Analysis of immune subtypes across the epithelial-mesenchymal plasticity spectrum

Chakraborty, Priyanka; Chen, Emily L.; McMullen, Isabelle; Armstrong, Andrew J.; Jolly, Mohit Kumar; Somarelli, Jason A.

doi:10.1016/j.csbj.2021.06.023

Cited by 19 publications

(27 citation statements)

References 103 publications

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“…They compared their metrics to the C1–C6 immune subtypes of Thorsson et al Their predictive analysis showed that different EMT scores were associated with specific immune subtypes. According to the authors, a more epithelial-like score is related to wound healing and IFN-γ subtypes (C1 and C2, respectively), while the most mesenchymal-like score was related to the quiescence subtype (C5), which would be consistent with an increased mesenchymal score also having stemness features [ 69 ]. This was the first step toward a prolific use of molecular signatures associated with EMT in precision therapy, since different molecular subtypes could be efficient prognostic and predictive biomarkers to stratify patient populations.…”

Section: Transcriptional Signatures and Emt Pathways Of Progressionmentioning

confidence: 94%

Epithelial–Mesenchymal Transition Signaling and Prostate Cancer Stem Cells: Emerging Biomarkers and Opportunities for Precision Therapeutics

Chaves

Melo

Saggioro

et al. 2021

Genes

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Prostate cancers may reactivate a latent embryonic program called the epithelial–mesenchymal transition (EMT) during the development of metastatic disease. Through EMT, tumors can develop a mesenchymal phenotype similar to cancer stem cell traits that contributes to metastasis and variation in therapeutic responses. Some of the recurrent somatic mutations of prostate cancer affect EMT driver genes and effector transcription factors that induce the chromatin- and androgen-dependent epigenetic alterations that characterize castrate-resistant prostate cancer (CRPC). EMT regulators in prostate cancer comprise transcription factors (SNAI1/2, ZEB1, TWIST1, and ETS), tumor suppressor genes (RB1, PTEN, and TP53), and post-transcriptional regulators (miRNAs) that under the selective pressures of antiandrogen therapy can develop an androgen-independent metastatic phenotype. In prostate cancer mouse models of EMT, Slug expression, as well as WNT/β-Catenin and notch signaling pathways, have been shown to increase stemness potential. Recent single-cell transcriptomic studies also suggest that the stemness phenotype of advanced prostate cancer may be related to EMT. Other evidence correlates EMT and stemness with immune evasion, for example, activation of the polycomb repressor complex I, promoting EMT and stemness and cytokine secretion through RB1, TP53, and PRC1. These findings are helping clinical trials in CRPC that seek to understand how drugs and biomarkers related to the acquisition of EMT can improve drug response.

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Section: Transcriptional Signatures and Emt Pathways Of Progressionmentioning

confidence: 94%

Epithelial–Mesenchymal Transition Signaling and Prostate Cancer Stem Cells: Emerging Biomarkers and Opportunities for Precision Therapeutics

Chaves

Melo

Saggioro

et al. 2021

Genes

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“…KLF4 correlated negatively with the KS and MLR EMT scoring metrics (higher KS or MLR scores denote a mesenchymal phenotype [56]) but positively with the 76GS scores (higher 76GS scores denote a more epithelial phenotype [56]) (Figure 2D,i). Most EMT-TFs were found to be correlated positively with each other (SNAI1/2, ZEB1/2, and TWIST1) and negatively with KLF4 and the other MET drivers, such as ESRP1/2, OVOL1/2, and GRHL2 [57], which were all positively corelated with KLF4 (Figure 2D,i). Consistent correlations were recapitulated in the RACIPE simulation data for the KLF4-EMT network (Figure 2D,ii), thus underscoring that the gene regulatory network considered in Figure 1A can explain these observed experimental trends for the existence of 'teams' [58] of EMT and MET inducers.…”

Section: Klf4 Promotes An Epithelial Phenotypementioning

confidence: 95%

KLF4 Induces Mesenchymal–Epithelial Transition (MET) by Suppressing Multiple EMT-Inducing Transcription Factors

