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In this study, we propose a simple and computationally efficient method based on the multifactor dimensional reduction algorithm to identify gene-gene interactions associated with the survival phenotype. The proposed method, referred to as KM-MDR, uses the Kaplan-Meier median survival time as a classifier. The KM-MDR method classifies multilocus genotypes into a binary attribute for high- or low-risk groups using median survival time and replaces balanced accuracy with log-rank test statistics as a score to determine the best model. Through intensive simulation studies, we compared the power of KM-MDR with that of Surv-MDR, Cox-MDR, and AFT-MDR. It was found that KM-MDR has a similar power to that of Surv-MDR, with less computing time, and has comparable power to that of Cox-MDR and AFT-MDR, even when there is a covariate effect. Furthermore, we apply KM-MDR to a real dataset of ovarian cancer patients from The Cancer Genome Atlas (TCGA).
In general, the individual patient-level data (IPD) collected in clinical trials are not available to independent researchers to conduct economic evaluations; researchers only have access to published survival curves and summary statistics. Thus, methods that use published survival curves and summary statistics to reproduce statistics for economic evaluations are essential. Four methods have been identified: two traditional methods 1) least squares method, 2) graphical method; and two recently proposed methods by 3) Hoyle and Henley, 4) Guyot et al. The four methods were first individually reviewed and subsequently assessed regarding their abilities to estimate mean survival through a simulation study.
Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.
Apolipoprotein B (apoB) has additional benefits over conventional lipid measurements in predicting future cardiovascular disease (CVD). We aimed to validate the clinical relevance of our equation to estimate apoB in a large-scale, prospective, community-based cohort study (Ansung-Ansan cohort study).A total of 9001 Korean subjects were assessed. We excluded subjects with history of CVD (n = 228), taking lipid-lowering medications (n = 51), and those whose outcome data were not available (n = 33). Finally, a total of 8713 subjects (4126 men and 4587 women) with a mean age of 52.2 years were enrolled and followed up biannually for a mean 8.1 years.At baseline, 24.9% of subjects were current smokers, 12.5% had diabetes, and 22.2% had hypertension. Incident case of CVD occurred in 600 of the study subjects (493 ischemic heart disease and 424 stroke). Independent variables included in the models were age, sex, waist circumference, current smoking, and presence of diabetes and hypertension. Both non-HDL cholesterol (HR per 1-SD [95% CI]; 1.13 [1.05-1.23], P = 0.002) and estimated apoB (HR per 1-SD [95% CI]; 1.14 [1.05-1.24], P = 0.001) were independently associated with the development of CVD; however, the LDL cholesterol level was not predictive of future CVD (HR per 1-SD [95% CI]; 1.07 [0.99-1.16], P = 0.08).Both non-HDL cholesterol and estimated apoB level were independently associated with the development of CVD. Because LDL cholesterol has limited value to predict incident CVD, we recommend calculating non-HDL cholesterol or apoB with our equation to predict risk of incident CVD in the general Korean population.
Hazard ratios are ubiquitously used in time to event analysis to quantify treatment effects. Although hazard ratios are invaluable for hypothesis testing, other measures of association, both relative and absolute, may be used to fully elucidate study results. Restricted mean survival time (RMST) differences between groups have been advocated as useful measures of association. Recent work focused on model-free estimates of the difference in restricted mean survival through follow-up times, instead of focusing on a single time horizon. The resulting curve can be used to quantify the association in time units with a simultaneous confidence band. In this work a model-based estimate of the curve is proposed using pseudo-values allowing for possible covariate adjustment. The method is easily implementable with available software and makes possible to compute a simultaneous confidence region for the curve. The pseudo-values regression using multiple restriction times is in good agreement with the estimates obtained by standard direct regression models fixing a single restriction time. Moreover, the proposed method is flexible enough to reproduce the results of the non-parametric approach when no covariates are considered. Examples where it is important to adjust for baseline covariates will be used to illustrate the different methods together with some simulations.
