Xevinapant plus radiotherapy in resected, high-risk, cisplatin-ineligible LA SCCHN: the phase III XRay Vision study design.

There is a significant unmet need and lack of treatment options for patients with resected, high-risk, cisplatin-ineligible locally advanced squamous cell carcinoma of the head and neck (LA SCCHN). Xevinapant, a first-in-class, potent, oral, small-molecule IAP inhibitor, is thought to restore cancer cell sensitivity to chemotherapy and radiotherapy in clinical and preclinical studies. We describe the design of XRay Vision (NCT05386550), an international, randomized, double-blind, phase III study. Approximately 700 patients with resected, high-risk, cisplatin-ineligible LA SCCHN will be randomized 1:1 to receive 6 cycles of xevinapant or placebo, in combination with radiotherapy for the first 3 cycles. The primary end point is disease-free survival, and secondary end points include overall survival, health-related quality of life, and safety.

Squamous cell carcinoma is the most common form of head and neck cancer (SCCHN) and includes cancers of the lips, mouth, throat, tongue and voice box. It is called 'locally advanced' when the cancer has spread to nearby areas but not to other parts of the body. Few treatment options are available for people with locally advanced SCCHN who have had surgery and are unable to receive a type of chemotherapy called cisplatin. Xevinapant is being developed as a possible new type of cancer treatment. It is a liquid that is taken by mouth or given through a feeding tube. Adding xevinapant to the standard treatment ­ called radiotherapy ­ aims to make radiotherapy more effective against the cancer. Researchers have started a large, international, phase III study called XRay Vision to see if adding xevinapant to radiotherapy can help stop the cancer from coming back after surgery and help people live longer. Clinical Trial Registration: NCT05386550 (ClinicalTrials.gov).

Big data simulations for capacity improvement in a general ophthalmology clinic.

PURPOSE: Long total waiting times (TWT) experienced by patients during a clinic visit have a significant adverse effect on patient's satisfaction. Our aim was to use big data simulations of a patient scheduling calendar and its effect on TWT in a general ophthalmology clinic. Based on the simulation, we implemented changes to the calendar and verified their effect on TWT in clinical practice. DESIGN AND METHODS: For this retrospective simulation study, we generated a discrete event simulation (DES) model based on clinical timepoints of 4.401 visits to our clinic. All data points were exported from our clinical warehouse for further processing. If not available from the electronic health record, manual time measurements of the process were used. Various patient scheduling models were simulated and evaluated based on their reduction of TWT. The most promising model was implemented into clinical practice in 2017. RESULTS: During validation of our simulation model, we achieved a high agreement of mean TWT between the real data (229 ± 100 min) and the corresponding simulated data (225 ± 112 min). This indicates a high quality of the simulation model. Following the simulations, a patient scheduling calendar was introduced, which, compared with the old calendar, provided block intervals and extended time windows for patients. The simulated TWT of this model was 153 min. After implementation in clinical practice, TWT per patient in our general ophthalmology clinic has been reduced from 229 ± 100 to 183 ± 89 min. CONCLUSION: By implementing a big data simulation model, we have achieved a cost-neutral reduction of the mean TWT by 21%. Big data simulation enables users to evaluate variations to an existing system before implementation into clinical practice. Various models for improving patient flow or reducing capacity loads can be evaluated cost-effectively.

Stem Cell Transcriptome Responses and Corresponding Biomarkers That Indicate the Transition from Adaptive Responses to Cytotoxicity.

Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as "tolerance" (minor transcriptome changes), "functional adaptation" (moderate/strong transcriptome responses, but no cytotoxicity), and "degeneration". The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate "biomarkers of cytotoxicity". These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied.

Design principles of concentration-dependent transcriptome deviations in drug-exposed differentiating stem cells.

