Search

Li et al. show that dexamethasone and lactoferrin induced polymorphonuclear myeloid-derived suppressor cells relieve inflammatory conditions in mouse models with minimal effect on tumour growth. This work demonstrates promise for combating inflammatory adverse events during anti-cancer treatments.

Abstract Background Deregulation of protein translation control is a hallmark of cancers. Eukaryotic initiation factor 4A2 (EIF4A2) is required for mRNA binding to ribosome and plays an important role in translation initiation. However, little is known about its functions in colorectal cancer (CRC). Methods Analysis of CRC transcriptome data from TCGA identified that EIF4A2 was associated with poor prognosis. Immunohistochemistry study of EIF4A2 was carried out in 297 paired colorectal tumor and adjacent normal tissue samples. In vitro and in vivo cell-biological assays were performed to study the biological functions of EIF4A2 on experimental metastasis and sensitivity to oxaliplatin treatment. Bioinformatic prediction, chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assay were carried out to unveil the transcription factor of EIF4A2 regulation. Results EIF4A2 Expression is significantly higher in colorectal tumors. Multivariate analysis suggests EIF4A2 as an independent predictor of overall, disease-free and progression-free survival. Dysfunction of EIF4A2 by genetic knock-down or small-molecule inhibitor silvestrol dramatically inhibited CRC invasion and migration, sphere formation and enhanced sensitivity to oxaliplatin treatment in vitro and in vivo. Notably, EIF4A2 knock-down also suppressed lung metastasis in vivo. qRT-PCR and immunoblotting analyses identified c-Myc as a downstream target and effector of EIF4A2. ChIP and dual-luciferase reporter assays validated the bioinformatical prediction of ZNF143 as a specific transcription factor of EIF4A2. Conclusions EIF4A2 promotes experimental metastasis and oxaliplatin resistance in CRC. Silvestrol inhibits tumor growth and has synergistic effects with oxaliplatin to induce apoptosis in cell-derived xenograft (CDX) and patient-derived xenograft (PDX) models.

Background: Blood-based tumor mutational burden (bTMB) was recently found to be suboptimal in predicting overall survival (OS) benefits of atezolizumab over docetaxel among patients with advanced non-small cell lung cancer (NSCLC). The maximum somatic allele frequency (MSAF) is an indicator of the proportion of tumor-derived plasma DNA, which could affect the concordance between bTMB and tissue-based TMB. Therefore, we aimed to evaluate the utility of MSAF, alone or in combination with bTMB, to identify NSCLC patients with or without survival benefit from atezolizumab over docetaxel.Methods: We analyzed the individual patient-level data from the randomized POPLAR and OAK studies. The bTMB and MSAF were derived from the pre-treatment blood-based genomic data.Results: In both the bTMB-high (i.e., bTMB ≥ 13) and bTMB-low subgroups, atezolizumab significantly improved OS compared with docetaxel (hazard ratio [HR] = 0.43 [95% CI, 0.29–0.65], P < 0.001 and HR = 0.73 [95% CI, 0.61–0.87], P < 0.001, respectively). Among patients with a low MSAF (i.e., MSAF < 10.3%), OS significantly favored atezolizumab (HR = 0.59 [95% CI, 0.48–0.72], P < 0.001), whereas OS with atezolizumab was similar to that with docetaxel in the MSAF-high subgroup (HR = 0.91 [95% CI, 0.68–1.20], P = 0.500; interaction test P = 0.017). Among patients from the bTMB-low and MSAF-high subgroup, OS was numerically worse with atezolizumab than with docetaxel (HR = 1.06 [95% CI, 0.78–1.45], P = 0.710); in contrast, OS was significantly improved with atezolizumab compared with docetaxel in those with either a high bTMB or low MSAF (HR = 0.57 [95% CI, 0.47–0.69], P < 0.001; interaction test P < 0.001). Consistent findings were obtained for progression-free survival data.Conclusions: MSAF alone or in combination with bTMB can effectively distinguish patients with or without survival benefit from atezolizumab compared with docetaxel. MSAF and the combined bTMB-MSAF classification may become practical predictive markers for atezolizumab in advanced NSCLC.

