Einar K. Rofstad

Tumor hypoxia is associated with resistance to treatment, aggressive growth, metastatic dissemination, and poor clinical outcome in many cancer types. The potential of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to assess the extent of hypoxia in tumors has been investigated in several studies in our laboratory. Cervical carcinoma, melanoma, and pancreatic ductal adenocarcinoma (PDAC) xenografts have been used as models of human cancer, and the transfer rate constant (K^trans) and the extravascular extracellular volume fraction (v_e) have been derived from DCE-MRI data by using Tofts standard pharmacokinetic model and a population-based arterial input function. K^trans was found to reflect naturally occurring and treatment-induced hypoxia when hypoxia was caused by low blood perfusion, radiation responsiveness when radiation resistance was due to hypoxia, and metastatic potential when metastasis was hypoxia-induced. K^trans was also associated with outcome for patients with locally-advanced cervical carcinoma treated with cisplatin-based chemoradiotherapy. Together, the studies imply that DCE-MRI can provide valuable information on the hypoxic status of cervical carcinoma, melanoma, and PDAC. In this communication, we review and discuss the studies and provide some recommendations as to how DCE-MRI data can be analyzed and interpreted to assess tumor hypoxia.

Most tumors develop abnormal fibrotic regions consisting of fibroblasts, immune cells, and a dense extracellular matrix (ECM) immersed in a viscous interstitial fluid, and an abundant fibrotic tumor microenvironment (TME) is associated with poor outcome of treatment. It has been hypothesized that the treatment of cancer may be improved by interventions aiming to normalize this TME. The approaches used in attempts to normalize the fibrotic TME can be categorized into three strategies of targeted antifibrotic therapy: targeting of components of the ECM, targeting of the producers of the ECM components—the activated cancer-associated fibroblasts (CAFs), and targeting of the signaling pathways activating CAFs. To target the ECM, enzymes against components of the ECM have been used, including collagenase, relaxin, hyaluronidase, and lyxyl oxidase. Targeting of CAFs have been investigated by using agents aiming to eliminate or reprogram CAFs. CAFs are activated primarily by transforming growth factor-β (TGF-β), hedgehog, or focal adhesion kinase signaling, and several agents have been used to target these signaling pathways, including angiotensin II receptor I blockers (e.g., losartan) to inhibit the TGF-β pathway. Taken together, these studies have revealed that antifibrotic therapy is a two-edged sword: while some studies suggest enhanced response to treatment after antifibrotic therapy, others suggest that antifibrotic therapy may lead to increased tumor growth, metastasis, and impaired outcome of treatment. There are several possible explanations of these conflicting observations. Most importantly, tumors contain different subpopulations of CAFs, and while some subpopulations may promote tumor growth and metastasis, others may inhibit malignant progression. Furthermore, the outcome of antifibrotic therapy may depend on stage of disease, duration of treatment, treatment-induced activation of alternative profibrotic signaling pathways, and treatment-induced recruitment of tumor-supporting immune cells. Nevertheless, losartan-induced suppression of TGF-β signaling appears to be a particularly promising strategy. Losartan is a widely prescribed antihypertensive drug and highly advantageous therapeutic effects have been observed after losartan treatment of pancreatic cancer. However, improved understanding of the mechanisms governing the development of fibrosis in tumors is needed before safe antifibrotic treatments can be established.

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide biomarkers of the outcome of locally-advanced cervical carcinoma (LACC). There is, however, no agreement on how DCE-MR recordings should be analyzed. Previously, we have analyzed DCE-MRI data of LACC using non-model-based strategies. In the current study, we analyzed DCE-MRI data of LACC using the Tofts pharmacokinetic model, and the biomarkers derived from this analysis were compared with those derived from the non-model-based analyses. Methods: Eighty LACC patients given cisplatin-based chemoradiotherapy with curative intent were included in the study. Treatment outcome was recorded as disease-free survival (DFS) and overall survival (OS). DCE-MRI series were analyzed voxelwise to produce K^trans and v_e frequency distributions, and ROC analysis was used to identify the parameters of the frequency distributions having the greatest potential as biomarkers. The prognostic power of these parameters was compared with that of the non-model-based parameters LETV (low-enhancing tumor volume) and TVIS (tumor volume with increasing signal). Results: Poor DFS and OS were associated with low values of K^trans, whereas there was no association between treatment outcome and v_e. The K^trans parameters having the greatest prognostic value were p35-K^trans (the K^trans value at the 35 percentile of a frequency distribution) and RV-K^trans (the tumor subvolume with K^trans values below 0.13 min^− 1). Multivariate analysis including clinical parameters and p35-K^trans or RV-K^trans revealed that RV-K^trans was the only independent prognostic factor of DFS and OS. There were significant correlations between RV-K^trans and LETV and between RV-K^trans and TVIS, and the prognostic power of RV-K^trans was similar to that of LETV and TVIS. Conclusions: Biomarkers of the outcome of LACC can be provided by analyzing DCE-MRI series using the Tofts pharmacokinetic model. However, these biomarkers do not appear to have greater prognostic value than biomarkers determined by non-model-based analyses.

