Every Patient Deserves Their Own Equation
Room 16107: 16th Floor (By the Tower Elevators) 7:30 AM to 9 AM
“DISCRIMINATING SURVIVAL OUTCOMES IN PATIENTS WITH GLIOBLASTOMA USING A SIMULATION-BASED PATIENT-SPECIFIC RESPONSE METRIC”
By
KRISTIN RAE SWANSON, PhD
&
“FERTILITY PRESERVATION OPTIONS FOR YOUNG WOMEN AND GIRLS DIAGNOSED WITH CANCER”
By
MARY ELLEN PAVONE, MD, MSCI
Modeling glioma-associated edema during anti-angiogenic therapy
Andrea Hawkins-Daarud, Russell Rockne, Alexander R. A. Anderson, Kristin R. Swanson
Frontiers in Molecular and Cellular Oncology, 3(66), 2013 doi: 10.3389/fonc.2013.00066 *Ranked as #1 Paper in this journal – May 2013
https://www.frontiersin.org/Molecular_and_Cellular_Oncology/10.3389/fonc.2013.00066/abstract
From Patient-Specific Mathematical Neuro-Oncology Towards Precision Medicine.
Anne L. Baldock, Russell Rockne, Addie Boone, Maxwell Neal, Maciej. M. Mrugala, Jason K. Rockhill, Kristin R. Swanson
Frontiers in Molecular and Cellular Oncology, 2013 3(62) doi: 10.3389/fonc.2013.00062 *Ranked as #3 Paper in this journal – May 2013
https://www.frontiersin.org/molecular_and_cellular_oncology/10.3389/fonc.2013.00062/abstract
Gliomas are notoriously aggressive, malignant brain tumors that have variable response to treatment. These patients often have poor prognosis, informed primarily by histopathology. Mathematical neuro-oncology (MNO) is a young and burgeoning field that leverages mathematical models to predict and quantify response to therapies. These mathematical models can form the basis of modern “precision medicine” approaches to tailor therapy in a patient-specific manner. Patient-specific models (PSMs) can be used to overcome imaging limitations, improve prognostic predictions, stratify patients, and assess treatment response in silico. The information gleaned from such models can aid in the construction and efficacy of clinical trials and treatment protocols, accelerating the pace of clinical research in the war on cancer. This review focuses on the growing translation of PSM to clinical neuro-oncology. It will also provide a forward-looking view on a new era of patient-specific MNO.
Gliomas are notoriously aggressive, malignant brain tumors that have variable response to treatment. These patients often have poor prognosis, informed primarily by histopathology. Mathematical neuro-oncology (MNO) is a young and burgeoning field that leverages mathematical models to predict and quantify response to therapies. These mathematical models can form the basis of modern “precision medicine” approaches to tailor therapy in a patient-specific manner. Patient-specific models (PSMs) can be used to overcome imaging limitations, improve prognostic predictions, stratify patients, and assess treatment response in silico. The information gleaned from such models can aid in the construction and efficacy of clinical trials and treatment protocols, accelerating the pace of clinical research in the war on cancer. This review focuses on the growing translation of PSM to clinical neuro-oncology. It will also provide a forward-looking view on a new era of patient-specific MNO.
Predicting brain tumors like weather forecasters predict an upcoming storm. For patients, that means they can better prepare and make choices for how to better protect themselves.
WGN meteorologist Tom Skilling surrounds himself with mathematical data. Wind speed, barometric pressure, temperature rise and fall. Armed with that knowledge, he creates the daily and weekly forecast.
In this lab, researchers are doing the exact same thing, except they are forecasting how quickly a brain tumor will erupt and spread. It lets patients know if they should ditch the raincoat for a stronger protector from the elements.
Prof. Kristin Swanson, Northwestern Medicine Brain Researcher: “So if a patient comes in and gets some new treatment, you say ‘I predicted the disease was going to be this big at some future time point,’ and the disease was only this big, then that difference is a measure of how well the treatment worked. And that difference is actually very predictive of how well a patient does in overall survival.”
Look at this image. Think of the white spot as an iceberg, the part you can see above water. Then look at the image below. The red represents the true size — the growth and tumor spread through the brain, like the iceberg below the water.
Prof. Kristin Swanson: “It’s all about seeing what you can’t see. The imaging doesn’t tell you the whole answer so how can you infer where the disease is?”
MRI is good but limited. Instead, researchers at Northwestern Medicine are using a mathematical model comparing the rate of growth on MRI and analyzing cell density.
Prof. Kristin Swanson: “If you’ve got two pre-treatment MRI’s, you can tune the mathematical model to each patient. One of the nice things about this tool set is that you can not only predict what the disease extent is, but you can say, ‘Hey, this is what this patient looks like today, this is what I think the patient’s going to look like in six weeks, this is what I think the patient’s disease is going to look like in six months.’ And when you combine all that information together, you can generate a baseline against which to compare treatments.”
Plotting points on a graph, in this case the line represents how large and fast the tumor will grow. But then treatment is started. Now look at the red box below the line. It represents how much smaller the mass was from the original prediction. The cancer is no longer rising and growing, but the angle of progression is shrinking … the therapy is working. It tells doctors to stay the course.
Prof. Kristin Swanson: “They need to know whether the treatment that they are on is doing the right thing. So that’s one of the nice things about this tool is that it says, ‘Hey, these patients are actually getting a lot of bang for their buck, they’re getting a ton from this given therapy, where these patients over here, they may not be getting as much.’”
The ultimate goal is to accurately predict exactly when to change therapies. Currently, some patients stick with what they know without opting for a clinical trial when it may save their lives, others give up on the strongest standard therapy too soon. The researchers are hoping prediction will mean life extension.
https://wgntv.com/2013/01/30/forecasting-brain-tumors/
Maxwell Lewis Neal, Andrew D. Trister, Tyler Cloke, Rita Sodt, Sunyoung Ahn,
Anne L. Baldock, Carly A. Bridge, Albert Lai, Timothy F. Cloughesy, Maciej M. Mrugala,
Jason K. Rockhill, Russell C. Rockne, Kristin R. Swanson
Accurate clinical assessment of a patient’s response to treatment for glioblastoma multiforme (GBM), the most malignant type of primary brain tumor, is undermined by the wide patient-to-patient variability in GBM dynamics and responsiveness to therapy. Using computational models that account for the unique geometry and kinetics of individual patients’ tumors, we developed a method for assessing treatment response that discriminates progression-free and overall survival following therapy for GBM. Applying these models as untreated virtual controls, we generate a patient-specific “Days Gained” response metric that estimates the number of days a therapy delayed imageable tumor progression. We assessed treatment response in terms of Days Gained scores for 33 patients at the time of their first MRI scan following first-line radiation therapy. Based on Kaplan-Meier analyses, patients with Days Gained scores of 100 or more had improved progression-free survival, and patients with scores of 117 or more had improved overall survival. Our results demonstrate that the Days Gained response metric calculated at the routinely acquired first post-radiation treatment time point provides prognostic information regarding progression and survival outcomes. Applied prospectively, our model-based approach has the potential to improve GBM treatment by accounting for patient-to-patient heterogeneity in GBM dynamics and responses to therapy.
https://dx.plos.org/10.1371/journal.pone.0051951
Department of Radiation Oncology, University of Washington Medical Center, 1959 N E Pacific Street, Seattle, WA 98195, USA. Department of Radiation Oncology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115, USA.
We demonstrate a patient-specific method of adaptive IMRT treatment for glioblastoma using a multiobjective evolutionary algorithm (MOEA). The MOEA generates spatially optimized dose distributions using an iterative dialogue between the MOEA and a mathematical model of tumor cell proliferation, diffusion and response. Dose distributions optimized on a weekly basis using biological metrics have the potential to substantially improve and individualize treatment outcomes. Optimized dose distributions were generated using three different decision criteria for the tumor and compared with plans utilizing standard dose of 1.8 Gy/fraction to the CTV (T2-visible MRI region plus a 2.5 cm margin). The sets of optimal dose distributions generated using the MOEA approach the Pareto Front (the set of IMRT plans that delineate optimal tradeoffs amongst the clinical goals of tumor control and normal tissue sparing). MOEA optimized doses demonstrated superior performance as judged by three biological metrics according to simulated results. The predicted number of reproductively viable cells 12 weeks after treatment was found to be the best target objective for use in the MOEA.
New method is first to predict brain cancer outcome and quickly show if therapy is effective
CHICAGO — The critical question shortly after a brain cancer patient starts treatment: how well is it working? But there hasn’t been a good way to gauge that.
Now Northwestern Medicine researchers have developed a new method — similar to forecasting storms with computer models — to predict an individual patient’s brain tumor growth. This growth forecast will enable physicians to rapidly identify how well the tumor is responding to a particular therapy. The approach allows a quick pivot to a new therapy in a critical time window if the current one isn’t effective.
The study is based on 33 patients with glioblastoma, the most common and aggressive form of brain cancer. The paper will be published Jan. 23 in the journal PLOS ONE.
“When a hurricane is approaching, weather models tell us where it’s going,” said senior author Kristin Swanson, professor and vice chair of research for neurological surgery at Northwestern University Feinberg School of Medicine. “Our brain tumor model does the same thing. We know how much and where the tumor will grow. Then we can know how much the treatment deflected that growth and directly relate that to impact on patient survival.”
Swanson also is a member of the Northwestern Brain Tumor Institute and the Robert H. Lurie Comprehensive Cancer Center of Northwestern University. Maxwell Neal, lead author, is a post-doctoral researcher in bioengineering at the University of Washington.
The method will advance brain tumor treatment, Swanson said, by helping distinguish effective treatments from ineffective ones and enabling clinicians to optimize treatment plans on a patient-by-patient basis.
Muddy Zone Right After Treatment
“There is this muddy zone right after the first round of treatments when it’s hard for the clinician to know whether to change therapy because she doesn’t have the metrics that correlate to outcome,” Swanson said. “The doctor can’t yet gauge how much it helped.”
If the doctor determines the treatment isn’t effective, she can try a different type of treatment or help the patient enroll in a clinical trial with a new drug being tested. The information also is helpful to the patient.
“The patient wants to know the therapy is doing something for them,” Swanson said. “On the flip side, if the therapy isn’t helping, then it may not be worth the side affects he is enduring.”
Not All Brain Tumors are the Same
Brain cancer patients are in great need of an approach to find optimal personalized treatments.
Brain tumors vary in their growth rate, shape and density but existing methods for measuring a treatment’s impact ignore this variation. The methods (and thus physicians) cannot distinguish between a patient with a fast-growing tumor that responds well to treatment and a patient with a slow-growing tumor that responds poorly.
By using a personalized, patient-specific approach that accounts for tumor features such as 3-dimensional shape, density and growth rate, the new Northwestern method can make this distinction.
Is it Working? How the Model Forecasts Growth and Measures Effectiveness
To measure a treatment’s effectiveness, the scientists performing the study created a unique computer model of each patient’s tumor and predicted how it would grow in the absence of treatment, explained Neal.
The prediction model was based on the MRI scans that the patient received on the day of diagnosis and on the day of surgery. The difference between these two scans enabled researchers to estimate how fast the tumor was growing along with the density of tumor cells throughout the brain.
Researchers then scored the effectiveness of the patient’s treatment by comparing the size of the patient’s tumor after treatment to the model-predicted size if untreated.
“The study demonstrated that higher-scoring patients survived significantly longer than lower-scoring patients and their tumors took significantly longer to recur,” Neal said. “The score can guide clinicians in determining the effectiveness of the therapy.”
Northwestern researchers hope to make the computer model an iPad app or offer it on a website where a clinician can simply enter a patients’ MRI data to calculate the response score.
The research was supported by the National Cancer Institute of the National Institutes of Health, grants R01 CA164371, R01 NS 060752, U54 CA143970. In addition, the research was funded by the McDonnell Foundation, the Brain Tumor Funders Collaborative, the University of Washington Academic Pathology Fund, and the James D. Murray Endowed Chair.
NORTHWESTERN NEWS: https://www.eurekalert.org/pub_releases/2013-01/nu-fbt011813.php
Complex mathematical models are helping researchers understand cancer’s evolution and providing clues on how to thwart drug resistance.
https://www.nature.com/nature/journal/v491/n7425_supp/full/491S66a.html