An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Circular RNAs in Clear Cell Renal Cell Carcinoma: Their Microarray-Based Identification, Analytical Validation, and Potential Use in a Clinico-Genomic Model to Improve Prognostic Accuracy

Circular RNAs (circRNAs) may act as novel cancer biomarkers. However, a genome-wide evaluation of circRNAs in clear cell renal cell carcinoma (ccRCC) has yet to be conducted. Therefore, the objective of this study was to identify and validate circRNAs in ccRCC tissue with a focus to evaluate their potential as prognostic biomarkers. A genome-wide identification of circRNAs in total RNA extracted from ccRCC tissue samples was performed using microarray analysis. Three relevant differentially expressed circRNAs were selected (circEGLN3, circNOX4, and circRHOBTB3), their circular nature was experimentally confirmed, and their expression-along with that of their linear counterparts-was measured in 99 malignant and 85 adjacent normal tissue samples using specifically established RT-qPCR assays. The capacity of circRNAs to discriminate between malignant and adjacent normal tissue samples and their prognostic potential (with the endpoints cancer-specific, recurrence-free, and overall survival) after surgery were estimated by C-statistics, Kaplan-Meier method, univariate and multivariate Cox regression analysis, decision curve analysis, and Akaike and Bayesian information criteria. CircEGLN3 discriminated malignant from normal tissue with 97% accuracy. We generated a prognostic for the three endpoints by multivariate Cox regression analysis that included circEGLN3, circRHOBT3 and linRHOBTB3. The predictive outcome accuracy of the clinical models based on clinicopathological factors was improved in combination with this circRNA-based signature. Bootstrapping as well as Akaike and Bayesian information criteria confirmed the statistical significance and robustness of the combined models. Limitations of this study include its retrospective nature and the lack of external validation. The study demonstrated the promising potential of circRNAs as diagnostic and particularly prognostic biomarkers in ccRCC patients

Efficacy and safety of anticancer drug combinations: a meta-analysis of randomized trials with a focus on immunotherapeutics and gene-targeted compounds.

Hundreds of trials are being conducted to evaluate combination of newer targeted drugs as well as immunotherapy. Our aim was to compare efficacy and safety of combination versus single non-cytotoxic anticancer agents. We searched PubMed (01/01/2001 to 03/06/2018) (and, for immunotherapy, ASCO and ESMO abstracts (2016 through March 2018)) for randomized clinical trials that compared a single non-cytotoxic agent (targeted, hormonal, or immunotherapy) versus a combination with another non-cytotoxic partner. Efficacy and safety endpoints were evaluated in a meta-analysis using a linear mixed-effects model (guidelines per PRISMA Report).We included 95 randomized comparisons (single vs. combination non-cytotoxic therapies) (59.4%, phase II; 41.6%, phase III trials) (29,175 patients (solid tumors)). Combinations most frequently included a hormonal agent and a targeted small molecule (23%). Compared to single non-cytotoxic agents, adding another non-cytotoxic drug increased response rate (odds ratio [OR]=1.61, 95%CI 1.40-1.84)and prolonged progression-free survival (hazard ratio [HR]=0.75, 95%CI 0.69-0.81)and overall survival (HR=0.87, 95%CI 0.81-0.94) (all p<0.001), which was most pronounced for the association between immunotherapy combinations and longer survival. Combinations also significantlyincreased the risk of high-grade toxicities (OR=2.42, 95%CI 1.98-2.97) (most notably for immunotherapy and small molecule inhibitors) and mortality at least possibly therapy related (OR: 1.33, 95%CI 1.15-1.53) (both p<0.001) (absolute mortality = 0.90% (single agent) versus 1.31% (combinations)) compared to single agents. In conclusion, combinations of non-cytotoxic drugs versus monotherapy in randomized cancer clinical trials attenuated safety, but increased efficacy, with the balance tilting in favor of combination therapy, based on the prolongation in survival

Complete mesocolic excision does not increase short-term complications in laparoscopic left-sided colectomies : a comparative retrospective single-center study

Background: Since the implementation of total mesorectal excision (TME) in rectal cancer surgery, oncological outcomes improved dramatically. With the technique of complete mesocolic excision (CME) with central vascular ligation (CVL), the same surgical principles were introduced to the field of colon cancer surgery. Until now, current literature fails to invariably demonstrate its oncological superiority when compared to conventional surgery, and there are some concerns on increased morbidity. The aim of this study is to compare short-term outcomes after left-sided laparoscopic CME versus conventional surgery. Methods: In this retrospective analysis, data on all laparoscopic sigmoidal resections performed during a 3-year period (October 2015 to October 2018) at our institution were collected. A comparative analysis between the CME group-for sigmoid colon cancer-and the non-CME group-for benign disease-was performed. Results: One hundred sixty-three patients met the inclusion criteria and were included for analysis. Data on 66 CME resections were compared with 97 controls. Median age and operative risk were higher in the CME group. One leak was observed in the CME group (1/66) and 3 in the non-CME group (3/97), representing no significant difference. Regarding hospital stay, postoperative complications, surgical site infections, and intra-abdominal collections, no differences were observed. There was a slightly lower reoperation (1.5% versus 6.2%, p = 0.243) and readmission rate (4.5% versus 6.2%, p = 0.740) in the CME group during the first 30 postoperative days. Operation times were significantly longer in the CME group (210 versus 184 min, p < 0.001), and a trend towards longer pathological specimens in the CME group was noted (21 vs 19 cm, p = 0.059). Conclusions: CME does not increase short-term complications in laparoscopic left-sided colectomies. Significantly longer operation times were observed in the CME group

