Chemotherapy and radiotherapy are first-line treatments in the management of advanced solid tumors. Whereas these treatments are directed at eliminating cancer cells, they cause significant adverse effects that can be detrimental to a patient’s quality of life and even life-threatening. Diet is a modifiable risk factor that has been shown to affect cancer risk, recurrence, and treatment toxicity, but little information is known how diet interacts with cancer treatment modalities. Although dietary interventions, such as intermittent fasting and ketogenic diets, have shown promise in pre-clinical studies by reducing the toxicity and increasing the efficacy of chemotherapeutics, there remains a limited number of clinical studies in this space. This review surveys the impact of dietary interventions (caloric restriction, intermittent and short-term fasting, and ketogenic diet) on cancer treatment outcomes in both pre-clinical and clinical studies. Early studies support a complementary role for these dietary interventions in improving patient quality of life across multiple cancer types by reducing toxicity and perhaps a benefit in treatment efficacy. Larger, phase III, randomized clinical trials are ultimately necessary to evaluate the efficacy of these dietary interventions in improving oncologic or quality of life outcomes for patients that are undergoing chemotherapy or radiotherapy.

e22021 Background: Melanoma brain metastases (MBM) are common with a median overall survival of 4-5 months. Although immunotherapies have improved clinical outcomes and have doubled overall survival in MBM, there is a high incidence rate of relapse caused by drug resistance. AXL, a receptor tyrosine kinase (RTK), is associated with drug resistance and metastasis in many cancers. The activation of AXL via trans-phosphorylation regulates multiple signaling pathways that induce tumor survival, metastasis, drug resistance, and epithelial-to-mesenchymal transition (EMT). In MBM, AXL is upregulated and associated with disease progression, promoting cell invasion and migration. This suggests that targeting AXL can be a novel strategy to overcome treatment-related resistance in MBM. TP-0903, an investigational small molecule inhibitor of AXL, has shown efficacy in reversing the mesenchymal phenotype and re-sensitizing resistant cancer cells to targeted therapies in heme malignancies, pancreatic, and breast cancer. We aim to investigate the efficacy of TP-0903 in MBM. Methods: The Cancer Genome Atlas (TCGA) data was utilized to investigate the signaling pathways downstream of AXL that are upregulated in advanced melanoma. Nine signaling molecules including AKT1, mTOR, and PAK4 were analyzed to identify any correlation between gene expression levels and overall survival. Four metastatic melanoma cell lines were used to evaluate the effect of TP-0903 on cell viability and active AXL downregulation was assessed in vitro through MTS cell viability assays and Immunoblotting. Wound closure assays were executed to understand the functional consequences of AXL downregulation. Results: In all nine genes, high expression levels confer poor survival probability. Cell viability assays of four malignant melanoma cell lines showed that TP-0903 treatment resulted in IC50 values ranging from 32 – 692 nM. Western blot analysis indicated that TP-0903 reduced the levels of phosphorylated AXL in malignant melanoma cell lines. In addition, increasing TP-0903 concentrations reduced the rate of cell migration in these malignant melanoma cell lines. Conclusions: AXL plays a role in EMT, treatment resistance, and metastasis in MBM, resulting in poor survival. Our findings suggest TP-0903 is effective in reducing cell migration, inhibit metastasis, and can be a potential therapeutic option in MBM.

Introduction: High-grade glioma (HGG) is the most common subtype of primary brain tumors with high recurrence rate and poor survival. The emergence of targeted molecular and cellular therapies (e.g., pembrolizumab, Chimeric Antigen Receptor [CAR] T-cell therapy) are potentially promising in improving overall survival. Due to increased intratumor heterogeneity and inhomogeneous treatment response, there is an unmet need of imaging biomarkers predictive of treatment response and survival. Radiomics using machine learning methods have shown promise in predicting treatment response in various solid tumors, including HGG. In this study, we compare the survival prediction performance using machine learning models with different radiomic features individually derived from T1- and T2-weighted MR images in patients suffering from HGG treated with CAR-T cell therapy. Methods: In this IRB-approved phase 1 clinical trial, 61 patients (39 males, median age = 49) suffering from recurrent HGG underwent surgical resection and CAR T-cell therapy1. All patients underwent baseline MRI scans prior to both surgical resection and CAR T-cell administration in the resection cavity. For patients with a complete set of T1- and T2-weighted MRIs (n = 50), we generated segmentations in a semi-automated manner, labeling with each tumorous voxel as either contrast-enhanced tumor (ET), non-enhancing tumor (NET) and edema. From each tumor label, we extracted shape-based, texture-based and image-filtered radiomic features2. We utilized gradient-boosted tree models (lightGBM) to classify whether survival is above or below group median (188 days) by using two nested loops of 10-fold cross validations each. For the inner validation loop, we determined the optimal model from hyper-parameters including regularization. For the outer validation loop, we tested this model on the hold-out data and the predictions were used as radiomic risk scores. Results: For each of the ET, NET, and edema tumor ROIs, we extracted 1313 radiomic features for predictive modeling. The outer validation loop Area Under the Receiver Operating Characteristic Curve (AUC) for ET, NET and edema were 0.55, 0.70, and 0.46, respectively, suggesting that radiomic features calculated from NET voxels are the most predictive of survival compared to features from ET and edema voxels. We also stratified the patients into two distinct prognostic sub-groups (25 patients each group) using the NET radiomic risk scores obtained from the outer validation loop, with a log-rank test p-value of 0.01. Conclusions: In patients suffering from recurrent HGG who were treated with CAR T-cell therapy, we found that radiomic features derived from NET voxels are predictive of survival while the other two tumorous voxel types (ET and edema) are not. Further work is needed to incorporate clinical and molecular features that also may be predictive of survival. 1N Engl J Med 2016; 375:2561-2569. 2Cancer Research, 77(21), e104-e107 Citation Format: Chi Wah Wong, Sohaib Naim, Vincent La, Seth Michael Hilliard, Eemon Tizpa, Rashi Ranjan, Hannah Jade Young, Kimberly Jane Bonjoc, Aleksandr Filippov, Saman Tabassum Khan, Christine Brown, Behnam Badie, Ammar Ahmed Chaudhry. Radiomic prediction of survival in recurrent high-grade glioma patients treated with CAR T-cell therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 866.