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The proposal of improving reproducibility by lowering the significance threshold to 0.005 has been discussed, but the impact on conducting clinical trials has yet to be examined from a study design perspective. The impact on sample size and study duration was investigated using design setups from 125 phase II studies published between 2015 and 2022. The impact was assessed using percent increase in sample size and additional years of accrual with the medians being 110.97% higher and 2.65 years longer respectively. The results indicated that this proposal causes additional financial burdens that reduce the efficiency of conducting clinical trials.

Abstract Background Amiodarone is one of the most commonly used anti-arrhythmic agents. Amiodarone pulmonary toxicity is a potentially fatal adverse effect associated with amiodarone use. Previous studies on the epidemiology and risk factors for amiodarone pulmonary toxicity showed diverse results. Methods A multicenter retrospective cohort study was conducted to identify clinic-epidemiologic markers associated with amiodarone pulmonary toxicity for development of a prediction rule. Patients taking amiodarone who were managed in 3 centres in Hong Kong from 2005 to 2015 were included in this study. Penalized logistic regression was used to model the outcome as it is rare. Results A total of 34 cases with amiodarone pulmonary toxicity were identified among 1786 patients taking amiodarone for at least 90 days from 2005 to 2015. The incidence of amiodarone pulmonary toxicity was estimated to be 1.9%. The risk factors for amiodarone pulmonary toxicity included advanced age (OR 1.047, 95% CI 1.010–1.085, p = 0.013), ventricular arrhythmia (OR 2.703, 95% CI 1.053–6.935, p = 0.039), underlying lung disease (OR 2.511, 95% CI 1.146–5.501, p = 0.021) and cumulative dose of amiodarone (OR 4.762, 95% CI 1.310–17.309 p = 0.018). Conclusions The incidence of amiodarone pulmonary toxicity in Chinese patients in Hong Kong is estimated to be 1.9% in this study. Age, underlying lung disease, ventricular arrhythmia and cumulative dose of amiodarone are associated with the development of amiodarone pulmonary toxicity. A prediction rule was developed to inform the risk of developing amiodarone pulmonary toxicity.

Abstract Clofarabine is active in refractory/relapsed acute myeloid leukemia (AML). In this phase 2 study, we treated 18‐ to 65‐year‐old AML patients refractory to first‐line 3 + 7 daunorubicin/cytarabine induction or relapsing after 3 + 7 induction and high‐dose cytarabine consolidation, with clofarabine (30 mg/m2/d, Days 1‐5), cytarabine (750 mg/m2/d, Days 1‐5), and mitoxantrone (12 mg/m2/d, Days 3‐5) (CLAM). Patients achieving remission received up to two consolidation cycles of 50% CLAM, with eligible cases bridged to allogeneic hematopoietic stem cell transplantation (allo‐HSCT). The mutational profile of a 69‐gene panel was evaluated. Twenty‐six men and 26 women at a median age of 46 (22‐65) years were treated. The overall response rate after the first cycle of CLAM was 90.4% (complete remission, CR: 69.2%; CR with incomplete hematologic recovery, CRi: 21.2%). Twenty‐two CR/CRi patients underwent allo‐HSCT. The 2‐year overall survival (OS), relapse‐free survival (RFS), and event‐free survival (EFS) were 65.8%, 45.7%, and 40.2%, respectively. Multivariate analyses showed that superior OS was associated with CR after CLAM (P = .005) and allo‐HSCT (P = .005), and superior RFS and EFS were associated with allo‐HSCT (P

BackgroundHepatocellular carcinoma (HCC) is a rare hepatic malignancy in children. Hepatitis B virus (HBV) infection is a key predisposing factor in endemic regions but its impact on outcome has not been studied. We aim to evaluate the prognostic implication of HBV seropositivity and role of cancer surveillance in children with HCC from East Asian populations with national HBV vaccination.MethodsReview of population-based databases for patients (< 18 years old) diagnosed with HCC from 1993 to 2017 in two Southeast Asian regions with universal HBV vaccination (instituted since 1988 and 1987 in Hong Kong and Singapore, respectively).ResultsThirty-nine patients were identified (Hong Kong, 28; Singapore, 11). Thirty were male; median age at diagnosis was 10.8 years (range, 0.98–16.6). Abdominal pain was the commonest presentation while five patients were diagnosed through surveillance for underlying condition. Alpha-fetoprotein was raised in 36 patients (mean, 500,598 ng/ml). Nineteen had bilobar involvement, among the patients in whom pretreatment extent of disease (PRETEXT) staging could retrospectively be assigned, 3 had stage I, 13 had stage II, 4 had stage III, and 11 had stage IV disease. Seventeen had distant metastasis. HBsAg was positive in 19 of 38 patients. Two patients had fibrolamellar HCC. Upfront management involved tumor resection in 16 (liver transplantation, 2), systemic chemotherapy in 21, interventional procedures in 6 [transarterial chemoembolization (TACE), 5, radiofrequency ablation (RFA), 1], and radiotherapy in 4 (selective internal radiation, 3, external beam radiation, 1). Five-year event-free survival (EFS) and overall survival (OS) were 15.4 ± 6.0 and 26.1 ± 7.2%, respectively. Patient’s HBsAg positivity, metastatic disease and inability to undergo definitive resection represent poor prognostic factors in univariate and multivariable analyses. Patients diagnosed by surveillance had significantly better outcome.ConclusionPediatric HCC has poor outcome. HBV status remains relevant in the era of universal HBV vaccination. HBV carrier has inferior outcome and use of surveillance may mitigate disease course.

