SAGA: Sparse And Geometry-Aware non-negative matrix factorization through non-linear local embedding

International audienceThis paper presents a new non-negative matrix factorization technique which (1) allows the decomposition of the original data on multiple latent factors accounting for the geometrical structure of the manifold embedding the data; (2) provides an optimal representation with a controllable level of sparsity; (3) has an overall linear complexity allowing handling in tractable time large and high dimensional datasets. It operates by coding the data with respect to local neighbors with non-linear weights. This locality is obtained as a consequence of the simultaneous sparsity and convexity constraints. Our method is demonstrated over several experiments, including a feature extraction and classification task, where it achieves better performances than the state-of-the-art factorization methods, with a shorter computational time

Efficient Deep Image Denoising via Class Specific Convolution

Deep neural networks have been widely used in image denoising during the past few years. Even though they achieve great success on this problem, they are computationally inefficient which makes them inappropriate to be implemented in mobile devices. In this paper, we propose an efficient deep neural network for image denoising based on pixel-wise classification. Despite using a computationally efficient network cannot effectively remove the noises from any content, it is still capable to denoise from a specific type of pattern or texture. The proposed method follows such a divide and conquer scheme. We first use an efficient U-net to pixel-wisely classify pixels in the noisy image based on the local gradient statistics. Then we replace part of the convolution layers in existing denoising networks by the proposed Class Specific Convolution layers (CSConv) which use different weights for different classes of pixels. Quantitative and qualitative evaluations on public datasets demonstrate that the proposed method can reduce the computational costs without sacrificing the performance compared to state-of-the-art algorithms.Comment: The Thirty-Fifth AAAI Conference on Artificial Intelligence(AAAI-21

TGLI1 transcription factor mediates breast cancer brain metastasis via activating metastasis-initiating cancer stem cells and astrocytes in the tumor microenvironment

Mechanisms for breast cancer metastasis remain unclear. Whether truncated glioma-associated oncogene homolog 1 (TGLI1), a transcription factor known to promote angiogenesis, migration and invasion, plays any role in metastasis of any tumor type has never been investigated. In this study, results of two mouse models of breast cancer metastasis showed that ectopic expression of TGLI1, but not GLI1, promoted preferential metastasis to the brain. Conversely, selective TGLI1 knockdown using antisense oligonucleotides led to decreased breast cancer brain metastasis (BCBM) in vivo. Immunohistochemical staining showed that TGLI1, but not GLI1, was increased in lymph node metastases compared to matched primary tumors, and that TGLI1 was expressed at higher levels in BCBM specimens compared to primary tumors. TGLI1 activation is associated with a shortened time to develop BCBM and enriched in HER2-enriched and triple-negative breast cancers. Radioresistant BCBM cell lines and specimens expressed higher levels of TGLI1, but not GLI1, than radiosensitive counterparts. Since cancer stem cells (CSCs) are radioresistant and metastasis-initiating cells, we examined TGLI1 for its involvement in breast CSCs and found TGLI1 to transcriptionally activate stemness genes CD44, Nanog, Sox2, and OCT4 leading to CSC renewal, and TGLI1 outcompetes with GLI1 for binding to target promoters. We next examined whether astrocyte-priming underlies TGLI1-mediated brain tropism and found that TGLI1-positive CSCs strongly activated and interacted with astrocytes in vitro and in vivo. These findings demonstrate, for the first time, that TGLI1 mediates breast cancer metastasis to the brain, in part, through promoting metastasis-initiating CSCs and activating astrocytes in BCBM microenvironment

Single-Cell Sequencing Reveals the Landscape of the Human Brain Metastatic Microenvironment

Brain metastases is the most common intracranial tumor and account for approximately 20% of all systematic cancer cases. It is a leading cause of death in advanced-stage cancer, resulting in a five-year overall survival rate below 10%. Therefore, there is a critical need to identify effective biomarkers that can support frequent surveillance and promote efficient drug guidance in brain metastasis. Recently, the remarkable breakthroughs in single-cell RNA-sequencing (scRNA-seq) technology have advanced our insights into the tumor microenvironment (TME) at single-cell resolution, which offers the potential to unravel the metastasis-related cellular crosstalk and provides the potential for improving therapeutic effects mediated by multifaceted cellular interactions within TME. In this study, we have applied scRNA-seq and profiled 10,896 cells collected from five brain tumor tissue samples originating from breast and lung cancers. Our analysis reveals the presence of various intratumoral components, including tumor cells, fibroblasts, myeloid cells, stromal cells expressing neural stem cell markers, as well as minor populations of oligodendrocytes and T cells. Interestingly, distinct cellular compositions are observed across different samples, indicating the influence of diverse cellular interactions on the infiltration patterns within the TME. Importantly, we identify tumor-associated fibroblasts in both our in-house dataset and external scRNA-seq datasets. These fibroblasts exhibit high expression of type I collagen genes, dominate cell-cell interactions within the TME via the type I collagen signaling axis, and facilitate the remodeling of the TME to a collagen-I-rich extracellular matrix similar to the original TME at primary sites. Additionally, we observe M1 activation in native microglial cells and infiltrated macrophages, which may contribute to a proinflammatory TME and the upregulation of collagen type I expression in fibroblasts. Furthermore, tumor cell-specific receptors exhibit a significant association with patient survival in both brain metastasis and native glioblastoma cases. Taken together, our comprehensive analyses identify type I collagen-secreting tumor-associated fibroblasts as key mediators in metastatic brain tumors and uncover tumor receptors that are potentially associated with patient survival. These discoveries provide potential biomarkers for effective therapeutic targets and intervention strategies

