Citations

Abstract The clinical success of immune checkpoint inhibitors in treating metastatic and refractory cancers has generated significant interest in investigating their role in treating locally advanced diseases, thus requiring them to be combined with standard treatments in the hope of producing synergistic antitumor responses. Radiotherapy, in particular, has long been hypothesized to have actions complementary to those of immune checkpoint blockade, and a growing body of evidence indicates that cancer immunotherapy may also have radiosensitizing effects, which would provide unique benefit for locoregional treatments. Recent studies have demonstrated that when immune cells are activated by immunotherapeutics, they can reprogram the tumor microenvironment in ways that may potentially increase the radiosensitivity of the tumor. In this review, we highlight the evidence that supports reciprocal interactions between cancer immunotherapy and radiotherapy, where in addition to the traditional notion that radiation serves to enhance the activation of antitumor immunity, an alternative scenario also exists in which T-cell activation by cancer immunotherapy may sensitize tumors to radiation treatment through mechanisms that include normalization of the tumor vasculature and tissue hypoxia. We describe the empirical observations from preclinical models that support such effects and discuss their implications for future research and trial design.

Abstract Background Axitinib is an oral small molecule that inhibits receptor tyrosine kinases vascular endothelial growth factor receptors 1 to 3. A phase 1 and pharmacokinetic (PK) trial evaluating axitinib was conducted in children with refractory solid tumors. Methods Axitinib was administered orally twice daily in continuous 28‐day cycles. Dose levels (2.4 mg/m 2 /dose and 3.2 mg/m 2 /dose) were evaluated using a rolling 6 design. Serial PKs (cycle 1, days 1 and 8) and exploratory biomarkers were analyzed. Results A total of 19 patients were enrolled; 1 patient was ineligible due to inadequate time having elapsed from prior therapy. The median age of the patients was 13.5 years (range, 5‐17 years). Two of 5 patients who were treated at dose level 2 experienced dose‐limiting toxicities (palmar‐plantar erythryodysesthesia syndrome in 1 patient and intratumoral hemorrhage in 1 patient). Frequent (>20%) grade 1 to 2 toxicities during cycle 1 included anemia, anorexia, fatigue, diarrhea, nausea, and hypertension. Nonhematological toxicities of grade ≥3 in subsequent cycles included hypertension and elevated serum lipase. PK analysis demonstrated variability in axitinib exposure, the median time to peak plasma concentration was 2 hours, and the half‐life ranged from 0.7 to 5.2 hours. Exposure and dose were not found to be significantly associated with hypertension. Five patients achieved stable disease for ≥6 cycles as their best response, including patients with malignant peripheral nerve sheath tumor (1 patient), Ewing sarcoma (1 patient), hepatocellular carcinoma (1 patient), and osteosarcoma (2 patients). One patient with alveolar soft part sarcoma achieved a partial response. Kidney injury biomarkers were found to be elevated at baseline; no trends were identified. Conclusions In children with refractory solid tumors, the maximum tolerated and recommended dose of axitinib appears to be 2.4 mg/m 2 /dose, which provides PK exposures similar to those of adults.

PurposeIntensity‐modulated proton therapy (IMPT) is known to be sensitive to patient setup and range uncertainty issues. Multiple robust optimization methods have been developed to mitigate the impact of these uncertainties. Here, we propose a new robust optimization method, which provides an alternative way of robust optimization in IMPT, and is clinically practical, which will enable users to control the balance between nominal plan quality and plan robustness in a user‐defined fashion.MethodWe calculated nine individual dose distributions which corresponded to one nominal and eight extreme scenarios caused by patient setup and proton beam's range uncertainties. For each voxel, the normalized dose interval (NDI) is defined as the full dose range variation divided by the maximum dose in all uncertainty scenarios (NDI = [max – min dose]/max dose), which was then used to calculate the normalized dose interval volume histogram (NDIVH) curves. The areas under the NDIVH curves were used to quantify plan robustness. A normalized dose interval volume constraint (NDIVC) applied to the target was incorporated to specify the desired robustness which was user‐defined. Users could then explore the trade‐off between nominal plan quality and plan robustness by adjusting the position of the NDIVCs on the NDIVH curves freely. We benchmarked our method using one lung, five head and neck (H&N), and three prostate cases by comparing our results to those derived using the voxel‐wise worst‐case robust optimization.ResultsUsing the benchmark cases, our new method achieved quality IMPT plans comparable to those derived from the voxel‐wise worst‐case robust optimization for both nominal plan quality and plan robustness in general; even more conformal and more homogeneous target dose distributions in some cases, if proper NDIVCs were applied. The AUC under NDIVH, as a precise quantitative index of plan robustness, was consistent with DVH bandwidths. Additionally, we demonstrated the feasibility of adjusting the position of NDIVCs in the NDIVH curves which allowed users to explore the trade‐off between nominal plan quality and plan robustness.ConclusionsThe NDIVH‐based robust optimization method provided a novel and individualized way of robust optimization in IMPT, and enables users to adjust the balance between nominal plan quality and plan robustness in a user‐defined fashion. This method is applicable for continued improvement and developing the next generation of IMPT planning algorithms in the future.

