Search

Chemotherapy plays an important role in the treatment of cancer. While clinical chemotherapy protocols can lead to remission in some patients, in many cases tumor progression occurs despite continued treatment. In the present study we summarize mathematical approaches to model tumor growth and response to treatment, focusing on anticancer therapy resistance. We present results obtained at the recently founded Cybermedical Competence Center at Óbuda University, focusing on the development of a new therapy optimization concept that aims to optimize traditional chemotherapy.

There are about 14 million new cancer cases and 8 million deaths every year. Every second man and one in every three women will get cancer during their lifetimes. Following decades of steady increase, death rates have stabilized due to increased awareness and prevention, early detection, and the emergence of more effective therapy. Yet despite all the advances cancer remains a major killer. Despite improved therapies, nearly all current treatments face the same problem: for many patients, they ultimately stop working. Therapy resistance is the ultimate challenge facing cancer researchers and patients today. In this review we present an overview of the most important resistance mechanisms, discussing progress in therapies designed to prevent or overcome anticancer therapy resistance. Finally, we present recent findings from our own laboratory on the development of new experimental models and new therapeutic approaches to combat multidrug resistant cancer.

Anticancer compounds are typically identified in in vitro screens. Unfortunately, the in vitro drug sensitivity of cell lines does not reflect treatment efficiency in animal models, and neither show acceptable correlation to clinical results. While cell lines and laboratory animals can be readily "cured", the treatment of malignancies remains hampered by the multidrug resistance (MDR) of tumors. Genetically engineered mouse models (GEMMs) giving rise to spontaneous tumors offer a new possibility to characterize the evolution of drug resistance mechanisms and to target multidrug resistant cancer.A tumorellenes szerek alapjául szolgáló vegyületeket többnyire in vitro kísérletekben azonosítják. Problémát jelent, hogy a szûrésekhez használt sejtvonalak érzékenysége nem mutat teljes átfedést az állatmodellek eredményeivel, illetve a klinikai tapasztalatokkal: míg a sejtvonalakat és a laboratóriumi egereket hatékonyan „gyógyíthatjuk”, a daganatos megbetegedések eredményes kezelése a terápia során rendszerint kialakuló multidrog-rezisztencia (MDR) miatt a mai napig nem megoldott. Új irányt jelentenek a genetikailag módosított egerekben kialakuló spontán tumor modellek, melyek a kemoterápiás kezelés következtében válnak ellenállóvá. A spontán fejlõdõ tumorok evolúciójának, szövettanának, terápiára adott válaszának tanulmányozása révén megismerhetõvé válnak a klinikailag releváns rezisztenciamechanizmusok, és lehetõség nyílik a multidrogrezisztens tumorokat célzó kemoterápia fejlesztésére.