Herpes zoster in SLE: prevalence, incidence and risk factors

ObjectivesThis study aimed to evaluate the prevalence and incidence of herpes zoster (HZ) events and describe its associated factors in a study of patients with SLE.Methods491 consecutive SLE participants were screened for HZ events using a patient-reported questionnaire to capture outcomes on pain and other characteristics associated with HZ events. Sociodemographic, clinical and laboratory measures were also analysed, and time-dependent Cox regression survival analyses were performed to investigate factors associated with HZ events.ResultsPrevalence of HZ was 30.5%, incidence was 14.3 cases per 1000 person-years. Lymphopenia and glucocorticoid dosing were significantly associated with HZ events.ConclusionsHZ is highly prevalent in SLE, which may be linked to disease-related and treatment-related effects on cellular immunity. Our results suggest that the presence of certain risk factors may be useful to allow identification of patients at risk of HZ and improve its management in patients with SLE.

The Effect of Intraocular Pressure-Lowering Medication on Metastatic Uveal Melanomas

Background: There has been speculation that IOP-lowering medication, which increases aqueous humor outflow, increases the risk of metastatic uveal melanoma (UM). This hypothesis has not been studied previously but is relevant for UM patients who use IOP-lowering medication. The aim of the current study is to assess the association between the use of intraocular pressure (IOP)-lowering medication and the risk of metastatic UM, and mortality. Methods: A retrospective cohort study, in which patients from the Rotterdam Ocular Melanoma Study were included from 1986 onwards. Medical records were evaluated for use of IOP-lowering medication at baseline (i.e., before diagnosis). For each IOP-lowering medication, we divided patients into two groups for comparison (e.g., patients with alpha2-agonist use and patients without alpha2-agonist use). All patients underwent regular ophthalmic examinations and routine screening for metastasis. Survival analyses were initiated to compare groups in each IOP-lowering medication group. In addition, secondary analyses were performed to examine the association between IOP and the development of metastatic UM, and mortality. Results: A total of 707 patients were included of whom 13 patients used prostaglandin or pilocarpine at baseline. For alpha2-agonist, beta-blocker, carbonic anhydrase inhibitor, and oral IOP-lowering medication these were 4, 14, 11, and 12 patients, respectively. The risk of metastatic UM (choroid and ciliary body melanoma) among the prostaglandin/pilocarpine users was significantly higher than controls (HR [95% CI]: 4.840 [1.452–16.133]). Mortality did not differ significantly among the IOP-lowering medications groups, except for the prostaglandin or pilocarpine group (HR [95% CI]: 7.528 [1.836–30.867]). If we combined all IOP-lowering medication that increase aqueous humor outflow, the risk (HR [95% CI]) of metastatic UM and mortality was 6.344 (1.615–24.918) and 9.743 (2.475–38.353), respectively. There was an association between IOP and mortality, but not for the onset of metastatic UM. Conclusion: The use of topical prostaglandin or pilocarpine may increase the risk of metastatic UM and mortality compared to patients without prostaglandin or pilocarpine use. Therefore, use of IOP-lowering medication which increases aqueous humor outflow, should be avoided in patients with (presumed) UM.

Survival Genie: A Web Portal for Single-Cell Data, Gene-Ratio, and Cell Composition-Based Survival Analyses

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. Many packages and tools have been developed to correlate single-gene features to clinical outcomes, but lack in performing such analysis based on multiple-genes, gene sets, and genes ratio. Furthermore, cluster marker genes associated with cell types, states and function from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates associations between the enrichment of known cell types and survival outcome across cancers. We have developed Survival Genie (https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/, a web tool to perform survival analysis on single-cell RNA-seq data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden (Fig. 1). For comprehensive survival evaluation, the Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs for both adult and pediatric cancers including different types of leukemia. Users can upload single-cell data or gene sets and select partitioning methods (i.e., mean, median, quartile, cutp) to determine the effect of their levels on patient survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile (Fig. 1). The Survival Genie source code is written in the R programming language and the interactive web application with the R Shiny framework. We demonstrate the application of the Survival Genie tool by exploring the prognostic utility of blast cell and immune cell markers generated from single cell RNA-seq analysis of paired pediatric AML bone marrow samples taken at the time of diagnosis and end of induction (Thomas, Perumalla et al. 2020) . We identified AML blast specific signature consisting of 7 genes (CLEC11A, PRAME, AZU1, NREP, ARMH1, C1QBP, TRH) that depicted significant association with poor survival (HR=2.3 and Log Rank P-value=.007). Further analysis of AML relapse-associated single cell clusters showed increased levels of individual markers, including CRIP1, FLNA, and RFLNB/FAM101B and their significant association with poor survival in TARGET AML dataset. Additionally, expression of combined RFLNB/FAM101B and WDFY4 genes was associated with poor overall survival (HR=1.8 Log Rank P-value=0.01) and shorter event-free survival (HR=1.9, Log Rank P-value<0.0001). This clearly shows the usefulness of Survival Genie tool in exploring the prognostic association of genes as well as gene sets. Survival Genie is a one-stop web-portal for single-cell phenotype clusters, list of genes, and cell composition-based survival analyses across multiple cancer datasets including hematological malignancies. The analytical options and harmonized collection of multiple cancer types makes Survival Genie a comprehensive resource to correlate gene sets, pathways, cellular enrichment, and single cell clusters to clinical outcome to assist in developing next generation prognostic and therapeutic biomarkers. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Incidence and survival analyses for occult lung cancer between 2004 and 2015: a population-based study

Background This study aimed to investigate the incidence and long-term survival outcomes of occult lung cancer between 2004 and 2015. Methods A total of 2958 patients were diagnosed with occult lung cancer in the 305,054 patients with lung cancer. The entire cohort was used to calculate the crude incidence rate. Eligible 52,472 patients (T1-xN0M0, including 2353 occult lung cancers) were selected from the entire cohort to perform survival analyses after translating T classification according to the 8th TNM staging system. Cancer-specific survival curves for different T classifications were presented. Results The crude incidence rate of occult lung cancer was 1.00 per 100 patients, and it was reduced between 2004 and 2015 [1.4 per 100 persons in 2004; 0.6 per 100 persons in 2015; adjusted risk ratio = 0.437, 95% confidence interval (CI) 0.363–0.527]. In the survival analysis, there were 2206 death events in the 2353 occult lung cancers. The results of the multivariable analysis revealed that the prognoses with occult lung cancer were similar to patients with stage T3N0M0 (adjusted hazard ratio = 1.054, 95% CI 0.986–1.127, p = 0.121). Adjusted survival curves presented the same results. In addition, adjusted for other confounders, female, age ≤ 72 years, surgical treatment, radiotherapy, adenocarcinoma, and non-squamous and non-adenocarcinoma non-small cell carcinoma were independent protective prognostic factors (all p < 0.05). Conclusions Occult lung cancer was uncommon. However, the cancer-specific survival of occult lung cancer was poor, therefore, we should put the assessment of its prognoses on the agenda. Timely surgical treatment and radiotherapy could improve survival outcomes for those patients. Besides, we still need more research to confirm those findings.

Analytic approaches to clinical validation of results from preclinical models of glioblastoma: a systematic review

BackgroundAnalytic approaches to clinical validation of results from preclinical models are important in assessment of their relevance to human disease. This systematic review examined consistency in reporting of glioblastoma cohorts from The Cancer Genome Atlas (TCGA) and assessed whether studies included patient characteristics in their survival analyses.MethodsWe searched Embase and Medline on 02Feb21 for studies using preclinical models of glioblastoma published after Jan2008 that used data from TCGA to validate the association between at least one molecular marker and overall survival in adult patients with glioblastoma. Main data items included cohort characteristics, statistical significance of the survival analysis, and model covariates.ResultsThere were 58 eligible studies from 1,751 non-duplicate records investigating 126 individual molecular markers. In 14 studies published between 2017 and 2020 using TCGA RNA microarray data that should have the same cohort, the median number of patients was 464.5 (interquartile range 220.5-525). Of the 15 molecular markers that underwent more than one univariable or multivariable survival analyses, five had discrepancies between studies. Covariates used in the 17 studies that used multivariable survival analyses were age (76.5%), pre-operative functional status (35.3%), sex (29.4%) MGMT promoter methylation (29.4%), radiotherapy (23.5%), chemotherapy (17.6%), IDH mutation (17.6%) and extent of resection (5.9%).ConclusionsPreclinical glioblastoma studies that used TCGA for validation did not provide sufficient information about their cohort selection and there were inconsistent results. Transparency in reporting and the use of analytic approaches that adjust for clinical variables can improve the reproducibility between studies.Importance of the StudyDespite using the same data from The Cancer Genome Atlas, translational preclinical studies in glioblastoma research included different numbers of patients into their analyses and their results were inconsistent.Fewer than a third of the studies used multivariable survival analysis to adjust for clinical variables but most did not take treatment factors into account.Greater transparency in cohort selection from open access data and integration of clinical variables into analyses will help improve reproducibility in glioblastoma research.