Information on design principles governing transcriptome changes upon transition from safe to hazardous drug concentrations or from tolerated to cytotoxic drug levels are important for the application of toxicogenomics data in developmental toxicology. Here, we tested the effect of eight concentrations of valproic acid (VPA; 25-1000 µM) in an assay that recapitulates the development of human embryonic stem cells to neuroectoderm. Cells were exposed to the drug during the entire differentiation process, and the number of differentially regulated genes increased continuously over the concentration range from zero to about 3000. We identified overrepresented transcription factor binding sites (TFBS) as well as superordinate cell biological processes, and we developed a gene ontology (GO) activation profiler, as well as a two-dimensional teratogenicity index. Analysis of the transcriptome data set by the above biostatistical and systems biology approaches yielded the following insights: (i) tolerated (≤25 µM), deregulated/teratogenic (150-550 µM), and cytotoxic (≥800 µM) concentrations could be differentiated. (ii) Biological signatures related to the mode of action of VPA, such as protein acetylation, developmental changes, and cell migration, emerged from the teratogenic concentrations range. (iii) Cytotoxicity was not accompanied by signatures of newly emerging canonical cell death/stress indicators, but by catabolism and decreased expression of cell cycle associated genes. (iv) Most, but not all of the GO groups and TFBS seen at the highest concentrations were already overrepresented at 350-450 µM. (v) The teratogenicity index reflected this behavior, and thus differed strongly from cytotoxicity. Our findings suggest the use of the highest noncytotoxic drug concentration for gene array toxicogenomics studies, as higher concentrations possibly yield wrong information on the mode of action, and lower drug levels result in decreased gene expression changes and thus a reduced power of the study.

Total HIV-1 DNA Dynamics and Influencing Factors in Long-Term ART-Treated Japanese Adults: A Retrospective Longitudinal Analysis.

BACKGROUND: Understanding HIV persistence in treated patients is an important milestone toward drug-free control. We aimed at analyzing total HIV DNA dynamics and influencing factors in Japanese patients who received more than a decade of suppressive antiretroviral treatment (ART). METHODS: A retrospective study including clinical records and 840 peripheral blood mononuclear cells samples (mean 14 samples/patient) for 59 patients (92% male) was performed. Subjects were divided into 2 groups: with and without hematological comorbidity (mainly hemophilia) plus hepatitis C virus coinfection. Total HIV DNA was measured in peripheral blood mononuclear cells by quantitative polymerase chain reaction. The dynamics, regression over time, and influence of antiretrovirals by group were estimated using a novel regression model (R software v 3.2.3). RESULTS: Total HIV DNA decreased on ART initiation, and subsequently, its dynamics varied between groups with previously undescribed fluctuations. If calculated by on-treatment, the mean total HIV DNA levels were similar. The comorbidity group had unstable levels showing different regression over time (P = 0.088/0.094 in year 1/after year 8 of ART) and significantly different treatment responses as shown by antiretroviral group switching estimates. Furthermore, curing hepatitis C virus in hemophiliacs did not significantly alter total HIV DNA levels or regression. CONCLUSIONS: Our data identified some effects of the long-term treatment on total HIV DNA levels and highlighted the partial influence of comorbidities and coinfections. Total HIV DNA monitoring contributed to therapy response estimates and HIV reservoir quantification. The results suggest that HIV DNA monitoring during ART might be useful as a persistence marker in both HIV-monoinfected patients and those with comorbidities and coinfections.

Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis, and tissue microarray validation.

PURPOSE: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multigene signatures in clinical practice is unclear, and the biologic importance of individual genes is difficult to assess, as the published signatures virtually do not overlap. EXPERIMENTAL DESIGN: Here, we describe a novel single institute cohort, including 196 non-small lung cancers (NSCLC) with clinical information and long-term follow-up. Gene expression array data were used as a training set to screen for single genes with prognostic impact. The top 450 probe sets identified using a univariate Cox regression model (significance level P < 0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n = 860). RESULTS: The meta-analysis revealed 14 genes that were significantly associated with survival (P < 0.001) with a false discovery rate <1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on tissue microarrays from 2 independent NSCLC cohorts, altogether including 617 NSCLC samples. Low CADM1 protein expression was significantly associated with shorter survival, with particular influence in the adenocarcinoma patient subgroup. CONCLUSIONS: Using a novel NSCLC cohort together with a meta-analysis validation approach, we have identified a set of single genes with independent prognostic impact. One of these genes, CADM1, was further established as an immunohistochemical marker with a potential application in clinical diagnostics.