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Multiparametric classification links tumor microenvironments with tumor cell phenotype.

ABSTRACT: While it has been established that a number of microenvironment components can affect the likelihood of metastasis, the link between microenvironment and tumor cell phenotypes is poorly understood. Here we have examined microenvironment control over two different tumor cell motility phenotypes required for metastasis. By high-resolution multiphoton microscopy of mammary carcinoma in mice, we detected two phenotypes of motile tumor cells, different in locomotion speed. Only slower tumor cells exhibited protrusions with molecular, morphological, and functional characteristics associated with invadopodia. Each region in the primary tumor exhibited either fast- or slow-locomotion. To understand how the tumor microenvironment controls invadopodium formation and tumor cell locomotion, we systematically analyzed components of the microenvironment previously associated with cell invasion and migration. No single microenvironmental property was able to predict the locations of tumor cell phenotypes in the tumor if used in isolation or combined linearly. To solve this, we utilized the support vector machine (SVM) algorithm to classify phenotypes in a nonlinear fashion. This approach identified conditions that promoted either motility phenotype. We then demonstrated that varying one of the conditions may change tumor cell behavior only in a context-dependent manner. In addition, to establish the link between phenotypes and cell fates, we photoconverted and monitored the fate of tumor cells in different microenvironments, finding that only tumor cells in the invadopodium-rich microenvironments degraded extracellular matrix (ECM) and disseminated. The number of invadopodia positively correlated with degradation, while the inhibiting metalloproteases eliminated degradation and lung metastasis, consistent with a direct link among invadopodia, ECM degradation, and metastasis. We have detected and characterized two phenotypes of motile tumor cells in vivo, which occurred in spatially distinct microenvironments of primary tumors. We show how machine-learning analysis can classify heterogeneous microenvironments in vivo to enable prediction of motility phenotypes and tumor cell fate. The ability to predict the locations of tumor cell behavior leading to metastasis in breast cancer models may lead towards understanding the heterogeneity of response to treatment.

SUBMITTER: Gligorijevic B

PROVIDER: S-EPMC4227649 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Multiparametric classification links tumor microenvironments with tumor cell phenotype.

Gligorijevic Bojana B Bergman Aviv A Condeelis John J

PLoS biology 20141111 11

While it has been established that a number of microenvironment components can affect the likelihood of metastasis, the link between microenvironment and tumor cell phenotypes is poorly understood. Here we have examined microenvironment control over two different tumor cell motility phenotypes required for metastasis. By high-resolution multiphoton microscopy of mammary carcinoma in mice, we detected two phenotypes of motile tumor cells, different in locomotion speed. Only slower tumor cells exh ...[more]

PMID: 25386698

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Project description:Global mRNA expression analysis is efficient for phenotypic profiling of tumours, and has been used to define molecular subtypes for almost every major tumour type. A key limitation is that most tumours are communities of both tumour and non-tumour cells. This problem is particularly pertinent for analysis of advanced invasive tumours, which are known to induce major changes and responses in both the tumour and the surrounding tissue. To identify bladder cancer tumour-cell phenotypes and compare classification by tumour-cell phenotype with classification by global gene expression analysis, we analysed 307 advanced bladder cancers (cystectomized) both by genome gene expression analysis and by immunohistochemistry with antibodies for 28 proteins. According to systematic analysis of gene and protein expression data, focusing on key molecular processes, we describe five tumour-cell phenotypes of advanced urothelial carcinoma: urothelial-like, genomically unstable, basal/SCC-like, mesenchymal-like, and small-cell/neuroendocrine-like. We provide molecular pathological definitions for each subtype. Tumours expressing urothelial differentiation factors show inconsistent and abnormal protein expression of terminal differentiation markers, suggesting pseudo-differentiation. Cancers with different tumour-cell phenotypes may co-cluster (converge), and cases with identical tumour-cell phenotypes may cluster apart (diverge), in global mRNA analyses. This divergence/convergence suggests that broad global commonalities related to the invasive process may exist between muscle-invasive tumours regardless of specific tumour-cell phenotype. Hence, there is a systematic disagreement in subtype classification determined by global mRNA profiling and by immunohistochemical profiling at the tumour-cell level. We suggest that a combination of molecular pathology (tumour-cell phenotype) and global mRNA profiling (context) is required for adequate subtype classification of muscle-invasive bladder cancer. © 2017 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Project description:BackgroundImmunotherapy has demonstrated a limited clinical efficacy in approximately 5% of cholangiocarcinoma. The main challenges for an effective immunotherapy response in cholangiocarcinoma arise from the tumor microenvironment, which is poorly understood.MethodsFor a comprehensive analysis of the tumor microenvironment in cholangiocarcinoma, we performed multiplex immunohistochemistry with two 15-marker immune panels and Nanostring assays for a comprehensive analysis of 104 surgically resected cholangiocarcinomas including intrahepatic, hilar, and distal cholangiocarcinoma. We also validated some key findings with a batch integration analysis of published single cell RNA sequencing data.ResultsThis study found that natural killer cells occupy the largest immune cell compartment in cholangiocarcinoma. Granzyme-B+CD8+ effector T cells are significantly associated with better overall survival in both intrahepatic and distal cholangiocarcinoma. Above 85% of intrahepatic cholangiocarcinomas with higher density of PD-1-EOMES-CD8+ effector T cells are associated with long-term survival. However, only the density of PD-1-EOMES-CD8+ T cells in the tumor areas, but not in the peripheries of the tumors, is prognostic. In all three cholangiocarcinoma subtypes, T regulator cells are significantly associated with a poor prognosis; however, M1 and M2 tumor-associated macrophages or PD-L1+ tumor-associated macrophage demonstrate different prognostic values. Combining PD-L1+ M1 or M2, PD-L1- M1 or M2 tumor-associated macrophages, and T regulator cells to subgroup intrahepatic and distal cholangiocarcinoma, the prognosis is significantly better distinguished. Moreover, PD-L1- M2 tumor-associated macrophages is associated with a good prognosis in intrahepatic and distal cholangiocarcinoma, suggesting this subtype of M2 tumor-associated macrophages may be antitumoral. Interestingly, lower densities of various types of immunosuppressive cells are associated with decreased infiltration of effector T cells in distal and hilar cholangiocarcinoma, but not in intrahepatic cholangiocarcinoma. In intrahepatic cholangiocarcinoma, PD-L1+ tumor-associated macrophages exert their immunosuppressive function likely through promoting T cell exhaustion.ConclusionsThis study suggests that the densities of Granzyme-B+CD8+ effector T cells and non-exhausted PD-1-EOMES-CD8+ T cells and the PD-L1 status in the tumor-associated macrophages are prognostic makers in cholangiocarcinomas. The study also supports targeting PD-L1+ tumor-associated macrophages as the immunotherapy for cholangiocarcinoma.

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Multiparametric classification links tumor microenvironments with tumor cell phenotype.

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Multiparametric classification links tumor microenvironments with tumor cell phenotype.

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