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Supplementary Data from Extracellular Vesicle–Packaged CDH11 and ITGA5 Induce the Premetastatic Niche for Bone Colonization of Breast Cancer Cells

Although most breast cancer metastases in bone cause osteolytic lesions, the osteogenic niche has commonly been described as an initiator of early-stage bone colonization of disseminated cancer cells. Tumor cell–derived extracellular vesicles (EV) have been shown to determine the organotropism of cancer cells by transferring their cargo, such as nucleic acids and proteins, to resident cells at future metastatic sites and preparing a favorable premetastatic niche. Runt-related transcription factor 2 (RUNX2) and its regulated genes have been shown to facilitate the acquisition of osteomimetic features and to enhance the bone metastatic potential of breast cancer cells. In this study, we present in vivo and in vitro evidence to clarify the role of EVs released by breast cancer cells with high RUNX2 expression in the education of osteoblasts to form an osteogenic premetastatic niche. Furthermore, different extracellular vesicular proteins were identified that mediate events subsequent to the specific recognition of tumor-derived EVs by osteoblasts via cadherin 11 (CDH11) and the induction of the osteogenic premetastatic niche by integrin α5 (ITGA5). CDH11high/ITGA5high EVs were demonstrated to be responsible for the formation of a premetastatic niche that facilitates RUNX2 high-expressing breast cancer cell colonization in bone, revealing a potential EV-based premetastatic niche blockage strategy.Significance:This study provides mechanistic insights into the generation of an osteogenic premetastatic niche by breast cancer–derived EVs and identifies potential EV-derived diagnostic biomarkers and targets for breast cancer bone metastasis.

Although most breast cancer metastases in bone cause osteolytic lesions, the osteogenic niche has commonly been described as an initiator of early-stage bone colonization of disseminated cancer cells. Tumor cell–derived extracellular vesicles (EV) have been shown to determine the organotropism of cancer cells by transferring their cargo, such as nucleic acids and proteins, to resident cells at future metastatic sites and preparing a favorable premetastatic niche. Runt-related transcription factor 2 (RUNX2) and its regulated genes have been shown to facilitate the acquisition of osteomimetic features and to enhance the bone metastatic potential of breast cancer cells. In this study, we present in vivo and in vitro evidence to clarify the role of EVs released by breast cancer cells with high RUNX2 expression in the education of osteoblasts to form an osteogenic premetastatic niche. Furthermore, different extracellular vesicular proteins were identified that mediate events subsequent to the specific recognition of tumor-derived EVs by osteoblasts via cadherin 11 (CDH11) and the induction of the osteogenic premetastatic niche by integrin α5 (ITGA5). CDH11high/ITGA5high EVs were demonstrated to be responsible for the formation of a premetastatic niche that facilitates RUNX2 high-expressing breast cancer cell colonization in bone, revealing a potential EV-based premetastatic niche blockage strategy. Significance: This study provides mechanistic insights into the generation of an osteogenic premetastatic niche by breast cancer–derived EVs and identifies potential EV-derived diagnostic biomarkers and targets for breast cancer bone metastasis.

LJM, a 41-year-old schizophrenic Chinese man with bilateral anterior cingulate gyrus (Brodmann's area 24) lesions and also a small lesion in right amygdala after an operation, was compared with normal as well as brain-damaged and schizophrenic controls in identification of morphed facial expressions of six basic emotions. In repeated administrations of the test for recognition of facial emotions, over a 1- year period, LJM performed significantly worse for expressions of fear compared with the three groups of controls. Recognition of other emotions was not significantly different from that of the controls, except that his recognition of disgust during the first session (but not in two subsequent sessions) was worse than normal and brain-damaged controls but not worse than schizophrenic controls. The dissociation between recognition of fear and other emotions supported the view that the brain has separable networks for processing different emotions, and that the right amygdala as well as the anterior part of bilateral cingulate gyrus are possible substrates involved in the special network for perception of fear. The results from the various groups of Chinese subjects indicate that they perceive emotions in a categorical manner, and that the six basic emotions are likely to be cross-cultural universals.

Human eyes can detect visual salient region easily, but computational modeling of this basic intelligent behavior still remains a challenge. Here this paper presents a salient region detection method based on multi-resolution which can highlight salient regions with well-defined boundaries of object. First, the original image is sub-sampled into three multi-resolution layers. Then for each layer the luminance and color salient features are extracted in frequency domain and the significant values are calculated by using invariant laws of Euclidean distance in Lab space. In order to remove noise and enhance the correlation among the vicinity pixels, the normal distribution function is used to specify the salient map in each layer. Finally, the final saliency map can be obtained by normalizing and merging the multi-resolution salient maps.

Human visual system can detect salient region fast and reliably, however, it is a big challenge to build a corresponding visual computing model. In this paper, a model of salient region detection based on local and regional features is presented. Firstly, the image is divided into 8 × 8 sub-blocks. Secondly, the local feature and regional feature of each sub-block are calculated. Local feature which reflects the sub-block's salient character locally is obtained through the calculation of color features in sub-block's 8 neighbors; regional feature which reflects the sub-block's character globally is calculated in the 15×15 block size areas which center on each sub-block. In order to avoid losing some detail features in the single scale, the saliency feature is extracted from multi-scale, and the salient sub-block is combined with the above features together as the block's salient value. Secondly, salient edge has been calculated by the contrast of the color values in 4 color channels. Finally, Salient map can be extracted by combining the salient feature and salient edge together. The experimental results show that our model can extract salient objects in images fast and exactly, which can be used in the areas of image retrieval, compression, and so on.