DNA hydroxymethylation in high-grade gliomas

Background and Study Aims Since the new WHO classification of nervous system tumors (2016 revised 4th edition) has been released, gliomas are classified depending on molecular and genetic markers in connection with histopathology, instead of histopathology itself as it was in the previous classification. Over the last years, epigenetic analysis has taken on increased importance in the diagnosis and treatment of different cancers. Multiple studies confirmed that DNA methylation and hydroxymethylation play an important role in the regulation of gene expression during carcinogenesis. In this review, we aim to present the current state of knowledge on DNA hydroxymethylation in human high-grade gliomas (WHO grade III and IV). Results The correlation of DNA hydroxymethylation and survival in glioblastoma patients was evaluated by different studies. The majority of them showed that the expression of 5-hydroxymethylcytosine (5-hmC) and Ten-eleven translocation (TET) enzymes were significantly reduced, sometimes almost undetectable in high-grade gliomas in comparison with the control brain. A decreased level of 5-hmC was associated with poor survival in patients, but high expression of the TET3 enzyme was related to a better prognosis for GBM patients. This points to the relevance of DNA hydroxymethylation in molecular diagnostics of human gliomas, including survival estimation or differentiating patients in terms of response to the treatment. Conclusion Future studies may shed some more light on this epigenetic mechanism involved in the pathogenesis of human high-grade gliomas and help to develop new targeted therapies.

SORG Algorithm to Predict 3- and 12-month Survival in Metastatic Spinal Disease: A Cross-Sectional Population-Based Retrospective Study.

In this study, we recruited 40 patients with spinal metastatic disease who were operated at Geneva University Hospitals by the Neurosurgery or Orthopedic teams between the years of 2015 and 2020. We did an ROC analysis in order to determine the accuracy of the SORG ML algorithm and nomogram versus the Tokuhashi Original and Revised Scores. The analysis of data of our independent cohort shows a clear advantage in terms of predictive ability of the SORG ML algorithm and nomogram in comparison with the Tokuhashi scores. The SORG ML had an AUC of 0.87 for 90-days and 0.85 for 1-year. The SORG Nomogram showed a predictive ability at 90-days and 1-year with AUC’s of 0.87 and 0.76 respectively. These results showed excellent discriminative ability as compared with the Tokuhashi Original Score which achieved AUC’s of 0.70 and 0.69 and the Tokuhashi Revised Score which had AUC’s of 0.65 and 0.71 for 3-month and 1-year respectively. The predictive ability of the SORG ML algorithm and nomogram were superior to currently used preoperative survival estimation scores for spinal metastatic disease.

Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient’s high-risk markers, and support treatment decisions.

Analysis of the Efficiency and Prognostic Value of the Sentinel Node Technique in Oral Squamous Cell Carcinoma after Seven Years

Background and objectives: The purpose of this study was to analyse the diagnostic and prognostic efficiency of the sentinel lymph node biopsy technique (SLNB). Materials and Methods: This is a prospective observational study performed by the Hospital Complex in Santiago de Compostela (CHUS) in Spain, between February 2013 and June 2020. The study included 60 patients, who had been diagnosed with OSCC in stage T1/T2N0M0. Results: 10 patients (16.7%) presented with SN+ (sentinel node positive). The majority (80%) only presented subcapsular affection, however one case also presented with extracapsular affection. Using the Kaplan–Meier curves, we determined that the average survival estimation for SN- patients was 74.0 months (CI95% 67.6–80.5) and it was 45.4 months (CI95% 10.9–24.0) for SN+ patients (p = 0.002). SN+ patients presented an OR = 11.000 (CI95% 2.393–50.589, p = 0.002) for cancer-related mortality. In terms of the diagnostic performance of the SN (sentinel node) test, a 55% sensitivity, a 100% specificity, 100% PPV and a 84% NPV were obtained. The analysis using ROC (receiver operating characteristic) curves revealed an AUC = 0.671 (CI95% 0.492–0.850, p = 0.046). Conclusions: SLNB seems to be an adequate technique for the detection of hidden metastases

Development and validation of patient-level prediction models for symptoms, hospitalization and treatment initiation amongst prostate cancer patients on watchful waiting

The objective of this study is to develop and validate patient-level prediction models for patients on watchful waiting (WW) estimating the risk of developing symptomatic progression, hospitalization, ER visit, initiation of curative or palliative treatment, and survival. Estimation for all clinical models will be done based on 1) age and clinical measurements (e.g., PSA) 6 months before diagnosis, 2) age, clinical measurements 6 months before diagnosis, and clinical conditions one year before diagnosis. Finally, a clinically usable model will be developed based on expert clinical input. All prediction models will be implemented using Lasso logistic regression for the time at risk analyses.

Comparative survival analysis using the International Stratification Score (ISS) in newly-diagnosed multiple myeloma in the Uruguayan population

Background and aims. Multiple myeloma is a frequent hematologic malignancy, in which the International Stratification Score (ISS) is widely used to estimate the overall survival. However, there are no studies in Latin America evaluating its performance. This study aims to describe the ISS performance in the overall survival estimation for newly diagnosed multiple myeloma patients in Uruguay. Methods. This is a retrospective registry‐based survival analysis through the Grupo Uruguayo de Mieloma Múltiple (GUMMA) database, including newly diagnosed multiple myeloma patients from January 2001 until May 2019. Results. 249 patients were included, 51.81% males and an average age of 63.49 years. According to ISS and Durie-Salmon score (DSS), 47.79% and 82.3% were ISS III and DSS III, respectively. Also, 32.3% were DSS B. Auto hematopoietic stem cell transplantation was performed in 31.73% of patients, and bortezomib was used in 44.18% as frontline therapy. The overall survival was 80% for ISS1, 64.9% ISS2, and 48.6% ISS3 (Log-Rank; p <0.01). The average overall survival was 116.5 months for ISS 1, 77.6 months for ISS 2, and 57.8 months for ISS 3. The hazard ratio between ISS II and ISS I was 2.42 (95% CI 1.10-5.33; p<0.05), and 3.94 (95% CI 1.88-8.26; p<0.05) between ISS III and ISS II. Conclusion. The ISS staging system allows an adequate stratification of patients according to overall survival in the real-practice setting. However, considering the relevance of the new cytogenetic advances, it is necessary to increase the availability and quality of iFISH in Latin America.

Abstract 13604: Virulence of Staphylococcus Infection in Surgically Treated Patients With Endocarditis - A Multi-center Analysis

Introduction: Infective endocarditis (IE) caused by Staphylococcus species have been noted to increase and are believed to be associated with higher morbidity and mortality rates. Hypothesis: Staphylococcus species are more virulent compared to other commonly causative bacteria of IE. Methods: The database of the Clinical Multicenter Project for Analysis of Infective Endocarditis in Germany (CAMPAIGN), comprising 4917 consecutive patients (mean age 62.2±14.6 years) undergoing heart valve surgery, was retrospectively analyzed. Uni- and multivariable regression analyses were used for comparison and risk stratification. The Kaplan Meier method was used for long-term survival estimation of the respective groups. Results: Staphylococcus patients (n=1260) were significantly more morbid than the Non-Staphylocccus group (n=3657) with regard to NYHA IV (21.4% vs. 16.7%*), CAD (29.0% vs. 25.7%; p =0.027), arterial hypertension (64.8% vs. 45.8%*), diabetes (29.9% vs. 24.8%*), renal failure (47.5% vs. 35.2%*), COPD (12.8% vs. 9.9%; p =0.007), PAD (11.3% vs. 6.4%*), preoperative stroke (35.1% vs. 18.0%*) and need for mechanical ventilation (18.2% vs. 6.4%*). Overall, Staphylococcus infections were more prevalent on mitral (51.0% vs. 42.0%*) and tricuspid (9.4% vs. 4.4%*) valves and showed higher incidences of large vegetations (87.0% vs. 56.0%*), mitral regurgitation (62.5% vs. 41.5%*) and preoperative septic embolism (51.8% vs. 28.9%*). Postoperatively, need for tracheostomy (13.7% vs. 7.6%*) and dialysis (29.4% vs. 13.6%*) were also significantly increased. The 30-day mortality for Staphylococcus was significantly higher (21.3% vs. 15.9%*) and long-term survival was significantly worse ( Fig. 1 ). Conclusions: Staphylococcus endocarditis is associated with significantly worse outcome compared to IE by other commonly causative bacteria. Early surgery should be considered to avoid preoperative septic embolism, deterioration and death. *= p <0.0001

Estimating fecundity and density dependence from mark-recapture data for making population projections

Forecasting changes in size and distributions of populations is at the forefront of ecological sciences in the 21st century. Such forecasts require robust estimators of fecundity, survival and density-dependence. While survival estimation is the main focus of mark-recapture modelling, fecundity and density dependence are rarely the subject of these models. Here, we demonstrate that these parameters can be simultaneously estimated in a Bayesian framework using only robust design mark-recapture data. Using simulated capture histories, we show that this framework (which we named CJS-pop) can estimate vital rates and their density dependence with little bias. When CJS-pop is applied to capture history data from Brown Creeper (Certhia americana), it provides estimates of fecundity that is expected from the breeding biology of this species. Finally, we illustrate that density dependence, even when estimated with uncertainty in the CJS-pop framework, regularizes population dynamics and reduces the frequent population extinctions and explosions observed under density-independent models. While CJS-pop as a whole is a useful addition to the current mark-recapture modelling toolbox, we argue that the independent components of this framework in estimating fecundity and density dependence can be integrated to other CJS frameworks, potentially creating models capable of population projections.