Stromal responses among common carcinomas correlated with clinicopathologic features

Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC)

BACKGROUND

Predictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality.

METHODS

RPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models.

RESULTS

The distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06-2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56-2.62; P = 0.00647).

CONCLUSIONS

RPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.

Targeting the tumour stroma to improve cancer therapy

Cancers are not composed merely of cancer cells alone; instead, they are complex 'ecosystems' comprising many different cell types and noncellular factors. The tumour stroma is a critical component of the tumour microenvironment, where it has crucial roles in tumour initiation, progression, and metastasis. Most anticancer therapies target cancer cells specifically, but the tumour stroma can promote the resistance of cancer cells to such therapies, eventually resulting in fatal disease. Therefore, novel treatment strategies should combine anticancer and antistromal agents. Herein, we provide an overview of the advances in understanding the complex cancer cell-tumour stroma interactions and discuss how this knowledge can result in more effective therapeutic strategies, which might ultimately improve patient outcomes.

A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer

The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35-40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name "Radiomic Prognostic Vector" (RPV). RPV reliably identifies the 5% of patients with median overall survival less than 2 years, significantly improves established prognostic methods, and is validated in two independent, multi-center cohorts. Furthermore, genetic, transcriptomic and proteomic analysis from two independent datasets elucidate that stromal phenotype and DNA damage response pathways are activated in RPV-stratified tumors. RPV and its associated analysis platform could be exploited to guide personalized therapy of EOC and is potentially transferrable to other cancer types.

Repurposed drug screen identifies cardiac glycosides as inhibitors of TGF-β-induced cancer-associated fibroblast differentiation

The tumor microenvironment, primarily composed of myofibroblasts, directly influences the progression of solid tumors. Through secretion of growth factors, extracellular matrix deposition, and contractile mechanotransduction, myofibroblasts, or cancer-associated fibroblasts (CAFs), support angiogenesis and cancer cell invasion and metastasis. The differentiation of fibroblasts to CAFs is primarily induced by TGF-β from cancer cells. To discover agents capable of blocking CAF differentiation, we developed a high content immunofluorescence-based assay to screen repurposed chemical libraries utilizing fibronectin expression as an initial CAF marker. Screening of the Prestwick chemical library and NIH Clinical Collection repurposed drug library, totaling over 1700 compounds, identified cardiac glycosides as particularly potent CAF blocking agents. Cardiac glycosides are traditionally used to regulate intracellular calcium by inhibiting the Na+/K+ ATPase to control cardiac contractility. Herein, we report that multiple cardiac glycoside compounds, including digoxin, are able to inhibit TGF-β-induced fibronectin expression at low nanomolar concentrations without undesirable cell toxicity. We found this inhibition to hold true for multiple fibroblast cell lines. Using real-time qPCR, we determined that digoxin prevented induction of multiple CAF markers. Furthermore, we report that digoxin is able to prevent TGF-β-induced fibroblast contraction of extracellular matrix, a major phenotypic consequence of CAF differentiation. Assessing the mechanism of inhibition, we found digoxin reduced SMAD promoter activity downstream of TGF-β, and we provide data that the effect is through inhibition of its known target, the Na+/K+ ATPase. These findings support a critical role for calcium signaling during CAF differentiation and highlight a novel, repurposable modality for cancer therapy.

Tumor-extracellular matrix interactions: Identification of tools associated with breast cancer progression

Several evidences support the concept that cancer development and progression are not entirely cancer cell-autonomous processes, but may be influenced, and possibly driven, by cross-talk between cancer cells and the surrounding microenvironment in which, besides immune cells, stromal cells and extracellular matrix (ECM) play a major role in regulating distinct biologic processes. Stroma and ECM-related signatures proved to influence breast cancer progression, and to contribute to the identification of tumor phenotypes resistant to cytotoxic and hormonal treatments. The possible clinical implications of the interplay between tumor cells and the microenvironment, with special reference to ECM remodelling, will be discussed in this review.

Genetic instability in the tumor microenvironment: a new look at an old neighbor

The recent exponential increase in our knowledge of cellular and molecular mechanisms involved in carcinogenesis has largely failed to translate into new therapies and clinical practices. This lack of success may result in part from the fact that most studies focus on tumor cells as potential therapeutic targets and neglect the complex microenvironment that undergoes profound changes during tumor development. Furthermore, an unfortunate association of factors such as tumor genetic complexity, overestimation of biomarker and drug potentials, as well as a poor understanding of tumor microenvironment in diagnosis and prognosis leads to the current levels of treatment failure regarding a vast majority of cancer types. A growing body of evidence points to the importance of the functional diversity of immune and structural cells during tumor development. In this sense, the lack of technologies that would allow for molecular screening of individual stromal cell types poses a major challenge for the development of therapies targeting the tumor microenvironment. Progress in microenvironment genetic studies represents a formidable opportunity for the development of new selective drugs because stromal cells have lower mutation rates than malignant cells, and should prove to be good targets for therapy.

