Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma.

Distant metastasis-free survival (DMFS) curves are widely used in oncology. They are classically analyzed using the Kaplan-Meier estimator or agnostic statistical models from survival analysis. Here we report on a method to extract more information from DMFS curves using a mathematical model of primary tumor growth and metastatic dissemination. The model depends on two parameters, α and µ, respectively quantifying tumor growth and dissemination. We assumed these to be lognormally distributed in a patient population. We propose a method for identification of the parameters of these distributions based on least-squares minimization between the data and the simulated survival curve. We studied the practical identifiability of these parameters and found that including the percentage of patients with metastasis at diagnosis was critical to ensure robust estimation. We also studied the impact and identifiability of covariates and their coefficients in α and µ, either categorical or continuous, including various functional forms for the latter (threshold, linear or a combination of both). We found that both the functional form and the coefficients could be determined from DMFS curves. We then applied our model to a clinical dataset of metastatic relapse from kidney cancer with individual data of 105 patients. We show that the model was able to describe the data and illustrate our method to disentangle the impact of three covariates on DMFS: a categorical one (Führman grade) and two continuous ones (gene expressions of the macrophage mannose receptor 1 (MMR) and the G Protein-Coupled Receptor Class C Group 5 Member A (GPRC5a) gene). We found that all had an influence in metastasis dissemination (µ), but not on growth (α).

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression.

BACKGROUND: Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. METHODS: In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. RESULTS: Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. CONCLUSION: A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Computational Modelling of Metastasis Development in Renal Cell Carcinoma.

The biology of the metastatic colonization process remains a poorly understood phenomenon. To improve our knowledge of its dynamics, we conducted a modelling study based on multi-modal data from an orthotopic murine experimental system of metastatic renal cell carcinoma. The standard theory of metastatic colonization usually assumes that secondary tumours, once established at a distant site, grow independently from each other and from the primary tumour. Using a mathematical model that translates this assumption into equations, we challenged this theory against our data that included: 1) dynamics of primary tumour cells in the kidney and metastatic cells in the lungs, retrieved by green fluorescent protein tracking, and 2) magnetic resonance images (MRI) informing on the number and size of macroscopic lesions. Critically, when calibrated on the growth of the primary tumour and total metastatic burden, the predicted theoretical size distributions were not in agreement with the MRI observations. Moreover, tumour expansion only based on proliferation was not able to explain the volume increase of the metastatic lesions. These findings strongly suggested rejection of the standard theory, demonstrating that the time development of the size distribution of metastases could not be explained by independent growth of metastatic foci. This led us to investigate the effect of spatial interactions between merging metastatic tumours on the dynamics of the global metastatic burden. We derived a mathematical model of spatial tumour growth, confronted it with experimental data of single metastatic tumour growth, and used it to provide insights on the dynamics of multiple tumours growing in close vicinity. Together, our results have implications for theories of the metastatic process and suggest that global dynamics of metastasis development is dependent on spatial interactions between metastatic lesions.

Metalloproteinase-dependent and -independent processes contribute to inhibition of breast cancer cell migration, angiogenesis and liver metastasis by a disintegrin and metalloproteinase with thrombospondin motifs-15.

The ADAMTS proteinases are a family of secreted, matrix-associated enzymes that have diverse roles in the regulation of tissue organization and vascular homeostasis. Several of the 19 human family members have been identified as having either tumor promoting or suppressing roles. We previously demonstrated that decreased ADAMTS15 expression correlated with a worse clinical outcome in mammary carcinoma (e.g., Porter et al., Int J Cancer 2006;118:1241-7). We have explored the effects of A Disintegrin and Metalloproteinase with Thrombospondin motifs-15 (ADAMTS-15) on the behavior of MDA-MB-231 and MCF-7 breast cancer cells by stable expression of either a wild-type (wt) or metalloproteinase-inactive (E362A) protein. No effects on mammary cancer cell proliferation or apoptosis were observed for either form of ADAMTS-15. However, both forms reduced cell migration on fibronectin or laminin matrices, though motility on a Type I collagen matrix was unimpaired. Knockdown of syndecan-4 attenuated the inhibitory effects of ADAMTS-15 on cell migration. In contrast to its effects on cell migration, wt ADAMTS-15 but not the E362A inactive mutant inhibited endothelial tubulogenesis in 3D collagen gels and angiogenesis in the aortic ring assay. In experimental metastasis assays in nude mice, MDA-MB-231 cells expressing either form of ADAMTS-15 showed reduced spread to the liver, though lung colonization was enhanced for cells expressing wt ADAMTS-15. These studies indicate that extracellular ADAMTS-15 has multiple actions on tumor pathophysiology. Via modulation of cell-ECM interactions, which likely involve syndecan-4, it attenuates mammary cancer cell migration independent of its metalloproteinase activity; however, its antiangiogenic action requires catalytic functionality, and its effects on metastasis in vivo are tissue niche-dependent.

