LRP6 promotes invasion and metastasis of colorectal cancer through cytoskeleton dynamics LRP6 promotes invasion and metastasis of colorectal cancer through cytoskeleton dynamics

The tumor microenvironment plays a vital role in tumor progression and treatment response. However, the association between immune cell concentrations in primary tumor and blood indexes remains unknown. Thus, we enrolled patients with gastric cancer (GC) in two cohorts. We used multiplexed immunohistochemistry to quantify in situ proteins covering rare cell types at sub-cellular resolution in 80 patients with GC in the first cohort. A high correlation between the LMR (lymphocyte-to-monocyte ratio)/NLR (neutrophil-to-lymphocyte ratio) and tumor immune microenvironment was found. The density of exhausted CD8 T cells including CD8⁺PD1⁻TIM3⁺, CD8⁺LAG3⁺PD1⁺, CD8⁺LAG3⁺PD1⁻, CD8⁺LAG3⁺PD1⁺TIM3⁻ was negatively associated with LMR and positively associated with NLR (p < 0.05). Additionally, the higher density of macrophages in tumor core was associated with a higher platelet-to-lymphocyte ratio and systemic immune-inflammation index. Furthermore, we validated the prognostic value of LMR and NLR in an independent cohort of 357 gastric cancer patients receiving immunotherapy. Higher LMR at baseline was significantly associated with superior immune-related PFS (irPFS) and a trend of superior immune-related OS (irOS). Higher NLR was associated with inferior irOS. In conclusion, blood indexes were associated with immune cells infiltrating in primary tumors of GC. NLR and LMR are associated with the density of exhausted CD8⁺ T immune cells, which leads to prognostic values of immunotherapy.

Proliferative and Inhibitory Activity of Siberian ginseng (Eleutherococcus senticosus) Extract on Cancer Cell Lines; A-549, XWLC-05, HCT-116, CNE and Beas-2b Eleutherococcus senticosus;A-549 (small cell lung cancer);XWLC-05 (Yunnan lung cancer cell line); Siberian ginseng (Eleutherococcus senticosus) is used primarily as an adaptogen herb and also for its immune stimulant properties in Western herbal medicine. Another closely related species used in East Asian medicine systems i.e. Kampo, TCM (Manchuria, Korea, Japan and Ainu of Hokkaido) and also called Siberian ginseng (Acanthopanax senticosus) also displays immune-stimulant and anti-cancer properties. These may affect tumour growth and also provide an anti-fatigue effect for cancer patients, in particular for those suffering from lung cancer. There is some evidence that a carbohydrate in Siberian ginseng may possess not only immune stimulatory but also anti-tumour effects and also display other various anti-cancer properties. Our study aimed to determine the inhibitory and also proliferative effects of a methanol plant extract of Siberan ginseng (E. senticosus) on various cancer and normal cell lines including: A-549 (small cell lung cancer), XWLC-05 (Yunnan lung cancer cell line), CNE (human nasopharyngeal carcinoma cell line), HCT-116 (human colon cancer) and Beas-2b (human lung epithelial). These cell lines were treated with an extract from E. senticosus that was evaporated and reconstituted in DMSO. Treatment of A-549 (small cell lung cancer) cells with E. senticosus methanolic extract showed a concentration-dependent inhibitory trend from $12.5-50{\mu}g/mL$, and then a plateau, whereas at 12.5 and $25{\mu}g/mL$, there is a slight growth suppression in QBC-939 cells, but then a steady suppression from 50, 100 and $200{\mu}g/mL$. Further, in XWLC-05 (Yunnan lung cancer cell line), E. senticosus methanolic extract displayed an inhibitory effect which plateaued with increasing dosage. Next, in CNE (human nasopharyngeal carcinoma cell line) there was a dose dependent proliferative response, whereas in Beas-2 (human lung epithelial cell line), an inhibitory effect. Finally in colon cancer cell line (HCT-116) we observed an initially weak inhibitory effect and then plateau.

Accurate detection of HER2 expression through immunohistochemistry (IHC) is of great clinical significance in the treatment of breast cancer. However, manual interpretation of HER2 is challenging, due to the interobserver variability among pathologists. We sought to explore a deep learning method to predict HER2 expression level and gene status based on a Whole Slide Image (WSI) of the HER2 IHC section. When applied to 228 invasive breast carcinoma of no special type (IBC-NST) DAB-stained slides, our GrayMap+ convolutional neural network (CNN) model accurately classified HER2 IHC level with mean accuracy 0.952 ± 0.029 and predicted HER2 FISH status with mean accuracy 0.921 ± 0.029. Our result also demonstrated strong consistency in HER2 expression score between our system and experienced pathologists (intraclass correlation coefficient (ICC) = 0.903, Cohen’s κ = 0.875). The discordant cases were found to be largely caused by high intra-tumor staining heterogeneity in the HER2 IHC group and low copy number in the HER2 FISH group.