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Microenvironmental independence associated with tumor progression.

ABSTRACT: Tumor-microenvironment interactions are increasingly recognized to influence tumor progression. To understand the competitive dynamics of tumor cells in diverse microenvironments, we experimentally parameterized a hybrid discrete-continuum mathematical model with phenotypic trait data from a set of related mammary cell lines with normal, transformed, or tumorigenic properties. Surprisingly, in a resource-rich microenvironment, with few limitations on proliferation or migration, transformed (but not tumorigenic) cells were most successful and outcompeted other cell types in heterogeneous tumor simulations. Conversely, constrained microenvironments with limitations on space and/or growth factors gave a selective advantage to phenotypes derived from tumorigenic cell lines. Analysis of the relative performance of each phenotype in constrained versus unconstrained microenvironments revealed that, although all cell types grew more slowly in resource-constrained microenvironments, the most aggressive cells were least affected by microenvironmental constraints. A game theory model testing the relationship between microenvironment resource availability and competitive cellular dynamics supports the concept that microenvironmental independence is an advantageous cellular trait in resource-limited microenvironments.

SUBMITTER: Anderson AR

PROVIDER: S-EPMC2783510 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Microenvironmental independence associated with tumor progression.

Anderson Alexander R A AR Hassanein Mohamed M Branch Kevin M KM Lu Jenny J Lobdell Nichole A NA Maier Julie J Basanta David D Weidow Brandy B Narasanna Archana A Arteaga Carlos L CL Reynolds Albert B AB Quaranta Vito V Estrada Lourdes L Weaver Alissa M AM

Cancer research 20091103 22

Tumor-microenvironment interactions are increasingly recognized to influence tumor progression. To understand the competitive dynamics of tumor cells in diverse microenvironments, we experimentally parameterized a hybrid discrete-continuum mathematical model with phenotypic trait data from a set of related mammary cell lines with normal, transformed, or tumorigenic properties. Surprisingly, in a resource-rich microenvironment, with few limitations on proliferation or migration, transformed (but ...[more]

PMID: 19887618

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Project description:BackgroundThe intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.Methods and findingsWe developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10-4, hazard ratio = 1.47, 95% CI 1.17-1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26-2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.ConclusionsTo our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.

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Microenvironmental independence associated with tumor progression.

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Microenvironmental independence associated with tumor progression.

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