Although there are considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically relevant oncologic models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathologic response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled) reaction–diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications. Cancer Res; 75(22); 1–11. ©2015 AACR.

While there is considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically-relevant oncological models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathological response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled) reaction-diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (RECIST; 0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications.

Colorectal cancer (CRC) pathogenesis remains incompletely understood. Here we report that the heterochromatin protein HP1γ is upregulated commonly in human CRC where it promotes cell proliferation in vitro and in vivo. Gene expression and promoter binding experiments demonstrated that HP1γ directly regulated CDKN1A (p21Waf1/Cip1) in a manner associated with methylation of histone H3K9 on its promoter. We identified miR-30 as a tumor suppressive microRNA that targets HP1γ in vitro and in vivo to specifically suppress the growth of colorectal cancer in mouse xenograft models. MiR-30a was widely downregulated in primary human CRC tissues where its expression correlated inversely with high levels of HP1γ protein. Our results identify a new miR-30a/HP1γ/p21 regulatory axis controlling CRC development which may offer prognostic and therapeutic opportunities.

Tumor cells counteract innate and adaptive antitumor immune responses by recruiting regulatory T cells (Treg) and innate myeloid-derived suppressor cells (MDSC), which facilitate immune escape and metastatic dissemination. Here we report a role in these recruitment processes for CD81, a member of the tetraspanin family of proteins that have been implicated previously in cancer progression. We found that genetic deficiency in CD81 reduced tumor growth and metastasis in two genetic mouse backgrounds and multiple tumor models. Mechanistic investigations revealed that CD81 was not required for normal development of Treg and MDSC but was essential for immunosuppressive functions. Notably, adoptive transfer of wildtype Treg into CD81-deficient mice was sufficient to promote tumor growth and metastasis. Our findings suggested that CD81 modulates adaptive and innate immune responses, warranting further investigation of CD81 in immunomodulation in cancer and its progression.

The presence of differentiated thyroid cells in thyroid cancer is critical for the antitumor response to radioactive iodide treatment, and loss of the differentiated phenotype is a key hallmark of iodide-refractory metastatic disease. The role of microRNAs (miRs) in fine-tuning gene expression has become a major regulatory mechanism by which developmental and pathological processes occur. In this study, we performed next-generation sequencing and expression analysis of eight papillary thyroid carcinomas (PTC) to comprehensively characterize miRs involved in loss of differentiation. We found that only a small set of abundant miRs is differentially expressed between PTC tissue and normal tissue from the same patient. In addition, we integrated computational prediction of potential targets and mRNA sequencing and identified a master miR regulatory network involved in essential biological processes such as thyroid differentiation. Both mature products of miR-146b (miR-146b-5p and -3p) were among the most abundantly expressed miRs in tumors. Specifically, we found that miR-146b-3p binds to the 3`UTR of PAX8 and Sodium/Iodide Symporter (NIS) leading to impaired protein translation and a subsequent reduction in iodide uptake. Furthermore, our findings show that miR-146b and PAX8 regulate each other and share common target genes, thus highlighting a novel regulatory circuit that governs the differentiated phenotype of PTC. In conclusion, our study has uncovered the existence of a miR-146b-3p-PAX8-NIS regulatory circuit that may be exploited therapeutically to modulate thyroid cell differentiation and iodide uptake for improved treatment of advanced thyroid cancer.