A systems biology investigation of curcumin potency against TGF-β-induced EMT signaling in lung cancer

Curcumin (diferuloylmethane) is bioactive phenolic compound which exerts diverse antimetastatic effect. Several studies have reported the antimetastatic effect of curcumin by its ability to modulate the epithelial-to-mesenchymal transition (EMT) process in different cancers, but underlying molecular mechanism is poorly understood. EMT is a highly conserved biological process in which epithelial cells acquire mesenchymal-like characteristics by losing their cell-cell junctions and polarity. As a consequence, deviation in cellular mechanism leads to cancer metastasis and thereby death. In this perspective, we explored the antimetastatic potential and mechanism of curcumin on the EMT process by establishing in vitro EMT model in lungs cancer (A549) cells induced by TGF-β1. Our results showed that curcumin mitigates EMT by regulating the expression of crucial mesenchymal markers such as MMP2, vimentin and N-cadherin. Besides, the transcriptional analysis revealed that the curcumin treatment differentially regulated the expression of 75 genes in NanoString nCounter platform. Further protein-protein interaction network and clusters analysis of differentially expressed genes revealed their involvement in essential biological processes that plays a key role during EMT transition. Altogether, the study provides a comprehensive overview of the antimetastatic potential of curcumin in TGF-β1-induced EMT in lung cancer cells.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03360-7.

RNA Exosome Component EXOSC4 Amplified in Multiple Cancer Types Is Required for the Cancer Cell Survival

The RNA exosome is a multi-subunit ribonuclease complex that is evolutionally conserved and the major cellular machinery for the surveillance, processing, degradation, and turnover of diverse RNAs essential for cell viability. Here we performed integrated genomic and clinicopathological analyses of 27 RNA exosome components across 32 tumor types using The Cancer Genome Atlas PanCancer Atlas Studies' datasets. We discovered that the EXOSC4 gene, which encodes a barrel component of the RNA exosome, was amplified across multiple cancer types. We further found that EXOSC4 alteration is associated with a poor prognosis of pancreatic cancer patients. Moreover, we demonstrated that EXOSC4 is required for the survival of pancreatic cancer cells. EXOSC4 also repressed BIK expression and destabilized SESN2 mRNA by promoting its degradation. Furthermore, knockdown of BIK and SESN2 could partially rescue pancreatic cells from the reduction in cell viability caused by EXOSC4 knockdown. Our study provides evidence for EXOSC4-mediated regulation of BIK and SESN2 mRNA in the survival of pancreatic tumor cells.

Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue-residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

Clinical and molecular predictors of long-term response in HER2 positive metastatic breast cancer patients

BACKGROUND

HER2+ metastatic breast cancer (MBC) is a poor prognosis disease, unusually curable. To date, no predictive factors have been clearly correlated with long-term response to anti-HER2 agents.

METHODS

54 HER2+ MBC patients treated with HER2 targeted therapy as first line treatment were analysed: 40 with a time to progression longer than 3 years in Long Responders (LR) group and 14 with a progression disease within one year of anti-HER2 therapy in a control group named Early Progressors (EP). The expression of 770 genes and 13 molecular pathways were evaluated using Nanostring PanCancer pathway panel performed on FFPE BC tissues.

RESULTS

Considering baseline patients and tumor characteristics, EP women had more CNS spread and more metastatic burden of disease compared to LR (p > 0.05). Gene expression analysis identified 30 genes with significantly different expression in the two cohorts; five were driver genes (BRCA1, PDGFRA, AR, PHF6 and MSH2). The majority of these genes were over-expressed, mainly in LR patients, and encoded growth factors, pro- or anti-inflammatory interleukins and DNA repair factors. Only four genes were down regulated, all in EP group (TNFSF10, CACNG1, IL20RB and BRCA1). Most of these genes were involved in MAPK and PI3K pathways. MAPK pathway was differently expressed between LR and EP (p = 0.05). PI3K was the only pathway overexpressed in EP patients.

CONCLUSIONS

Whole genome expression analysis comparing LR vs. EP identified a group of genes that may predict more favourable long-term outcomes. Up-regulation of MAPK and down-regulation of PI3K pathway could be a positive predictive factors. Further clinical implications are warranted.

ABBREVIATIONS

BC: breast cancer; MBC: metastatic breast cancer; LR: long responder; EP: early progressor; FFPE: formalin-fixed paraffin-embedded; CNS: central nervous system; PFS: progression free survival; OS: overall survival.

PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective

The Cancer Genome Atlas (TCGA) represents one of several international consortia dedicated to performing comprehensive genomic and epigenomic analyses of selected tumour types to advance our understanding of disease and provide an open-access resource for worldwide cancer research. Thirty-three tumour types (selected by histology or tissue of origin, to include both common and rare diseases), comprising >11 000 specimens, were subjected to DNA sequencing, copy number and methylation analysis, and transcriptomic, proteomic and histological evaluation. Each cancer type was analysed individually to identify tissue-specific alterations, and make correlations across different molecular platforms. The final dataset was then normalized and combined for the PanCancer Initiative, which seeks to identify commonalities across different cancer types or cells of origin/lineage, or within anatomically or morphologically related groups. An important resource generated along with the rich molecular studies is an extensive digital pathology slide archive, composed of frozen section tissue directly related to the tissues analysed as part of TCGA, and representative formalin-fixed paraffin-embedded, haematoxylin and eosin (H&E)-stained diagnostic slides. These H&E image resources have primarily been used to verify diagnoses and histological subtypes with some limited extraction of standard pathological variables such as mitotic activity, grade, and lymphocytic infiltrates. Largely overlooked is the richness of these scanned images for more sophisticated feature extraction approaches coupled with machine learning, and ultimately correlation with molecular features and clinical endpoints. Here, we document initial attempts to exploit TCGA imaging archives, and describe some of the tools, and the rapidly evolving image analysis/feature extraction landscape. Our hope is to inform, and ultimately inspire and challenge, the pathology and cancer research communities to exploit these imaging resources so that the full potential of this integral platform of TCGA can be used to complement and enhance the insightful integrated analyses from the genomic and epigenomic platforms. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.