What drives prostate cancer evolution?

I usually start our papers on prostate cancer to bone metastases by explaining how common prostate cancer is and how little can we do for a patient when the disease metastasizes. But the good news is that only about 1 in 10 patients die of prostate cancer. One of the questions when dealing with a new prostate cancer patient is not how to treat the cancer but whether treatment should be given.

To make this decision, pathologists look at tissue sections (see the featured image) and decide, based on the size and shape of the cancer cells and whether tissue architecture seems to be destroyed. With that information (and two representative examples) they produce a Gleason score with values ranging from 2 (no tumor) to 10 (most aggressive one). One thing that is generally overlooked is the prostate ecosystem. Prostate cancer cells emerge from epithelial cells whose work is supported by smooth muscle cells, fibroblasts and others. These stromal cells can produce an environment that is permisive to the tumor (in the case of reactive stroma). Why haven’t we used information about stroma before? This week we have released a bioRxiv preprint that shows how this can be done. This manuscript constitutes a substantial body of work including a sophisticated computational model (Sandy Anderson, Ziv Frankenstein and yours truly) to explore our hypothesis that led to a clinical biomarker tested on historical clinical data (Gustavo Ayala) and in vivo validation (Simon Hayward, Omar Franco and Doug Strand).

Using a computational model we can explore how the interactions between a heterogeneous cancer population (more or less aggressive cancer cells) and an also heterogeneous stroma (more or less permissive stromal cells) can lead to vastly different evolutionary paths and thus, clinical outcomes. Here’s an example where we look at the tumor growing, starting from one of the glands and gradually taking over the prostate:

We can also see the tumor phenotypes leading the charge:

Using this model we learned the impact that a permissive stroma has on the evolutionary dynamics of prostate cancer.

The more help we get, the less we need to change
Highlighting the importance of computationally driven approaches (which can study vastly more scenarios more efficiently and cheaply than any experimental model) is the fact that this dialogue can lead to rather counterintuitive outcomes. For instance that less permissive microenvironments (with more reactive stroma) can, paradoxically, select for tumor cells that are (relatively) less aggressive.
With these insights in mind, we propose an Integrated Cancer Biomarker (ICB) which we tested using a cohort of historical data from 870 patients.
Patient stratification of Gleason categories using the ICB

Clearly , integrating pathological information from both the tumor and the stroma allows us to better predict how the tumor will turn to be from the clinical perspective. Adding further credence to the results is that we decided to validate the biological mechanism we used to build the computational model with an animal model.

I am excited about this paper because it shows what a mathematical model can do when mathematicians, biologists and clinical researchers work together. If you want to know the details, check out the bioRxiv version here.

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