Research on sex differences in renal cancer-specific mortality (RCSM), which considered the sex effect to be constant throughout life, has yielded conflicting results. This study hypothesized the sex effect may be modified by age, which is a proxy for hormonal status. Data from the Surveillance, Epidemiology and End Results database (1988-2010) were used to identify 114,539 patients with renal cell carcinoma (RCC). The study cohort was divided into three age groups using cutoffs of 42 and 58 years, which represent the premenopausal and postmenopausal periods. The cumulative incidence function and competing risks analyses were used to examine the effect of covariates on RCSM and other-cause mortality (OCM). In premenopausal period, male sex was a significant predictor of poor RCSM for both localized (adjusted subdistribution hazard ratio [aSHR] = 1.63, P = 0.002) and advanced (aSHR = 1.20, P = 0.041) disease. In postmenopausal period, the sex disparity diminished (aSHR = 1.05, P = 0.16) and reversed (aSHR = 0.95, P = 0.017) in localized and advanced disease, respectively. On the contrary, similar trend was not found for OCM across all age groups. Our results demonstrated the sex effect on RCSM was strongly modified by age. These findings may aid in clinical practice and need further evaluation of underlying biological mechanisms.
Cord blood (CB) has been used as an important and ethical source for hematopoietic stem cell transplantation (SCT) as well as cell therapy by manufacturing mesenchymal stem cell, induced pleuripotential stem cell or just isolating mononuclear cell from CB. Recently, the application of cell-based therapy using CB has expanded its clinical utility, particularly, by using autologous CB in children with refractory diseases. For these purposes, CB has been stored worldwide since mid-1990. In this review, I would like to briefly present the historical development of clinical uses of CB in the fields of SCT and cell therapy, particularly to review the experiences in Korea. Furthermore, I would touch the recent banking status of CB.
The value of postoperative radiotherapy in the treatment of medullary thyroid carcinoma (MTC) has not been unequivocally demonstrated. Therefore our study aimed to answer the question of whether adjuvant radiotherapy showed any impact on the risk of local recurrence and whether there were any differences in response to radiotherapy between hereditary and sporadic MTC.
Haemangioblastomas are rare, highly vascularised tumours that typically occur in the cerebellum, brain stem and spinal cord. Up to a third of individuals with a haemangioblastoma will have von Hippel-Lindau (VHL) disease. Individuals with haemangioblastoma and underlying VHL disease present, on average, at a younger age and frequently have a personal or family history of VHL disease-related tumours (e.g., retinal or central nervous system (CNS) haemangioblastomas, renal cell carcinoma, phaeochromocytoma). However, a subset present an apparently sporadic haemangioblastoma without other features of VHL disease. To detect such individuals, it has been recommended that genetic testing and clinical/radiological assessment for VHL disease should be offered to patients with a haemangioblastoma. To assess "real-world" clinical practice, we undertook a national survey of clinical genetics centres. All participating centres responded that they would offer genetic testing and a comprehensive assessment (ophthalmological examination and CNS and abdominal imaging) to a patient presenting with a CNS haemangioblastoma. However, for individuals who tested negative, there was variability in practice with regard to the need for continued follow-up. We then reviewed the results of follow-up surveillance in 91 such individuals seen at four centres. The risk of developing a potential VHL-related tumour (haemangioblastoma or RCC) was estimated at 10.8% at 10 years follow-up. The risks of developing a recurrent haemangioblastoma were higher in those who presented <40 years of age. In the light of these and previous findings, we propose an age-stratified protocol for surveillance of VHL-related tumours in individuals with apparently isolated haemangioblastoma.
To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.
Alectinib and brigatinib are second-generation anaplastic lymphoma receptor tyrosine kinases (ALKs) that are widely used as first-line therapy for treating ALK-positive advanced non-small cell lung cancer (NSCLC). Given the lack of a head-to-head comparison of these drugs as first-line therapies, this retrospective observational study aimed to compare the real-world efficacy and safety of alectinib and brigatinib.
It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.
