20 and 5/6), and thyroid transcription factor (TTF1). In addition, EGFR mutation analysis was performed utilizing previously published primers. Data from QIFM with CK 18, pKDR and pERK was also collected.Twenty out of 70 cases (29%) required re-categorization, 19 reclassified according to the IHC profiles and one based on morphology. EGFR IHC with clone 31G7 was more robust than E30, SCC tumors exhibiting a higher score (2-3+ overall) compared to AC. EGFR mutation in exon 19 was identified in one BAC tumor sample (female) out of 31 cases evaluated. Two tumor samples (ASC and AC) contained intronic point mutations.The integration of clinical data, IHC profiles, EGFR status and additional marker studies (QIFM) has generated an improved model for NSCLC classification. This further demonstrates the benefit of a systems pathology approach, integrating biomarker analysis, clinical data, molecular assays and image based IF, to refine diagnostic pathology as it is performed today. Individualized patient management requires a more comprehensive molecular profile of the actual tumor specimen in order to select appropriate therapeutics and define relevant parameters of prognosis. [Table: see text].


PubMed | Aureon Biosciences Inc.
Type: Journal Article | Journal: Journal of clinical oncology : official journal of the American Society of Clinical Oncology | Year: 2016

56 Background: A systems-based model was previously developed and validated to predict disease progression (DP) using pretreatment clinical data and standardized, robust prostate needle biopsy (PNB) tissue metrics. We sought to apply novel, advanced immunofluorescent (IF) image analysis methods to re-assess PNB androgen receptor (AR) and Ki67 expression profiles and identify cut-points useful for understanding and guiding therapeutic decision making.Pretreatment clinical features and PNB H&E / IF images on 306 patients (91% cT1-T2b, 66% PSA

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