Non-Hodgkin's lymphoma is a disseminated, highly malignant
cancer, with resistance to drug treatment based on molecular- and tissue-scale characteristics that are intricately linked. A critical
element of molecular resistance has been traced to the loss of functionality in
proteins such as the
tumor suppressor p53. We investigate the tissue-scale physiologic effects of this loss by integrating in vivo and immunohistological data with computational modeling to study the spatiotemporal physical dynamics of
lymphoma growth. We compare between drug-sensitive Eμ-myc Arf-/- and drug-resistant Eμ-myc p53-/-
lymphoma cell
tumors grown in live mice. Initial values for the model parameters are obtained in part by extracting values from the cellular-scale from whole-
tumor histological staining of the
tumor-infiltrated inguinal lymph node in vivo. We compare model-predicted
tumor growth with that observed from intravital microscopy and macroscopic imaging in vivo, finding that the model is able to accurately predict
lymphoma growth. A critical physical mechanism underlying drug-resistant phenotypes may be that the Eμ-myc p53-/- cells seem to pack more closely within the
tumor than the Eμ-myc Arf-/- cells, thus possibly exacerbating diffusion gradients of
oxygen, leading to cell quiescence and hence resistance to cell-cycle specific drugs. Tighter cell packing could also maintain steeper gradients of drug and lead to insufficient toxicity. The transport phenomena within the
lymphoma may thus contribute in nontrivial, complex ways to the difference in drug sensitivity between Eμ-myc Arf-/- and Eμ-myc p53-/-
tumors, beyond what might be solely expected from loss of functionality at the molecular scale. We conclude that computational modeling tightly integrated with experimental data gives insight into the dynamics of
Non-Hodgkin's lymphoma and provides a platform to generate confirmable predictions of
tumor growth.