BACKGROUND Post-transplant lymphoproliferative disorder (PTLD) is a rare complication following solid organ transplantation and allogeneic hematopoietic stem cell transplantation (Allo-HSCT), which gives rise to high mortality rates. MATERIAL AND METHODS This was a single-center retrospective analysis based on 27 patients who were diagnosed with PTLD following Allo-HSCT between January 1, 2007 and June 2018 at the Chinese PLA General Hospital. The purpose of this analysis was to investigate responses and prognostic factors of rituximab-based treatment. RESULTS Twenty-seven patients were treated with rituximab. Among them, 20 of 27 patients (74.07%) had a complete response, 2 of 27 patients (7.41%) had a partial response, 5 of 27 patients (18.52%) had no response, and 22 of 27 patients (81.48%) cleared Epstein-Barr virus (EBV) copies. There were no obvious side effects. The 1-year overall survival (OS) estimate was 46.8% (95% CI, 23.1-65.5%). Univariate analysis revealed that lower OS was correlated with Eastern Cooperative Oncology Group (ECOG) score standard (3-4), Epstein-Barr virus (EBV) viral load (≥10⁶ copies/mL), bacteria or fungal infection, and EBV reactivation were positive after treatment with 1 or 2 doses of rituximab (P<0.05). Multivariate analysis showed that each of the following were independently associated with lower OS (P<0.05): female, ECOG score standard (3-4), and EBV reactivation were positive after treatment with 1 or 2 doses of rituximab. CONCLUSIONS Our results demonstrated that rituximab-based treatment was a safe and effective strategy for patients who were diagnosed with PTLD following Allo-HSCT. The identified prognostic factors may help to detect which PTLD patients are at a higher risk of mortality.
Calf morbidity and mortality are serious constraints in the success of dairy calf production. Thus, the current study was carried out with the objective to estimate the incidence of calf morbidity and mortality and associated risk factors in milk-shed districts of Gamo Zone, Southern Ethiopia. A prospective cohort and cross-sectional survey were employed from November 2019 to April 2020. A total of 196 calves were recruited by simple random sampling. Recruitment of calves was deployed by both the concurrent and prospective cohorts in calves aged below three months in study herds. The crude incidence of calf morbidity and mortality was 30.9% and 8.64%, respectively. The most frequently encountered disorder was calf diarrhea (10.17%), followed by pneumonia (6.5%). The other disorders were septicemia, omphalitis, arthritis, eye problem and miscellaneous cases. Multivariable Cox regression was revealed significant association for the calf vigor status, colostrum ingestion time, colostrum feeding status, dam parity, age at first calving, and related disorders were found risk factors of calf morbidity; likewise, calf vigor status at birth, time of colostrum ingestion and weaning were risk factors determining calf mortality. Calf morbidity and mortality rates recorded in this study were marginally higher than economically tolerable level, therefore, could affect the productivity of smallholder dairying by decreasing the obtainability of replacement heifers. Among significant explanatory factors investigated, colostrum ingestion time, method and amount were found important determinant factors of calf mortality and morbidity under the small-holder farming in the milk-shed districts of the Gamo zone. Therefore, rigorous calf husbandry practice is a need to manipulate the aforementioned calf determinants with subsequent application of tailor-made interventions.
Survival analysis is a collection of statistical procedures employed on time-to-event data. The outcome variable of interest is time until an event occurs. Conventionally, it dealt with death as the event, but it can handle any event occurring in an individual like disease, relapse from remission, and recovery. Survival data describe the length of time from a time of origin to an endpoint of interest. By time, we mean years, months, weeks, or days from the beginning of being enrolled in the study. The major limitation of time-to-event data is the possibility of an event not occurring in all the subjects during a specific study period. In addition, some of the study subjects may leave the study prematurely. Such situations lead to what is called censored observations as complete information is not available for these subjects. Life table and Kaplan-Meier techniques are employed to obtain the descriptive measures of survival times. The main objectives of survival analysis include analysis of patterns of time-to-event data, evaluating reasons why data may be censored, comparing the survival curves, and assessing the relationship of explanatory variables to survival time. Survival analysis also offers different regression models that accommodate any number of covariates (categorical or continuous) and produces adjusted hazard ratios for individual factor.
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