Little is known about the overall trend and prioritisations of past and current antimicrobial resistance (AMR) policies. Here we introduce a quantitative method to analyse AMR policies. The AMR-Policy Analysis Coding Toolkit (AMR-PACT) uses several categorical variables. Thirteen AMR action plans from five countries (China, Japan, Norway, the UK and the USA) were used to develop the tool and identify possible values for each variable. The scope and capability of AMR-PACT is demonstrated through the 2015 WHO’s Global Action Plan and 2017 Hong Kong AMR Action Plan (HKAP). Majority of policies were aimed at either human or animal sector with less attention given to the environment, plant or food sector. Both plans shared the same two strategic focus areas, namely the conservation of antibiotics and the improved surveillance of resistance. There were no policies dedicated to improving access to antibiotics in the HKAP. These empirical results provide useful insights into the priorities and gaps of AMR policies. The method proposed here can help understand countries’ priorities regarding AMR, support the creation of AMR policy database and foster innovative policymaking.

There has been a growing trend that activities relating to clinical trials take place at locations other than traditional trial sites (hence decentralized clinical trials or DCTs), some of which are at settings of real-world clinical practice. Although there are numerous benefits of DCTs, this also brings some implications on a number of issues relating to the design, conduct, and analysis of DCTs. The Real-World Evidence Scientific Working Group of the American Statistical Association Biopharmaceutical Section has been reviewing the field of DCTs and provides in this paper considerations for decentralized trials from a statistical perspective. This paper first discusses selected critical decentralized elements that may have statistical implications on the trial and then summarizes regulatory guidance, framework, and initiatives on DCTs. More discussions are presented by focusing on the design (including construction of estimand), implementation, statistical analysis plan (including missing data handling), and reporting of safety events. Some additional considerations (e.g., ethical considerations, technology infrastructure, study oversight, data security and privacy, and regulatory compliance) are also briefly discussed. This paper is intended to provide statistical considerations for decentralized trials of medical products to support regulatory decision-making., (© 2025 Novartis Pharmaceuticals Corporation. Pfizer Inc. AbbVie Inc and The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Whereas AI/ML methods were considered experimental tools in clinical development for some time, nowadays they are widely available. However, stakeholders in the health care industry still need to answer the question which role these methods can realistically play and what standards should be adhered to. Clinical research in late-stage clinical development has particular requirements in terms of robustness, transparency and traceability. These standards should also be adhered to when applying AI/ML methods. Currently there is some formal regulatory guidance available, but this is more directed at settings where a device or medical software is investigated. Here we focus on the application of AI/ML methods in late-stage clinical drug development, i.e. in a setting where currently less guidance is available. This is done via first summarizing available regulatory guidance and work done by regulatory statisticians followed by the presentation of an industry application where the influence of extensive sets of baseline characteristics on the treatment effect can be investigated by applying ML-methods in a standardized manner with intuitive graphical displays leveraging explainable AI methods. The paper aims at stimulating discussions on the role such analyses can play in general rather than advocating for a particular AI/ML-method or indication where such methods could be meaningful., (© 2024. The Author(s), under exclusive licence to The Drug Information Association, Inc.)

Background: The use of historical external control data in clinical trials has grown in interest and needs when considering the design of future trials. Hybrid control designs can be more efficient to achieve the same power with fewer patients and limited resources. The literature is sparse on appropriate statistical methods which can account for the differences between historical external controls and the control patients in a study. In this article, we illustrate the analysis framework of a clinical trial if a hybrid control design was used after determining an RCT may not be feasible., Methods: We utilize two previously completed RCTs in nonsquamous NSCLC and a nationwide electronic health record derived de-identified database as examples and compare 5 analysis methods on each trial, as well as a set of simulations to determine operating characteristics of such designs., Results: In single trial estimation, the Case Weighted Adaptive Power Prior provided estimated treatment hazard ratios consistent with the original trial's conclusions with narrower confidence intervals. The simulation studies showed that the Case Weighted Adaptive Power Prior achieved the highest power (and well controlled type-1 error) across all 5 methods with consistent study sample size., Conclusions: By following the proposed hybrid control framework, one can design a hybrid control trial transparently and accounting for differences between control groups while controlling type-1 error and still achieving efficiency gains from the additional contribution from external controls., (© 2024. The Author(s).)

