We just got word from the USC Undergraduate Research Associates Program (URAP) that I will have funding for a 3-person, multidisciplinary team of undergraduates starting summer 2013. This project will aim to assemble a team consisting of a computer programmer, a mathematician, and a biologist to jointly develop and refine user interfaces to make computational modeling of cancer faster and more accessible to a wider group of students. This work goes hand-in-hand with our educational goals in the Consortium for Integrative Comptuational Oncology. More details to come (including a job poster and selection details), but this should be a very fun and worthwhile project.
I’m really grateful to the URAP for this opportunity to fund some bright USC undergraduates in a neat project. Last year, two of our interns (Margy Gunnar and Alice Hyun) were funded under this program, and it was a fantastic experience (at least for me!)
Here is my current speaking schedule for 2013. Please join me if you can!
- March 22, 2013: Mathematical Biology Seminar, Department of Mathematics, Duke University, Durham, North Carolina
- Title: From integration of multiscale data to emergent phenomena: the prognosis for patient-calibrated computational oncology [abstract]
- April 19, 2013: Fourth Annual National Cancer Institute Physical Sciences in Oncology Center (NCI PS-OCs) Network Investigators’ Meeting, Phoenix, Arizona
- Title: Exploring possibilities for next-generation computational cancer models that work
together (a conversation starter) [abstract]
- Plenary talk
- May 30, 2013: Mathways into Cancer II International Workshop, Carmona, Spain
- Title: From multiscale data integration to predictions of emergent phenomena: the
prognosis for patient-calibrated computational oncology [abstract]
- Plenary talk
- June 12, 2013: Annual Meeting for the Society of Mathematical Biology (SMB), Mini-Symposium 11: Agent-based simulations in oncology: applications to therapeutics, Tempe, Arizona.
- Title: Progress towards user-friendly, 3-D multiscale agent-based simulators for large (500k+ cells) cancer systems: application to in situ growth and tumor-stroma interactions [abstract]
- June 12, 2013: Annual Meeting for the Society of Mathematical Biology (SMB), Mini-Symposium 26: Patient-Specific Modeling of Cancer, Tempe, Arizona.
- Title: Patient-calibrated 3-D simulations of ductal carcinoma in situ (DCIS) with comedonecrosis and calcification [abstract]
*** UPDATE: Registration has been extended to September 19, 2012, 5:00 pm Pacific Time. ***
The NCI-funded, Physical Sciences in Oncology Center (PSOC) at USC is hosting its second annual symposium on interdisciplinary cancer research on September 27, 2012 from 7 am to 6 pm. The event is free but registration is required.
The symposium will include a great diversity of expertise, spanning cell analysis, cancer evolution, modeling, drug delivery, and therapeutic response. We also have two fascinating keynote speakers: David A. Kirby, author of Lab Coats in Hollywood, and Corby Kummer, senior editor and acclaimed food critic for the Atlantic Monthly. Here’s the confirmed lineup:
Eun Sok Kim (University of Southern California)
Cagri Savran (Purdue University)
Paul Newton (University of Southern California, and Co-Director of CICO)
Paul M. Kulesa (Stowers Institute)
It should be a wonderful event, and I hope you can attend!
You can find the full flyer and description here.
You can find the agenda here.
On May 18th, Paul Newton and I received received significant startup funding from the USC James H. Zumberge Research and Innovation Fund to establish the Consortium for Integrative Computational Oncology (CICO). We’re grateful for this opportunity to build a new resource for USC and the broader cancer community!
CICO seeks to develop and promote cross-disciplinary, integrative collaborations across the USC (particularly the Viterbi School of Engineering and the Keck School of Medicine) in clinically-oriented cancer modeling. Among our guiding principles:
- Computational modeling of cancer must be driven by clinical needs. Modelers need to work hand-in-hand with clinicians at all steps of the modeling process.
- Computational oncology works at its fullest potential when working with clinical data. This focus:
- drives advances in mathematical model design,
- allows us to evaluate and choose between competing models,
- helps biologists to test, validate, and refine current cancer biology orthodoxy,
- helps clinicians to better interpret their data, and
- is most likely to lead to computational tools that will make an impact in the clinic.
- Integrative computational oncology holds the potential to integrate advances from mathematical modeling, experiments, and clinical data into comprehensive tools that give a better understanding of cancer than any of these individual pieces alone.
- Integrative computational oncology must include student education at its core, to create a true “ecosystem” of clinically-focused modeling students from the undergraduate to postdoctoral level.
You’ll be hearing a lot more about CICO as we ramp up in the coming year!
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*
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
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.
This is really exciting–I’ll be giving a talk at the weekly applied math seminar at the University of California at Irvine in November. (4:00 pm on November 21, 2011) This will be a fun homecoming, and I can’t wait to catch up with old friends (and potential collaborators)! Many thanks to John Lowengrub for the invitation!
Here is my current list of upcoming talks in 2011-2012:
- September 30, 2011 (noon): Millind Tambe’s Teamcore group, University of Southern California (not a public talk)
- October 17, 2011 (Session II): USC PSOC short course: “physical sciences approach to understanding cancer”, University of Southern California
- November 21, 2011 (4:00 pm): Applied Mathematics Seminar, Department of Mathematics, University of California at Irvine
- Spring 2012: Mathematics Biology and Ecology Seminar, Centre for Mathematical Biology, University of Oxford (UK)
- May 18, 2012: USC PSOC seminar, Center for Applied Molecular Medicine, University of Southern California
- We used my agent model calibration technique, plus a volume-averaging upscaling to calibrate a simplified continuum model of DCIS growth in the breast.
- We used the steady-state approximation of the continuum model to estimate the DCIS volume.
- We applied this technique to 17 cases and found a very good match in predicted vs. pathology volume in 14 of 17 cases.
- We also found that the model was a much better predictor of volume than mammographic estimates (although this can vary with how the mammography is processed).
- We found that the mathematical theory predicted that a single variable A–a ratio of the apoptotic index, the proliferative index, and the estimated intraductal oxygenation–is a better predictor of tumor volume than grade, PI, or AI alone.
Edit on Sept. 8, 2011 at 11:22 am PDT:
Preprint is now online: