Cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs) are important components of the tumor microenvironment, which have been reported to localize in colorectal carcinomas where they promote tumor progression. One of the crucial effects they exerted is immune-suppression, which was reported recently, however, the overall mechanism has not been fully addressed. In this study, it was shown that TAMs were enriched in colorectal cancer, and their infiltration was associated with VCAM-1 expression. Human colorectal cancer-derived CAFs can promote the adhesion of monocytes by up-regulating VCAM-1 expression in colorectal cancer cells. Furthermore, CAFs can attract monocytes by secreting IL-8 rather than SDF-1 and subsequently promote M2 polarization of macrophages, which synergize with CAFs in suppressing the functioning of natural killer (NK) cells. It was also found that CAFs promoted M2 macrophages recruitment in tumor tissue in vivo, and after VCAM-1 knocking-down in tumor cells or depletion of macrophages, the pro-tumor effect of CAFs was partly abolished, but no change was observed in NK cells infiltration. Collectively, the findings in this work show that TAMs and CAFs function synergistically in the tumor microenvironment and have the capacity to regulate NK cells in colorectal cancer and this presents a novel mechanism.

PurposeIn the present study, we investigated the incidence of cardiotoxicity within 5 years of trastuzumab treatment and evaluated potential risk factors in clinical practice.MethodsThe study cohort included 415 patients diagnosed with early breast cancer (EBC). Cardiotoxicity incidence was evaluated in patients receiving trastuzumab and those who did not. Multivariate Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals of potential risk factors for trastuzumab-related cardiotoxicity after appropriate adjustments.ResultsIncidence of cardiotoxicity in patients treated with trastuzumab was significantly higher than that in controls (23.7% vs. 10.8%, p<0.001). This result was adjusted for factors that might increase the risk of cardiotoxicity, such as history of coronary artery diseases or the use of anthracyclines for more than four cycles.ConclusionOur findings indicated that treatment with trastuzumab was strongly associated with cardiotoxicity in EBC patients.

Ovarian cancer is a kind of gynecological malignancy with high mortality. Ferroptosis is a new type of iron-dependent cell death characterized by the formation of lipid peroxides and excessive accumulation of reactive oxygen species. Studies have shown that ferroptosis modulates tumor genesis, progression, and invasion, including ovarian cancer. Based on the mRNA expression data from TCGA, we construct a scoring system using consensus clustering analysis, univariate Cox regression analysis, and least absolute selection operator. Then, we systematically evaluate the relationship between score and clinical characteristics of ovarian cancer. The result from the prediction of biofunction pathways shows that score serves as an independent prognostic marker for ovarian cancer and affects tumor progression by modulating tumor metastasis. Moreover, immunocytes such as activated CD4 T cell, activated CD8 T cell, regulatory T cells, macrophage, and stromal cells, including adipocytes, epithelial cells, and fibroblast infiltrate more in the tumor microenvironment in a high-score group, indicating ferroptosis can also affect tumor immune landscape. Critically, four potentially sensitive drugs, including staurosporine, epothilone B, DMOG, and HG6-64-1 based on the scores, are predicted, and DMOG is recognized as a novel targeted drug for ovarian cancer. In general, we construct the scoring system based on ferroptosis-related genes that can predict the prognosis of ovarian cancer patients and propose that ferroptosis may affect ovarian cancer progression by mediating tumor metastasis and immune landscape. Novel drugs to target ovarian cancer are also predicted.

Background Carcinoma-associated fibroblasts (CAFs) are dominant components of tumor microenvironment, which has been reported to promote development, progression, and metastasis of cancer. However, the role of CAFs during adhesion process remains unknown. It has been hypothesized that CAFs contribute to adhesion to endothelial cells of colorectal cancer (CRC) via HGF/c-Met pathway. Methods Clinical specimen and orthotopic liver metastasis model was used to investigate association between CD44 expression and propensity of metastasis in CRC. Human CRC derived cancer associated fibroblasts was isolated and its effect on migration and adhesion of CRC cells was investigated. We also confirm the conclusion on animal metastasis model. Results In this study, clinical specimen and orthotopic liver metastatic model indicated that overexpression of CD44 is associated with CRC metastasis, and we found that colorectal cancer-derived CAFs (CC-CAFs) increased the adhesion and migration of CRC cells in vitro through up-regulation of CD44, we also found that CC-CAFs promoted adhesion and liver or lung metastasis in vivo. Mechanistically, we found that the expression of HGF increased tenfolds compared CC-CAFs with adjacent normal fibroblasts, and HGF promoted adhesion through up-regulation of CD44 via HGF/c-MET signal pathway. Conclusions These results indicated that CC-CAFs-derived HGF induced up-regulation of CD44 which mediated adhesion of CRC cells to endothelial cells, and subsequently resulted in enhancement of metastasis of CRC cells, it could provide a novel therapeutic or preventive target.