We consider the challenges associated with causal inference in settings where data from a randomized trial are augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE). This question is motivated by the SUNFISH trial, which investigated the effect of risdiplam on motor function in patients with spinal muscular atrophy. While the original analysis used only data generated by the trial, we explore an alternative analysis incorporating external controls from the placebo arm of a historical trial. We cast the setting into a formal causal inference framework and show how these designs are characterized by a lack of full randomization to treatment and heightened dependency on modeling. To address this, we outline sufficient causal assumptions about the exchangeability between the internal and external controls to identify the ATE and establish a connection with novel graphical criteria. Furthermore, we propose estimators, review efficiency bounds, develop an approach for efficient doubly robust estimation even when unknown nuisance models are estimated with flexible machine learning methods, suggest model diagnostics, and demonstrate finite-sample performance of the methods through a simulation study. The ideas and methods are illustrated through their application to the SUNFISH trial, where we find that external controls can increase the efficiency of treatment effect estimation., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

In clinical trials, it is common to design a study that permits the administration of an experimental treatment to participants in the placebo or standard of care group post primary endpoint. This is often seen in the open-label extension phase of a phase III, pivotal study of the new medicine, where the focus is on assessing long-term safety and efficacy. With the availability of external controls, proper estimation and inference of long-term treatment effect during the open-label extension phase in the absence of placebo-controlled patients are now feasible. Within the framework of causal inference, we propose several difference-in-differences (DID) type methods and a synthetic control method (SCM) for the combination of randomized controlled trials and external controls. Our realistic simulation studies demonstrate the desirable performance of the proposed estimators in a variety of practical scenarios. In particular, DID methods outperform SCM and are the recommended methods of choice. An empirical application of the methods is demonstrated through a phase III clinical trial in rare disease.

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls from one or more sources of historical trial data [HTD]) can be used to increase statistical precision. Here we assess combining two sources of Roche HTD to construct a hybrid control in targeted therapy for decision-making via an extensive simulation study. Our simulations are based on the real data of one of the experimental arms and the control arm of the MORPHEUS-UC Phase Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider potential complications such as model misspecification, unmeasured confounding, different sample sizes of current treatment groups, and heterogeneity among the three trials. We evaluate two frequentist methods (with both Cox and Weibull accelerated failure time [AFT] models) and three different commensurate priors in Bayesian dynamic borrowing (with a Weibull AFT model), and modifications within each of those, when estimating the effect of treatment on survival outcomes and measures of effect such as marginal hazard ratios. We assess the performance of these methods in different settings and the potential of generalizations to supplement decisions in early-phase oncology trials. The results show that the proposed joint frequentist methods and noninformative priors within Bayesian dynamic borrowing with no adjustment on covariates are preferred, especially when treatment effects across the three trials are heterogeneous. For generalization of hybrid control methods in such settings, we recommend more simulation studies., (© 2023 John Wiley & Sons Ltd.)

Background: Despite the growth in primary cancer (PC) survivors, the trends and disparities in this population have yet to be comprehensively examined using competing risk analysis. The objective is to examine trends in time to second primary cancer (SPC) and to characterize age, sex, and racial disparities in time-to-SPC., Methods: A retrospective analysis was conducted based on Surveillance, Epidemiology, and End Results (SEER). Two datasets for this study are (1) the discovery dataset with patients from SEER-8 (1990-2019) and (2) the validation dataset with patients from SEER-17 (2000-2019), excluding those in the discovery dataset. Patients were survivors of lung, colorectal, breast (female only), and prostate PCs., Results: The 5-year SPC cumulative incidences of lung PC increased from 1990 to 2019, with the cumulative incidence ratio being 1.73 (95% confidence intervals [CI], 1.64-1.82; p < 0.001). Age disparities among all PCs remained from 2010 to 2019, and the adjusted HRs (aHRs) of all PCs were above 1.43 when those below 65 were compared with those 65 and above. Sex disparity exists among colorectal and lung PC survivors. Racial disparities existed among non-Hispanic (NH) Black breast PC survivors (aHR: 1.11; 95% CI: 1.07-1.17; p < 0.001). The types of SPC vary according to PC and sex., Conclusions: Over the past three decades, there has been a noticeably shortened time-to-SPC among lung PC survivors. This is likely attributed to the reduced number of lung cancer deaths due to advancements in effective treatments. However, disparities in age, sex, and race still exist, indicating that further effort is needed to close the gap., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Purpose: To investigate the enrollment success rate of cancer clinical trials conducted in 2008-2019 and various factors lowering the enrollment success rate., Methods: This is a cross-sectional study with clinical trial information from the largest registration database ClinicalTrials.gov. Enrollment success rate was defined as actual enrollment greater or equal to 85% of the estimated enrollment goal. The association between trial characteristics and enrollment success was evaluated using the multivariable logistic regression., Results: A total of 4,004 trials in breast, lung, and colorectal cancers were included. The overall enrollment success rate was 49.1%. Compared with 2008-2010 (51.5%) and 2011-2013 (52.1%), the enrollment success rate is lower in 2014-2016 (46.5%) and 2017-2019 (36.4%). Regression analyses found trial activation year, phase I, phase I/phase II, and phase II ( v phase III), sponsor agency of government ( v industry), not requiring healthy volunteers, and estimated enrollment of 50-100, 100-200, 200, and >500 ( v 0-50) were associated with a lower enrollment success rate ( P < .05). However, trials with placebo comparator, ≥5 locations ( v 1 location), and a higher number of secondary end points (eg, ≥5 v 0) were associated with a higher enrollment success rate ( P < .05). The AUC for prediction of the final logistic regression models for all trials and specific trial groups ranged from 0.69 to 0.76., Conclusion: This large-scale study supports a lower enrollment success rate over years in cancer clinical trials. Identified factors for enrollment success can be used to develop and improve recruitment strategies for future cancer trials.

