Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer

Nawaz, Sidra; Heindl, Andreas; Koelble, Konrad; Yuan, Yinyin

doi:10.1038/modpathol.2015.37

Cited by 129 publications

(116 citation statements)

References 38 publications

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“…Several commercial applications have been developed or adopted for CAIA-based Ki67 quantification. As a fully automated, solely computer-based detection of the tumor cell compartment raises questions regarding the diagnostic responsibility [26][27][28], many of these commercial applications rely on a measurement in a single, user-defined region of interest (ROI) [18,[29][30][31]. However, because of intratumoral proliferative heterogeneity, definition of the ROI, in terms of size and location, remains an important step in the analytical procedure [20].…”

Section: Introductionmentioning

confidence: 99%

The region-of-interest size impacts on Ki67 quantification by computer-assisted image analysis in breast cancer

Christgen¹,

Ahsen²,

Christgen³

et al. 2015

Human Pathology

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Section: Introductionmentioning

confidence: 99%

The region-of-interest size impacts on Ki67 quantification by computer-assisted image analysis in breast cancer

Christgen¹,

Ahsen²,

Christgen³

et al. 2015

Human Pathology

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“…Cold Spring Harbor Laboratory Press on May 10, 2018 -Published by genome.cshlp.org Downloaded from best prognostic predictions based on the discovery cohort (Methods; Nawaz et al 2015). Patients with high values of the cell area and cell W/L metagenes have significantly better survival in both the discovery and validation cohorts (log-rank test P < 0.01) (Fig.…”

Section: Studying Mechanotransduction By Image-omicsmentioning

confidence: 99%

Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics

Sailem¹,

Bakal²

2016

Genome Res.

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The associations between clinical phenotypes (tumor grade, survival) and cell phenotypes, such as shape, signaling activity, and gene expression, are the basis for cancer pathology, but the mechanisms explaining these relationships are not always clear. The generation of large data sets containing information regarding cell phenotypes and clinical data provides an opportunity to describe these mechanisms. Here, we develop an image-omics approach to integrate quantitative cell imaging data, gene expression, and protein-protein interaction data to systematically describe a "shape-gene network" that couples specific aspects of breast cancer cell shape to signaling and transcriptional events. The actions of this network converge on NF-κB, and support the idea that NF-κB is responsive to mechanical stimuli. By integrating RNAi screening data, we identify components of the shape-gene network that regulate NF-κB in response to cell shape changes. This network was also used to generate metagene models that predict NF-κB activity and aspects of morphology such as cell area, elongation, and protrusiveness. Critically, these metagenes also have predictive value regarding tumor grade and patient outcomes. Taken together, these data strongly suggest that changes in cell shape, driven by gene expression and/or mechanical forces, can promote breast cancer progression by modulating NF-κB activation. Our findings highlight the importance of integrating phenotypic data at the molecular level (signaling and gene expression) with those at the cellular and tissue levels to better understand breast cancer oncogenesis.[Supplemental material is available for this article.]

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“…Moreover, the tumor vasculature may also influence tumor-immune interactions, given that endothelial cells has a regulatory role in T cell migration into tumors [170]. Lastly, the recruitment and spatial localization of immune cells, which can include multiple cell types with different functions, varies within tumors and has been shown to associate with clinical outcome [4, 171]. …”

Section: Modeling Tumor Heterogeneity and Evolution In 3dmentioning

confidence: 99%

Heralding a new paradigm in 3D tumor modeling

et al. 2016

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Numerous studies to date have contributed to a paradigm shift in modeling cancer, moving from the traditional two-dimensional culture system to three-dimensional (3D) culture systems for cancer cell culture. This led to the inception of tumor engineering, which has undergone rapid advances over the years. In line with the recognition that tumors are not merely masses of proliferating cancer cells but rather, highly complex tissues consisting of a dynamic extracellular matrix together with stromal, immune and endothelial cells, significant efforts have been made to better recapitulate the tumor microenvironment in 3D. These approaches include the development of engineered matrices and co-cultures to replicate the complexity of tumor-stroma interactions in vitro. However, the tumor engineering and cancer biology fields have traditionally relied heavily on the use of cancer cell lines as a cell source in tumor modeling. While cancer cell lines have contributed to a wealth of knowledge in cancer biology, the use of this cell source is increasingly perceived as a major contributing factor to the dismal failure rate of oncology drugs in drug development. Backing this notion is the increasing evidence that tumors possess intrinsic heterogeneity, which predominantly homogeneous cancer cell lines poorly reflect. Tumor heterogeneity contributes to therapeutic resistance in patients. To overcome this limitation, cancer cell lines are beginning to be replaced by primary tumor cell sources, in the form of patient-derived xenografts and organoids cultures. Moving forward, we propose that further advances in tumor engineering would require that tumor heterogeneity (tumor variants) be taken into consideration together with tumor complexity (tumor-stroma interactions). In this review, we provide a comprehensive overview of what has been achieved in recapitulating tumor complexity, and discuss the importance of incorporating tumor heterogeneity into 3D in vitro tumor models. This work carves out the roadmap for 3D tumor engineering and highlights some of the challenges that need to be addressed as we move forward into the next chapter.

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Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer

Cited by 129 publications

References 38 publications

The region-of-interest size impacts on Ki67 quantification by computer-assisted image analysis in breast cancer

The region-of-interest size impacts on Ki67 quantification by computer-assisted image analysis in breast cancer

Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics

Heralding a new paradigm in 3D tumor modeling

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