Effect of Everolimus on Heterogenous Renal Cancer Cells Populations Including Renal Cancer Stem Cells

Triple-negative expression (ALDH1A1-/CD133-/mutant p53-) cases in lung adenocarcinoma had a good prognosis

Cancer stem cells (CSCs) are major contributors to the malignant transformation of cells because of their capacity for self-renewal. Aldehyde dehydrogenase1A1 (ALDH1A1) and CD133 are promising candidate of CSC markers in non-small cell lung cancer (NSCLC). Furthermore, TP53 is frequently mutated in lung cancer, and the loss of its function is associated with malignant characteristics. However, the relationship between CSCs and mutant p53 in lung adenocarcinoma is not well-established. We examined the expression of ALDH1A1, CD133, and mutant p53 in lung adenocarcinoma patients and conducted a clinicopathological study. Triple-negative cases without ALDH1A1, CD133, and mutant p53 expression in lung adenocarcinoma were shown to have a much better prognosis than others. Our present results suggest that detection of CSC markers and mutant p53 by immunohistochemical staining may be effective in therapeutic strategies for lung adenocarcinoma.

ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles

Determining the tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. Many components of these biological pathways still remain unknown, hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (https://github.com/caanene1/ACSNI).

Targeting of Lung Cancer Stem Cell Self-Renewal Pathway by a Small Molecule Verrucarin J

Despite considerable advances made in understanding of lung cancer biology, there has been meek improvement in lung cancer treatment outcome with 4% to 5% increase in 5-year survival rates in the last four decades. Underlying problem of lung cancer recurrence and poor prognosis is attributed to the presence of cancer stem cells (CSCs) which possess the potential to differentiate, proliferate and trigger chemo-resistance, tumor progression and metastasis, despite initial elimination of the tumor. To address specific targeting of CSCs, we investigated the effects of a small molecule Verrucarin J (VJ) on lung cancer cell lines A549 and H1793. VJ significantly inhibited cell proliferation of both cell lines, with IC₅₀ values of approximately 10 nM for A549 and 20 nM for H1793 respectively after 48 h of treatment. A549 cell line when treated with VJ, induced cell apoptosis with concomitant down regulation of key CSC specific genes- ALDH1, LGR5, OCT4 and CD133 in a dose-dependent manner. To delineate the molecular mechanism by which VJ targets lung cancer cells and CSCs, we determined the effects of VJ on CSC self-renewal pathways Wnt1/β-catenin and Notch1. Treatment of A549 cell line with VJ inhibited significantly both the signalling pathways, suggesting inhibition of expression of CSC genes by VJ through the inhibition of CSC self-renewal signalling pathways. Taken together, our results suggest that VJ may serve as a potent anticancer drug to target cancer cells and CSCs.