Background:The addition of anti-human epidermal growth factor receptor 2 (HER2)-targeted drugs, such as trastuzumab, lapatinib, and trastuzumab emtansine (T-DM1), to chemotherapy significantly improved prognosis of HER2-positive breast cancer patients. However, it was confused that metastatic patients vary in the response of targeted drug. Therefore, methods of accurately predicting drug response were really needed. To overcome the spatial and temporal limitations of biopsies, we aimed to develop a more sensitive and less invasive method of detecting mutations associated with anti-HER2 therapeutic response through circulating-free DNA (cfDNA).Methods:From March 6, 2014 to December 10, 2014, 24 plasma samples from 20 patients with HER2-positive metastatic breast cancer who received systemic therapy were eligible. We used a panel for detection of hot-spot mutations from 50 oncogenes and tumor suppressor genes, and then used targeted next-generation sequencing (NGS) to identify somatic mutation of these samples in those 50 genes. Samples taken before their first trastuzumab administration and subsequently proven with clinical benefit were grouped into sensitive group. The others were collected after disease progression of the trastuzumab-based therapy and were grouped into the resistant group.Results:A total of 486 single-nucleotide variants from 46 genes were detected. Of these 46 genes, phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), proto-oncogene c-Kit (KIT), and tumor protein p53 (TP53) were the most common mutated genes. Seven genes, including epidermal growth factor receptor (EGFR), G protein subunit alpha S (GNAS), HRas proto-oncogene (HRAS), mutL homolog 1 (MLH1), cadherin 1 (CDH1), neuroblastoma RAS viral oncogene homolog (NRAS), and NOTCH1, that only occurred mutations in the resistant group were associated with the resistance of targeted therapy. In addition, we detected a HER2 S855I mutation in two patients who had persistent benefits from anti-HER2 therapy.Conclusion:Targeted NGS of cfDNA has potential clinical utility to detect biomarkers from HER2-targeted therapies.

The location and occurrence time of convective rainfalls have attracted great public concern as they can lead to terrible disasters. However, the simulation results of convective rainfalls in the Pearl River Delta region often show significant discrepancies from the observations. One of the major causes lies in the inaccurate geographic distribution of land surface properties used in the model simulation of the heavy precipitation. In this study, we replaced the default soil and vegetation datasets of Weather Research and Forecasting (WRF) model with two refined datasets, i.e. the GlobCover 2009 (GLC2009) land cover map and the Harmonized World Soil Database (HWSD) soil texture, to investigate the impact of vegetation and soil on the rainfall patterns. The result showed that the simulation patterns of convective rainfalls obtained from the coupled refined datasets are more consistent with the observations than those obtained from the default ones. By using the coupled refined land surface datasets, the overlap ratio of high precipitation districts reached 36.3% with a variance of 28.5 km from the observed maximum rainfall position, while those of the default United States Geological Survey (USGS) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) dataset are 17.0%/ 32.8 km and 24.9%/49.0 km, respectively. The simulated total rainfall amount and occurrence time using the coupled refined datasets are the closest to the observed peak values. In addition, the HWSD soil data has improved the accuracy of the simulated precipitation amount, and the GLC2009 land cover data also did better in catching the early peak time.

No abstract

Limited by the number of ground observation stations, PM 2.5 retrieval from the remote sensing data is an effective complement to conventional ground observations and is a current research hotspot. The general principle behind the remote sensing retrieval of PM 2.5 is to first retrieve the aerosol optical depth (AOD) and calculate the PM 2.5 via the AOD-based statistical relationships. This method is likely to cause error propagation, which leads to instability in the retrieval model. In this paper, we propose a PM 2.5 remote sensing retrieval method via an ensemble random forest machine learning method to directly establish the relationship between the moderate-resolution imaging spectroradiometer (MODIS) images and ground observational PM 2.5 to avoid retrieval errors from the atmospheric aerosol optical depths and obtain PM 2.5 retrieval results with higher precision and spatial resolution. The proposed method first uses a random forest to train and validate the MODIS images and ground observation station PM 2.5 data; then, an optimal multimodel group, according to the determination coefficient R-square (R 2 ) index, is selected. Finally, the optimal multi-model group is used on the whole MODIS image to obtain the PM 2.5 retrieval result for the whole area. In an attempt to use machine learning technology to retrieve PM 2.5 , the experiments selected a substantial amount of MODIS image data during four seasons in Guangdong Province for validation and compared three performance indicators (R 2 , RMSE, and correlation coefficient (CC) to verify the superiority of the proposed algorithm.INDEX TERMS Ensemble random forest, machine learning, remote sensing based PM 2.5 retrieval, Kriging interpolation, aerosol optical depth.