The prognostic significance of tumour–stroma ratio in oestrogen receptor-positive breast cancer

Downey, Candice; Simpkins, Steven; White, James T.; Holliday, DL; Jones, J. Louise; Jordan, Lee B.; Kulka, Janina; Pollock, Samuel B.; Rajan, Sreekumar Sundara; Thygesen, Helene; Hanby, AM; Speirs, Valerie

doi:10.1038/bjc.2014.69

Cited by 88 publications

(93 citation statements)

References 10 publications

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“…The implication of tumor stroma to prognosis could be different for different cancer types. For instance, high percentage of tumor stroma is associated with poor prognosis in triple-negative disease but good prognosis in estrogen receptor-positive disease (48,49). Here, we found in this study that for ccRCC both image features and gene expression signatures revealed that a large percentage of tumor stroma predicted poor prognosis.…”

Section: Discussionsupporting

confidence: 53%

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

et al. 2017

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In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n ¼ 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. Ó2017 AACR.

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Section: Discussionsupporting

confidence: 53%

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

et al. 2017

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“…It is increasingly recognized that stroma plays an important role in breast cancer progression (31–33). It has also been shown that expression of adipokines may be altered with obesity and in the peritumoral tissue of advanced breast tumors, suggesting a role for breast fat and adipokines in tumorigenesis (34).…”

Section: Discussionmentioning

confidence: 99%

Benign Breast Tissue Composition in Breast Cancer Patients: Association with Risk Factors, Clinical Variables, and Gene Expression

Sun¹,

Sandhu

Figueroa

et al. 2014

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Introduction Breast tissue composition (epithelium, non-fatty stroma, and adipose) changes qualitatively and quantitatively throughout the lifespan, and may mediate relationships between risk factors and breast cancer initiation. We sought to identify relationships between tissue composition, risk factors, tumor characteristics, and gene expression. Methods Participants were 146 patients from the Polish Breast Cancer Study, with data on risk factor and clinicopathological characteristics. Benign breast tissue composition was evaluated using digital image analysis of histological sections. Whole genome microarrays were performed on the same tissue blocks. Results Mean epithelial, non-fatty stromal, and adipose proportions were 8.4% (SD=4.9%), 27.7% (SD=24.0%), and 64.0% (SD=24.0%), respectively. Among women < 50 years old, stroma proportion decreased and adipose proportion increased with age, with approximately 2% difference per year (p <0.01). The variation in epithelial proportion with age was modest (0.1% per year). Higher epithelial proportion was associated with obesity (7.6% in non-obese vs 10.1% in obese; p=0.02) and with poorly differentiated tumors (7.8% in well/moderate vs 9.9% in poor; p=0.05). Gene expression signatures associated with epithelial and stromal proportion were identified and validated. Stroma-associated genes were in metabolism and stem cell maintenance pathways, while epithelial genes were enriched for cytokine and immune response pathways. Conclusions Breast tissue composition was associated with age, BMI, and tumor grade, with consequences for breast gene expression. Impact Breast tissue morphologic factors may influence breast cancer etiology. Composition and gene expression may act as biomarkers of breast cancer risk and progression.

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“…We therefore designed our study using cell types that were likely to influence each other’s gene expression when in co- and tri-culture. Because interactions between cancer, mesenchymal (Downey et al 2014; Amornsupak et al 2014; Luo et al 2015), and immune compartments (Nagalla et al 2013; Maley et al 2015; Stewart et al 2012; Tabariès et al 2015; Goswami et al 2005) play critical roles in a number of aspects of cellular phenotypic and functional modification in cancer progression, our models included combinations of one of two distinct breast cancers cell lines, SKBR3 and BT474, with the bone marrow stromal cell line, HS5, and ThP1 monocytes differentiated to M2, TAM-like macrophages. SKBR3 and BT474 are representative of the HER2-expressing subtype of breast cancer which comprises approximately 20 percentage of breast cancers and in which a stromal-derived gene signature was shown to have strongest prognostic value in clinical samples (Finak et al 2008).…”

Section: Resultsmentioning

confidence: 99%

Transitions from mono- to co- to tri-culture uniquely affect gene expression in breast cancer, stromal, and immune compartments

Regier

Maccoux

Weinberger

et al. 2016

Biomed Microdevices

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Heterotypic interactions in cancer microenvironments play important roles in disease initiation, progression, and spread. Co-culture is the predominant approach used in dissecting paracrine interactions between tumor and stromal cells, but functional results from simple co-cultures frequently fail to correlate to in vivo conditions. Though complex heterotypic in vitro models have improved functional relevance, there is little systematic knowledge of how multi-culture parameters influence this recapitulation. We therefore have employed a more iterative approach to investigate the influence of increasing model complexity; increased heterotypic complexity specifically. Here we describe how the compartmentalized and microscale elements of our multi-culture device allowed us to obtain gene expression data from one cell type at a time in a heterotypic culture where cells communicated through paracrine interactions. With our device we generated a large dataset comprised of cell type specific gene-expression patterns for cultures of increasing complexity (three cell types in mono-, co-, or tri-culture) not readily accessible in other systems. Principal component analysis indicated that gene expression was changed in co-culture but was often more strongly altered in tri-culture as compared to mono-culture. Our analysis revealed that cell type identity and the complexity around it (mono-, co-, or tri-culture) influence gene regulation. We also observed evidence of complementary regulation between cell types in the same heterotypic culture. Here we demonstrate the utility of our platform in providing insight into how tumor and stromal cells respond to microenvironments of varying complexities highlighting the expanding importance of heterotypic cultures that go beyond conventional co-culture.

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The prognostic significance of tumour–stroma ratio in oestrogen receptor-positive breast cancer

Cited by 88 publications

References 10 publications

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

Benign Breast Tissue Composition in Breast Cancer Patients: Association with Risk Factors, Clinical Variables, and Gene Expression

Transitions from mono- to co- to tri-culture uniquely affect gene expression in breast cancer, stromal, and immune compartments

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