Abstract Introduction The apolipoprotein E (APOE) e4 allele is a major genetic risk factor of late‐onset Alzheimer's disease. However, its interaction with two other canonical risk factors, age and sex, is not clear. Previous studies have reported conflicting results on its differential effects in men and women, its association with young‐onset AD before the age of 65 years, and its significance in genetic admixture populations. In these studies, the hazard of the e4 allele was assumed to be constant during aging. However, this hypothesis has not been tested and its violation may lead to significant biases and contribute to such discrepant findings. Methods In a prospective cohort of 4727 subjects, we performed Cox regression analysis of the association of the e4 allele with AD age of onset. We then performed diagnostics on the resulting model and tested if the hazard of the e4 allele violated the assumption of proportionality during aging. We examined whether incorporating age stratifications and time‐dependent coefficients could restore the proportionality. We then validated our findings in four independent cohorts. Results Hazard of the e4 allele for AD was nonproportional. It took a stepwise decline around the age of 80 years for men and around the age of 75 years for women. By stratifying subjects into a younger group and an older group, we detected more consistent effects of the e4 allele across multiple independent cohorts. We also found that the e4 allele was a significant risk factor for young‐onset AD with age of onset before 65 years. Discussion Age compositions of study cohorts can significantly bias the estimated effect of the APOE genotype. Studies of AD should consider hidden age structures among subjects and routinely employ appropriate age and sex stratification strategies or nonparametric modeling in experimental designs and data analysis. Finally, we argue that the e4 allele is a risk factor not only for late‐onset AD but also for young‐onset AD.

Pancreatic cancer, mostly pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancer types, with an estimated 44,330 death in 2018 in the US alone. While targeted therapies and immune checkpoint inhibitors have significantly improved treatment options for patients with lung cancer and renal cell carcinomas, little progress has been made in pancreatic cancer, with a dismal 5-year survival rate currently at ~8%. Upon diagnosis, the majority of pancreatic cancer cases (~80%) are already metastatic. Thus, identifying ways to reduce pancreatic cancer metastasis is an unmet medical need. Furthermore, pancreatic cancer is notorious resistant to chemotherapy. While Kirsten RAt Sarcoma virus oncogene (K-RAS) mutation is the major driver for pancreatic cancer, specific inhibition of RAS signaling has been very challenging, and combination therapy is thought to be promising. In this study, we report that combination of hedgehog (Hh) and Mitogen-activated Protein/Extracellular Signal-regulated Kinase Kinase (MEK) signaling inhibitors reduces pancreatic cancer metastasis in mouse models. In mouse models of pancreatic cancer metastasis using human pancreatic cancer cells, we found that Hh target gene Gli1 is up-regulated during pancreatic cancer metastasis. Specific inhibition of smoothened signaling significantly altered the gene expression profile of the tumor microenvironment but had no significant effects on cancer metastasis. By combining Hh signaling inhibitor BMS833923 with RAS downstream MEK signaling inhibitor AZD6244, we observed reduced number of metastatic nodules in several mouse models for pancreatic cancer metastasis. These two inhibitors also decreased cell proliferation significantly and reduced CD45+ cells (particularly Ly6G+CD11b+ cells). We demonstrated that depleting Ly6G+ CD11b+ cells is sufficient to reduce cancer cell proliferation and the number of metastatic nodules. In vitro, Ly6G+ CD11b+ cells can stimulate cancer cell proliferation, and this effect is sensitive to MEK and Hh inhibition. Our studies may help design novel therapeutic strategies to mitigate pancreatic cancer metastasis.