Methodology of a reevaluation of cardiovascular outcomes in the RECORD trial: study design and conduct

Background In 2010, after regulatory review of rosiglitazone licensing, the US Food and Drug Administration (FDA) requested a reevaluation of cardiovascular end points in the RECORD trial.&lt;p&gt;&lt;/p&gt; Methods Automated screening of the original clinical trial database and manual case report form review were performed to identify all potential cardiovascular and noncardiovascular deaths, and nonfatal myocardial infarction (MI) and stroke events. Search techniques were used to find participants lost to follow-up, and sites were queried for additional source documents. Suspected events underwent blinded adjudication using both original RECORD end point definitions and new FDA end point definitions, before analysis by the Duke Clinical Research Institute.&lt;p&gt;&lt;/p&gt; Results The reevaluation effort included an additional 328 person-years of follow-up. Automated screening identified 396 suspected deaths, 2,052 suspected MIs, and 468 suspected strokes. Manual review of documents by Duke Clinical Research Institute clinical events classification (CEC) coordinators identified an additional 31 suspected deaths, 49 suspected MIs, and 28 suspected strokes. There were 127 CEC queries issued requesting additional information on suspected deaths; 43 were closed with no site response, 61 were closed with a response that no additional data were available, and additional data were received for 23. Seventy CEC queries were issued requesting additional information for suspected MI and stroke events; 31 were closed with no site response, 20 were closed with a response that no additional data were available, and 19 resulted in additional data.&lt;p&gt;&lt;/p&gt; Conclusions Comprehensive procedures were used for rigorous event reascertainment and readjudication in a previously completed open-label, global clinical trial. These procedures used in this unique situation were consistent with other common approaches in the field, were enhanced to address the FDA concerns about the original RECORD trial results, and could be considered by clinical trialists designing event readjudication protocols for drug development programs that have been completed.&lt;p&gt;&lt;/p&gt

Model-based Recursive Partitioning for Subgroup Analyses

The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.Comment: 26 pages, 6 figure

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

Cyclooxygenase-2 Expression in Bladder Cancer and Patient Prognosis: Results from a Large Clinical Cohort and Meta-Analysis

Aberrant overexpression of cyclooxygenase-2 (COX2) is observed in urothelial carcinoma of the bladder (UCB). Studies evaluating COX2 as a prognostic marker in UCB report contradictory results. We determined the prognostic potential of COX2 expression in UCB and quantitatively summarize the results with those of the literature through a meta-analysis. Newly diagnosed UCB patients recruited between 1998–2001 in 18 Spanish hospitals were prospectively included in the study and followed-up (median, 70.7 months). Diagnostic slides were reviewed and uniformly classified by expert pathologists. Clinical data was retrieved from hospital charts. Tissue microarrays containing non-muscle invasive (n = 557) and muscle invasive (n = 216) tumours were analyzed by immunohistochemistry using quantitative image analysis. Expression was evaluated in Cox regression models to assess the risk of recurrence, progression and disease-specific mortality. Meta-hazard ratios were estimated using our results and those from 11 additional evaluable studies. COX2 expression was observed in 38% (211/557) of non-muscle invasive and 63% (137/216) of muscle invasive tumors. Expression was associated with advanced pathological stage and grade (p<0.0001). In the univariable analyses, COX2 expression - as a categorical variable - was not associated with any of the outcomes analyzed. As a continuous variable, a weak association with recurrence in non-muscle invasive tumors was observed (p-value = 0.048). In the multivariable analyses, COX2 expression did not independently predict any of the considered outcomes. The meta-analysis confirmed these results. We did not find evidence that COX2 expression is an independent prognostic marker of recurrence, progression or survival in patients with UCB.The work was partially supported by the Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III, Ministry of Science and Innovation, Spain (G03/174, 00/0745, PI051436, PI061614 and G03/174); Red Temática de Investigación Cooperativa en Cáncer- RD06/0020-RTICC; Consolider ONCOBIO; EU-FP6-STREP-37739-DRoP-ToP; EU-FP7-HEALTH-F2-2008-201663-UROMOL; EU-FP7-HEALTH-F2-2008-201333-DECanBio; USA-NIH-RO1-CA089715; and a PhD fellowship awarded to MJC from the ‘‘la Caixa’’ foundation, Spain, and a postdoctoral fellowship awarded to AFSA from the Fundación Científica de la AEC