Background and Objective: The application of immunotherapy in cancers, including liver cancer, has been increasing. However, non-proportional hazard (NPH) is often observed in cancer immunotherapy trials. In presence of violation of proportional hazard (PH) assumption, restricted mean survival time (RMST) ratio was proposed as an alternative to hazard ratio (HR) for evaluating the treatment effects of such trials. To shorten the total study duration, an intermediate endpoint with shorter follow-up such as progression-free survival (PFS) is used as the primary endpoint. Our aim is to evaluate the applicability of RMST ratio in addition to the HR in assessing the level of PFS serving as a surrogacy of overall survival (OS)., Methods: Phase II or phase III hepatocellular carcinoma (HCC) immunotherapy studies that were published between January 2013 and August 2022 were identified via the search in PubMed. Weighted least-square regression (WLSR) was applied to analyze the trial level data with the sample size of study being set as the weight. The evaluation was conducted twice with RMST ratio and HR being applied in respective evaluation to examine the level of PFS as a surrogacy for OS., Key Content and Findings: Based on the results of eight included trials, the R-square values of WLSR with either HR or RMST ratio being applied were 0.31 and 0.16 separately, indicating a moderate and low correlation between PFS and OS respectively., Conclusions: In this study, our results demonstrated the potential of RMST ratio in addition to HR for evaluating the level of surrogacy in immunotherapy trials. Furthermore, including more large scale and homogeneous studies into the research may help better understand the level of surrogacy in liver cancer.

According to the Global Burden of Disease Study, 3 million deaths were attributable to high sodium intake and low intake of whole grains. With the rapid evolution of the food industry, we hypothesize that dietary intake of sodium has increased and that dietary intake of whole grains or fibers has decreased because of easier access to highly processed food. Country-level data on dietary factors and country income levels from 1990 to 2018 were collected from 3 public databases. The trend of dietary intake was modeled using the linear mixed model accounting for random effects of individual countries. The country-level differences in dietary factors between males and females were calculated, and the trends were also modeled accounting for the random effects of countries. Both males and females consumed increasing amounts of dietary sodium from 1990 to 2018 in high-income, middle- to high-, middle-, and low-income countries. Dietary fiber intake increased in low-to-middle, middle-, and middle-to-high income countries for both men and women over the past 3 decades. Men tend to consume more sodium and less fiber and whole grains in their diets than women, the trend of which is statistically significant in middle-income countries. Over the past 3 decades, the macrotrend of dietary sodium has increased around the globe. To reduce the sodium intake level, nutrition policy should emphasize sodium reduction, especially in high-income, middle- to high-income, middle-income, and low-income countries., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Borrowing data from external control has been an appealing strategy for evidence synthesis when conducting randomized controlled trials (RCTs). Often named hybrid control trials, they leverage existing control data from clinical trials or potentially real-world data (RWD), enable trial designs to allocate more patients to the novel intervention arm, and improve the efficiency or lower the cost of the primary RCT. Several methods have been established and developed to borrow external control data, among which the propensity score methods and Bayesian dynamic borrowing framework play essential roles. Noticing the unique strengths of propensity score methods and Bayesian hierarchical models, we utilize both methods in a complementary manner to analyze hybrid control studies. In this article, we review methods including covariate adjustments, propensity score matching and weighting in combination with dynamic borrowing and compare the performance of these methods through comprehensive simulations. Different degrees of covariate imbalance and confounding are examined. Our findings suggested that the conventional covariate adjustment in combination with the Bayesian commensurate prior model provides the highest power with good type I error control under the investigated settings. It has desired performance especially under scenarios of different degrees of confounding. To estimate efficacy signals in the exploratory setting, the covariate adjustment method in combination with the Bayesian commensurate prior is recommended., (© 2023 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

Background and Objective: Fully-supervised learning approaches have shown promising results in some health status prediction tasks using Electronic Health Records (EHRs). These traditional approaches rely on sufficient labeled data to learn from. However, in practice, acquiring large-scaled labeled medical data for various prediction tasks is often not feasible. Thus, it is of great interest to utilize contrastive pre-training to leverage the unlabeled information., Methods: In this work, we propose a novel data-efficient framework, contrastive predictive autoencoder (CPAE), to first learn without labels from the EHR data in the pre-training process, and then fine-tune on the downstream tasks. Our framework comprises of two parts: (i) a contrastive learning process, inherited from contrastive predictive coding (CPC), which aims to extract global slow-varying features, and (ii) a reconstruction process, which forces the encoder to capture local features. We also introduce the attention mechanism in one variant of our framework to balance the above two processes., Results: Experiments on real-world EHR dataset verify the effectiveness of our proposed framework on two downstream tasks (i.e., in-hospital mortality prediction and length-of-stay prediction), compared to their supervised counterparts, the CPC model, and other baseline models., Conclusions: By comprising of both contrastive learning components and reconstruction components, CPAE aims to extract both global slow-varying information and local transient information. The best results on two downstream tasks are all achieved by CPAE. The variant AtCPAE is particularly superior when fine-tuned on very small training data. Further work may incorporate techniques of multi-task learning to optimize the pre-training process of CPAEs. Moreover, this work is based on the benchmark MIMIC-III dataset which only includes 17 variables. Future work may extend to a larger number of variables., Competing Interests: Declaration of Competing Interest Authors declare that they have no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Bayesian historical borrowing has recently attracted growing interest due to the increasing availability of historical control data, as well as improved computational methodology and software. In this article, we argue that the statistical models used for borrowing may be suboptimal when they do not adjust for differing factors across historical studies such as covariates, dosing regimen, etc. We propose an alternative approach to address these shortcomings. We start by constructing a historical model based on subject-level historical data to accurately characterize the control treatment by adjusting for known between trials differences. This model is subsequently used to predict the control arm response in the current trial, enabling the derivation of a model-informed prior for the treatment effect parameter of another (potentially simpler) model used to analyze the trial efficacy (i.e. the trial model). Our approach is applied to neovascular age-related macular degeneration trials, employing a cross-sectional regression trial model, and a longitudinal non-linear mixed-effects drug-disease-trial historical model. The latter model characterizes the relationship between clinical response, drug exposure and baseline covariates so that the derived model-informed prior seamlessly adapts to the trial population and can be extrapolated to a different dosing regimen. This approach can yield a more accurate prior for borrowing, thus optimizing gains in efficiency (e.g. increasing power or reducing the sample size) in future trials.

Background: Appropriate analyses and reporting are essential to the reproducibility and interpretation of clinical trials. However, the coronavirus disease 19 (COVID-19) pandemic and other force majeure events, like the war in Ukraine, have impacted the conduct of clinical trials., Methods: The number of clinical trials potentially impacted were estimated from clinicaltrials.gov. To identify reporting items considered vital for assessing the impact of COVID-19, we reviewed 35 randomized phase III trials from three top oncology journals published between July and December 2020. For validation, we reviewed 29 phase III trials published between January and December 2021., Results: Our results show that the number of clinical trials being potentially impacted in cancer, cardiovascular diseases, and diabetes is at least 1,484, 535, and 145, respectively. The magnitude of disruption is most significant in oncology trials. Based on the review of 35 trials, a modified checklist with ten new and four modified items covering pandemic's impact on trial conduct, protocol changes, delays, data capture, analysis and interpretation was developed to ensure comprehensive and transparent reporting. Our validation shows that six out of seven applicable reporting items were reported in less than 21% of the articles., Conclusions: Our recommendations were proposed to improve the reporting of randomized clinical trials impacted by COVID-19 and force majeure events that are broadly applicable to different areas of medical research., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-2160/coif). THL reports this study partially supported by University Postgraduate Fellowships of HKU Foundation. EEV reports consulting fees and payment/honoraria from AbbVie, AstraZeneca, Beigene, BioNTech, Eli Lilly, EMD Serono, Genentech/Roche, GlaxoSmithKline, Merck and Novatis, outside the submitted work. HP reports personal fees from Genentech, outside the submitted work. The other authors have no conflicts of interest to declare., (2023 Annals of Translational Medicine. All rights reserved.)

Dietary factors show different effects on genetically diverse populations. Scientific research uses gene-environment interaction models to study the effects of dietary factors on genetically diverse populations for lung cancer risk. However, previous study designs have not investigated the degree of type I error inflation and, in some instances, have not corrected for multiple testing. Using a motivating investigation of diet-gene interaction and lung cancer risk, we propose a training and testing strategy and perform real-world simulations to select the appropriate statistical methods to reduce false-positive discoveries. The simulation results show that the unconstrained maximum likelihood (UML) method controls the type I error better than the constrained maximum likelihood (CML). The empirical Bayesian (EB) method can compete with the UML method in achieving statistical power and controlling type I error. We observed a significant interaction between SNP rs7175421 with dietary whole grain in lung cancer prevention, with an effect size (standard error) of -0.312 (0.112) for EB estimate. SNP rs7175421 may interact with dietary whole grains in modulating lung cancer risk. Evaluating statistical methods for gene-diet interaction analysis can help balance the statistical power and type I error.

