Predictive Modeling of Drug Response in Non-Hodgkin’s Lymphoma

Abstract

We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors to chemotherapy drug resistance, which are difficult to understand solely through experimentation. Experiments in cell culture and in mice with drug-sensitive (Eµ-myc/Arf-/-) and drug-resistant (Eµ-myc/p53-/-) lymphoma cell lines were conducted to calibrate and validate a mechanistic mathematical model. Inputs to inform the model include tumor drug transport characteristics, such as blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response in cell culture. Model results show that the drug response in mice, represented by the fraction of dead tumor volume, can be reliably predicted from these inputs. Hence, a proof-of-principle for predictive quantification of lymphoma drug therapy was established based on both cellular and tissue-scale physiological contributions. We further demonstrate that, if the in vitro cytotoxic response of a specific cancer cell line under chemotherapy is known, the model is then able to predict the treatment efficacy in vivo. Lastly, tissue blood volume fraction was determined to be the most sensitive model parameter and a primary contributor to drug resistance.

Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies

“…The semiautomated histology analysis described here can potentially be used for other solid tumors, although thresholding based on vascular staining and tumor types may need to be optimized for each. The general applicability of the mechanistic f kill model to predict response has been examined and confirmed in several other cancer types, including CRC with metastasis to liver, glioblastoma, pancreatic cancer, and lymphoma (13,16,18,21). The observed consistency across tumor types is attributed to the fact that the f kill model was derived from fundamental principles of mass transport common to many solid tumor types (13) and evaluates vasculature characteristics in the tumor prior to treatment, thereby determining the efficiency of the vascular network to deliver drugs to the tumor.…”

“…Over the years, our group has proposed that the characteristics of the tumor vasculature might be a biologic predictor of response to chemotherapy. This mechanistic hypothesis has been examined in a series of modeling studies to evaluate the prediction of treatment outcomes based on chemotherapy drug diffusion and the physical properties of several tumor types (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). We and other investigators have proposed that diffusion barriers may prevent drugs from reaching malignant tumor cells, a functional mechanism that might partially underlie drug resistance (30).…”

What does not kill a tumour may make it stronger: In silico insights into chemotherapeutic drug resistance

“…Drug resistance is a multipart phenomenon which can be derived from several origins, in fact a cancer cell or tumour may express drug resistance in various ways [35,46,61]. Drug resistance may arise due to micro-environmental or intrinsic cell factors [19] and cells can acquire drug resistance by for example amplifying drug target molecules, activating DNA-repair, inducing drug transporters or altering their drug metabolism [61]. Phenotypical variations in cells, such as drug resistance, can be inherited or acquired and further, cells may be resistant to one specific drug or to multiple drugs, the latter phenomenon is known as multidrug resistance (MDR) [33,35,41,68].…”

The influence of soluble fragments of extracellular matrix (ECM) on tumor growth and morphology

“…Model predictions are also validated using experiments on an in vivo breast cancer mouse model with different drug delivery methods. By combining mathematical modeling with experiments in mice for predicting chemotherapy drug response, Frieboes et al [39] showed that the drug response in mice (represented by the fraction of dead tumor volume) can be predicted from drug transport characteristics (blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response cell culture). Koay et al [40] developed a mass transport model for measuring mass transport properties to describe qualities of the pancreatic tissue and its surrounding vasculature during routine contrast-enhanced CT scans of human pancreatic ductal adenocarcinoma (PDAC).…”