Reversible transdifferentiation of blood vascular endothelial cells to a lymphatic-like phenotype in vitro.

Blood vascular cells and lymphatic endothelial cells (BECs and LECs, respectively) form two separate vascular systems and are functionally distinct cell types or lineages with characteristic gene expression profiles. Interconversion between these cell types has not been reported. Here, we show that in conventional in vitro angiogenesis assays, human BECs of fetal or adult origin show altered gene expression that is indicative of transition to a lymphatic-like phenotype. This change occurs in BECs undergoing tubulogenesis in fibrin, collagen or Matrigel assays, but is independent of tube formation per se, because it is not inhibited by a metalloproteinase inhibitor that blocks tubulogenesis. It is also reversible, since cells removed from 3D tubules revert to a BEC expression profile upon monolayer culture. Induction of the lymphatic-like phenotype is partially inhibited by co-culture of HUVECs with perivascular cells. These data reveal an unexpected plasticity in endothelial phenotype, which is regulated by contact with the ECM environment and/or cues from supporting cells.

New insights into the plasticity of the endothelial phenotype.

The mammalian vascular system consists of two distinct, but closely related, networks: the blood vasculature (itself divided into arterial and venous networks) and the lymphatic vasculature. EC (endothelial cell) lineage specification has been proposed to be determined during embryonic development, after which the ECs are committed to their fate. However, increasing evidence suggests that ECs retain various degrees of plasticity, and have the ability to express characteristics of alternative cell lineages. Therapeutic control of endothelial plasticity will allow greater understanding of the genesis and treatment of several vascular diseases.

Plk1, upregulated by HIF-2, mediates metastasis and drug resistance of clear cell renal cell carcinoma.

Polo-like kinase 1 (Plk1) expression is inversely correlated with survival advantages in many cancers. However, molecular mechanisms that underlie Plk1 expression are poorly understood. Here, we uncover a hypoxia-regulated mechanism of Plk1-mediated cancer metastasis and drug resistance. We demonstrated that a HIF-2-dependent regulatory pathway drives Plk1 expression in clear cell renal cell carcinoma (ccRCC). Mechanistically, HIF-2 transcriptionally targets the hypoxia response element of the Plk1 promoter. In ccRCC patients, high expression of Plk1 was correlated to poor disease-free survival and overall survival. Loss-of-function of Plk1 in vivo markedly attenuated ccRCC growth and metastasis. High Plk1 expression conferred a resistant phenotype of ccRCC to targeted therapeutics such as sunitinib, in vitro, in vivo, and in metastatic ccRCC patients. Importantly, high Plk1 expression was defined in a subpopulation of ccRCC patients that are refractory to current therapies. Hence, we propose a therapeutic paradigm for improving outcomes of ccRCC patients.

Sunitinib Stimulates Expression of VEGFC by Tumor Cells and Promotes Lymphangiogenesis in Clear Cell Renal Cell Carcinomas.

Sunitinib is an antiangiogenic therapy given as a first-line treatment for renal cell carcinoma (RCC). While treatment improves progression-free survival, most patients relapse. We hypothesized that patient relapse can stem from the development of a lymphatic network driven by the production of the main growth factor for lymphatic endothelial cells, VEGFC. In this study, we found that sunitinib can stimulate vegfc gene transcription and increase VEGFC mRNA half-life. In addition, sunitinib activated p38 MAPK, which resulted in the upregulation/activity of HuR and inactivation of tristetraprolin, two AU-rich element-binding proteins. Sunitinib stimulated a VEGFC-dependent development of lymphatic vessels in experimental tumors. This may explain our findings of increased lymph node invasion and new metastatic sites in 30% of sunitinib-treated patients and increased lymphatic vessels found in 70% of neoadjuvant treated patients. In summary, a therapy dedicated to destroying tumor blood vessels induced the development of lymphatic vessels, which may have contributed to the treatment failure. Cancer Res; 77(5); 1212-26. ©2017 AACR.