Background: Melanoma patients with metastatic growth in the meninges have poor prognosis and few treatment options. Although treatment with BRAF inhibitors or immune checkpoint inhibitors has provided promising results, most patients with advanced melanoma are resistant to these treatments and develop severe side effects. Novel treatment strategies are needed for patients with meningeal melanoma metastases, and the potential of antiangiogenic therapy was investigated in this preclinical study. Methods: Two GFP-transfected melanoma models (A-07 and D-12) differing substantially in VEGF-A expression were included in the study, and the anti-VEGF-A antibody bevacizumab was used as therapeutic agent. Meningeal metastases were initiated in BALB/c nu/nu mice by intracranial inoculation of melanoma cells, and bevacizumab treatment was given twice a week in i.p. doses of 10 mg/kg until the mice became moribund. Therapeutic effects were evaluated by determining tumor host survival time, assessing tumor growth and angiogenic activity by quantitative analyses of histological preparations, and measuring the expression of angiogenesis-related genes by quantitative PCR. Results: Meningeal A-07 melanomas showed higher expression of VEGF-A than meningeal D-12 melanomas, whereas the expression of ANGPT2 and IL8, two important angiogenesis drivers in melanoma, was much higher in D-12 than in A-07 tumors. Bevacizumab treatment inhibited tumor angiogenesis and prolonged host survival in mice with A-07 tumors but not in mice with D-12 tumors. Meningeal A-07 tumors in bevacizumab-treated mice compensated for the reduced VEGF-A activity by up-regulating a large number of angiogenesis-related genes, including ANGPT2 and its receptors TIE1 and TIE2. Melanoma cells migrated from meningeal tumors into the cerebrum, where they initiated metastatic growth by vessel co-option. In the A-07 model, the density of cerebral micrometastases was higher in bevacizumab-treated than in untreated mice, either because bevacizumab treatment increased mouse survival or induced increased tumor gene expression. Conclusions: The development of antiangiogenic strategies for the treatment of meningeal melanoma metastases is a challenging task because the outcome of treatment will depend on the angiogenic signature of the tumor tissue, treatment-induced alterations of the angiogenic signature, and the treatment sensitivity of metastatic lesions in other intracranial sites.

Background: There is significant evidence that DCE-MRI may have the potential to provide clinically useful biomarkers of the outcome of locally advanced cervical carcinoma. However, there is no consensus on how to analyze DCE-MRI data to arrive at the most powerful biomarkers. The purpose of this study was to analyze DCE-MRI data of cervical cancer patients by using the Brix pharmacokinetic model and to compare the biomarkers derived from the Brix analysis with biomarkers determined by non-model-based analysis [i.e., low-enhancing tumor volume (LETV) and tumor volume with increasing signal (TVIS)] of the same patient cohort. Material and methods: DCE-MRI recordings of 80 patients (FIGO stage IB–IVA) treated with concurrent cisplatin-based chemoradiotherapy were analyzed voxel-by-voxel, and frequency distributions of the three parameters of the Brix model (A_Brix, k_ep, and k_el) were determined. Moreover, risk volumes were calculated from the Brix parameters and termed RV-A_Brix, RV-k_ep, and RV-k_el, where the RVs represent the tumor volume with voxel values below a threshold value determined by ROC analysis. Disease-free survival (DFS) and overall survival (OS) were used as measures of treatment outcome. Results: Significant associations between the median value or any other percentile value of A_Brix, k_ep, or k_el and treatment outcome were not found. However, RV-A_Brix, RV-k_ep, and RV-k_el correlated with DFS and OS. Multivariate analysis revealed that the prognostic power of RV-A_Brix, RV-k_ep, and RV-k_el was independent of well-established clinical prognostic factors. RV-A_Brix, RV-k_ep, and RV-k_el correlated with each other as well as with LETV and TVIS. Conclusion: Strong biomarkers of the outcome of locally advanced cervical carcinoma can be provided by subjecting DCE-MRI series to pharmacokinetic analysis using the Brix model. The prognostic power of these biomarkers is not necessarily superior to that of biomarkers identified by non-model-based analyses.

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