Dysregulation of EMT Drives the Progression to Clinically Aggressive Sarcomatoid Bladder Cancer

Guo, Charles C.; Majewski, Tadeusz; Zhang, Li; Yao, Hui; Bondaruk, Jolanta; Wang, Yan; Zhang, Shizhen; Wang, Ziqiao; Lee, June Goo; Lee, Sang Kyou; Cogdell, David; Zhang, Miao; Wei, Peng; Grossman, H. Barton; Kamat, Ashish M.; Duplisea, Jonathan J.; Ferguson, James E.; Huang, He; Dadhania, Vipulkumar; Gao, Jianjun; Dinney, Colin P.; Weinstein, John N.; Baggerly, Keith A.; McConkey, David J.; Czerniak, Bogdan

doi:10.1016/j.celrep.2019.04.048

Cited by 111 publications

(103 citation statements)

References 70 publications

(109 reference statements)

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“…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). These three methods use different combinations of genes and metrics; however, they show a very good concordance amongst them in terms of identifying an empirical trend along the EMT axis.…”

Section: Discussionmentioning

confidence: 99%

“…The EMT scores were calculated based on a 76-gene expression signature reported (Byers et al, 2013) (Table S1) and the metric mentioned based on that gene signature (Guo et al, 2019). For each sample, the score was calculated as a weighted sum of 76 gene expression levels and the scores were centered by subtracting the mean across all tumor samples so that the grand mean of the score was zero.…”

Section: Gsmentioning

confidence: 99%

“…First, a 76-gene EMT signature (76GS; hereafter referred to as 76GS method) was developed and validated using gene expression from non-small cell lung cancer (NSCLC) cell lines and patients treated in the BATTLE trial (Byers et al, 2013). This scoring method calculates EMT scores based on a weighted sum of the expression levels of 76 genes; the weight factor of a gene is the correlation coefficient between the expression level of that gene and that of CDH1 (E-cadherin) in that dataset; thus, the absolute EMT scores of epithelial samples using the 76 GS method are relatively higher than those of mesenchymal samples (Guo et al, 2019). Second, an EMT score separately for cell lines and tumors was developed based on a two-sample Kolmogorov-Smirnov test (KS; hereafter referred to as the KS method).…”

Section: Introductionmentioning

confidence: 99%

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

Chakraborty

George

Tripathi

et al. 2020

Preprint

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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 immune system. Recent in silico, in vitro, and in vivo evidence indicates that EMT is not an all-or-none process; instead, cells stably acquire one or more hybrid epithelial/ mesenchymal (E/M) phenotypes which often can be more aggressive than purely epithelial or mesenchymal 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 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 epithelial (E) and mesenchymal (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.

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Section: Discussionmentioning

confidence: 99%

Section: Gsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chakraborty

George

Tripathi

et al. 2020

Preprint

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show abstract

“…The EMT scores were calculated based on a 76-gene expression signature reported (Byers et al, 2013; Supplementary Table S1) and the metric mentioned based on that gene signature (Guo et al, 2019). For each sample, the score was calculated as a weighted sum of 76 gene expression levels and the scores were centered by subtracting the mean across all tumor samples so that the grand mean of the score was zero.…”

Section: Gsmentioning

confidence: 99%

“…First, a 76-gene EMT signature (76GS; hereafter referred to as the 76GS method) was developed and validated using gene expression from non-small cell lung cancer (NSCLC) cell lines and patients treated in the BATTLE trial (Byers et al, 2013). This scoring method calculates EMT scores based on a weighted sum of the expression levels of 76 genes; the weight factor of a gene is the correlation coefficient between the expression level of that gene and that of CDH1 (E-cadherin) in that dataset; thus, the absolute EMT scores of E samples using the 76 GS method are relatively higher than those of M samples (Guo et al, 2019). Second, an EMT score separately for cell lines and tumors was developed based on a two-sample Kolmogorov-Smirnov test (KS; hereafter referred to as the KS method).…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Transcriptomics-Based Scoring Metrics for the Epithelial-Hybrid-Mesenchymal Spectrum

Chakraborty

George

Tripathi

et al. 2020

Front. Bioeng. Biotechnol.

106

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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.

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Greater utility of molecular subtype rather than epithelial‐to‐mesenchymal transition (EMT) markers for prognosis in high‐risk non‐muscle‐invasive (HGT1) bladder cancer

Ottley

Pell

Brazier

et al. 2020

The Journal of Pathology CR

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Approximately 75% of bladder cancers are non-muscle invasive (NMIBC). Of these, up to 53% of cases progress to life-threatening muscle-invasive bladder cancer (MIBC). Patients with high-grade stage T1 (HGT1) NMIBC frequently undergo radical cystectomy (RC), although this represents overtreatment for many. Identification of progressors versus non-progressors could spare unnecessary treatment. Recent studies have confirmed that urothelial carcinoma is composed of two main molecular subtypes, basal and luminal, with 12% of basal tumours exhibiting epithelial-to-mesenchymal transition (EMT). Levels of immune cell infiltration have been shown to be subtype-specific. Here, we performed immunohistochemistry (IHC) for 11 antibodies relating to molecular subtypes or EMT in 26 cases of HGT1 urothelial carcinoma cases with 6 matched samples subsequently obtained at cystectomy (n = 6; 1 muscle-invasive, 5 non-muscle-invasive; 3 = pTis, 1 = pT1, 1 = pTa) and at recurrence (n = 2, pT2). RNAScope was also conducted in a subset. Expression patterns in HGT1 specimens versus MIBC (pT2 +) were examined, and correlated with disease-specific survival (DSS). Levels of stromal tumour-infiltrating lymphocytes (sTILs) were assessed manually to determine whether lymphocyte infiltration was associated with DSS and whether differences existed between HGT1 and MIBC. Molecular subtype markers demonstrated increased prognostic potential compared to the EMT markers assessed. Increased expression of the luminal markers FOXA1 and SCUBE2, were found to be significantly associated with better DFS. No EMT markers were significantly associated with DFS. In areas of non-invasive papillary urothelial carcinoma, but not invasive carcinoma, sTIL levels were found to be significantly associated with DFS. While differences were observed between HGT1 cases that progressed versus those that did not, a larger cohort study is required for validation of these findings. Taken together, an emphasis on molecular subtype markers, rather than EMT markers, may be preferable when studying biomarkers of HGT1 urothelial carcinoma in the future.

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Dysregulation of EMT Drives the Progression to Clinically Aggressive Sarcomatoid Bladder Cancer

Cited by 111 publications

References 70 publications

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

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

Comparative Study of Transcriptomics-Based Scoring Metrics for the Epithelial-Hybrid-Mesenchymal Spectrum

Greater utility of molecular subtype rather than epithelial‐to‐mesenchymal transition (EMT) markers for prognosis in high‐risk non‐muscle‐invasive (HGT1) bladder cancer

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