Autophagy can be tumor suppressive as well as promotive in regulation of tumorigenesis and disease progression. Accordingly, the prognostic significance of autophagy key regulator Beclin 1 was varied among different tumors. Here, we detected the clinicopathological and prognostic effect of Beclin 1 in the subtypes of intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC). Beclin 1 expression level was detected by immunohistochemistry staining in 106 ICC and 74 ECC patients. We found that Beclin 1 was lowly expressed in 126 (70%) cholangiocarcinoma patients, consist of 72 ICC and 54 ECC. Moreover, the cholangiocarcinoma patients with lymph node metastasis (N1) had a lower Beclin 1 level than that of N0 subgroup (P=0.012). However, we did not detect any correlations between Beclin 1 and other clinicopathological features, including tumor subtypes, vascular invasion, HBV infection, liver cirrhosis, cholecystolithiasis and TNM stage. Survival analysis showed that, compared with the high expression subset, Beclin 1 low expression was correlated with a poorer 3-year progression-free survival (PFS, 69.1% VS 46.8%, P=041) for cholangiocarcinoma. Importantly, our stratified univariate and multivariate analysis confirmed that Beclin 1 lowly expressed ICC had an inferior PFS as well as overall survival than ECC, particularly than that of Beclin 1 highly expressed ECC patients. Thus, our study demonstrated that Beclin 1low expression, correlated with lymph node metastasis, and might be a negative prognostic biomarker for cholangiocarcinoma. Combined Beclin 1 level with the anatomical location might lead to refined prognosis for the subtypes of ICC and ECC.

Cancer-associated fibroblasts (CAFs) are reported to support tumorigenesis by stimulating angiogenesis, cancer cell proliferation, and invasion in most solid tumors. However, the roles of CAFs in the liver cancer microenvironment have not been thoroughly studied. In our previous study, we successfully isolated CAFs from hepatocellular carcinoma (HCC) (H-CAFs) and proved that H-CAFs suppressed the activation of NK cells and thereby created favorable conditions for HCC progression. In our present study, we found that the proliferation of MHCC97L and Hep3B cells was significantly promoted by treatment with conditioned medium from H-CAFs. Pathological analysis also revealed that H-CAFs increased the proportion of Ki-67 (+) malignant cells and prevented them from undergoing necrosis. Moreover, the concentration of hepatocyte growth factor (HGF) cytokine in the conditioned medium of H-CAFs was higher than conditioned medium from normal skin fibroblasts (NSFs). Anti-HGF significantly reduced the proliferation-promoting capability of H-CAFs. In addition, we found that the abundance of H-CAFs correlated positively with tumor size. These results indicate that H-CAFs are an important factor for promoting the growth of HCC in vitro and in vivo, and that HGF plays a key role in HCC proliferation induced by H-CAFs.

BACKGROUND:Accurate prognostication of locally advanced nasopharyngeal carcinoma (NPC) will benefit patients for tailored therapy. Here, we addressed this issue by developing a mathematical algorithm based on support vector machine (SVM) through integrating the expression levels of multi-biomarkers. METHODOLOGY/PRINCIPAL FINDINGS:Ninety-seven locally advanced NPC patients in a randomized controlled trial (RCT), consisting of 48 cases serving as training set and 49 cases as testing set of SVM models, with 5-year follow-up were studied. We designed SVM models by selecting the variables from 38 tissue molecular biomarkers, which represent 6 tumorigenesis signaling pathways, and 3 EBV-related serological biomarkers. We designed 3 SVM models to refine prognosis of NPC with 5-year follow-up. The SVM1 displayed highly predictive sensitivity (sensitivity, specificity were 88.0% and 81.9%, respectively) by integrating the expression of 7 molecular biomarkers. The SVM2 model showed highly predictive specificity (sensitivity, specificity were 84.0% and 94.5%, respectively) by grouping the expression level of 12 molecular biomarkers and 3 EBV-related serological biomarkers. The SVM3 model, constructed by combination SVM1 with SVM2, displayed a high predictive capacity (sensitivity, specificity were 88.0% and 90.3%, respectively). We found that 3 SVM models had strong power in classification of prognosis. Moreover, Cox multivariate regression analysis confirmed these 3 SVM models were all the significant independent prognostic model for overall survival in testing set and overall patients. CONCLUSIONS/SIGNIFICANCE:Our SVM prognostic models designed in the RCT displayed strong power in refining patient prognosis for locally advanced NPC, potentially directing future target therapy against the related signaling pathways.