Grant submitted to the NSF

Today, Paul Newton and I submitted a joint grant to the National Science Foundation in the Physical and Engineering Sciences in Oncology (PESO) program.  PESO is a neat program jointly run by the NSF and NCI, that has spun off the NCI's recent physical sciences approach to cancer. Our proposal brings together a a variety of techniques (spanning agent-based models, signaling, tissue biomechanics, fluid flow, nonlinear transport, and Markov chains) to study targeted aspects of cancer metastasis, from early microinvasion to circulating tumor cells (CTCs) to whole-body dissemination of metastatic disease.

On a personal note, this is my first proposal as a Co-PI.  *fingers crossed*

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Giving a talk at USC on Monday, February 6

For those of you in the neighborhood, I'll be giving a on patient-calibrated computational modeling of breast cancer, and on the role of mathematical modeling in facilitating a deeper understanding of pathology and mammography.

Monday, February 6, Center for the Applied Mathematical Sciences (CAMS) at the University of Southern California. 

Link and abstract: http://cams.usc.edu/Colloquia/2-6-2012.html 

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DCIS modeling paper accepted

Recently, I wrote about a major work we submitted to the Journal of Theoretical Biology: "Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression."  

I am pleased to report that our paper has now been accepted.  You can download the accepted preprint here. We also have a lot of supplementary material, including simulation movies, simulation datasets (for 0, 15, 30, adn 45 days of growth), and open source C++ code for postprocessing and visualization.

I discussed the results in detail here, but here's the short version:

1. We use a mechanistic, agent-based model of individual cancer cells growing in a duct. Cells are moved by adhesive and repulsive forces exchanged with other cells and the basement membrane.  Cell phenotype is controlled by stochastic processes.
2. We constrained all parameter expected to be relatively independent of patients by a careful analysis of the experimental biological and clinical literature.
3. We developed the very first patient-specific calibration method, using clinically-accessible pathology.  This is a key point in future patient-tailored predictions and surgical/therapeutic planning. 
4. The model made numerous quantitative predictions, such as: 
1. The tumor grows at a constant rate, between 7 to 10 mm/year. This is right in the middle of the range reported in the clinic. 
2. The tumor's size in mammgraphy is linearly correlated with the post-surgical pathology size.  When we linearly extrapolate our correlation across two orders of magnitude, it goes right through the middle of a cluster of 87 clinical data points.
3. The tumor necrotic core has an age structuring: with oldest, calcified material in the center, and newest, most intact necrotic cells at the outer edge.  
4. The appearance of a "typical" DCIS duct cross-section varies with distance from the leading edge; all types of cross-sections predicted by our model are observed in patient pathology. 
5. The model also gave new insight on the underlying biology of breast cancer, such as: 
1. The split between the viable rim and necrotic core (observed almost universally in pathology) is not just an artifact, but an actual biomechanical effect from fast necrotic cell lysis.
2. The constant rate of tumor growth arises from the biomechanical stress relief provided by lysing necrotic cells. This points to the critical role of intracellular and intra-tumoral water transport in determining the qualitative and quantitative behavior of tumors. 
3. Pyknosis (nuclear degradation in necrotic cells), must occur at a time scale between that of cell lysis (on the order of hours) and cell calcification (on the order of weeks).  
4. The current model cannot explain the full spectrum of calcification types; other biophysics, such as degradation over a long, 1-2 month time scale, must be at play.

I hope you enjoy this article and find it useful. It is our hope that it will help drive our field from qualitative theory towards quantitative, patient-tailored predictions. 

Direct link to the preprint: http://www.mathcancer.org/Publications.php#macklin12_jtb

I want to express my greatest thanks to my co-authors, colleagues, and the editorial staff at the Journal of Theoretical Biology. 

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