Five-Year Survivors From Brain Metastases Treated With Stereotactic Radiosurgery: Biology, Improving Treatments, or Just Plain Luck?

BACKGROUND: Improvements in therapies have led to an increasing number of long-term survivors of brain metastases. The present series compares a population of 5-year survivors of brain metastases to a generalized brain metastases population to assess for factors attributable to long-term survival. METHODS: A single institution retrospective review was performed to identify 5-year survivors of brain metastases who received stereotactic radiosurgery (SRS). A historical control population of 737 patients with brain metastases was used to assess similarities and differences between the long-term survivor population and the general population treated with SRS. RESULTS: A total of 98 patients with brain metastases were found to have survived over 60 months. No differences between long-term survivors and controls were identified with regards to the age at first SRS ( CONCLUSION: Five-year survivors of brain metastases represent a diverse histologic population, suggesting a small population of oligometastatic and indolent cancers exist for each cancer type

The multiple team formation problem using sociometry

The Team Formation problem (TFP) has become a well-known problem in the OR literature over the last few years. In this problem, the allocation of multiple individuals that match a required set of skills as a group must be chosen to maximise one or several social positive attributes. Specifically, the aim of the current research is two-fold. First, two new dimensions of the TFP are added by considering multiple projects and fractions of people's dedication. This new problem is named the Multiple Team Formation Problem (MTFP). Second, an optimization model consisting in a quadratic objective function, linear constraints and integer variables is proposed for the problem. The optimization model is solved by three algorithms: a Constraint Programming approach provided by a commercial solver, a Local Search heuristic and a Variable Neighbourhood Search metaheuristic. These three algorithms constitute the first attempt to solve the MTFP, being a variable neighbourhood local search metaheuristic the most efficient in almost all cases. Applications of this problem commonly appear in real-life situations, particularly with the current and ongoing development of social network analysis. Therefore, this work opens multiple paths for future research

Common susceptibility variants are shared between schizophrenia and psoriasis in the Han Chinese population

Previous studies have shown that individuals with schizophrenia have a greater risk for psoriasis than a typical person. This suggests that there might be a shared genetic etiology between the 2 conditions. We aimed to characterize the potential shared genetic susceptibility between schizophrenia and psoriasis using genome-wide marker genotype data

Recurrent Fusion Genes in Gastric Cancer: CLDN18-ARHGAP26 Induces Loss of Epithelial Integrity.

Genome rearrangements, a hallmark of cancer, can result in gene fusions with oncogenic properties. Using DNA paired-end-tag (DNA-PET) whole-genome sequencing, we analyzed 15 gastric cancers (GCs) from Southeast Asians. Rearrangements were enriched in open chromatin and shaped by chromatin structure. We identified seven rearrangement hot spots and 136 gene fusions. In three out of 100 GC cases, we found recurrent fusions between CLDN18, a tight junction gene, and ARHGAP26, a gene encoding a RHOA inhibitor. Epithelial cell lines expressing CLDN18-ARHGAP26 displayed a dramatic loss of epithelial phenotype and long protrusions indicative of epithelial-mesenchymal transition (EMT). Fusion-positive cell lines showed impaired barrier properties, reduced cell-cell and cell-extracellular matrix adhesion, retarded wound healing, and inhibition of RHOA. Gain of invasion was seen in cancer cell lines expressing the fusion. Thus, CLDN18-ARHGAP26 mediates epithelial disintegration, possibly leading to stomach H(+) leakage, and the fusion might contribute to invasiveness once a cell is transformed. Cell Rep 2015 Jul 14; 12(2):272-285

Multi-Omics Analysis of Brain Metastasis Outcomes Following Craniotomy

Background: The incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death. Methods: We executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization. Results: We discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference. Conclusion: A multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease