ONCO Subgroup Contributed Talks

Wednesday, June 16 at 02:15pm (PDT)
Wednesday, June 16 at 10:15pm (BST)
Thursday, June 17 06:15am (KST)

SMB2021 SMB2021 Follow Wednesday (Thursday) during the "CT07" time block.
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Pirmin Schlicke

Technical University of Munich, Germany
"Bringing math into medical clinics: a model framework quantifying treatment outcomes in metastatic cancer"
Since roughly 90% of lung cancer deaths occur due to the presence of metastases and their resulting symptoms. Therefore the identification and evaluation of metastases is of utter importance for optimal treatment. Modern imaging technology leaves most metastases undiscovered as their size is too small to be recognised. Nonetheless, they play an important role in therapy success. We quantified the metastatic size distribution in cancer patients and estimated the effects of possible different treatment applications. The framework presented is a coupled ODE/PDE model based on a McKendrick-von-Foerster equation introduced by Iwata et al. and modified along its characteristics to account for therapeutic effects in treatment applications. The continuous definition also allows to model the metastatic cascade, thus the transition of a single primary tumor to a metastatic disease. These simulations could help clinicians to compare outcomes and to choose among treatment possibilities based on different therapy goals. Retrospective analysis with clinical data allows for follow-up prognostic possibilities that will be shown in this presentation.

Daniel Glazar

Moffitt Cancer Center
"Predicting Advanced Head and Neck Cancer Patients with High Risk of Early Treatment Failure"
IntroductionThere is a need to discover better treatment strategies for patients with advanced head and neck squamous cell carcinoma (HNSCC). 45 patients with advanced HNSCC were treated with combination cetuximab (anti-EGFR) and nivolumab (anti-PD-1) every 2 weeks with a 2-week lead-in of cetuximab alone in a phase I/II clinical trial. However, not every patient responded to the protocol therapy. Therefore, there is a clinical need to identify high-risk patients. MethodsAvailable patient-specific information includes CT-derived sum of longest diameters every 8 weeks. We train a tumor growth inhibition (TGI) ODE model describing a uniform growth rate and initial treatment sensitivity and patient-specific rate of evolution of resistance. We forecast tumor burden and predict risk level at the second and third observations.ResultsThe TGI model is able to accurately represent tumor burden dynamics (R2=0.98). However, forecasts for tumor burden are rather poor. However, since our main concern is predicting risk level, we continue with the study and achieve decent predictions at second and third observations (n=25,14 patients, accuracy=0.64,0.71, respectively).ConclusionGiven enough on-treatment information, a clinician can use the TGI model to predict high-risk patients on the trial protocol.

Maximilian Strobl

University of Oxford & Moffitt Cancer Center
"Using eco-evolutionary modelling to improve the management of PARPi resistance in ovarian cancer maintenance therapy"
PARP inhibitors (PARPis) represent a great advancement in the treatment of ovarian cancer, yet these drugs still often fail after a few months due to emerging drug resistance. A recent clinical trial in prostate cancer showed that evolutionary-inspired, adaptive drug scheduling significantly delayed time to progression. This approach modulated treatment to maintain a pool of drug-sensitive cells that suppress resistant cells through competition. Here, we present results from a combined modelling and experimental study in which we investigated whether adaptive therapy can delay resistance to the PARPi Olaparib. We performed a series of in vitro experiments in which we used time-lapse microscopy to characterise the cell population dynamics under different PARPi schedules. Our work reveals a delay in drug response, and that cells recover quickly upon drug withdrawal. Thus, treatment interruptions or modulations need to be carefully timed. To explain this behaviour we develop an ODE model which attributes the dynamics to the fact that PARPis induce cell cycle arrest from which cells may still recover. This model can not only fit the in vitro data, but it also accurately predicts the response to unseen drug schedules. We conclude with in silico trials of a plausible adaptive PARPi strategy.

Ryan Murphy

Queensland University of Technology
"Looking beneath the surface of tumour spheroids: insights from mathematical models parameterised to experimental data"
In 1972 H. P. Greenspan proposed one of the first mathematical models to describe avascular tumour spheroid growth. He suggested that his work be experimentally validated when improved technology was available. Remarkably, even though his paper has been highly influential and well-cited it has not yet been experimentally validated. In this presentation we will directly connect the Greenspan model to experimental data for the first time. Using live-dead cell staining and fluorescent ubiquitination-based cell cycle indicator (FUCCI) technology, we reveal and measure necrotic, quiescent, and proliferative regions inside growing tumour spheroids. These novel data, that we collect across a number of initial tumour spheroid sizes, cell lines, and experimental designs, allows us to test the Greenspan model and form confidence intervals for its parameters.

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Virtual conference of the Society for Mathematical Biology, 2021.