Category: DCIS

Paul Macklin calls for common standards in cancer modeling

At a recent NCI-organized mini-symposium on big data in cancer, Paul Macklin called for new data standards in Multicellular data in simulations, experiments, and clinical science. USC featured the talk (abstract here) and the work at news.usc.edu.

Read the article: http://news.usc.edu/59091/usc-researcher-calls-for-common-standards-in-cancer-modeling/ (Feb. 21, 2014)

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Paul Macklin interviewed at 2013 PSOC Annual Meeting

Paul Macklin gave a plenary talk at the 2013 NIH Physical Sciences in Oncology Annual Meeting. After the talk, he gave an interview to the Pauline Davies at the NIH on the need for data standards and model compatibility in computational and mathematical modeling of cancer. Of particular interest:

Pauline Davies: How would you ever get this standardization? Who would be responsible for saying we want it all reported in this particular way?

Paul Macklin: That’s a good question. It’s a bit of the chicken and the egg problem. Who’s going to come and give you data in your standard if you don’t have a standard? How do you plan a standard without any data? And so it’s a bit interesting. I just think someone needs to step forward and show leadership and try to get a small working group together, and at the end of the day, perfect is the enemy of the good. I think you start small and give it a go, and you add more to your standard as you need it. So maybe version one is, let’s say, how quickly the cells divide, how often they do it, how quickly they die, and what their oxygen level is, and maybe their positions. And that can be version one of this standard and a few of us try it out and see what we can do. I think it really comes down to a starting group of people and a simple starting point, and you grow it as you need it.

Shortly after, the MultiCellDS project was born (using just this strategy above!), with the generous assistance of the Breast Cancer Research Foundation.

Read / Listen to the interview: http://physics.cancer.gov/report/2013report/PaulMacklin.aspx (2013)

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Macklin Lab featured on March 2013 cover of Notices of the American Mathematical Society

I’m very excited to be featured on this month’s cover of the Notices of the American Mathematical Society. The cover shows a series of images from a multiscale simulation of a tumor growing in the brain, made with John Lowengrub while I was a Ph.D. student at UC Irvine. (See Frieboes et al. 2007, Macklin et al. 2009, and Macklin and Lowengrub 2008.) The “about the cover” write-up (Page 325) gives more detail.

The inside has a short interview on our more current work, particularly 3-D agent-based modeling. You should also read Rick Durrett‘s perspective piece on cancer modeling (Page 304)—it’s a great read! (And yup, Figure 3 is from our patient-calibrated breast cancer modeling in Macklin et al. 2012. 😉 )

The entire March 2013 issue can be accessed for free at the AMS Notices website:

http://www.ams.org/notices/201303/

I want to thank Bill Casselman and Rick Durrett for making this possible. I had a lot of fun in the process, and I’m grateful for the opportunity to trade ideas!


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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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PSOC Short Course on Multidisciplinary Cancer Modeling

Next Monday (October 17, 2011), the USC-led Physical Sciences Oncology Center / CAMM will host a short course on multidisciplinary cancer modeling, combining the expertise of biologists, oncologists, and physical scientists. I’ll attach a PDF flyer of the schedule below.  I am giving a talk during “Session II – The Physicist Perspective on Cancer.”  I will focus on tailoring mathematical models from the ground up to clinical data from individual patients, with an emphasis on using computational models to make testable clinical predictions, and using these models a platforms to generate hypotheses on cancer biology.

The response to our short course has been overwhelming (in a good way), with around 200 registrants! So, registration is unfortunately closed at this time.  However, the talks will be broadcast live via a webcast.  The link and login details are in the PDF below. I hope to see you there! — Paul

Agenda
7:00 am – 8:25 am : Registration, breakfast, and opening comments, etc.
David B. Agus, M.D. (Director of USC CAMM)
W. Daniel Hillis, Ph.D. (PI of USC PSOC, Applied Minds)
Larry A. Nagahara (NCI PSOC Program Director)

8:30 am – 10:15 am : Session I – Cancer Biology and the Cancer Genome
Paul Mischel, UCLA – The Biology of Cancer from Cell to Patient, Oncogenesis to Therapeutic Response
Matteo Pellegrini, UCLA – Evolution in Cancer
Mitchelll Gross, USC – Historical Perspective on Cancer Diagnosis and Treatment

10:30 am – 12:15 pm : Session II – The Physicist Perspective on Cancer
Dan Ruderman, USC – Cancer as a Multi-scale Problem
Paul Macklin, USC – Computational Models of Cancer Growth
Tom Tombrello, Cal-Tech – Perspective: Big Problems in Physics vs. Cancer

1:45 pm. – 3:10 pm : Session III – Novel Measurement Platforms & Data Management & Integration
Michelle Povinelli, USC – The Role of Novel Microdevices in Dissecting Cellular Phenomena
Carl Kesselman, USC – Data Management & Integration Challenges in Interdisciplinary Studies

3:10 pm – 3:40 pm : Session IV – Creativity in Research at the Interface between the Life and Physical Sciences
‘Fireside Chat’ David Agus and Danny Hillis, USC

4:00 pm – 5:00 pm : Capstone – Keynote Speaker
Tim Walsh, Game Inventor, Keynote Speaker

5:30 pm – 8:00 pm : Poster Session and Reception

Click here for the poster, including webcast information: http://mathcancer.org/publications/PSOC_short_course_2011.pdf

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DCIS paper resubmitted; lots of clinical predictions, lots of validation

After a lot of revision, I have merged my two papers on ductal carcinoma in situ (DCIS) in to a single manuscript and resubmitted to the Journal of Theoretical Biology for final review.  A preprint is posted at my website, along with considerable supplementary material (data sets and source code, and animations).  Thanks to my co-authors and other friends for all the help in the revisions. Thanks also the reviewers for insightful comments. I think this revised manuscript is all the better for it.