Background: Weight loss (WL) has been associated with shorter survival in patients with advanced cancer, while obesity has been associated with longer survival. Integrating body mass index (BMI) and WL provides a powerful prognostic tool but has not been well-studied in lung cancer patients, particularly in the setting of clinical trials., Methods: We analysed patient data (n = 10 128) from 63 National Cancer Institute sponsored advanced non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) trials. Risk matrices were created using BMI and WL percentage, which were divided into 'grades' based on median survival. Relationships between survival, BMI and WL percentage were examined using Kaplan-Meier estimators and Cox proportional hazards (PH) models with restricted cubic splines., Results: For NSCLC, a twofold difference was noted in median survival between the BMI > 28 and WL ≤ 5% group (13.5 months) compared with the BMI < 20 and WL > 5% group (6.6 months). These associations were less pronounced in SCLC. Kaplan-Meier curves showed significant survival differences between grades for both NSCLC and SCLC (log-rank, P < 0.0001). In Stage IV NSCLC, Cox PH analyses with restricted cubic splines demonstrated significant associations between BMI and survival in both WL ≤ 5% (P = 0.0004) and >5% (P = 0.0129) groups, as well as in WL > 5% in Stage III (P = 0.0306). In SCLC, these relationships were more complex., Conclusions: BMI and WL have strong associations with overall survival in patients with advanced lung cancer, with a greater impact seen in NSCLC compared with SCLC. The integration of a BMI/WL grading scale may provide additional prognostic information and should be included in the evaluation of therapeutic interventions in future clinical trials in advanced lung cancer., (© 2022 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

Background: While the efficacy of immune checkpoint inhibitors (ICIs) is increasingly recognized in advanced gastric cancer (aGC), overall survival (OS) has not been consistently improved across the different randomized controlled trials (RCTs). This meta-analysis aimed to quantify the efficacy and safety of ICI and explore potential predictive tumor tissue biomarkers in aGC., Methods: A random-effect pairwise meta-analysis was used to evaluate the primary outcome of OS. Sensitivity analysis was performed to investigate the effects of ICIs on PD-L1 status, TMB, MSI-H, and the Asian patient population. We extracted the OS Kaplan-Meier curves from the included trials to compare the effect of PD-L1 status on response to ICIs using DigitizeIt 2.5 and Guyot's algorithm., Results: A pairwise meta-analysis of seven RCTs included in this study showed that ICIs were more effective than the comparator in improving OS (pooled HR: 0.84). We demonstrated that PD-1 ICIs were additive when combined with the comparator arm (pooled HR: 0.79). A sensitivity analysis showed that PD-1 ICIs were associated with better OS outcomes in the Asian patient population as monotherapy (pooled HR: 0.66) or in combination with chemotherapy (pooled HR: 0.83). We demonstrated that tumors with PD-L1 ≥1 ( P = 0.02) and PD-L1 ≥10 ( P = 0.006) derived OS benefit from ICI monotherapy. Equally, MSI-H ( P < 0.00001) and TMB-high ( P < 0.0001) tumors derived favorable survival benefits from ICIs., Conclusions and Relevance: The results of this meta-analysis suggest that ICIs result in improved OS outcomes in aGC. The benefits varied with different ethnicities, class of ICI, PD-L1 expression, MSI status, and TMB., Systematic Review Registration: https://www.crd.york.ac.uk/prospero, identifier (CRD42019137829)., Competing Interests: AE disclosure: Advisory role: Pfizer, Bayer, Almac Discovery, Almac Diagnostics Research funding: Almac Diagnostics. MM disclosure: Honoraria: Taiho Pharmaceutical, Eli Lilly/ImClone, Amgen, Roche/Genentech, Merck KGaA, Darmstadt, Germany, MSD Oncology, Bristol Myers Squibb, AstraZeneca/MedImmune, Servier Consulting or Advisory Role: Bayer, Merck Sharp & Dohme, Merck KGaA, Darmstadt, Germany, Amgen, Taiho Pharmaceutical, Nordic Group, Pfizer, Yakult, Roche, Eli Lilly, Servier Research Funding: Amgen Inst, Leap Therapeutics Inst, Merck KGaA, Darmstadt, Germany Inst, Jennerex Inst, AstraZeneca Inst, Merck Sharp & Dohme Inst Travel, Accommodations, Expenses: Amgen, Merck KGaA, Darmstadt, Germany, Roche, Bayer, American Society of Clinical Oncology, German Cancer Society, Merck Sharp & Dohme, European Society for Medical Oncology Kohei Shitara disclosure: Honoraria—Abbvie; Novartis; Yakult Pharmaceutical Consulting or Advisory Role—Abbvie; Amgen; Astellas Pharma; Boehringer Ingelheim; Bristol-Myers Squibb; Daiichi Sankyo; GlaxoSmithKline; Lilly; MSD; Novartis; Ono Pharmaceutical; Pfizer; Taiho Pharmaceutical; Takeda; Novartis; Merck Pharmaceutical Research Funding—Astellas Pharma Inst; Chugai Pharma Inst; Daiichi Sankyo Inst; Dainippon Sumitomo Pharma Inst; Eisai Inst; Lilly Inst; Mediscience Planning Inst; MSD Inst; Ono Pharmaceutical Inst; Taiho Pharmaceutical Inst; Merck Pharmaceutical. NB disclosure: Honoraria—Bristol-Myers Squibb Japan; Ono Pharmaceutical; Taiho Pharmaceutical Research Funding—Ono Pharmaceutical Inst; Takeda Inst. HP disclosure: Personal fees from Genentech outside the submitted work. KL disclosure: Honoraria: Eli Lilly; Bristol-Myers Squibb; Daiichi Sankyo; Taiho Pharmaceutical; Merck Pharmaceutical; Amgen; BAYER; MSD; Sanofi-Aventis Consulting or Advisory Role: Eli Lilly; Bristol-Myers Squibb; Daiichi Sankyo; Taiho Pharmaceutical; Merck Pharmaceutical; Amgen; Bayer; MSD Research Funding: Taiho Pharmaceutical; Bayer; Roche. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Reviewer SM declared a shared affiliation with the author KS to the handling editor at the time of review., (Copyright © 2022 El Helali, Tao, Wong, Chan, Mok, Wu, Shitara, Mohler, Boku, Pang and Lam.)

Background: For patients with locally advanced non-small cell lung cancer (NSCLC), concurrent chemoradiotherapy is the foundational treatment strategy. Adding induction chemotherapy did not achieve a superior efficacy but increased the burden from toxicity. Accordingly, we retrospectively investigated the toxicity patterns through pooling individual patient data of the Cancer and Leukemia Group B (CALGB)/Alliance trials., Methods: We included a total of 637 patients with unresectable stage III NSCLC who received induction chemotherapy with a platinum doublet and concurrent chemoradiotherapy and experienced at least one adverse event (AE) in CALGB 9130, 9431, 9534, 30105, 30106 and 39801 trials. The following toxicity occurrence patterns were evaluated: top 10 most frequent AEs, AE distribution by grade, rate of treatment discontinuation due to AEs, associations of AE occurrence with patient characteristics and treatment phase, the time to the first grade ≥3 AE occurrence and its associations with patient characteristics and treatment phase., Results: The occurrence of AEs was the main reason accounting for treatment discontinuation (60 of 637 among all patients; 18 of 112 patients who experienced the induction phase only; 42 of 525 patients who experienced both phases). All patients experienced a total of 11,786 AEs (grade ≥3: 1,049 of 5,538 in induction phase, 1,382 of 6,248 in concurrent phase). Lymphocytes and white blood count were of top 3 grade ≥3 AEs that patients experienced the most in the either phase. Multivariable analysis found AE occurrence was associated with age ≥65 [any grade: odds ratio (OR) =1.44, 95% confidence interval (CI): 1.12-1.86] and the concurrent phase (grade ≥3: OR =1.86, 95% CI: 1.41-2.47; any grade: OR =1.47, 95% CI: 1.19-1.81). Patients in the concurrent phase were more likely and earlier to develop grade ≥3 AEs than those in the induction phase [hazard ratio (HR) =4.37, 95% CI: 2.52-7.59]., Conclusions: The report provides a better understanding regarding the toxicity occurrence patterns in concurrent chemoradiotherapy after induction chemotherapy., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-2006/coif). HP has an NIHU01 grant from the FDA, owns stock from Roche, and received personal fees from Genentech, outside the submitted work. XW holds NIH grants but not related to this work. The other authors have no conflicts of interest to declare., (2022 Translational Cancer Research. All rights reserved.)

Background: Proportional hazards (PH) assumption is often violated in cancer immunotherapy studies. Restricted mean survival time (RMST) ratio is a valid metric to quantify the size of treatment effect when non-proportional hazard (NPH) is present. This study investigated the use of RMST ratio and hazard ratio (HR) in studying progression-free survival (PFS) as a surrogate endpoint for overall survival (OS) in non-small cell lung cancer immunotherapy trials., Methods: Trial level data were collected from 14 phase III trials published between 2012 and 2018. A weighted least-square regression (WLSR) was performed to evaluate the trial-level surrogacy. Surrogacy was evaluated via the association between RMST ratios for PFS and OS and between HRs for PFS and OS., Results: Using data extracted from published articles, low to moderate correlation (0.49) between PFS and OS was observed for HR while low correlation (0.35) was observed for RMST ratio. When trials violating PH in PFS were included, more consistent correlations for both HR (0.43) and RMST ratio (0.44) were observed., Conclusions: In summary, the strength of PFS surrogacy for OS depends on whether HR or RMST ratio are chosen. RMST ratio and additional sensitivity analysis should be considered in addition to HR.

