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We consider the problem of recovering 2D block-sparse signals with unknown cluster patterns. The 2D block-sparse patterns arise naturally in many practical applications, such as foreground detection and inverse synthetic aperture radar imaging. To exploit the underlying block-sparse structure, we propose a 2D pattern-coupled hierarchical Gaussian prior model. The proposed pattern-coupled hierarchical Gaussian prior model imposes a soft coupling mechanism among neighboring coefficients through their shared hyperparameters. This coupling mechanism enables effective and automatic learning of the underlying irregular cluster patterns, without requiring any a priori knowledge of the block partition of sparse signals. We develop a computationally efficient Bayesian inference method, which integrates the generalized approximate message passing technique with the proposed prior model. Simulation results show that the proposed method offers competitive recovery performance for a range of 2D sparse signal recovery and image processing applications over the existing method, meanwhile achieving a significant reduction in the computational complexity.

We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target images. A two-dimensional pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring scatterers on the target scene. An expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.Index Terms-Block-sparse structure, expectation-maximization (EM), ISAR, pattern-coupled sparse bayesian learning.1070-9908

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Cisplatin-based neoadjuvant chemotherapy has been shown to improve survival in patients with squamous cell carcinoma (SCC), but clinical biomarkers to predict chemosensitivity remain elusive. Here, we show the long noncoding RNA (lncRNA) LINC01011, which we termed cisplatin-sensitivity-associated lncRNA (CISAL), controls mitochondrial fission and cisplatin sensitivity by inhibiting BRCA1 transcription in tongue SCC (TSCC) models. Mechanistically, we found CISAL directly binds the BRCA1 promoter and forms an RNA-DNA triplex structure, sequestering BRCA1 transcription factor-GABPA away from the downstream regulatory binding region. Importantly, the clinical relevance of these findings is suggested by the significant association of CISAL and BRCA1 expression levels in TSCC tumors with neoadjuvant chemosensitivity and overall survival. We propose a new model where lncRNAs are tethered at gene promoter by RNA-DNA triplex formation, spatially sequestering transcription factors away from DNA-binding sites. Our study uncovers the potential of CISAL-BRCA1 signaling as a potential target to predict or improve chemosensitivity.

Purpose: The limited efficacy of chimeric antigen receptor (CAR) T-cell therapies with solid malignancies prompted us to test whether epigenetic therapy could enhance the antitumor activity of B7-H3.CAR T cells with several solid cancer types.Experimental Design: We evaluated B7-H3 expression in many human solid cancer and normal tissue samples. The efficacy of the combinatorial therapy with B7-H3.CAR T cells and the deacetylase inhibitor SAHA with several solid cancer types and the potential underlying mechanisms were characterized with in vitro and ex vivo experiments.Results: B7-H3 is expressed in most of the human solid tumor samples tested, but exhibits a restricted expression in normal tissues. B7-H3.CAR T cells selectively killed B7-H3 expressing human cancer cell lines in vitro. A low dose of SAHA upregulated B7-H3 expression in several types of solid cancer cells at the transcriptional level and B7-H3.CAR expression on human transgenic T-cell membrane. In contrast, the expression of immunosuppressive molecules, such as CTLA-4 and TET2, by T cells was downregulated upon SAHA treatment. A low dose of SAHA significantly enhanced the antitumor activity of B7-H3.CAR T cells with solid cancers in vitro and ex vivo, including orthotopic patient-derived xenograft and metastatic models treated with autologous CAR T-cell infusions.Conclusions: Our results show that our novel strategy which combines SAHA and B7-H3.CAR T cells enhances their therapeutic efficacy with solid cancers and justify its translation to a clinical setting.

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ObjectiveTo investigate whether TCF7+ T cells constitute an important factor to improve the existing postoperative prediction model for patients with oral squamous cell carcinoma.MethodTCF7+ T cells were detected in the tissues of 167 OSCC patients by multiplex immunofluorescence. The percentage of TCF7+ T cells was transformed into a dichotomous variable, combined with the clinicopathological data for the OSCC patients, and then subjected to univariate and multivariate analyses. The derived independent predictors were then incorporated into risk models to analyze their relationship with the prognosis of patients.ResultsThe high TCF7+ group had a better prognosis than the low TCF7+ group (OS: p<0.001; RFS: p<0.001). Univariate and multivariate analyses showed that TCF7+ T cells serve as an independent predictor of OSCC (univariate/multivariate analysis: p<0.001). In Cox risk progression models, inclusion of the TCF7+ T cell percentage improved the predictive accuracy of Grade and TNM stage (Grade-OS/RFS: p<0.001; TNM-OS/RFS: p<0.001; TNM+Grade-OS: p<0.001, TNM+Grade-RFS: p=0.004). Inclusion of the TCF7+ T cell percentage improved the clinical utility.ConclusionsTCF7+ T cells can act as an independent predictor for postoperative OSCC patients. The inclusion of TCF7+ T cells improved the predictive accuracy and clinical utility of the nomograms to different degrees.