P13.04 Survival analyses for determining clinical relevance of molecular markers in translational glioblastoma research - a systematic review

BACKGROUND Pre-clinical glioblastoma studies can assess the relevance of their findings to patient survival using integrated clinical and genomic data. Validity of univariable analyses requires an assumption that molecular markers are randomly distributed across patient characteristics to mitigate the confounding effects of clinical variables. Multivariable survival analyses adjusting for clinical variables do not change the association if this assumption holds. We aimed to assess this by summarising the types of survival analyses and their results in translational glioblastoma research. MATERIAL AND METHODS We systematically searched Medline and Embase Jan 2008 to Feb 2021 for glioblastoma cell line or animal studies validating their molecular markers in The Cancer Genome Atlas (TCGA) or the Chinese Glioma Genome Atlas (CGGA) using survival analyses. Studies that exclusively used genomic data without laboratory findings were excluded. Two reviewers independently assessed study eligibility and extracted data. Data items included patient inclusion criteria, characteristics of survival analyses, and whether molecular markers had statistically significant association with overall survival. RESULTS Of 1,047 potentially eligible studies, we included 59 pre-clinical glioblastoma studies that tested the association between their molecular markers and survival using TCGA or CGGA data. All studies used TCGA data and 2 also used CGGA data. Sixteen (27%) studies specified their patient inclusion criteria from TCGA for survival analysis. Eight studies exclusively investigated sets of molecular markers, leaving 51 studies reporting 126 molecular markers. Eighteen (31%) studies used multivariable survival analysis in addition to univariable analyses. All molecular markers underwent univariable analyses, of which 12 (10%) molecular markers had additional multivariable survival analyses. In the 13 multivariable analyses on 12 molecular markers, four (31%) markers were associated with survival in the univariable analyses but not in the multivariable analyses. CONCLUSION Most pre-clinical studies used univariable survival analyses alone in public genomic repositories to assess the relevance of their results to patient survival. Our findings demonstrated that multivariable analyses are needed to account for confounding effects of clinical variables. Using relevant components from reporting guidelines for observational studies can improve the transparency and quality of translational studies.

Survival analyses

Survival analyses are a key tool for demographers, ecologists, and evolutionary biologists. This chapter presents the most common methods and illustrates their use for species across the Tree of Life. It discusses the challenges associated with various types of survival data, how to model species with a complex life cycle, and includes the impact of environmental factors and individual heterogeneity. It covers the analysis of ‘known-fate’ data collected in lab conditions, using the Kaplan–Meier estimator and Cox’s proportional hazard regression analysis. Alternatively, survival data collected on free-ranging populations usually involve individuals missing at certain monitoring occasions and unknown time at death. The chapter provides an overview of capture–mark–recapture (CMR) models, from single-state to multi-state and multi-event models, and their use in animal and plant demography to estimate demographic parameters while correcting for imperfect detection of individuals. It discusses various inference frameworks available to implement CMR models using a frequentist or Bayesian approach. Only humans are an exception among free-ranging populations, with the existence of several consequent databases with perfect knowledge of age and cause of death for all individuals. The chapter presents an overview of the most common models used to describe mortality patterns over age and time using human mortality data. Throughout, focus is placed on eight case studies, which involve lab organisms, free-ranging animal populations, plant populations, and human populations. Each example includes data and codes, together with step-by-step guidance to run the survival analysis.

Age-Based Survival Analysis of Coniferous and Broad-Leaved Trees: A Case Study of Preserved Forests in Northern Japan

Scientifically sound methods are essential to estimate the survival of trees, as they can substantially support sustainable management of natural forest resources. Tree mortality assessments have mainly been based on forest inventories and are mostly limited to planted forests; few studies have conducted age-based survival analyses in natural forests. We performed survival analyses of individual tree populations in natural forest stands to evaluate differences in the survival of two coniferous species (Abies sachalinensis (F. Schmidt) Mast. and Picea jezoensis var. microsperma) and all broad-leaved species. We used tree rings and census data from four preserved permanent plots in pan-mixed and sub-boreal natural forests obtained over 30 years (1989–2019). All living trees (diameter at breast height ≥ 5 cm in 1989) were targeted to identify tree ages using a Resistograph. Periodical tree age data, for a 10-year age class, were obtained during three consecutive observation periods. Mortality and recruitment changes were recorded to analyze multi-temporal age distributions and mean lifetimes. Non-parametric survival analyses revealed a multi-modal age distribution and exponential shapes. There were no significant differences among survival probabilities of species in different periods, except for broad-leaved species, which had longer mean lifetimes in each period than coniferous species. The estimated practical mean lifetime and diameter at breast height values of each coniferous and broad-leaved tree can be applied as an early identification system for trees likely to die to facilitate the Stand-based Silvicultural Management System of the University of Tokyo Hokkaido Forest. However, the survival probabilities estimated in this study should be used carefully in long-term forest dynamic predictions because the analysis did not include the effects of catastrophic disturbances, which might significantly influence forests. The mortality patterns and survival probabilities reported in this study are valuable for understanding the stand dynamics of natural forests associated with the mortality of individual tree populations.