TGFβ promotes YAP‐dependent AXL induction in mesenchymal‐type lung cancer cells

The acquisition of chemoresistance remains a major cause of cancer mortality due to the limited accessibility of targeted or immune therapies. However, given that severe alterations of molecular features during epithelial‐to‐mesenchymal transition (EMT) lead to acquired chemoresistance, emerging studies have focused on identifying targetable drivers associated with acquired chemoresistance. Particularly, AXL, a key receptor tyrosine kinase that confers resistance against targets and chemotherapeutics, is highly expressed in mesenchymal cancer cells. However, the underlying mechanism of AXL induction in mesenchymal cancer cells is poorly understood. Our study revealed that the YAP signature, which was highly enriched in mesenchymal‐type lung cancer, was closely correlated to AXL expression in 181 lung cancer cell lines. Moreover, using isogenic lung cancer cell pairs, we also found that doxorubicin treatment induced YAP nuclear translocation in mesenchymal‐type lung cancer cells to induce AXL expression. Additionally, the concurrent activation of TGFβ signaling coordinated YAP‐dependent AXL expression through SMAD4. These data suggest that crosstalk between YAP and the TGFβ/SMAD axis upon treatment with chemotherapeutics might be a promising target to improve chemosensitivity in mesenchymal‐type lung cancer.

Abbreviations

AUC

area under the curve

AXL

AXL receptor tyrosine kinase

BCL2

B‐cell lymphoma 2

CTD2

cancer target discovery and development

CTGF

connective tissue growth factor

DEG

differentially expressed genes

DOXO

doxorubicin

EMT

epithelial–mesenchymal transition

Eto

etoposide

FDA

Food and Drug Administration

ITGB3

integrin beta‐3

MAPK

mitogen‐activated protein kinase

MMP2

matrix metalloproteinase‐2

MMP9

matrix metalloproteinase‐9

mRNA

messenger RNA

NF‐κB

nuclear factor kappa‐light‐chain‐enhancer of activated B cells

SBE

SMAD binding element

SERPINE1

serpin family E member 1

siRNA

small interfering RNA

ssGSEA

single‐sample gene set enrichment analysis

TCGA

The Cancer Genome Atlas

TGFβ

transforming growth factor beta

YAP

Yes‐associated protein

YAP8SA

mutants of inhibitory phosphorylation site at eight serine to Alanine of YAP

ZEB1

zinc finger E‐box binding homeobox 1

ZEB2

zinc finger E‐box‐binding homeobox 2

     

Coronary Artery Lumen Segmentation Using Location–Adaptive Threshold in Coronary Computed Tomographic Angiography: A Proof-of-Concept

ObjectiveTo compare the lumen parameters measured by the location-adaptive threshold method (LATM), in which the inter- and intra-scan attenuation variabilities of coronary computed tomographic angiography (CCTA) were corrected, and the scan-adaptive threshold method (SATM), in which only the inter-scan variability was corrected, with the reference standard measurement by intravascular ultrasonography (IVUS).Materials and MethodsThe Hounsfield unit (HU) values of whole voxels and the centerline in each of the cross-sections of the 22 target coronary artery segments were obtained from 15 patients between March 2009 and June 2010, in addition to the corresponding voxel size. Lumen volume was calculated mathematically as the voxel volume multiplied by the number of voxels with HU within a given range, defined as the lumen for each method, and compared with the IVUS-derived reference standard. Subgroup analysis of the lumen area was performed to investigate the effect of lumen size on the studied methods. Bland-Altman plots were used to evaluate the agreement between the measurements.ResultsLumen volumes measured by SATM was significantly smaller than that measured by IVUS (mean difference, 14.6 mm³; 95% confidence interval [CI], 4.9–24.3 mm³); the lumen volumes measured by LATM and IVUS were not significantly different (mean difference, −0.7 mm³; 95% CI, −9.1–7.7 mm³). The lumen area measured by SATM was significantly smaller than that measured by LATM in the smaller lumen area group (mean of difference, 1.07 mm²; 95% CI, 0.89–1.25 mm²) but not in the larger lumen area group (mean of difference, −0.07 mm²; 95% CI, −0.22–0.08 mm²). In the smaller lumen group, the mean difference was lower in the Bland-Altman plot of IVUS and LATM (0.46 mm²; 95% CI, 0.27–0.65 mm²) than in that of IVUS and SATM (1.53 mm²; 95% CI, 1.27–1.79 mm²).ConclusionSATM underestimated the lumen parameters for computed lumen segmentation in CCTA, and this may be overcome by using LATM.     

Non alcoholic fatty liver disease and risk of incident diabetes in subjects who are not obese

Background and aims

It is not known whether non alcoholic fatty liver disease (NAFLD) is a risk factor for diabetes in non obese, non centrally-obese subjects. Our aim was to investigate relationships between fatty liver, insulin resistance and a biomarker score for liver fibrosis with incident diabetes at follow up, in subjects who were neither obese nor centrally-obese.

Genetic toxicity assessment using liver cell models: past,present, and future

ABSTRACT

Genotoxic compounds may be detoxified to non-genotoxic metabolites while many pro-carcinogens require metabolic activation to exert their genotoxicity in vivo. Standard genotoxicity assays were developed and utilized for risk assessment for over 40 years. Most of these assays are conducted in metabolically incompetent rodent or human cell lines. Deficient in normal metabolism and relying on exogenous metabolic activation systems, the current in vitro genotoxicity assays often have yielded high false positive rates, which trigger unnecessary and costly in vivo studies. Metabolically active cells such as hepatocytes have been recognized as a promising cell model in predicting genotoxicity of carcinogens in vivo. In recent years, significant advances in tissue culture and biological technologies provided new opportunities for using hepatocytes in genetic toxicology. This review encompasses published studies (both in vitro and in vivo) using hepatocytes for genotoxicity assessment. Findings from both standard and newly developed genotoxicity assays are summarized. Various liver cell models used for genotoxicity assessment are described, including the potential application of advanced liver cell models such as 3D spheroids, organoids, and engineered hepatocytes. An integrated strategy, that includes the use of human-based cells with enhanced biological relevance and throughput, and applying the quantitative analysis of data, may provide an approach for future genotoxicity risk assessment.