Unlu, M. B. | Search bioRxiv

bioRxiv2021

Simulations on the efficacy of radiotherapy with different time schemes of antiangiogenic therapy

The combination of radiotherapy and antiangiogenic agents has been suggested to be potent in tumor growth control compared to the application of antiangiogenic therapy or radiotherapy alone. Since radiotherapy is highly dependent on the oxygen level of the tumor area, antiangiogenic agents are utilized for the reoxygenation of tumor vasculature. We present a mathematical framework to investigate the efficacy of radiotherapy combined with antiangiogenic treatment. The framework consists of tumor cells, vasculature, and oxygenation levels evolving with time to mimic a tumor microenvironment. Non-linear partial differential equations (PDEs) are employed to simulate each component of the framework. Different treatment schemes are investigated to see the changes in tumor growth and oxygenation. To test combination schedules, radiation monotherapy, neoadjuvant, adjuvant, and concurrent cases are simulated. The efficiency of each therapy scheme on tumor growth control, the changes in tumor cell density, and oxygen levels shared by tumor cells are represented. The simulation results indicate that the application of radiotherapy after antiangiogenic treatment is more efficient in tumor growth control compared to other therapy schemes. The present study gives an insight into the possible interaction and timing of the combination of radiotherapy and antiangiogenic drug treatment.

cancer biology↗

bioRxiv2020

Chemotherapy response prediction with diffuser elapser network

In solid tumors, elevated fluid pressure and inadequate blood perfusion resulting from unbalanced angiogenesis are the prominent reasons for the ineffective drug delivery inside tumors. To normalize the heterogeneous and tortuous tumor vessel structure, antiangiogenic treatment is an effective approach. Additionally, the combined therapy of antiangiogenic agents and chemotherapy drugs has shown promising effects on enhanced drug delivery. However, the need to find the appropriate scheduling and dosages of the combination therapy is one of the main problems in anticancer therapy. Our study aims to generate a realistic response to the treatment schedule, making it possible for future works to use these patient-specific responses to decide on the optimal starting time and dosages of cytotoxic drug treatment. Our dataset is based on our previous in-silico model with a framework for the tumor microenvironment, consisting of a tumor layer, vasculature network, interstitial fluid pressure, and drug diffusion maps. In this regard, the chemotherapy response prediction problem is discussed in the study, putting forth a proof-of-concept for deep learning models to capture the tumor growth and drug response behaviors simultaneously. The proposed model utilizes multiple convolutional neural network submodels to predict future tumor microenvironment maps considering the effects of ongoing treatment. Since the model has the task of predicting future tumor microenvironment maps, we use two image quality evaluation metrics, which are structural similarity and peak signal-to-noise ratio, to evaluate model performance. We track tumor cell density values of ground truth and predicted tumor microenvironments. The model predicts tumor microenvironment maps seven days ahead with the average structural similarity score of 0.973 and the average peak signal ratio of 35.41 in the test set. It also predicts tumor cell density at the end day of 7 with the mean absolute percentage error of 2.292 {+/-} 1.820. Author summaryThe disorganized structure and leakiness of tumor vessels induce the inadequate blood supply and high fluid pressure within tumors. These features of the tumor microenvironment, identified as delivery barriers, lead to an insufficient amount of drugs to reach the interior parts of tumors. It is observed that the use of anti-vascular drugs makes the structure and function of the tumor vascular system more normal. Moreover, the combination of these drugs with cytotoxic agents provides favorable results with increased treatment response. But, it is also important to adjust the treatment schedule properly. In this regard, we build a deep learning model, designed to examine the tumor response with the ongoing treatment schedule. Our study suggests that deep learning models can be used to predict tumor growth and drug response in the scheduling of cytotoxic drugs.

systems biology↗