Blackboard to Bedside: Showcase of Translational Modeling

Tuesday, June 15 at 04:15am (PDT)
Tuesday, June 15 at 12:15pm (BST)
Tuesday, June 15 08:15pm (KST)

SMB2021 SMB2021 Follow Monday (Tuesday) during the "MS06" time block.
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Renee Brady-Nicholls (Moffitt Cancer Center, USA), Mohammad Zahid (Moffitt Cancer Center, USA), Stefano Pasetto (Moffitt Cancer Center, USA)


One of the most significant mathematical oncology capabilities lies in its ability to translate to the clinic and make patient-specific treatment recommendations that will ultimately prolong patient response and survival. This capability requires using clinical data as inputs to mathematical models to characterize individual patient responses and recommend clinically-relevant personalized treatment approaches and schedules. This common goal can be accomplished using various techniques. This mini-symposium will feature a varied selection of talks demonstrating how different approaches can support oncologists and patients in making clinical decisions. We will also highlight how this can improve the clinical trial design and how in silico modeling can investigate alternative therapy options.

Rene Bruno

(Genentech-Roche, France)
"Tumor dynamic modeling and overall survival predictions to support decisions in oncology clinical trials"
The key endpoints to support treatment approval in oncology and particularly for the treatment of advanced diseases is overall survival (OS). However, decisions to move to pivotal trials have to be made using earlier endpoints like overall response rate (ORR) or progression free survival (PFS) that often poorly predict OS and probability of success of a pivotal Phase III trial particularly with immunotherapies. Longitudinal tumor dynamic models estimate treatment effect on tumor growth inhibition (TGI)) and are linked to OS (TGI-OS models) in treatment independent biomarker-outcome models to offer a quantitative model-based approach that fully leverage to data generated in early trials. The use of TGI-OS models to simulate Phase III studies outcome and support early decisions will be illustrated (Bruno et al, Clin Cancer Res 2020;26:1787–95).

Pamela Jackson

(Mayo Clinic, USA)
"Instantiating an Imaging Digital Twin for a Brain Tumor Patient"
In medicine, digital twins are computational representations of some aspect of an individual patient and their disease. An effective digital twin can incorporate mathematical models to recapitulate the patient’s current disease state and predict the individual patient’s response to a therapeutic intervention, such that multiple interventions can be tested on the twin prior to selecting the most effective therapy. For brain tumors specifically, clinical imaging will be an important part of any digital twin given the eloquent nature of the brain and the integral part imaging plays in identifying suspected brain tumors and determining response to therapy. Thus, an imaging digital twin that can capture the dynamic visualization of the disease will be critical for comparison to actual patient images. Before the dynamics of the disease can be captured, we must first instantiate the simulated version of a patient’s imaging for the pre-treatment timepoint. Our objective is to demonstrate the identification of an imaging digital twin for an individual patient’s brain tumor at the pretreatment time-point using a brain tumor growth mathematical model coupled to an imaging simulation utilizing MRI physics. To instantiate the imaging digital twin, we generated multiple candidate brain tumors and their associated simulated images using the Proliferation-Invasion-Hypoxia-Necrosis-Angiogenesis-Edema (PIHNA-E) model coupled to an MRI signal model [1,2]. Using the PIHNA-E model [1] incorporating the patient’s imaging-based anatomy, we created twenty-five phantoms based on unique combinations of 5 different rates of migration (D [mm2/year]) and 5 different rates of proliferation (ρ [1/year])]. These patient-specific PIHNA-E simulations were then passed into an MRI signal model for simulating corresponding T2-weighted MRIs [2]. We then compared the acquired patient image to the candidate simulations with various combinations of D and ρ. To identify a “close” matching image, we calculated the L2-norm of twelve statistical features for both the acquired patient image and the simulated candidate images. The D and ρ of the acquired image with the lowest L2-norm relative to the candidate image was selected as the predictive parameter set. Additionally, we examined the effect of noise on the selection process. We were able to both create patient-specific simulated MRIs and select parameters for the PIHNA-E brain tumor growth model. [1] A. Hawkins-Daarud, R. C. Rockne, A. R. A. Anderson, and K. R. Swanson. 'Modeling tumor-associated edema in gliomas during anti-angiogenic therapy and its impact on imageable tumor.' Frontiers in oncology 3:66, 2013. [2] P.R. Jackson, A. Hawkins-Daarud, S. C. Partridge, P. E. Kinahan, and K. R. Swanson. 'Simulating magnetic resonance images based on a model of tumor growth incorporating microenvironment.' Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, International Society for Optics and Photonics 10577:105771D, 2018.

Elsa Hansen

(Penn State Huck Institutes of the Life Sciences, USA)
"Maintenance therapy: A case study in trial design"
Treatment efficacy is often measured in terms of progression free survival (PFS) or tumor response. Viewing cancer treatment from the perspective of resistance management changes how we interpret these measures. I will discuss these issues in the context of a recent clinical trial of maintenance therapy for multiple myeloma.

Sarah Brüningk

(ETH Zurich, Switzerland)
"Intermittent radiotherapy as alternative treatment for recurrent high grade glioma: A modeling study based on longitudinal tumor measurements"
Treatment options for recurrent high grade glioma are greatly limited and non-curative. Radiotherapy (RT) is an integral part of palliative patient care. A recent phase I clinical trial (NCT02313272) recently demonstrated the safety of a combination treatment of high dose hypofractionated stereotactic radiotherapy (HFSRT, ≥ 6 Gyx5 in daily fractions) with pembrolizumab (immuno therapy; anti PD1 antibody) and bevacizumab (aiming at vasculature normalization). In this presentation we show a simulation study of intermittent RT (iRT, delivering RT fractions in intervals of several weeks) suggested as a personalized treatment strategy to prolong tumor control rather than using debulking HFSRT. Simu- lations were performed using a mathematical model of tumor growth, radiation response and patient-specific evolution of resistance to additional treatments (pembrolizumab and bevacizumab). Four models comprising different levels of patient specific parameters were fitted from tumor growth curves of 16 patients enrolled in the NCT02313272 trial. The model ranking highest based on the Akaike information criterion was used for simulation of iRT and iRT plus boost (≥ 6 Gyx3 in daily fractions at time of progression) schedules for varying numbers of treatment fractions and time between fractions. Kapalan Meier curves scoring time to progression beyond the initial tumour volume were used to com- pare treatments. We show that iRT+boost(-boost) treatment was equal or superior to HFSRT in 15(11) out of 16 cases and that patients that remained responsive to pem- brolizumab and bevacizumab would benefit most from iRT. Time to progression could be prolonged through the application of additional, intermittently delivered fractions. iRT hence provides a promising treatment option for recurrent high grade glioma patients.

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Virtual conference of the Society for Mathematical Biology, 2021.