The records of 208.777 (100%) clinical trials registered at ClinicalTrials.gov were downloaded on the 19th of February 2016. Phase II and III trials including patients with glioblastoma were selected for further classification and analysis. Based on the disease settings, trials were classified into three groups: newly diagnosed glioblastoma, recurrent disease and trials with no differentiation according to disease setting. Furthermore, we categorized trials according to the experimental interventions, the primary sponsor, the source of financial support and trial design elements. Trends were evaluated using the autoregressive integrated moving average model. Two hundred sixteen (0.1%) trials were selected for further analysis. Academic centers (investigator initiated trials) were recorded as primary sponsors in 56.9% of trials, followed by industry 25.9%. Industry was the leading source of monetary support for the selected trials in 44.4%, followed by 25% of trials with primarily academic financial support. The number of newly initiated trials between 2005 and 2015 shows a positive trend, mainly through an increase in phase II trials, whereas phase III trials show a negative trend. The vast majority of trials evaluate forms of different systemic treatments (91.2%). In total, one hundred different molecular entities or biologicals were identified. Of those, 60% were involving drugs specifically designed for central nervous system malignancies. Trials that specifically address radiotherapy, surgery, imaging and other therapeutic or diagnostic methods appear to be rare. Current research in glioblastoma is mainly driven or sponsored by industry, academic medical oncologists and neuro-oncologists, with the majority of trials evaluating forms of systemic therapies. Few trials reach phase III. Imaging, radiation therapy and surgical procedures are underrepresented in current trials portfolios. Optimization in research portfolio for glioblastoma is needed.

Circulating exosomes from prostate cancer (PCa) patients undergoing radiotherapy are attractive candidate biomarkers for monitoring treatment response. Multiple workflows for isolation and content characterization of exosomes in biofluids have been attempted. We report a protocol to isolate and characterize exosomal miRNAs content and assess radiation-induced changes. In this pilot study, we performed targeted exosomal miRNA profiling of 25 serum samples obtained from PCa patients with intermediate- and high-risk disease treated with curative radiotherapy (RT), and controls. Post-treatment blood samples were collected at least 28 days after radiation therapy as a paired follow-up sample. The complete workflow consisted of two phases: I) filtration and polyethylene glycol salt precipitation phase which enriched particles below 200 nm in size followed by characterization using electron microscopy, and II) flow cytometry. Finally, miRNA expression analysis between untreated and treated patient samples was performed using RNA extraction kit, and qRT-PCR. In our preliminary data, 1 ml of serum from PCa patients showed higher exosomal concentration (3.68E+10) compared to controls (6.07E+08). The overall expression of exosomes after RT was found to be higher compared to untreated samples; the median value changed from 3.68E+10 to 5.40E+10; p = 0.52. Using electron microscopy, we were able to visualize cup-shaped vesicles with morphology and size compatible with exosomes. The bead-based flow cytometry showed positivity for exosomal tetraspanins surface markers CD63 and CD9. All five miRNAs (hsa-let-7a-5p, hsa-miR-141-3p, hsa-miR-145-5p, hsa-miR-21-5p, hsa-miR-99b-5p) have been identified in exosomes. Despite overall changes in hsa-let-7a-5p expression after radiation, the difference was significant only in the high-risk group (p = 0.037). In addition, the radiation response to hsa-miR-21-5p was elevated in the high-risk group compared to the intermediate group (p = 0.036). Herewith, we demonstrated a protocol for isolation of serum exosomes and exosomal miRNA amplification. The recovery of exosomal miRNAs and their differential expression after radiation treatment suggests promising biomarker potential that requires further investigation in larger patient cohorts.