DCIS is a precursor to invasive breast cancer, and it is generally detected by annual mammographic screening. More advanced DCIS (with greater risk) tends to have comedonecrosis–a type of cell death that leaves calcium phosphate deposits in the centers of the ducts. In fact, this is generally what’s detected in mammograms. DCIS is usually surgically removed by cutting out a small ball of tissue around what’s found in the mammograms (breast-conserving surgery, or lumpectomy).  But current planning isn’t so great. Even with the state-of-the-art in patient imaging and surgical planning, about 20%-50% of women need to get a second surgery because the first one didn’t get the entire tumor. 

So, there’s great need to understand calcifications, and how what you see in mammography relates to the actual tumor size and shape.  And if you do have a model to do this, there’s great need to calibrate it to patient pathology data (the stuff you get from your biopsies) so that the models say something meaningful about individual patients.  And there has been no method to do that. Until now. 


(As far as I know), this paper is the first to calibrate to individual patient immunohistochemistry and histopathology.  This, along with some parameter estimates to the theoretical and experimental biology literature, allows us to fully constrain the model. No free parameters to play with until it looks right. Any results are fully emergent from a mechanistic model and realistic parameter estimates rooted in the biology.



This model also includes the most detailed description of necrosis–the type of cell death that results in the comedonecrosis seen in mammograms. We include cell swelling, cell bursting, gradual loss of fluid, and the very first model of calcification.

Clinical predictions, with lots of validation:
All said and done, the model gives some big (and validated!) predictions:

  • The model predicts that a tumor grows through the duct at a constant rate.  This is consistent with what’s actually seen in mammography. 
  • The model gives a new explanation for the known trend: when necrotic cells burst and lose fluid, it makes it more mechanically favorable for proliferating cells to push into the center of the duct, rather than along the duct.  For this reason the model predicts faster growth in smaller ducts, and slower growth in larger ducts. 
  • The model predicts growth rates between 7.5 and 10 mm per year.  This is quantitatively consistent with published values in the clinical literature. 
  • The model predicts the difference between the size in a mammogram and the actual size (as measured by a pathologist after surgery) grows in time. This unfortunately means that it’s unlikely that there is some “fixed” safe distance to cut around the mammographic findings. 
  • On the other hand, the model predicts that there is a linear correlation between the size in a mammogram and the actual (pathology) tumor size. This bodes well for future surgical planning.  
  • Better still, the linear correlation we found quantitatively fits through 87 published patients, spanning two orders of magnitude.   
New insights on DCIS biology:
The model also makes several key predictions on the smaller-scale biology:

  • The model predicts that fast swelling of necrotic cells (on the order of 6 hours) is responsible for the tear between the viable rim and necrotic core seen in just about every pathology image of DCIS. 
  • The model predicts that the necrotic core is “age structured”, with newly necrotic cells (with relatively intact nuclei) on the outer edge, and interior band of mostly degraded but noncalcified cells, and a central core of oldest, calcified material.  This compares well with patient histopathology.
  • Comparing the model-predicted age structuring to histopathology predicts a sharper estimate on the various necrosis time scales: swelling and lysis (~6 h) < slow fluid loss (~days to a week) < pyknosis (~10+ days) < calcification (~2 weeks).
  • Because the model only predicts linear / casting-type calcifications (long “plugs” of calcification), other biophysics must be responsible for the variety of calcification types seen in mammography.  
  • Among other mechanisms, we postulate a very long-timescale (1-2 months) process of degradation of the phospholipid “backbone” of the calcifications, resulting in degradation of the calcifications. The cracks seen in the central portions of calcifications (in histopathology) supports this view. 
This last point is interesting: only 30-50% of solid-type DCIS has linear calcifications. This could provide an explanation for that, and may help improve the accuracy of diagnostic mammography. Furthermore, it may explain spontaneous resolution: where calcifications sometimes disappear from mammograms, while the underlying tumor is still present. 

Long-term outlook, and next steps:
In this work, we have taken a step towards moving mathematical models from the blackboard to the clinic. We actually can calibrate models to individual patient data. We actually can make testable predictions on things like growth rates and mammography sizes. 

The next step is to start validating the predictions in individual patients, rather than by the clinical literature. Our team has started doing just that.  Our pathologists are getting histopathology measurements from several patients.  Our mammographer is giving us calcification sizes and other data from 2 time points for each patient.  This will allow us to validate the predicted growth rate in each patient!

In the longer term, I’d like to use the model to develop a spatial mapping between the calcification appearance in the mammogram and its actual shape in the breast, as an improved surgical planning tool.  I’d like to study the impact of inadequate surgical margins in our simulated tumors.  And I’d like to expand the model to the next natural (and significant!) step of microinvasion, and progression to full invasive ductal carcinoma.

We’ve taken some nice baby steps towards making an impact in the clinic.  And that’s what this modeling is all about. 

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