Hierarchical kernel stick-breaking process for multi-task image analysis

The kernel stick-breaking process (KSBP) is employed to segment general imagery, imposing the condition that patches (small blocks of pixels) that are spatially proximate are more likely to be associated with the same cluster (segment). The number of clusters is not set a priori and is inferred from the hierarchical Bayesian model. Further, KSBP is integrated with a shared Dirichlet process prior to simultaneously model multiple images, inferring their inter-relationships. This latter application may be useful for sorting and learning relationships between multiple images. The Bayesian inference algorithm is based on a hybrid of variational Bayesian analysis and local sampling. In addition to providing details on the model and associated inference framework, example results are presented for several image-analysis problems. I

Pharmacogenetic Discovery in CALGB (Alliance) 90401 and Mechanistic Validation of a VAC14 Polymorphism That Increases Risk of Docetaxel-Induced Neuropathy

Purpose Discovery of single nucleotide polymorphisms (SNPs) that predict a patient\u27s risk of docetaxel-induced neuropathy would enable treatment individualization to maximize efficacy and avoid unnecessary toxicity. The objectives of this analysis were to discover SNPs associated with docetaxel-induced neuropathy and mechanistically validate these associations in preclinical models of drug-induced neuropathy. Experimental Design A genome-wide association study was conducted in metastatic castrate-resistant prostate cancer patients treated with docetaxel, prednisone and randomized to bevacizumab or placebo on CALGB 90401. SNPs were genotyped on the Illumina HumanHap610-Quad platform followed by rigorous quality control. The inference was conducted on the cumulative dose at occurrence of grade 3+ sensory neuropathy using a cause-specific hazard model that accounted for early treatment discontinuation. Genes with SNPs significantly associated with neuropathy were knocked down in cellular and mouse models of drug-induced neuropathy. Results 498,081 SNPs were analyzed in 623 Caucasian patients, 50 (8%) of whom experienced grade 3+ neuropathy. The 1000 SNPs most associated with neuropathy clustered in relevant pathways including neuropathic pain and axonal guidance. A SNP in VAC14 (rs875858) surpassed genome-wide significance (p=2.12×10-8 adjusted p=5.88×10-7). siRNA knockdown of VAC14 in stem cell derived peripheral neuronal cells increased docetaxel sensitivity as measured by decreased neurite processes (p=0.0015) and branches (p\u3c0.0001). Prior to docetaxel treatment VAC14 heterozygous mice had greater nociceptive sensitivity than wild-type litter mate controls (p=0.001). Conclusions VAC14 should be prioritized for further validation of its potential role as a predictor of docetaxel-induced neuropathy and biomarker for treatment individualization

Amplitude scale estimation for quantization-based watermarking

Quantization-based watermarking schemes are vulnerable to amplitude scaling. Therefore the scaling factor has to be accounted for either at the encoder, or at the decoder, prior to watermark decoding. In this paper we derive the marginal probability density model for the watermarked and attacked data, when the attack channel consists of amplitude scaling followed by additive noise. The encoder is Quantization Index Modulation with Distortion Compensation. Based on this model we obtain two estimation procedures for the scale parameter. The first approach is based on Fourier Analysis of the PDF. The estimation of the scaling parameter relies on the structure of the received data. The second approach that we obtain is the Maximum Likelihood estimator of the scaling factor. We study the performance of the estimation procedures theoretically and experimentally with real audio signals, and compare them to other well known approaches for amplitude scale estimation in the literature

<monospace>permGPU</monospace>: Using graphics processing units in RNA microarray association studies

<p>Abstract</p> <p>Background</p> <p>Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed.</p> <p>Results</p> <p>We have developed a CUDA based implementation, <monospace>permGPU</monospace>, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of <monospace>permGPU</monospace> within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using <monospace>permGPU</monospace> on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server.</p> <p>Conclusions</p> <p><monospace>permGPU</monospace> is available as an open-source stand-alone application and as an extension package for the <monospace>R</monospace> statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.</p

Gene Expression Markers of Efficacy and Resistance to Cetuximab Treatment in Metastatic Colorectal Cancer: Results from CALGB 80203 (Alliance)

PurposeFormalin-fixed, paraffin-embedded tumor samples from CALGB 80203 were analyzed for expression of EGFR axis-related genes to identify prognostic or predictive biomarkers for cetuximab treatment.Patients and methodsPatients (238 total) with first-line metastatic colorectal cancer (mCRC) were randomized to FOLFOX or FOLFIRI chemotherapy ± cetuximab. qRT-PCR analyses were conducted on tissues from 103 patients at baseline to measure gene expression levels of HER-related genes, including amphiregulin (AREG), betacellulin (BTC), NT5E (CD73), DUSP4, EGF, EGFR, epigen (EPGN), epiregulin (EREG), HBEGF, ERBB2 (HER2), ERBB3 (HER3), ERBB4 (HER4), PHLDA1, and TGFA. The interactions between expression levels and treatment with respect to progression-free survival (PFS) and overall survival (OS) were modeled using multiplicative Cox proportional hazards models.ResultsHigh tumor mRNA levels of HER2 [hazard ratio (HR), 0.64; P = 0.002] and EREG (HR, 0.89; P = 0.016) were prognostic markers associated with longer PFS across all patients. HER3 and CD73 expression levels were identified as potential predictive markers of benefit from cetuximab. In KRAS wild-type (WT) tumors, low HER3 expression was associated with longer OS from cetuximab treatment, whereas high HER3 expression was associated with shorter OS from cetuximab treatment (chemo + cetuximab: HR, 1.15; chemo-only: HR, 0.48; Pinteraction = 0.029). High CD73 expression was associated with longer PFS from cetuximab treatment in patients with KRAS-WT (chemo + cetuximab: HR, 0.91; chemo-only: HR, 1.57; Pinteraction = 0.026) and KRAS-mutant (Mut) tumors (chemo + cetuximab: HR, 0.80; chemo-only: HR, 1.29; P = 0.025).ConclusionsGene expression of HER3 and CD73 was identified as a potential predictive marker for cetuximab. These data implicate HER axis signaling and immune modulation as potential mechanisms of cetuximab action and sensitivity