To analyze the impact of weight loss before and during chemoradiation on survival outcomes in patients with locally advanced head and neck cancer. From 07/1994-07/2000 a total of 224 patients with squamous cell carcinoma of the head and neck were randomized to either hyperfractionated radiation therapy alone or the same radiation therapy combined with two cycles of concomitant cisplatin. The primary endpoint was time to any treatment failure (TTF); secondary endpoints were locoregional recurrence-free survival (LRRFS), distant metastasis-free survival (DMFS) and overall survival (OS). Patient weight was measured 6 months before treatment, at treatment start and treatment end. The proportion of patients with >5% weight loss was 32% before, and 51% during treatment, and the proportion of patients with >10% weight loss was 12% before, and 17% during treatment. After a median follow-up of 9.5 years (range, 0.1 – 15.4 years) weight loss before treatment was associated with decreased TTF, LRRFS, DMFS, cancer specific survival and OS in a multivariable analysis. However, weight loss during treatment was not associated with survival outcomes. Weight loss before and during chemoradiation was commonly observed. Weight loss before but not during treatment was associated with worse survival.

A high neutrophil-to-lymphocyte ratio (NLR) is a marker of systemic inflammation and together with the platelet-to-lymphocyte ratio (PLR) is associated with worse outcomes in several solid tumors. We investigated the prognostic value of NLR and PLR in patients with head and neck squamous cell carcinoma (HNSCC) treated with primary or adjuvant (chemo)radiotherapy ((C)RT). A retrospective chart review of consecutive patients with HNSCC was performed. Neutrophil-to-lymphocyte ratio and PLR were computed using complete blood counts (CBCs) performed within 10 days before treatment start. The prognostic role of NLR and PLR was evaluated with univariable and multivariable Cox regression analyses adjusting for disease-specific prognostic factors. NLR and PLR were assessed as log-transformed continuous variables (log NLR and log PLR). Endpoints of interest were overall survival (OS), locoregional recurrence-free survival (LRFS), distant recurrence-free survival (DRFS), and acute toxicity. We analyzed 186 patients treated from 2007 to 2010. Primary sites were oropharynx (45%), oral cavity (28%), hypopharynx (14%), and larynx (13%). Median follow-up was 49 months. Higher NLR was associated with OS (adjusted HR per 1 unit higher log NLR = 1.81 (1.16–2.81), p = 0.012), whereas no association could be shown with LRFS (HR = 1.49 (0,83-2,68), p = 0.182), DRFS (HR = 1.38 (0.65–3.22), p = 0.4), or acute toxicity grade ≥ 2. PLR was not associated with outcome, nor with toxicity. Our data suggest that in HNSCC patients treated with primary or adjuvant (C)RT, NLR is an independent predictor of mortality, but not disease-specific outcomes or toxicity. Neutrophil-to-lymphocyte ratio is a readily available biomarker that could improve pre-treatment prognostication and may be used for risk-stratification.

Background: This study was performed to evaluate the outcome after hemostatic radiotherapy (RT) of significant bleeding in incurable cancer patients. Methods: Patients treated by hemostatic RT between November 2006 and February 2010 were retrospectively analyzed. Bleeding was assessed according to the World Health Organization (WHO) scale (grade 0 = no bleeding, 1 = petechial bleeding, 2 = clinically significant bleeding, 3 = bleeding requiring transfusion, 4 = bleeding associated with fatality). The primary endpoint was bleeding at the end of RT. Key secondary endpoints included overall survival (OS) and acute toxicity. The bleeding score before and after RT were compared using the Wilcoxon signed rank test. Time to event endpoints were estimated using the Kaplan Meier method. Results: Overall 62 patients were analyzed including 1 patient whose benign cause of bleeding was pseudomyxoma peritonei. Median age was 66 (range, 37–93) years. Before RT, bleeding was graded as 2 and 3 in 24 (39%) and 38 (61%) patients, respectively. A median dose of 20 (range, 5–45) Gy of hemostatic RT was applied to the bleeding site. At the end of RT, there was a statistically significant difference in bleeding (p < 0.001); it was graded as 0 (n = 39), 1 (n = 12), 2 (n = 6), 3 (n = 4) and 4 (n = 1). With a median follow-up of 19.3 (range, 0.3-19.3) months, the 6-month OS rate was 43%. Forty patients died (65%); 5 due to bleeding. No grade 3 or above acute toxicity was observed. Conclusions: Hemostatic RT seems to be a safe and effective treatment for clinically and statistically significantly reducing bleeding in incurable cancer patients.

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.