This research involves mathematical models that ideally allow the personalization of radiation treatment based on clinical and physiological features of the patient and the tumor. I am mainly interested in statistical methods to model the response of normal and cancerous tissues to inonizing radiation in order to predict normal tissue complication (NTCP) or tumor control probabilities (TCP), respectively. Besides physical treatment parameters such as delivered dose, such models can use biological and clinical factors as predictors, e.g. tumor stage, hypoxia, certain blood parameters etc.
Besides classical approaches that usually model TCP or NTCP as generalized linear functions of certain variables, e.g. delivered dose to the tumor or organ at risk, respectively, there exist some data-driven approaches that try to incorporate all the relevant variables and their possible interactions to predict the outcome of interest. In particular, machine learning methods such as Support Vector Machines, have been shown to be effective tools for this aim. In addition, I try to make use of survival analysis techniques such as frailty models and cure rate models in order to account for the latent biological processes that take place during the time after treatment and influence NTCP, TCP or overall survival probability.
Nutrition and tumor cell metabolism
Nutrition is a commonly and often emotionally discussed topic in relation to cancer prevention and treatment. Unfortunately, the scientific facts often get mixed up with personal opinions or currently held beliefs that might be outdated.
Over the past years, more evidence has accumulated suggesting that cancer is not only a genetic disease, but also a metabolic one, with altered cellular metabolism possibly even the primal cause for malignancy. Thanks to the early work of Otto Warburg and colleagues we know for about 100 years now that rapidly growing tumor cells mainly rely on decomposition of glucose to lactate even if there is sufficient oxygen available which in normal cells would inhibit excessive lactate production. This phenomenon is known as “aerobic glycolysis” or the “Warburg effect” and is the underlying principle of positron emission tomography (PET) imaging.
It appears that there is a strong interaction between the patient’s metabolism and that of the tumor. For example, more and more studies point towards a highly increased risk of persons with Metabolic Syndrome for developing cancers of various origins. Increased levels of glucose, insulin and insulin-like growth factor (IGF) 1 are discussed as possible causes. Patients with advanced cancers, on the other hand, exhibit an altered metabolic state characterized through chronic inflammation with concurrent insulin resistance of normal tissues and muscle protein loss. This indicates that such patients might have altered nutritional needs.
My interest mainly lies on the impact of a ketogenic diet on cellular metabolism. The ketogenic diet is a very low carbohydrate, moderately protein containing and high fat diet in which the chronically low insulin levels lead to an increased production of ketone bodies from fatty acids. This induces a metabolic state very similar to fasting. Contrary to normal cells, most malignant cells are unable to fully metabolize ketone bodies. In addition, emerging evidence suggest that ketone bodies possess anti-oxidative properties and are able to modulate the expression of certain genes in tumor cells in ways that suppress their growth.
We have started a clinical trial investigating ketogenic interventions during radio(chemo-)therapy with respect to their impact on body composition, blood parameters and quality of life. details on this study (the KETOCOMP study) can be found on the clinicaltrials website or in our published study protocol.