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Stromal reactivity differentially drives tumour cell evolution and prostate cancer progression

Nature Ecology & Evolution volume 4, pages 870–884 (2020)Cite this article

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Abstract

Prostate cancer (PCa) progression is a complex eco-evolutionary process driven by the feedback between evolving tumour cell phenotypes and microenvironmentally driven selection. To better understand this relationship, we used a multiscale mathematical model that integrates data from biology and pathology on the microenvironmental regulation of PCa cell behaviour. Our data indicate that the interactions between tumour cells and their environment shape the evolutionary dynamics of PCa cells and explain overall tumour aggressiveness. A key environmental determinant of this aggressiveness is the stromal ecology, which can be either inhibitory, highly reactive (supportive) or non-reactive (neutral). Our results show that stromal ecology correlates directly with tumour growth but inversely modulates tumour evolution. This suggests that aggressive, environmentally independent PCa may be a result of poor stromal ecology, supporting the concept that purely tumour epithelium-centric metrics of aggressiveness may be incomplete and that incorporating markers of stromal ecology would improve prognosis.

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Fig. 1: In silico multiscale model of the prostate peripheral zone.
Fig. 2: Change in stromal reactivity phenotypes, tumour growth and invasiveness.
Fig. 3: In vivo stromogenic grade is linked to tumour growth and invasion but not to Gleason grade.
Fig. 4: The ICB stratifies all Gleason grades in a cohort of 1,291 PCa patients with over 20 years of follow-up.
Fig. 5: Stromal reactivity drives tumour cell evolution and progression: in silico and clinical analysis.
Fig. 6: Interactions between tumour cells and stroma shape the evolutionary dynamics of PCa and drive overall tumour aggressiveness.

Data availability

All clinical data used was de-identified. All clinical data elements exist at Baylor College of Medicine, but the authors no longer have direct access to them. Contact G. Ayala to discuss clinical data access.

Code availability

The code used to produce all the simulations in the paper is available at the https://github.com/MathOnco/PCASim github repository.

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Acknowledgements

A.R.A.A., G.A. and S.W.H. gratefully acknowledge National Cancer Institute funding for this work through grant no. U01CA151924.

Author information

Author notes

  1. These authors contributed equally: Simon W. Hayward, Gustavo Ayala, Alexander R. A. Anderson.

  2. These authors jointly supervised this work: Ziv Frankenstein, David Basanta.

Authors and Affiliations

  1. Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Ziv Frankenstein, David Basanta & Alexander R. A. Anderson

  2. Independent Researcher, New York, NY, USA

    Ziv Frankenstein

  3. Department of Surgery, NorthShore University HealthSystem Research Institute, Evanston, IL, USA

    Omar E. Franco, Rodrigo A. Javier & Simon W. Hayward

  4. Department of Pathology and Laboratory Medicine, University of Texas Health Science Center, Houston, TX, USA

    Yan Gao & Gustavo Ayala

  5. Department of Urology, UT Southwestern Medical Center, Dallas, TX, USA

    Douglas W. Strand

  6. Biostatistics/Epidemiology/Research Design Core, Department of Internal Medicine, University of Texas Health Science Center, Houston, TX, USA

    MinJae Lee

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Contributions

A.R.A.A., D.B., G.A. and S.W.H. developed the initial concept and obtained the funding. Z.F., D.B. and A.R.A.A. developed the mathematical model and simulation code. O.E.F., R.A.J., D.W.S. and S.W.H. conceived, designed and performed the in vitro and in vivo experiments. Y.G., M.L. and G.A. performed the statistical analysis of the clinical data. G.A. collated, stained and quantified the clinical data. Z.F. analysed the triple-stained clinical samples. Z.F., D.B., O.E.F., D.W.S., S.W.H., G.A. and A.R.A.A. co-wrote the paper.

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Correspondence to Alexander R. A. Anderson.

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G.A. declares an interest in Stromont (a virtual company). All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Representation of movement probabilities.

Representation of the probabilities of cell located at coordinates (i,j) moving to one of its four orthogonal neighbors (PM1-4) or remaining stationary (PM0).

Extended Data Fig. 2 Evolution of tumour cell MMP production under low and high SR conditions.

The evolution of tumour cell phenotypes through space and time (6-12.8 years) under low (blue) and high (red) SR conditions (this is the MMP equivalent of Fig. 5a–f). Heat map shows tumour cell phenotype (MMP production) distribution in low SR (a-c) and high SR (d, e). Tumor cell phenotypic change in MMP production from 8 different initiating phenotypes in high (red) and low (blue) SR environments (the average change and standard deviation across 100 simulations per initiating phenotype) is show in panel f.

Extended Data Fig. 3 SR drives tumour cell evolution and progression.

Extension of Fig. 5m–o. Single cell quantitative analysis of the triple immunostained (AKT, AR and NFkB) tissue sections for patients with RSG1 (blue) vs. RSG3 (red) in each Gleason category. Expression in all cells from each of the patient’s biopsies are shown, each individual bar represents the average (and deviation) for a single patient over all cells. Insets for AKT and NFkB have more appropriate y-axis scales to better illustrate differences.

Extended Data Fig. 4 Co-culture experiment details (Relevant to Fig. 5q, r).

a. Prostate Cancer cell lines LNCaP-BFP, C4-2B-RFP and PC3-GFP were cultured in the presence of conditioned medium (CM) from RSG1-CAF or RSG3-CAF for 4 weeks. Quantitation of individual cell populations was determined by FACS analysis. The number of each cell line out of 10,000 gated total number of cells (Y-axis) is shown for each cell line. b. Co-culture experiments using aggressive C4-2B and PC3 cells exposed to RSG1-CAF and RSG3-CAF. Quantitation and analysis were similar to those performed for the triple co-culture experiments. Data represents the mean of three different experiments performed in triplicate.

Extended Data Fig. 5 Calculating evolutionary gradients from patient biopsies.

Analysis of triple stained tissue samples (Gleason 7 with RSG1) illustrating our approach to identify the most statistically significant evolutionary gradient in AKT expression. To identify the most significant gradient across a given biopsy, we analyzed the rate of change in expression through space starting from the cell with highest individual level of expression. Slope was calculated across radial distance from the cell with highest expression in the biopsy, in the example shown here, coordinates (575,746) (a). Analysis was performed on patients with RSG1 and compared to RSG3 in each Gleason category (b) showing the most significant slope per patient for the 3 molecular markers, AKT (left), AR (middle) and NF-B (phospho-p65, right). The larger the slope the more quickly expression changes with distance from the highest expressing cell. per patient for the 3 molecular markers, AKT (left), AR (middle) and NF-B (phospho-p65, right). The larger the slope the more quickly expression changes with distance from the highest expressing cell.

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Additional details concerning the derivation and implementation of the hybrid discrete-continuum mathematical model.

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Frankenstein, Z., Basanta, D., Franco, O.E. et al. Stromal reactivity differentially drives tumour cell evolution and prostate cancer progression. Nat Ecol Evol 4, 870–884 (2020). https://doi.org/10.1038/s41559-020-1157-y

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