Systematic Identification of Druggable Epithelial–Stromal Crosstalk Signaling Networks in Ovarian Cancer

Yeung, Tsz-Lun; Sheng, Jianting; Leung, Cecilia S.; Li, Fuhai; Kim, Jaeyeon; Ho, Samuel; Matzuk, Martin M.; Lu, Karen H.; Wong, Stephen T.C.; Mok, Samuel C.

doi:10.1093/jnci/djy097

Cited by 42 publications

(43 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another key component of the cancer microenvironment are cancer associated fibroblasts (CAFs). Yeung et al examined the gene expression profile of microdissected ovarian cancer cells and CAFs derived from HGSOC tumors [37]. They observed that cancer cells overexpress TGF-β, while CAFs can respond through activated Smad signaling.…”

Section: The Role Of Fibronectin In Ovarian Cancermentioning

confidence: 99%

Fibronectin and Periostin as Prognostic Markers in Ovarian Cancer

Kujawa

Zembala‐Nożyńska

Cortez

et al. 2020

Cells

View full text Add to dashboard Cite

Previously, based on a DNA microarray experiment, we identified a 96-gene prognostic signature associated with the shorter survival of ovarian cancer patients. We hypothesized that some differentially expressed protein-coding genes from this signature could potentially serve as prognostic markers. The present study was aimed to validate two proteins, namely fibronectin (FN1) and periostin (POSTN), in the independent set of ovarian cancer samples. Both proteins are mainly known as extracellular matrix proteins with many important functions in physiology. However, there are also indications that they are implicated in cancer, including ovarian cancer. The expression of these proteins was immunohistochemically analyzed in 108 surgical samples of advanced ovarian cancer (majority: high-grade serous) and additionally on tissue arrays representing different stages of the progression of ovarian and fallopian tube epithelial tumors, from normal epithelia, through benign tumors, to adenocarcinomas of different stages. The correlation with clinical, pathological, and molecular features was evaluated. Kaplan–Meier survival analysis and Cox-proportional hazards models were used to estimate the correlation of the expression levels these proteins with survival. We observed that the higher expression of fibronectin in the tumor stroma was highly associated with shorter overall survival (OS) (Kaplan–Meier analysis, log-rank test p = 0.003). Periostin was also associated with shorter OS (p = 0.04). When we analyzed the combined score, calculated by adding together individual scores for stromal fibronectin and periostin expression, Cox regression demonstrated that this joint FN1&POSTN score was an independent prognostic factor for OS (HR = 2.16; 95% CI: 1.02–4.60; p = 0.044). The expression of fibronectin and periostin was also associated with the source of ovarian tumor sample: metastases showed higher expression of these proteins than primary tumor samples (χ2 test, p = 0.024 and p = 0.032). Elevated expression of fibronectin and periostin was also more common in fallopian cancers than in ovarian cancers. Our results support some previous observations that fibronectin and periostin have a prognostic significance in ovarian cancer. In addition, we propose the joint FN1&POSTN score as an independent prognostic factor for OS. Based on our results, it may also be speculated that these proteins are related to tumor progression and/or may indicate fallopian–epithelial origin of the tumor.

show abstract

Section: The Role Of Fibronectin In Ovarian Cancermentioning

confidence: 99%

Fibronectin and Periostin as Prognostic Markers in Ovarian Cancer

Kujawa

Zembala‐Nożyńska

Cortez

et al. 2020

Cells

View full text Add to dashboard Cite

show abstract

“…The tumor epithelium and surrounding microenvironment closely interact through the extracellular matrix or secreted factors such as exosomes, cytokines, and angiogenic factors (2). The depiction of a transcriptome map of the altered biological processes in the epithelial and stromal compartments will not only allow investigators to comprehensively understand the mechanism of cancer initiation and the complex coevolving relationships between the intrinsic and extrinsic factors of tumors (3) but also help in the detection of druggable epithelial-stromal crosstalk targets (4). Nishida previously used a laser capture microdissection (LCM)-processed miRNA and gene expression microarray to reveal two miRNA clusters with high expression in the cancer stroma (5).…”

Section: Introductionmentioning

confidence: 99%

Identification of Distinct Immune Subtypes in Colorectal Cancer Based on the Stromal Compartment

Shen

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

“…LRRC32 is a key regulator of TGF-β activation 52 . Together with increased TGFB1 secreted by CD73_1 and CD73_2 CAFs, LRRC32 may generate an immune-suppressive and pro-tumorigenic microenvironment to support the malignant phenotype of ovarian cancer cells, as we previously described 53,54 . Both FN1 and TNC encode extracellular proteins that have previously been shown to be associated with short progression-free survival and increased migration-inducing potential in HGSC 55,56 .…”

Section: Correlations Between Cell Subtype Density and Transcriptomicmentioning

confidence: 62%

“…Extensive details of specimen handling, RNA extraction and ampli cation, microarray hybridization, and quality-control procedures have been described previously 54 .…”

Section: Microdissection and Microarray Analysis Of Tissue Samplesmentioning

confidence: 99%

Deep learning on image-omics data in identifying prognostic immune biomarkers for ovarian cancer

Zhu¹,

Ferri-Borgogno²,

Sheng³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Although stromal and immune cells in the tumor microenvironment have been shown to directly affect tumor growth and chemoresistance, how the interactions among stromal and immune cells and their spatially resolved cell heterogeneity would influence ovarian patients’ survival remains largely unknown. To fill this gap, we developed a new imageomics method that (i) incorporates an artificial intelligence–based analytics pipeline for imaging mass cytometry (IMC) to increase the accuracy of cell segmentation and spatial information extraction in order to identify immune biomarkers and their interactions that can predict overall survival rates in patients with treatment-naïve ovarian cancer, and (ii) integrates quantitated spatial IMC image data with microdissected tumor and stromal transcriptomic data from the same patients as well as single-cell RNA sequencing data to detect genes correlated with the prognostic features and postulate novel mechanisms by which these genes contribute to the prognostic features.

show abstract

Systematic Identification of Druggable Epithelial–Stromal Crosstalk Signaling Networks in Ovarian Cancer

Abstract: Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer-stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.

Cited by 42 publications

References 41 publications

Fibronectin and Periostin as Prognostic Markers in Ovarian Cancer

Fibronectin and Periostin as Prognostic Markers in Ovarian Cancer

Identification of Distinct Immune Subtypes in Colorectal Cancer Based on the Stromal Compartment

Deep learning on image-omics data in identifying prognostic immune biomarkers for ovarian cancer

Contact Info

Product

Resources

About