Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test)

Background In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. Methods The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Results Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Conclusions Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients

Clinical trial protocol of the ASTER trial: a double-blind, randomized, placebo-controlled phase III trial evaluating the use of acetylsalicylic acid (ASA) for enhanced early detection of colorectal neoplasms

Immunochemical fecal occult blood tests (iFOBTs) are increasingly used for colorectal cancer (CRC) screening. In our preceding observational study, sensitivity for detecting advanced colorectal neoplasms by iFOBT was 70.8% among users of low-dose acetylsalicylic acid compared with 35.9% among non-users (p = 0.001), whereas there were only very small differences in specificity. In receiver operating characteristics (ROC) analyses, the area under the curve (AUC) was much higher for acetylsalicylic acid users than for non-users, with particularly strong differences in men (0.87 versus 0.68, p = 0.003). These findings suggested that use of acetylsalicylic acid before conduct of iFOBT might be a promising approach to improve non-invasive screening for CRC. Methods/design: In this randomized, double-blind, placebo-controlled trial, the diagnostic performance of two iFOBTs for detecting advanced colorectal neoplasms after a single low-dose of acetylsalicylic acid (300 mg) compared to placebo is evaluated. Acetylsalicylic acid or placebo is administered at least 5 days before a planned, study-independent colonoscopic screening in 2400 participants aged 40 to 80 years. Stool samples are obtained before and on three different days after the single dose of acetylsalicylic acid or placebo. In addition, optional blood samples are taken for future biomarker analyses. The diagnostic performance of the iFOBTs will be compared to the results of the colonoscopy as a gold standard for the diagnosis of colorectal neoplasms. Additionally, gender-specific performance of the tests and gain in diagnostic performance by test application on multiple days will be evaluated. Discussion: If the findings from our preceding observational study will be confirmed in this large trial, the proposed low-risk, inexpensive intervention would considerably improve the diagnostic accuracy of iFOBTs and thus lead to enhanced early detection of colorectal neoplasms. Thus, the results of this trial may have a large public health impact. Trial registration This trial was registered before recruitment of the participants in www.clinicaltrialsregister.eu on the 30th of May 2012: EudraCT No.: 2011–005603-32 and in www.drks.de on 13th of March 2012: German Clinical Trials Register DRKS-ID: DRKS00003252

Effects of radiofrequency electromagnetic fields (RF EMF) on cancer in laboratory animal studies

Background: The carcinogenicity of radiofrequency electromagnetic fields (RF EMF) has been evaluated by the International Agency for Research on Cancer (IARC) in 2011. Based on limited evidence of carcinogenicity in humans and in animals, RF EMF were classified as possibly carcinogenic to humans (Group 2B). In 2018, based on a survey amongst RF experts, WHO prioritized six major topics of potential RF EMF related human health effects for systematic reviews. In the current manuscript, we present the protocol for the systematic review of experimental laboratory animal studies (cancer bioassays) on exposure to RF fields on the outcome of cancer in laboratory animals. Objective: In the framework of WHO's Radiation Program, the aim of this work is to systematically evaluate effects of RF EMF exposure on cancer in laboratory animals. Study eligibility and criteria: WHO's Handbook (2014) for guideline development will be followed with appropriate adaptation. The selection of eligible studies will be based on Population, Exposures, Comparators, and Outcomes (PECO) criteria. We will include peer-reviewed articles and publicly available reports from government agencies reporting original data about animal cancer bioassays on exposure to RF EMF. The studies are identified by searching the following databases: MEDLINE (PubMed), Science Citation Index Expanded and Emerging Sources Citation Indes (Web of Science), Scopus, and the EMF Portal. No language or year-of-publication restrictions are applied. The methods and results of eligible studies will be presented in accordance with the PRISMA 2020 guidelines. Study appraisal method: Study evaluation of individual studies will be assessed using a risk of bias (RoB) tool developed by the Office of Health Assessment and Translation (OHAT) with appropriate considerations including sensitivity for evaluating RF EMF exposure in animal cancer bioassays. The final evaluation on the certainty of the evidence on a carcinogenic risk of RF EMF exposure in experimental animals will be performed using the OHAT Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach with appropriate considerations. The protocol has been registered in an open-source repository (PROSPERO)

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

Purpose: [18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients. Materials and methods: Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1–6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian 7 1.1 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 2: liver SUVmedian 7 1.5 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 3: liver SUVmedian 7 2 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients. Results: BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of β2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5. Conclusions: The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts

Selective inhibition of HDAC8 decreases neuroblastoma growth in vitro and in vivo and enhances retinoic acid-mediated differentiation

For differentiation-defective malignancies, compounds that modulate transcription, such as retinoic acid and histone deacetylase (HDAC) inhibitors, are of particular interest. HDAC inhibitors are currently under investigation for the treatment of a broad spectrum of cancer diseases. However, one clinical drawback is class-specific toxicity of unselective inhibitors, limiting their full anticancer potential. Selective targeting of individual HDAC isozymes in defined tumor entities may therefore be an attractive alternative treatment approach. We have previously identified HDAC family member 8 (HDAC8) as a novel target in childhood neuroblastoma. Using small-molecule inhibitors, we now demonstrate that selective inhibition of HDAC8 exhibits antineuroblastoma activity without toxicity in two xenograft mouse models of MYCN oncogene-amplified neuroblastoma. In contrast, the unselective HDAC inhibitor vorinostat was more toxic in the same models. HDAC8-selective inhibition induced cell cycle arrest and differentiation in vitro and in vivo. Upon combination with retinoic acid, differentiation was significantly enhanced, as demonstrated by elongated neurofilament-positive neurites and upregulation of NTRK1. Additionally, MYCN oncogene expression was downregulated in vitro and tumor cell growth was markedly reduced in vivo. Mechanistic studies suggest that cAMP-response element-binding protein (CREB) links HDAC8- and retinoic acid-mediated gene transcription. In conclusion, HDAC-selective targeting can be effective in tumors exhibiting HDAC isozyme-dependent tumor growth in vivo and can be combined with differentiation-inducing agents

BIAS: Transparent reporting of biomedical image analysis challenges

The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control), the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. This article describes how the BIAS statement was developed and presents a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit