Abstract
The Epithelial–mesenchymal transition (EMT) is a cellular process implicated in embryonic development, wound healing, and pathological conditions such as cancer metastasis and fibrosis. Cancer cells undergoing EMT exhibit enhanced aggressive behavior characterized by drug resistance, tumor-initiation potential, and the ability to evade the immune system. Recent in silico, in vitro, and in vivo evidence indicates that EMT is not an all-or-none process; instead, cells can stably acquire one or more hybrid epithelial/mesenchymal (E/M) phenotypes which often can be more aggressive than purely E or M cell populations. Thus, the EMT status of cancer cells can prove to be a critical estimate of patient prognosis. Recent attempts have employed different transcriptomics signatures to quantify EMT status in cell lines and patient tumors. However, a comprehensive comparison of these methods, including their accuracy in identifying cells in the hybrid E/M phenotype(s), is lacking. Here, we compare three distinct metrics that score EMT on a continuum, based on the transcriptomics signature of individual samples. Our results demonstrate that these methods exhibit good concordance among themselves in quantifying the extent of EMT in a given sample. Moreover, scoring EMT using any of the three methods discerned that cells can undergo varying extents of EMT across tumor types. Separately, our analysis also identified tumor types with maximum variability in terms of EMT and associated an enrichment of hybrid E/M signatures in these samples. Moreover, we also found that the multinomial logistic regression (MLR)-based metric was capable of distinguishing between “pure” individual hybrid E/M vs. mixtures of E and M cells. Our results, thus, suggest that while any of the three methods can indicate a generic trend in the EMT status of a given cell, the MLR method has two additional advantages: (a) it uses a small number of predictors to calculate the EMT score and (b) it can predict from the transcriptomic signature of a population whether it is comprised of “pure” hybrid E/M cells at the single-cell level or is instead an ensemble of E and M cell subpopulations.
Highlights
We used three different Epithelial–mesenchymal transition (EMT) scoring methods to quantify the extent of EMT in given transcriptomics data; each method utilizes a distinct gene set as well as a different underlying algorithm
This method has no specific predefined range of values, the range of values obtained are bounded by the maximal possible value of gene expression detected by microarray
While multinomial logistic regression (MLR) and Kolmogorov–Smirnov test (KS) methods are absolute, requiring a fixed transcript signature for EMT score calculation, the 76-gene EMT signature (76GS) method of EMT scoring depends on the number and nature of samples analyzed in a given dataset
Summary
The epithelial–mesenchymal transition (EMT) is a cell biological process crucial for various aspects of tumor aggressiveness – cancer metastasis (Jolly et al, 2017), resistance against cell death (Huang et al, 2013), metabolic reprogramming (Thomson et al, 2019), refractory response to chemotherapy and radiotherapy (Kurrey et al, 2009), tumor-initiation potential (Jolly et al, 2014), and immune evasion (Tripathi et al, 2016; Terry et al, 2017) – eventually affecting patient survival (Tan et al, 2014). Thought of as binary, EMT is considered as a complex process involving one or more hybrid epithelial/mesenchymal (E/M) states (Jolly and Celia-Terrassa, 2019). These hybrid E/M states can be more plastic and tumorigenic than “purely E” or “purely M” ones, constituting the “fittest” phenotype for metastasis (GrosseWilde et al, 2015; Bierie et al, 2017; Pastushenko et al, 2018; Kröger et al, 2019; Tripathi et al, 2020). Computational methods aimed at quantifying EMT on a continuous spectrum in order to enhance diagnostic, prognostic, and therapeutic intervention are indispensable
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