ONCO Subgroup Contributed Talks

Mohammad Zahid

H. Lee Moffitt Cancer Center & Research Institute

"In Silico Trial to Estimate Personalized Radiotherapy Dose in Head and Neck Cancer"

Current radiotherapy (RT) treatment schedules are not personalized for individual patients, with the prescribed dose being uniform for particular subtypes and stages of cancer, despite variable responses between patients. Our objective is to determine optimal personalized RT dose in order to minimize excess RT dose without sacrificing tumor control. Weekly tumor volume data were collected for 39 head and neck cancer patients from Moffitt and M.D. Anderson Cancer Centers that received RT over 6-8 weeks. Tumor growth was modeled as logistic growth, and the effect of RT was modeled as an instantaneous reduction in carrying capacity. Tumor volume reduction was connected to locoregional control (LRC) by a volume reduction threshold associated with LRC.The in silico trial was performed in a leave-one-out fashion where model parameters calibrated to tumor volume data from N-1 patients and then the calibrated model parameters were combined with the Nth patient's tumor volume data from weeks 1-4 of RT to simulate tumor volumes forward in order to estimate minimum dose required for LRC. We found that 87% of the patients received a higher total dose than estimated as necessary by our model, while the remaining patients were estimated to have received too little dose.

Linus Schumacher

University of Edinburgh

"Mutational fitness in age-related clonal haematopoiesis quantified from longitudinal data"

The production of blood can be disturbed by mutations in haematopoietic stem cells (HSCs). Though mostly inconsequential, some mutations confer fitness advantages resulting in growth of fitter clones (all progeny of a HSCs carrying the same mutation) that represent disproportionately large fractions of all blood cells. Clonal Haematopoiesis of Indeterminate Potential (CHIP) affects more than 10% of the population aged over 65 years and is currently diagnosed when 4% of blood cells carry the same mutation. CHIP is linked with a ten-fold increase in later onset of haematological cancers, highlighting the importance of detecting and predicting clonal growth early.We investigate CHIP in the Lothian Birth Cohort through targeted error-corrected sequencing of blood samples taken from participants every 3 years.Modelling the population dynamics of clones shows the commonly used threshold to diagnose CHIP can be reached due to neutral drift in synonymous mutations. This clinical detection method therefore leads to a ~50% false discovery rate of fit mutations. Using longitudinal data, we instead detect clones whose growth exceeds the distribution of fluctuations of neutral mutations. This allows us to uncover fitness-inducing mutations with high sensitivity and detect highly fit mutations before they achieve the threshold-based definition of CHIP.

Adam Malik

Uppsala University

"Using modelling to quantify the diversity of glioblastoma"

Glioblastoma grade IV is a highly aggressive form of brain cancer, with a short duration of survival after diagnosis even in the presence of treatment. A challenge with surgical removal is the diffuse nature of tumors, and the difficulty of removing the whole tumor when cancer cells have migrated away from the primary tumor site. During migration, cells are influenced by their microenvironment, and it has been observed that cells tend to migrate along white matter tracts or towards blood vessels. A great variation in growth patterns is found when glioblastoma cells from different patients are grown ex vivo in the brain of mice. In order to quantify these differences we construct an agent based model of tumor growth in the brain of mice. We make use of diffusion tensor imaging data to obtain information about the white matter tracts, as well as a dataset of the whole brain vasculature. The migration direction is biased by either the white matter, the blood vessels or both. The model is fitted to experimental data using Approximate Bayesian Computation to provide insights into the differences between both proliferation as well as migratory preference for white matter or blood vessels.

Marek Bodnar

Institute of Applied Mathematics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

"On the optimal use of bevacizumab in unresected glioblastoma: An evidence-based mathematical approach"

Glioblastoma (GBM) is the most common and aggressive type of brain tumor in adults, with a median patient survival slightly above one year, despite aggressive combination therapy with the alkilating agent temozolomide (TMZ) and radiation therapy. Phase III clinical trials of the combination of bevacizumab (BEV, anti-angiogenic drug) with the standard chemoradiation protocol were negative in terms of providing survival improvements. In a very interesting study, Balaña et al. (2016) sought to determine the impact of BEC on reduction of tumor size prior tochemoradiotherapy treatment in unresected GBM patients. They found that the combination of BEV and TMZ was more active than TMZ alone and may confer benefit in terms of tumor shrinkage in unresected patients. We propose a simple mathematical model of tumor growth taking into account hypoxic cells and treatments (radiotheraphy, chemotherapy and anti-angiogenic treatment). We study mathematical properties of the model. Moreover, we show that solution of the model mimic well results of clinical trials.