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Motivation Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In this letter, we propose an approach for multichannel spaceborne/stationary synthetic aperture radar (SAR) interferometry based on maximum a posteriori (MAP). Spaceborne/stationary SAR is a typical bistatic SAR configuration. In order to solve the phase disconnection problem while working with a low signal-to-noise ratio and a limited number of baselines as well as having large look angle variations, we use the height estimation results derived from iterative multibaseline unwrapping as the initial heights for the MAP estimation. The method presented here is highly experimental. An experiment is carried out to verify the effectiveness of the proposed approach. In this experiment, TerraSAR-X, working in the high-resolution spotlight mode with a 300-MHz bandwidth, acts as the transmitter. The receivers with three echo channels are placed on the ground to receive the reflected waveform. As proof of concept, we demonstrate the height estimation of several buildings. [ABSTRACT FROM PUBLISHER]

In this letter, a fast backprojection algorithm (FBPA) is proposed for bistatic synthetic aperture radar imaging. For range compression, the signal recorded by the synchronization channel of the receiver on the ground is used as a matched filter to compress the echo signal. This method can reduce the requirements of satellite-orbit measurement and synchronization precision. For azimuth compression, a secondary phase correction is implemented to reduce the effect caused by the approximation involved in the FBPA. The computational complexity of the proposed algorithm is \O(\N^2.5). The proposed algorithm is applied in a graphics processing unit and verified by simulation and real raw data. [ABSTRACT FROM PUBLISHER]

The flexible geometry configuration of the bistatic synthetic aperture radar (SAR) has many advantages. However, it causes serious measurement error in the bistatic SAR system, which degrades the quality of the SAR images and the precision of the digital elevation model (DEM) obtained using stereoscopy bistatic SAR. In this paper, the influence of the scene height estimation error, trigger delay, transmitter position measurement error, receiver position measurement error, and transmission line length measurement error are analyzed. These analyses are very useful in bistatic SAR system design. The scene height estimation error, trigger delay, transmitter position measurement error, and synchronization receiver position measurement error affect both the quality of the images and the precision of the DEM obtained by stereoscopy bistatic SAR slightly. The echo receiver position measurement error and transmission line length measurement error affect the quality of the imaging only slightly, but seriously affect the precision of the DEM obtained by stereoscopy bistatic SAR. Luckily, their measurement precision can be quite satisfactory. Simulations and real bistatic experimental results verify the proposed theoretical analysis. [ABSTRACT FROM AUTHOR]

Intravesical Bacillus Calmette-Guerin (BCG), a live attenuated tuberculosis vaccine that acts as a non-specific immune system stimulant, is the most effective adjuvant treatment for patients with intermediate or high-risk non-muscle-invasive bladder cancer (NMIBC). However, to date, there are no reliable tests that are predictive of BCG treatment response. In this study, we evaluated the performance of OncuriaTM, a bladder cancer detection test, to predict response to intravesical BCG. OncuriaTM data was evaluated in voided urine samples obtained from a prospectively collected cohort of 64 subjects with intermediate or high risk NMIBC prior to treatment with intravesical BCG. The OncuriaTM test, which measures 10 cancer-associated biomarkers was performed in an independent clinical laboratory. The ability of the test to identify those patients in whom BCG is ineffective against tumor recurrence was tested. Predictive models were derived using supervised learning and cross-validation analyses. Model performance was assessed using ROC curves. Pre-treatment urinary concentrations of MMP9, VEGFA, CA9, SDC1, PAI1, APOE, A1AT, ANG and MMP10 were increased in patients who developed disease recurrence. A combinatorial predictive model of treatment outcome achieved an AUROC 0.89 [95% CI: 0.80–0.99], outperforming any single biomarker, with a test sensitivity of 81.8% and a specificity of 84.9%. Hazard ratio analysis revealed that patients with higher urinary levels of ANG, CA9 and MMP10 had a significantly higher risk of disease recurrence. Monitoring the urinary levels of a cancer-associated biomarker panel enabled the discrimination of patients who did not respond to intravesical BCG therapy. With further study, the multiplex OncuriaTM test may be applicable for the clinical evaluation of bladder cancer patients considering intravesical BCG treatment. [ABSTRACT FROM AUTHOR]

Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multi-dimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from the TCGA study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer.