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Stromal reactivity differentially drives tumour cell evolution and prostate cancer progression

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.

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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.

References

  1. Strand, D. W., Franco, O. E., Basanta, D., Anderson, A. R. A. & Hayward, S. W. Perspectives on tissue interactions in development and disease. Curr. Mol. Med. 10, 95–112 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Simon-Assmann, P., Spenle, C., Lefebvre, O. & Kedinger, M. The role of the basement membrane as a modulator of intestinal epithelial-mesenchymal interactions. Prog. Mol. Biol. Transl. Sci. 96, 175–206 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Parmar, H. & Cunha, G. R. Epithelial–stromal interactions in the mouse and human mammary gland in vivo. Endocr. Relat. Cancer 11, 437–458 (2004).

    Article  CAS  PubMed  Google Scholar 

  4. Sugimoto, H., Mundel, T. M., Kieran, M. W. & Kalluri, R. Identification of fibroblast heterogeneity in the tumor microenvironment. Cancer Biol. Ther. 5, 1640–1646 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Orimo, A. et al. Stromal fibroblasts present in invasive human breast carcinomas promote tumor growth and angiogenesis through elevated SDF-1/CXCL12 secretion. Cell 121, 335–348 (2005).

    Article  CAS  PubMed  Google Scholar 

  6. Tuxhorn, J. A., Ayala, G. E. & Rowley, D. R. Reactive stroma in prostate cancer progression. J. Urol. 166, 2472–2483 (2001).

    Article  CAS  PubMed  Google Scholar 

  7. Tuxhorn, J. A., McAlhany, S. J., Dang, T. D., Ayala, G. E. & Rowley, D. R. Stromal cells promote angiogenesis and growth of human prostate tumors in a differential reactive stroma (DRS) xenograft model. Cancer Res. 62, 3298–3307 (2002).

    CAS  PubMed  Google Scholar 

  8. Ayala, G. et al. Reactive stroma as a predictor of biochemical-free recurrence in prostate cancer. Clin. Cancer Res. 9, 4792–4801 (2003).

    CAS  PubMed  Google Scholar 

  9. Olumi, A. F. et al. Carcinoma-associated fibroblasts direct tumor progression of initiated human prostatic epithelium. Cancer Res. 59, 5002–5011 (1999).

    CAS  PubMed  Google Scholar 

  10. Franco, O. E. et al. Altered TGF-β signaling in a subpopulation of human stromal cells promotes prostatic carcinogenesis. Cancer Res. 71, 1272–1281 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kiskowski, M. A. et al. Role for stromal heterogeneity in prostate tumorigenesis. Cancer Res. 71, 3459–3470 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bremnes, R. M. et al. The role of tumor stroma in cancer progression and prognosis: emphasis on carcinoma-associated fibroblasts and non-small cell lung cancer. J. Thorac. Oncol. 6, 209–217 (2011).

    Article  PubMed  Google Scholar 

  13. Tuxhorn, J. A. et al. Reactive stroma in human prostate cancer: induction of myofibroblast phenotype and extracellular matrix remodeling. Clin. Cancer Res. 8, 2912–2923 (2002).

    CAS  PubMed  Google Scholar 

  14. Levesque, C. & Nelson, P. S. Cellular constituents of the prostate stroma: key contributors to prostate cancer progression and therapy resistance. Cold Spring Harb. Perspect. Med. 8, a030510 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Yanagisawa, N. et al. Reprint of: Stromogenic prostatic carcinoma pattern (carcinomas with reactive stromal grade 3) in needle biopsies predicts biochemical recurrence-free survival in patients after radical prostatectomy. Hum. Pathol. 39, 282–291 (2008).

    Article  PubMed  Google Scholar 

  16. Diaz De Vivar, A. et al. Histologic features of stromogenic carcinoma of the prostate (carcinomas with reactive stroma grade 3). Hum. Pathol. 63, 202–211 (2017).

    Article  CAS  Google Scholar 

  17. Ayala, G. E. et al. Determining prostate cancer-specific death through quantification of stromogenic carcinoma area in prostatectomy specimens. Am. J. Pathol. 178, 79–87 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  18. San Martin, R. et al. Recruitment of CD34+ fibroblasts in tumor-associated reactive stroma: the reactive microvasculature hypothesis. Am. J. Pathol. 184, 1860–1870 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Potosky, A. L. et al. Five-year outcomes after prostatectomy or radiotherapy for prostate cancer: the prostate cancer outcomes study. J. Natl Cancer Inst. 96, 1358–1367 (2004).

    Article  PubMed  Google Scholar 

  20. Penson, D. F. et al. General quality of life 2 years following treatment for prostate cancer: what influences outcomes? Results from the Prostate Cancer Outcomes Study. J. Clin. Oncol. 21, 1147–1154 (2003).

    Article  PubMed  Google Scholar 

  21. Pound, C. R. et al. Natural history of progression after PSA elevation following radical prostatectomy. JAMA 281, 1591–1597 (1999).

    Article  CAS  PubMed  Google Scholar 

  22. Han, M., Partin, A. W., Pound, C. R., Epstein, J. I. & Walsh, P. C. Long-term biochemical disease-free and cancer-specific survival following anatomic radical retropubic prostatectomy. The 15-year Johns Hopkins experience. Urol. Clin. North Am. 28, 555–565 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Roehl, K. A., Han, M., Ramos, C. G., Antenor, J. A. & Catalona, W. J. Cancer progression and survival rates following anatomical radical retropubic prostatectomy in 3,478 consecutive patients: long-term results. J. Urol. 172, 910–914 (2004).

    Article  PubMed  Google Scholar 

  24. Hull, G. W. et al. Cancer control with radical prostatectomy alone in 1,000 consecutive patients. J. Urol. 167, 528–534 (2002).

    Article  PubMed  Google Scholar 

  25. Amling, C. L. et al. Long-term hazard of progression after radical prostatectomy for clinically localized prostate cancer: continued risk of biochemical failure after 5 years. J. Urol. 164, 101–105 (2000).

    Article  CAS  PubMed  Google Scholar 

  26. Moul, J. W. Treatment of PSA only recurrence of prostate cancer after prior local therapy. Curr. Pharm. Des. 12, 785–798 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Harrington, S., Lee, J., Colon, G. & Alappattu, M. Oncology section EDGE task force on prostate cancer: a systematic review of outcome measures for health-related quality of life. Rehabil. Oncol. 34, 27–35 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Basanta, D. et al. The role of transforming growth factor-β-mediated tumor–stroma interactions in prostate cancer progression: an integrative approach. Cancer Res. 69, 7111–7120 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Basanta, D. et al. Investigating prostate cancer tumour–stroma interactions: clinical and biological insights from an evolutionary game. Br. J. Cancer 106, 174–181 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Flach, E. H., Rebecca, V. W., Herlyn, M., Smalley, K. S. M. & Anderson, A. R. A. Fibroblasts contribute to melanoma tumor growth and drug resistance. Mol. Pharm. 8, 2039–2049 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kim, E. et al. Senescent fibroblasts in melanoma initiation and progression: an integrated theoretical, experimental, and clinical approach. Cancer Res. 73, 6874–6885 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Araujo, A., Cook, L. M., Lynch, C. C. & Basanta, D. An integrated computational model of the bone microenvironment in bone-metastatic prostate cancer. Cancer Res. 74, 2391–2401 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Picco, N., Sahai, E., Maini, P. K. & Anderson, A. R. Integrating models to quantify environment-mediated drug resistance. Cancer Res. 77, 5409–5418 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Kaznatcheev, A., Peacock, J., Basanta, D., Marusyk, A. & Scott, J. G. Fibroblasts and alectinib switch the evolutionary games played by non-small cell lung cancer. Nat. Ecol. Evol. 3, 450–456 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kim, Y. & Othmer, H. G. A hybrid model of tumor–stromal interactions in breast cancer. Bull. Math. Biol. 75, 1304–1350 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Martin, N. K., Gaffney, E. A., Gatenby, R. A. & Maini, P. K. Tumour–stromal interactions in acid-mediated invasion: a mathematical model. J. Theor. Biol. 267, 461–470 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  37. McKenney, J. K. et al. Histologic grading of prostatic adenocarcinoma can be further optimized: analysis of the relative prognostic strength of individual architectural patterns in 1275 patients from the canary retrospective cohort. Am. J. Surg. Pathol. 40, 1439–1456 (2016).

    Article  PubMed  Google Scholar 

  38. Quaranta, V., Weaver, A. M., Cummings, P. T. & Anderson, A. R. A. Mathematical modeling of cancer: the future of prognosis and treatment. Clin. Chim. Acta 357, 173–179 (2005).

    Article  CAS  PubMed  Google Scholar 

  39. Anderson, A. R. A. A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion. Math. Med. Biol. 22, 163–186 (2005).

    Article  PubMed  Google Scholar 

  40. Rejniak, K. A. & Anderson, A. R. A. Hybrid models of tumor growth. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 115–125 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Raman, D., Baugher, P. J., Thu, Y. M. & Richmond, A. Role of chemokines in tumor growth. Cancer Lett. 256, 137–165 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Grivennikov, S. I., Greten, F. R. & Karin, M. Immunity, inflammation, and cancer. Cell 140, 883–899 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Fluge, Ø. et al. Expression of EZH2 and Ki-67 in colorectal cancer and associations with treatment response and prognosis. Br. J. Cancer 101, 1282–1289 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ao, M. et al. Cross-talk between paracrine-acting cytokine and chemokine pathways promotes malignancy in benign human prostatic epithelium. Cancer Res. 67, 4244–4253 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. Maru, N., Ohori, M., Kattan, M. W., Scardino, P. T. & Wheeler, T. M. Prognostic significance of the diameter of perineural invasion in radical prostatectomy specimens. Hum. Pathol. 32, 828–833 (2001).

    Article  CAS  PubMed  Google Scholar 

  46. Li, R. et al. Prognostic value of Akt-1 in human prostate cancer: a computerized quantitative assessment with quantum dot technology. Clin. Cancer Res. 15, 3568–3573 (2009).

    Article  CAS  PubMed  Google Scholar 

  47. Li, R. et al. High level of androgen receptor is associated with aggressive clinicopathologic features and decreased biochemical recurrence-free survival in prostate: cancer patients treated with radical prostatectomy. Am. J. Surg. Pathol. 28, 928–934 (2004).

    Article  PubMed  Google Scholar 

  48. Altman, D. G., Lausen, B., Sauerbrei, W. & Schumacher, M. Dangers of using “optimal” cutpoints in the evaluation of prognostic factors. J. Natl Cancer Inst. 86, 829–835 (1994).

    Article  CAS  PubMed  Google Scholar 

  49. Dakhova, O. et al. Global gene expression analysis of reactive stroma in prostate cancer. Clin. Cancer Res. 15, 3979–3989 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hayashi, N. & Cunha, G. R. Mesenchyme-induced changes in the neoplastic characteristics of the Dunning prostatic adenocarcinoma. Cancer Res. 51, 4924–4930 (1991).

    CAS  PubMed  Google Scholar 

  51. Wheeler, T. M. & Lebovitz, R. M. Fresh tissue harvest for research from prostatectomy specimens. Prostate 25, 274–279 (1994).

    Article  CAS  PubMed  Google Scholar 

  52. Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, 2000).

  53. Grønnesby, J. K. & Borgan, Ø. A method for checking regression models in survival analysis based on the risk score. Lifetime Data Anal. 2, 315–328 (1996).

    Article  PubMed  Google Scholar 

  54. Grambsch, P. M., Therneau, T. M. & Fleming, T. R. Diagnostic plots to reveal functional form for covariates in multiplicative intensity models. Biometrics 51, 1469–1482 (1995).

    Article  CAS  PubMed  Google Scholar 

  55. Pepe, M. S., Janes, H., Longton, G., Leisenring, W. & Newcomb, P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am. J. Epidemiol. 159, 882–890 (2004).

    Article  PubMed  Google Scholar 

  56. Wu, H. C. et al. Derivation of androgen-independent human LNCaP prostatic cancer cell sublines: role of bone stromal cells. Int. J. Cancer 57, 406–412 (1994).

    Article  CAS  PubMed  Google Scholar 

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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.

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Authors and Affiliations

Authors

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.

Corresponding author

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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