miR-199a and miR-199b facilitate diffuse gastric cancer progression by targeting Frizzled-6.

Pathological markers that can monitor the progression of gastric cancer (GC) may facilitate the diagnosis and treatment of patients with diffuse GC (DGC). To identify microRNAs (miRNAs) that can differentiate between early and advanced DGC in the gastric mucosa, miRNA expression profiling was performed using the NanoString nCounter method in human DGC tumors. Ectopic expression of miR-199a and miR-199b (miR-199a/b) in SNU601 human GC cells accelerated the growth rate, viability, and motility of cancer cells and increased the tumor volume and weight in a mouse xenograft model. To study their clinicopathological roles in patients with GC, miR-199a/b levels were measured in human GC tumor samples using in situ hybridization. High miR-199a/b expression level was associated with enhanced lymphovascular invasion, advanced T stage, and lymph-node metastasis. Using the 3'-untranslated region (UTR) luciferase assay, Frizzled-6 (FZD6) was confirmed to be a direct target of miR-199a/b in GC cells. siRNA-mediated depletion of FZD6 enhanced the motility of SNU601 cells, and addback of FZD6 restored cancer cell motility stimulated by miR-199a/b. In conclusion, miR-199a/b promotes DGC progression by targeting FZD6, implying that miR-199a/b can be used as prognostic and diagnostic biomarkers for the disease.

Differential RNA expression of immune-related genes and tumor cell proximity from intratumoral M1 macrophages in acral lentiginous melanomas treated with PD-1 blockade.

Immune checkpoint inhibitors (ICIs) offer improved survival for patients with advanced malignant melanomas. However, only a subset of these patients exhibit an objective response rate of 10-40 % with ICIs. We aimed to ascertain the effects of RNA signatures and the spatial distribution of immune cells on the treatment outcomes of patients with malignant melanomas undergoing ICI therapy. Clinical data were retrospectively collected from ICI-treated patients with malignant melanoma; RNA expression profiles were examined via next-generation sequencing, whereas the composition, density, and spatial distribution of immune cells were determined via multiplex immunohistochemistry. Patients with poor and good responses to ICIs showed significant differences in mRNA expression profiles. Different spatial distributions of T-cells, macrophages, and NK cells as well as RNA signatures of immune-related genes were found to be closely related to therapeutic outcomes in ICI-treated patients with malignant melanomas. The spatial distributions of PD-1+ T-cells and activated M1 macrophages showed a significant correlation with favorable responses to ICIs. Our findings highlight the clinical relevance of the spatial proximity of immune cell subsets in the treatment outcomes of metastatic malignant melanoma.

Inhibition of STAT3 enhances sensitivity to tamoxifen in tamoxifen-resistant breast cancer cells.

BACKGROUND: The mechanisms of endocrine resistance are complex, and deregulation of several oncogenic signalling pathways has been proposed. We aimed to investigate the role of the EGFR and Src-mediated STAT3 signalling pathway in tamoxifen-resistant breast cancer cells. METHODS: The ER-positive luminal breast cancer cell lines, MCF-7 and T47D, were used. We have established an MCF-7-derived tamoxifen-resistant cell line (TamR) by long-term culture of MCF-7 cells with 4-hydroxytamoxifen. Cell viability was determined using an MTT assay, and protein expression levels were determined using western blot. Cell cycle and annexin V staining were analysed using flow cytometry. RESULTS: TamR cells showed decreased expression of estrogen receptor and increased expression of EGFR. TamR cells showed an acceleration of the G1 to S phase transition. The protein expression levels of phosphorylated Src, EGFR (Y845), and STAT3 was increased in TamR cells, while phosphorylated Akt was decreased. The expression of p-STAT3 was enhanced according to exposure time of tamoxifen in T47D cells, suggesting that activation of STAT3 can cause tamoxifen resistance in ER-positive breast cancer cells. Both dasatinib (Src inhibitor) and stattic (STAT3 inhibitor) inhibited cell proliferation and induced apoptosis in TamR cells. However, stattic showed a much stronger effect than dasatinib. Knockdown of STAT3 expression by siRNA had no effect on sensitivity to tamoxifen in MCF-7 cells, while that enhanced sensitivity to tamoxifen in TamR cells. There was not a significant synergistic effect of dasatinib and stattic on cell survival. TamR cells have low nuclear p21(Cip1) expression compared to MCF-7 cells and inhibition of STAT3 increased the expression of nuclear p21(Cip1) in TamR cells. CONCLUSIONS: The EGFR and Src-mediated STAT3 signalling pathway is activated in TamR cells, and inhibition of STAT3 may be a potential target in tamoxifen-resistant breast cancer. An increase in nuclear p21(Cip1) may be a key step in STAT3 inhibitor-induced cell death in TamR cells.

DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks.

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

Treatment Outcomes of 9,994 Patients With Extensive-Disease Small-Cell Lung Cancer From a Retrospective Nationwide Population-Based Cohort in the Korean HIRA Database.

To investigate the efficacy of irinotecan-based (IP) and etoposide-based (EP) platinum combinations, and of single-agent chemotherapy, for treatment of extensive-disease small cell lung cancer (ED-SCLC), we performed a large-scale, retrospective, nationwide, cohort study. The population data were extracted from the Health Insurance Review and Assessment Service of Korea database from January 1, 2008, to November 30, 2016. A total of 9,994 patients were allocated to ED-SCLC and analyzed in this study. The primary objectives were to evaluate the survival outcomes of systemic first-line treatments for ED-SCLC. For first-line treatment, patients who received IP showed a better time to first subsequent therapy (TFST) of 8.9 months (95% confidence interval [CI], 8.50-9.40) than those who received EP, who had a TFST of 6.8 months (95% CI, 6.77-6.97, P < 0.0001). In terms of overall survival (OS), IP was superior to EP (median OS, 10.8 months; 95% CI, 10.13-11.33 vs. 9.5 months; 95% CI, 9.33-9.73; P < 0.0001). Taken together, in the Korean population, first-line IP combination chemotherapy had significantly favorable effects on OS and TFST.

DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network.

Accurate prediction of non-small cell lung cancer (NSCLC) prognosis after surgery remains challenging. The Cox proportional hazard (PH) model is widely used, however, there are some limitations associated with it. In this study, we developed novel neural network models called binned time survival analysis (DeepBTS) models using 30 clinico-pathological features of surgically resected NSCLC patients (training cohort, n = 1,022; external validation cohort, n = 298). We employed the root-mean-square error (in the supervised learning model, s- DeepBTS) or negative log-likelihood (in the semi-unsupervised learning model, su-DeepBTS) as the loss function. The su-DeepBTS algorithm achieved better performance (C-index = 0.7306; AUC = 0.7677) than the other models (Cox PH: C-index = 0.7048 and AUC = 0.7390; s-DeepBTS: C-index = 0.7126 and AUC = 0.7420). The top 14 features were selected using su-DeepBTS model as a selector and could distinguish the low- and high-risk groups in the training cohort (p = 1.86 × 10-11) and validation cohort (p = 1.04 × 10-10). When trained with the optimal feature set for each model, the su-DeepBTS model could predict the prognoses of NSCLC better than the traditional model, especially in stage I patients. Follow-up studies using combined radiological, pathological imaging, and genomic data to enhance the performance of our model are ongoing.