Repurposing of the Syk inhibitor fostamatinib using a machine learning algorithm.

TAM (TYRO3, AXL, MERTK) receptor tyrosine kinases (RTKs) have intrinsic roles in tumor cell proliferation, migration, chemoresistance, and suppression of antitumor immunity. The overexpression of TAM RTKs is associated with poor prognosis in various types of cancer. Single-target agents of TAM RTKs have limited efficacy because of an adaptive feedback mechanism resulting from the cooperation of TAM family members.

Are 19del and L858R really different disease entities?

Clinicians have recognized the similarities and differences between the two subtypes of common epidermal growth factor receptor (EGFR) mutations, but actual treatment strategies have not yet changed. The L858R mutation can be understood by considering the pharmacological conformational plasticity of the receptor protein and the presence of other co-occurring mutations, whether subtypes of EGFR or non-EGFR mutations and differences in downstream signaling pathways. As long as we know that molecular differences lead to biological differences, it is a challenge for all of us that our treatment strategies must change.

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.

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.

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.

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.

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.

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).