ONCO-MS01

Mathematical approaches to advance clinical studies in oncology

Monday, June 14 at 09:30am (PDT)
Monday, June 14 at 05:30pm (BST)
Tuesday, June 15 01:30am (KST)

SMB2021 Follow Monday (Tuesday) during the "MS01" time block.

Note: this minisymposia has multiple sessions. The second session is MS02-ONCO (click here).

Heyrim Cho (University of California Riverside, USA), Russell Rockne (City of Hope Comprehensive Cancer Center, USA)

Description:

In this session, we bring together researchers in mathematical oncology to discuss methodologies for translational research in oncology. The advance of clinical data acquisition technologies, such as genome sequencers and new imaging techniques, are providing new opportunities in mathematical oncology. Mathematical modeling and quantitative framework can effectively leverage clinical data to describe various aspects of cancer progression and drug response. Moreover, models calibrated to individual patient data can predict treatment outcome and help design personalized treatment regarding the drug combination, dosage, and scheduling to improve treatment outcome. Here we highlight several applications of mathematical modeling to clinical oncology, including analysis of clinical data and clinical trials designed with mathematical modeling.

Hitesh Mistry

(University of Manchester, UK)

"Complexity/Simplicity of Oncology Pharmacodynamic Markers/Mathematical Models in the Clinic versus Drug Development"

Pharmacodynamic markers provide information on what the drug is doing to the body, in this talk its a measure of what the drug is doing to the disease, cancer. The number and types of biomarkers in Oncology has increased dramatically over the last 20-30 years. Our focus here will be on biomarkers that are used for selecting a dose/schedule. Many of these biomarkers are not typically used in the clinic but they do play a role in Oncology drug development. In this talk we shall compare the biomarkers/mathematical models in two Phase 1 Oncology trials, Rectal Carcinoma and metastatic Castrate Resistant Prostate Cancer, to those that are typically used in the clinic in the same settings. We shall highlight how the breadth and richness of data in Oncology drug development exceeds that in the clinic but that more complex mathematical models are used in the clinic versus drug development even though the question is the same - what dose/schedule should we use.

Renee Brady

(H. Lee Moffitt Cancer Center and Research Institute, USA)

"Predicting Response to Adaptive Therapy in Metastatic Prostate Cancer Using Prostate-Specific Antigen Dynamics"

Prostate cancer (PCa) remains the most prevalent cancer in men in the US. Standard treatment with androgen deprivation therapy (ADT) for localized disease often results in the competitive release of resistant cell phenotypes, causing patients to develop castration resistant PCa. Intermittent ADT has been shown to be a promising alternative to continuous treatment that can delay progression and may potentially reduce treatment-related adverse events. Second-line hormone therapy options, such as abiraterone acetate (AA), have been proven effective for metastatic castration resistant prostate cancer (mCRPC) and it has been proposed that similar to intermittent ADT, treatment with adaptive AA may reduce toxicity and prolong time to progression in mCRPC. We simulated and analyzed a simple quantitative model of prostate-specific antigen (PSA) dynamics to evaluate PCa stem cell enrichment as a plausible driver of treatment resistance. A Type 1b bootstrap internal validation leave-one-out analysis was used to calibrate and validate the model against longitudinal PSA data from 16 mCRPC patients receiving adaptive AA in a pilot study. Early PSA treatment response dynamics were then used to predict patient response to subsequent treatment. We extended the model to incorporate metastatic burden to improve predictive ability and also investigated the survival benefit of adding concurrent chemotherapy for patients predicted to become resistant. Model simulations demonstrated PCa stem cell self-renewal as a plausible driver of resistance to hormone therapy. The model was able to accurately describe patient-specific PSA dynamics and predict response with 78% accuracy. When incorporating metastatic burden, the predictive ability of the model increased to 81% (specificity = 92%, sensitivity = 50%). This study developed the first patient-specific mathematical model to use early treatment response dynamics to predict subsequent responses to adaptive AA.

Aleksandra Karolak

(City of Hope Comprehensive Cancer Center, USA)

"A Quantitative Systems Pharmacology Model to Improve Graft Versus Host Disease Outcomes"

Allogeneic hematopoietic cell transplant (HCT) cures patients of underlying disease by replacing their hematopoietic system with that of a healthy donor (non-malignant disease) or by the donor cells eradicating the patient’s malignancy (graft-versus-tumor effect). A post-transplant cyclophosphamide (PTCy) regimen was recently established as a standard of care for preventing graft versus host disease (GVHD), which is the most common cause of non-relapse mortality in HCT. The PTCy regimen consists of three drugs: cyclophosphamide (Cy), mycophenolate mofetil (MMF, active metabolite mycophenolic acid - MPA) and tacrolimus (TAC). All three drugs need to be optimized: PTCy has a narrow dose range based on preclinical data; clinical data in other GVHD regimens suggest that low plasma exposure to MPA and TAC are associated with GVHD. To address this need, we are constructing a Quantitative Systems Pharmacology (QSP) model to optimize the PTCy regimen. Guided by our preliminary preclinical and clinical data, our hypothesis is that QSP modeling can successfully predict immunologic reactions resulting from PTCy to subsequently: 1) simulate alternative doses and administration schedules for all three drugs; 2) identify the optimal PTCy dose and administration schedule; and 3) identify which model parameters introduce the greatest variability to design subsequent clinical trials and obtain more data to improve model reliability. In order to achieve these aims, we combine computational modeling with experimental data from HCT patients to develop and validate mathematical approach. The novelty of our approach comes from a joint application of population pharmacokinetic (popPK) model with the fully integrated immune response model (FIRM). The hybrid popPK-FIRM QSP model uses patient-specific metabolite data of PTCy drugs activity to guide dosing optimization. Simulations help predict pharmacokinetic characteristics of the PTCy regimen with correlation to drugs’ metabolites and evaluate the effects of implicit drug-drug interactions. The progress on implementations of the mathematical models, results of the simulations, and validation with the human samples collected at City of Hope will be presented.

Kit Curtius

(University of California San Diego, USA)

"Predicting Risk of Progression to Advanced Neoplasia in Patients with Ulcerative Colitis"

Patients with ulcerative colitis (UC) have an increased risk of developing colorectal cancer and thus are advised to participate in regular surveillance to remove pre-cancers that may be detected during colonoscopy. In order to translate a validated statistical model for predicting patient-specific risk of progression over time, we developed UC-CaRE (Ulcerative Colitis-Cancer Risk Estimator) as a tool that can be used to calculate and communicate individualized cancer risk estimates to UC patients with low-grade dysplasia based on their clinicopathological features. This visual aid facilitates the risk stratification of the lowest risk patients, who can be reassured to continue surveillance, versus those at the highest risk of cancer who may benefit from preventive surgery. Using shallow whole genome sequencing, we also found that the evolution of copy number alterations in UC predicts future neoplastic risk in patients. As molecular-based decision-making becomes more prominent in the clinical setting of early cancer detection, we propose that models of evolving genotypes can be integrated into and will enhance tools like UC-CaRE.

Hosted by SMB2021 Follow

Virtual conference of the Society for Mathematical Biology, 2021.