Genomic and clinical characterization of stromal infiltration markers in prostate cancer

Mahal, Brandon A.; Alshalalfa, Mohammed; Zhao, Shuang G.; Beltran, Himisha; Chen, William S.; Chipidza, Fallon E.; Davicioni, Elai; Karnes, R. Jeffrey; Ku, Sheng Yu; Lotan, Tamara L.; Muralidhar, Vinayak; Rebbeck, Timothy R.; Schaeffer, Edward M.; Spratt, Daniel E.; Feng, Felix Y.; Nguyen, Paul L.

doi:10.1002/cncr.32688

Cited by 10 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, low expression of desmin, an intermediate filament protein typical of differentiated smooth muscle cells [25], and α-SMA is associated with shorter biochemical recurrence (BCR)-free survival [16]. In addition, a high stromal gene expression score in primary PCa (determined on the expression of stromal infiltration markers) correlates with high genomic risk scores and metastatic progression [108]. Stromal cell infiltration correlates with decreased DNA repair gene expression, but also with better sensitivity to radiation therapies [108].…”

Section: The Reactive Stroma In Prostate Cancermentioning

confidence: 99%

“…In addition, a high stromal gene expression score in primary PCa (determined on the expression of stromal infiltration markers) correlates with high genomic risk scores and metastatic progression [108]. Stromal cell infiltration correlates with decreased DNA repair gene expression, but also with better sensitivity to radiation therapies [108]. Whole genome and whole exome sequencing analysis on PCa patient-derived xenografts has identified a 93-gene stroma-derived metastatic signature that can independently predict metastatic progression even in intermediate-risk primary PCa patients [109].…”

Section: The Reactive Stroma In Prostate Cancermentioning

confidence: 99%

“…Such 'stroma osteotropism' phenomenon could be explained as a secondary effect induced by the PCa cells, which upregulate osteoblast markers prior to metastasis (osteomimicry). Large-scale expression profiling of primary PCa tumors with follow-up clinical information has in fact revealed that high expression of stroma infiltration markers correlated with a higher risk of metastasis [108], highlighting the prognostic value of the primary stroma. Characterization of CAF subpopulations, based on multiple marker detection (α-SMA, caveolin-1, vimentin) by multiplex immunohistochemistry on primary PCa tissue microarray, indicated that high fibroblast infiltration significantly correlates to CRPC progression and PCa-related mortality, as assessed independently in two clinical cohorts [174].…”

Section: Role Of Cafs In Pca Bone Metastasismentioning

confidence: 99%

See 2 more Smart Citations

The Role of Cancer-Associated Fibroblasts in Prostate Cancer Tumorigenesis

Bonollo

Thalmann

Julio

et al. 2020

Cancers

View full text Add to dashboard Cite

Tumors strongly depend on their surrounding tumor microenvironment (TME) for growth and progression, since stromal elements are required to generate the optimal conditions for cancer cell proliferation, invasion, and possibly metastasis. Prostate cancer (PCa), though easily curable during primary stages, represents a clinical challenge in advanced stages because of the acquisition of resistance to anti-cancer treatments, especially androgen-deprivation therapies (ADT), which possibly lead to uncurable metastases such as those affecting the bone. An increasing number of studies is giving evidence that prostate TME components, especially cancer-associated fibroblasts (CAFs), which are the most abundant cell type, play a causal role in PCa since the very early disease stages, influencing therapy resistance and metastatic progression. This is highlighted by the prognostic value of the analysis of stromal markers, which may predict disease recurrence and metastasis. However, further investigations on the molecular mechanisms of tumor–stroma interactions are still needed to develop novel therapeutic approaches targeting stromal components. In this review, we report the current knowledge of the characteristics and functions of the stroma in prostate tumorigenesis, including relevant discussion of normal prostate homeostasis, chronic inflammatory conditions, pre-neoplastic lesions, and primary and metastatic tumors. Specifically, we focus on the role of CAFs, to point out their prognostic and therapeutic potential in PCa.

show abstract

Section: The Reactive Stroma In Prostate Cancermentioning

confidence: 99%

Section: The Reactive Stroma In Prostate Cancermentioning

confidence: 99%

Section: Role Of Cafs In Pca Bone Metastasismentioning

confidence: 99%

See 1 more Smart Citation

The Role of Cancer-Associated Fibroblasts in Prostate Cancer Tumorigenesis

Bonollo

Thalmann

Julio

et al. 2020

Cancers

View full text Add to dashboard Cite

show abstract

“…We estimated immune score and stromal score based on gene expression data using the ESTIMATE algorithm ( Xie et al, 2019 ; Mahal et al, 2020 ; Pan et al, 2020 ). Both scores were correlated with lymphatic invasion ( P = 0.0005 and P = 0.0008, respectively).…”

Section: Resultsmentioning

confidence: 99%

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Guo

Wang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Ovarian cancer (OC) is the deadliest gynecological cancer in women. Immune cell infiltration has a critical role in regulating carcinogenesis and prognosis in OC. To identify prognostic genes relevant to the tumor microenvironment in OC, we investigated the association between OC and gene expression profiles. Results obtained with the ESTIMATE R tool showed that immune score and stromal score were correlated with lymphatic invasion, and high immune score predicted a favorable prognosis. A total of 342 common differentially expressed genes were identified according to the two scores; these genes were mainly involved in immune response, extracellular region, and serine-type endopeptidase activity. Three immune-related prognostic genes were selected by univariate and multivariate Cox regression analysis. We further established a prognostic model and validated the prognostic value of three hub genes in different databases; our results showed that this model could accurately predict survival and evaluate prognosis independent of clinical characteristics. Three hub genes have prognostic value in OC. TIMER analysis revealed that the three genes were correlated with different immune cells. Low levels of macrophage infiltration and high levels of CD4+ T cell infiltration were associated with favorable survival outcomes. Arm-level gain of GYPC was correlated with neutrophils and dendritic cells. These findings indicate that CXCR4, GYPC, and MMP12 modulate prognosis via effects on the infiltration of immune cells. Thus, these genes represent potential targets for immune therapy in OC.

show abstract

“…Tumor tissues using Expression data) is an algorithm designed by Yoshihara et.al to calculate an immune score based on differential gene signatures between stromal cells and tumor cells 9 . This tool has been used to identify microenvironment-related markers in several solid tumors [10][11][12] .…”

Section: Estimate (Estimation Of Stromal and Immune Cells In Malignantmentioning

confidence: 99%

Establishment of a prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer

Kong¹,

Zhang²,

Gao³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Ovarian cancer is one of the most lethal gynecological cancers,and is in the top five cancer types associated with death in women. Immune infiltration of ovarian cancer is a critical factor in determining patient's prognosis. In addition to immune infiltration, key mutations also have a greater impact on the development and prognosis of OV.Methods Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on immune genes and clinical information. The IGCI score has been verified by K-M curves, ROC curves, C-index on test set. Based on mutation data from TCGA, we identified key mutations related to immune infiltration by Chi-squared tests and we also did survival analysis of these mutations.Results In this section, we found 46 genes related to disease occurrence and immune infiltration, IGCI score was established based on six factors: Age, White, Pharmaceutical, FGF7, CCR1 and CD14. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, P < 0.05) and CIN25 (C-index = 0.571, P < 0.05). Chi-squared tests revealed that 6 mutations are significantly (P < 0.05) related to immune infiltration : BRCA1, ZNF462, VWF, RBAK, RB1, and ADGRV1. According to mutation survival analysis, we found another 5 key mutations significantly related to patient prognosis (P < 0.05): CSMD3, FLG2, HMCN1, TOP2A, TRRAP. RB1 and CSMD3 mutations had small p-value (P < 0.1) in both Chi-squared tests and survival analysis. Then we conducted a drug sensitivity analysis of key mutation and found when RB1 mutation occurs, the efficacy of six anti-tumor drugs has changed significantly (P < 0.05).Conclusion Based on immune-related genes and clinical information, we have established an ovarian cancer prognosis score that is superior to other methods. At the same time, RB1 and CSMD3 gene mutations are considered to be closely related to ovarian cancer immunity and prognosis.

show abstract

Genomic and clinical characterization of stromal infiltration markers in prostate cancer

Cited by 10 publications

References 24 publications

The Role of Cancer-Associated Fibroblasts in Prostate Cancer Tumorigenesis

The Role of Cancer-Associated Fibroblasts in Prostate Cancer Tumorigenesis

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Establishment of a prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer

Contact Info

Product

Resources

About