Clinically Relevant Molecular Subtypes in Leiomyosarcoma

PURPOSE

Leiomyosarcoma is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for leiomyosarcoma. Recognition of different molecular subtypes is necessary to evaluate novel therapeutic options. In a previous study on 51 leiomyosarcomas, we identified three molecular subtypes in leiomyosarcoma. The current study was performed to determine whether the existence of these subtypes could be confirmed in independent cohorts.

EXPERIMENTAL DESIGN

Ninety-nine cases of leiomyosarcoma were expression profiled with 3'end RNA-Sequencing (3SEQ). Consensus clustering was conducted to determine the optimal number of subtypes.

RESULTS

We identified 3 leiomyosarcoma molecular subtypes and confirmed this finding by analyzing publically available data on 82 leiomyosarcoma from The Cancer Genome Atlas (TCGA). We identified two new formalin-fixed, paraffin-embedded tissue-compatible diagnostic immunohistochemical markers; LMOD1 for subtype I leiomyosarcoma and ARL4C for subtype II leiomyosarcoma. A leiomyosarcoma tissue microarray with known clinical outcome was used to show that subtype I leiomyosarcoma is associated with good outcome in extrauterine leiomyosarcoma while subtype II leiomyosarcoma is associated with poor prognosis in both uterine and extrauterine leiomyosarcoma. The leiomyosarcoma subtypes showed significant differences in expression levels for genes for which novel targeted therapies are being developed, suggesting that leiomyosarcoma subtypes may respond differentially to these targeted therapies.

CONCLUSIONS

We confirm the existence of 3 molecular subtypes in leiomyosarcoma using two independent datasets and show that the different molecular subtypes are associated with distinct clinical outcomes. The findings offer an opportunity for treating leiomyosarcoma in a subtype-specific targeted approach.

The polyphenols (-)-epigallocatechin-3-gallate and luteolin synergistically inhibit TGF-β-induced myofibroblast phenotypes through RhoA and ERK inhibition

The presence of reactive stroma, predominantly composed of myofibroblasts, is directly associated with and drives prostate cancer progression. We have previously shown that (−)-Epigallocatechin-3-gallate (EGCG), in the form of Polyphenon E, significantly decreases serum levels of HGF and VEGF in prostate cancer patients. Given that HGF and VEGF are secreted from surrounding tumor myofibroblasts, these observations suggested that EGCG may inhibit prostate cancer-associated myofibroblast differentiation. Herein, we demonstrate that micromolar combinations of EGCG and a second polyphenol, luteolin, synergistically inhibit TGF-β-induced myofibroblast phenotypes in prostate fibroblast cell lines, as observed primarily by potentiation of fibronectin expression. Functionally, EGCG and luteolin inhibited TGF-β-induced extracellular matrix contraction, an enhancer of tumor cell invasion. EGCG and luteolin inhibited downstream TGF-β-induced signaling, including activation of ERK and AKT, respectively, but mechanistically, only ERK appeared to be necessary for TGF-β-induced fibronectin expression. Furthermore, neither EGCG nor luteolin affected Smad signaling or nuclear translocation. Rho signaling was found to be necessary for TGF-β-induced fibronectin expression and EGCG and luteolin each reduced RhoA activation. Finally, EGCG and luteolin were shown to reverse TGF-β-induced fibronectin expression, implicating that these natural compounds may be useful not only in preventing but also in treating already activated myofibroblasts and the diseases they cause, including cancer. The ability of EGCG and luteolin to synergistically target myofibroblasts suggests that combined clinical use of these compounds could prevent or reverse cancer progression through targeting the tumor microenvironment, in addition to the tumor itself.

Cancer/stroma interplay via cyclooxygenase-2 and indoleamine 2,3-dioxygenase promotes breast cancer progression

Introduction

Expression of indoleamine 2,3-dioxygenase (IDO) in primary breast cancer increases tumor growth and metastasis. However, the clinical significance of stromal IDO and the regulation of stromal IDO are unclear.

Methods

Metabolomics and enzyme-linked immunosorbent assay (ELISA) were used to study the effect of cyclooxygenase-2 (COX-2)-overexpressing breast cancer cells on IDO expression in co-cultured human breast fibroblasts. Biochemical inhibitors and short-hairpin RNA (shRNA) were used to clarify how prostaglandin E₂ (PGE₂) upregulates IDO expression. Associations of stromal IDO with clinicopathologic parameters were tested in tumor specimens. An orthotopic animal model was used to examine the effect of COX-2 and IDO inhibitors on tumor growth.

Results

Kynurenine, the metabolite generated by IDO, increases in the supernatant of fibroblasts co-cultured with COX-2-overexpressing breast cancer cells. PGE₂ released by cancer cells upregulates IDO expression in fibroblasts through an EP4/signal transducer and activator of transcription 3 (STAT3)-dependent pathway. Conversely, fibroblast-secreted kynurenine promotes the formation of the E-cadherin/Aryl hydrocarbon receptor (AhR)/S-phase kinase-associated protein 2 (Skp2) complex, resulting in degradation of E-cadherin to increase breast cancer invasiveness. The enhancement of motility of breast cancer cells induced by co-culture with fibroblasts is suppressed by the IDO inhibitor 1-methyl-tryptophan. Pathological analysis demonstrates that upregulation of stromal IDO is a poor prognosis factor and is associated with of COX-2 overexpression. Co-expression of cancer COX-2 and stromal IDO predicts a worse disease-free and metastasis-free survival. Finally, COX-2 and IDO inhibitors inhibit tumor growth in vivo.

