A Transcriptional Program for Detecting TGFβ-Induced EMT in Cancer

Abstract

Most cancer deaths are due to metastasis, and epithelial-to-mesenchymal transition (EMT) plays a central role in driving cancer cell metastasis. EMT is induced by different stimuli, leading to different signaling patterns and therapeutic responses. TGFβ is one of the best-studied drivers of EMT, and many drugs are available to target this signaling pathway. A comprehensive bioinformatics approach was employed to derive a signature for TGFβ-induced EMT which can be used to score TGFβ-driven EMT in cells and clinical specimens. Considering this signature in pan-cancer cell and tumor datasets, a number of cell lines (including basal B breast cancer and cancers of the central nervous system) show evidence for TGFβ-driven EMT and carry a low mutational burden across the TGFβ signaling pathway. Furthermore, significant variation is observed in the response of high scoring cell lines to some common cancer drugs. Finally, this signature was applied to pan-cancer data from The Cancer Genome Atlas to identify tumor types with evidence of TGFβ-induced EMT. Tumor types with high scores showed significantly lower survival rates than those with low scores and also carry a lower mutational burden in the TGFβ pathway. The current transcriptomic signature demonstrates reproducible results across independent cell line and cancer datasets and identifies samples with strong mesenchymal phenotypes likely to be driven by TGFβ. The TGFβ-induced EMT signature may be useful to identify patients with mesenchymal-like tumors who could benefit from targeted therapeutics to inhibit promesenchymal TGFβ signaling and disrupt the metastatic cascade. .

Comparative study of transcriptomics-based scoring metrics for the epithelial-hybrid-mesenchymal spectrum

“…EMT can be driven by diverse biomechanical and/or biochemical stimuli in tumor microenvironments. TGFβ is one of the best-studied drivers of EMT, and a recent study identified a signature specific to TGFβ-induced EMT (Foroutan et al, 2017). EMT scores calculated via any of the three methods -KS, MLR, 76 GS -correlated well with the scores calculated for TGFβ-induced EMT gene signature ( Figure S2), further endorsing the equivalence of these methods in identifying the onset of EMT.…”

“…These approaches have highlighted the dynamical nature of EMT in driving cancer progression in patients (Jolly and Celia-Terrassa, 2019). Further, various approaches to quantify the EMT spectrum of samples based on different signatures of tumor types have been made (Foroutan et al, 2017;Puram et al, 2017). Among all the methods available for EMT scoring, we have compared the ones that are more generalized -KS (Tan et al, 2014), MLR (George et al, 2017), and 76 GS (Byers et al, 2013;Guo et al, 2019).…”

Epithelial‐mesenchymal transition is activated in CD44‐positive malignant ascites tumor cells of gastrointestinal cancer

“…Taking into account the signal transduction of the ATC, the genetic status of TGF‐beta‐related genes should be discussed. Generally, somatic mutations of TGF‐beta signaling‐related genes are reported to contribute to unresponsiveness to TGF‐beta‐induced EMT . In particular, SMAD4 mutation occurs in 32% of pancreatic cancer, 16% of non‐hyper‐mutated colorectal cancer and 8% of non‐hyper‐mutated gastric cancer patients .…”

“…Here, we showed reported to contribute to unresponsiveness to TGF-beta-induced EMT. 24 In particular, SMAD4 mutation occurs in 32% of pancreatic cancer, 25 16% of non-hyper-mutated colorectal cancer 26 and 8% of non-hyper-mutated gastric cancer patients. 27 Dominant negative mutation of SMAD2, SMAD3 and SMAD4 resulted in unresponsiveness to TGF-beta-induced EMT through disruption of canonical SMAD-mediated TGF-beta signaling, although EMT can be induced in control cells by TGF-beta stimulation.…”

A natural killer cell gene signature predicts melanoma patient survival

“…Phenotype-switching is an important regulatory program involved in the progression of melanoma [29] which has been linked to vemurafenib resistance [30] and general drug resistance [31]. It allows tumor cells to transition between proliferative ("epithelial-like") and invasive ("mesenchymal-like") behaviours, and in melanoma, there is strong evidence that TGF-β is an important driver of EMT/phenotype-switching programs [32], mediated in part by signaling molecules such as thrombospondin 1 [33]. This work examines the LM-MEL panel of melanoma cell lines [34] which we have extensively profiled in the context of phenotype switching [33,35].…”