Subbalakshmi

Sahoo

McMullen

et al. 2021

Cancers

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Epithelial–Mesenchymal Plasticity (EMP) refers to reversible dynamic processes where cells can transition from epithelial to mesenchymal (EMT) or from mesenchymal to epithelial (MET) phenotypes. Both these processes are modulated by multiple transcription factors acting in concert. While EMT-inducing transcription factors (TFs)—TWIST1/2, ZEB1/2, SNAIL1/2/3, GSC, and FOXC2—are well-characterized, the MET-inducing TFs are relatively poorly understood (OVOL1/2 and GRHL1/2). Here, using mechanism-based mathematical modeling, we show that transcription factor KLF4 can delay the onset of EMT by suppressing multiple EMT-TFs. Our simulations suggest that KLF4 overexpression can promote a phenotypic shift toward a more epithelial state, an observation suggested by the negative correlation of KLF4 with EMT-TFs and with transcriptomic-based EMT scoring metrics in cancer cell lines. We also show that the influence of KLF4 in modulating the EMT dynamics can be strengthened by its ability to inhibit cell-state transitions at the epigenetic level. Thus, KLF4 can inhibit EMT through multiple parallel paths and can act as a putative MET-TF. KLF4 associates with the patient survival metrics across multiple cancers in a context-specific manner, highlighting the complex association of EMP with patient survival.

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“…have shown coclustering of tumors with the tissue of origin. For example, coclustering was observed for gastrointestinal tumors including (COADREAD, STAD, and ESCA), kidney cancers (KIRP and KIRC), and squamous histology cancers (LUSC, HNSC, CESC, ESCA, and BLCA) (43)(44)(45). Our PCA did not show similar clustering; for example, lung cancers (LUSC and LUAD), head and neck cancers (HNSCC), and esophageal cancers (ESCA) were not clustering together in our analysis.…”

Section: Discussionmentioning

confidence: 55%

Pan-Cancer Analysis Shows Enrichment of Macrophages, Overexpression of Checkpoint Molecules, Inhibitory Cytokines, and Immune Exhaustion Signatures in EMT-High Tumors

et al. 2022

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Epithelial–mesenchymal transition (EMT) is a highly dynamic process that occurs under normal circumstances; however, EMT is also known to play a central role in tumor progression and metastasis. Furthermore, role of tumor immune microenvironment (TIME) in shaping anticancer immunity and inducing the EMT is also well recognized. Understanding the key features of EMT is critical for the development of effective therapeutic interventions. Given the central role of EMT in immune escape and cancer progression and treatment, we have carried out a pan-cancer TIME analysis of The Cancer Genome Atlas (TCGA) dataset in context to EMT. We have analyzed infiltration of various immune cells, expression of multiple checkpoint molecules and cytokines, and inflammatory and immune exhaustion gene signatures in 22 cancer types from TCGA dataset. A total of 16 cancer types showed a significantly increased (p < 0.001) infiltration of macrophages in EMT-high tumors (mesenchymal samples). Furthermore, out of the 17 checkpoint molecules we analyzed, 11 showed a significant overexpression (p < 0.001) in EMT-high samples of at least 10 cancer types. Analysis of cytokines showed significant enrichment of immunosuppressive cytokines—TGFB1 and IL10—in the EMT-high group of almost all cancer types. Analysis of various gene signatures showed enrichment of inflammation, exhausted CD8+ T cells, and activated stroma signatures in EMT-high tumors. In summary, our pan-cancer EMT analysis of TCGA dataset shows that the TIME of EMT-high tumors is highly immunosuppressive compared to the EMT-low (epithelial) tumors. The distinctive features of EMT-high tumors are as follows: (i) the enrichment of tumor-associated macrophages, (ii) overexpression of immune checkpoint molecules, (iii) upregulation of immune inhibitory cytokines TGFB1 and IL10, and (iv) enrichment of inflammatory and exhausted CD8+ T-cell signatures. Our study shows that TIMEs of different EMT groups differ significantly, and this would pave the way for future studies analyzing and targeting the TIME regulators for anticancer immunotherapy.

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Analysis of immune subtypes across the epithelial-mesenchymal plasticity spectrum

Cited by 19 publications

References 103 publications

Epithelial–Mesenchymal Transition Signaling and Prostate Cancer Stem Cells: Emerging Biomarkers and Opportunities for Precision Therapeutics

Epithelial–Mesenchymal Transition Signaling and Prostate Cancer Stem Cells: Emerging Biomarkers and Opportunities for Precision Therapeutics

KLF4 Induces Mesenchymal–Epithelial Transition (MET) by Suppressing Multiple EMT-Inducing Transcription Factors

Pan-Cancer Analysis Shows Enrichment of Macrophages, Overexpression of Checkpoint Molecules, Inhibitory Cytokines, and Immune Exhaustion Signatures in EMT-High Tumors

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