Conclusion

Integration of metabolomics and molecular and pathological approaches reveals the interplay between cancer and stroma via COX-2, and IDO promotes tumor progression and predicts poor patient survival.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0410-1) contains supplementary material, which is available to authorized users.

Superficial scrapings from breast tumors is a source for biobanking and research purposes

Breast cancer is a unique tumor disease in terms of the stringent requirement of predictive biomarker assessments. As recommended by current international guidelines, the established markers consist of estrogen receptor (ER), progesterone receptor, human epidermal growth factor and Ki67, and are primarily analyzed by immunohistochemistry. However, new diagnostic methods based on microarray or next-generation sequencing on DNA and mRNA level are gaining ground. These analyses require fresh-frozen tumor tissue that is generally not available from tumors <10 mm in diameter, comprising almost 25% of all resected breast cancer at our department. We here present a simple and standardized method to generate material from small tumors without risking the histopathological examination. Furthermore, we show that the quality of this material is sufficient for subsequent analysis on mRNA, DNA, and epigenetic level. We were also able to use this method for isolation and expansion of cancer stem cells from the majority of tumors. Consequently, researches can be provided with clinically relevant material for translational studies. In conclusion, this method opens up a new possibility for usage of valuable fresh tumor material for research purposes, biobanking, and next-generation sequencing.

Prognostic relevance of cancer-associated fibroblasts in human cancer

Prognostication is an integral part of cancer diagnostic and helps oncologists to guide treatment decisions and therapy intensity. Accumulating evidence suggest that the stroma compartment also contains independent prognostic information, best exemplified by the impact of immune cells and cells of the vasculature on cancer progression. Similarly, strong experimental evidence exist that stromal fibroblasts, often designated cancer associated fibroblasts (CAFs), are actively involved in tumorigenesis. Thus, it can be anticipated that the molecular repertoire of CAFs is likewise important for the clinical behavior of the tumor. In this review we present recent studies addressing the prognostic impact of CAFs, with the focus on human lung and breast cancer. Several single markers have been suggested, either CAF specific or CAF derived, that in immunohistochemical studies have demonstrated independent association with survival. This includes members of the platelet derived growth factor receptor (PDGFR) family, CAF-markers like podoplanin and fibroblast activation protein (FAP) as well as transcription factors (FoxF1) and secreted factors (matrix metalloproteinases (MMPs), SPARC). However, most studies are based on explorative evaluations on single patient cohorts and require further validation. Using a more comprehensive approach, microarray studies have been employed to create gene expression signatures that detect an activated fibroblast state. These "stroma signatures" have been applied to identify specific CAF features associated with prognosis in several independent data sets of breast and lung cancer patients. Early studies in breast cancer have also demonstrated that fibroblast features influence therapy response. Thus, many strategies have been used to present encouraging proof-of-concept findings that CAFs could be exploited for prognostication. However, these studies also highlight the difficulties to conclusively define an "activated stroma" and to identify the individual factors involved in clinically relevant tumor-stroma interactions.

A collagen-remodeling gene signature regulated by TGF-β signaling is associated with metastasis and poor survival in serous ovarian cancer

PURPOSE

To elucidate molecular pathways contributing to metastatic cancer progression and poor clinical outcome in serous ovarian cancer.

EXPERIMENTAL DESIGN

Poor survival signatures from three different serous ovarian cancer datasets were compared and a common set of genes was identified. The predictive value of this gene signature was validated in independent datasets. The expression of the signature genes was evaluated in primary, metastatic, and/or recurrent cancers using quantitative PCR and in situ hybridization. Alterations in gene expression by TGF-β1 and functional consequences of loss of COL11A1 were evaluated using pharmacologic and knockdown approaches, respectively.

RESULTS

We identified and validated a 10-gene signature (AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, and VCAN) that is associated with poor overall survival (OS) in patients with high-grade serous ovarian cancer. The signature genes encode extracellular matrix proteins involved in collagen remodeling. Expression of the signature genes is regulated by TGF-β1 signaling and is enriched in metastases in comparison with primary ovarian tumors. We demonstrate that levels of COL11A1, one of the signature genes, continuously increase during ovarian cancer disease progression, with the highest expression in recurrent metastases. Knockdown of COL11A1 decreases in vitro cell migration, invasion, and tumor progression in mice.

CONCLUSION

Our findings suggest that collagen-remodeling genes regulated by TGF-β1 signaling promote metastasis and contribute to poor OS in patients with serous ovarian cancer. Our 10-gene signature has both predictive value and biologic relevance and thus may be useful as a therapeutic target.

Can stroma reaction predict cancer lethality?

Stromal features in carcinomas may provide a relatively consistent means to stratify patients afflicted with solid tumors. Stroma-derived transcriptome signatures can now be used to make predictions about patient survival, suggesting the potential for their clinical application in precision medicine to predict disease progression and emergence of therapeutic resistance.