Objectives: Inconsistent findings have been reported on the link between dietary carbohydrates and lung cancer. This study aims to comprehensively evaluate the role of dietary carbohydrates on lung cancer risk., Materials and Methods: The prospective study is based on the PLCO trial, which recruited 113,096 eligible participants across the United States. Participants had to have completed baseline and diet history questionnaires. The incidence of lung cancer was acquired through self-report and medical record follow-up. A multivariable logistic model adjusted for confounders was used to estimate odds ratios (ORs) and 95 % confidence intervals (CIs) of dietary carbohydrates, fiber, whole grains, glycemic index (GI) and glycemic load (GL) for lung cancer. Similar methods were applied in analyzing the carbohydrates and fiber from different food sources. Multinomial logistic models were used for sensitivity analysis with lung cancer subtypes as outcomes., Results: Dietary carbohydrates and GL were inversely associated with lung cancer incidence in the PLCO population. Among various carbohydrates, 30-g daily consumption of dietary fiber was related to a lower risk of lung cancer (fourth vs first quartile OR: 0.62, 95 % CI: 0.54-0.72) compared with 8.8-g. Furthermore, consuming whole grains 2.3 servings per day as opposed to 0.3 servings per day was associated with a lower risk of lung cancer (OR: 0.73, 95 % CI: 0.64-0.83). A higher risk of lung cancer was seen for the consumption of high-GI food (OR: 1.19, 95 % CI: 1.05-1.35) and refined carbohydrates from soft drinks (OR: 1.23, 95 % CI: 1.04-1.46)., Conclusion: Carbohydrates and fiber from fruits, vegetables and whole grains are associated with lower lung cancer risk. Refined carbohydrates from processed food, such as soft drinks, appear to increase risk., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The aim of this study was to identify key microbes associated with change in skin status (lesional vs normal). Longitudinal changes in the skin microbiome between patients with psoriasis and healthy family controls living in the same household were studied using whole genome metagenomic shotgun sequencing at 4 time-points. There were significant changes in abundance of the pathogen Campylobacter jejuni and its higher taxonomic levels when the skin status of patients with psoriasis changed. There were significant longitudinal variations in alpha diveristy (p < 0.001) and beta diversity (p < 0.05) of the skin microbiome in patients with psoriasis, but not in the healthy control group, which indicated composition of skin microbiome in patients with psoriasis was different from healthy control and was dynamically less stable. This study will serve as the basis for future temporal studies of the skin microbiome and probiotic therapeutics.

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction., (© 2020 Wiley Periodicals LLC.)

Importance: Adherence to the Consolidated Standards of Reporting Trials (CONSORT) for randomized clinical trials is associated with improvingquality because inadequate reporting in randomized clinical trials may complicate the interpretation and the application of findings to clinical care., Objective: To evaluate an automated reporting checklist generation tool that uses natural language processing (NLP), called CONSORT-NLP., Design, Setting, and Participants: This study used published journal articles as training, testing, and validation sets to develop, refine, and evaluate the CONSORT-NLP tool. Articles reporting randomized clinical trials were selected from 25 high-impact-factor journals under the following categories: (1) general and internal medicine, (2) oncology, and (3) cardiac and cardiovascular systems., Main Outcomes and Measures: For an evaluation of the performance of this tool, an accuracy metric defined as the number of correct assessments divided by all assessments was calculated., Results: The CONSORT-NLP tool uses the widely used Portable Document Format as an input file. Of the 37 CONSORT reporting items, 34 (92%) were included in the tool. Of these 34 reporting items, 30 were fully implemented; 28 (93%) of the fully implemented CONSORT reporting items had an accuracy of more than 90% for the validation set. The remaining 2 (7%) had an accuracy between 80% and 90% for the validation set. Two to 5 articles were selected from each of these journals for a total of 158 articles to establish a training set of 111 articles to train CONSORT-NLP for CONSORT reporting items, a testing set of 25 articles to refine CONSORT-NLP, and a validation set of 22 articles to assess the performance of CONSORT-NLP. The CONSORT-NLP tool used the Portable Document Format of the articles as input files. A CONSORT-NLP graphical user interface was built using Java in 2019. The time required to complete the CONSORT checklist manually vs using the CONSORT-NLP tool was compared for 30 articles. Two case studies for randomized clinical trials are provided as an illustration for the CONSORT-NLP tool. For the 30 articles investigated, CONSORT-NLP required a mean (SD) 23.0 (4.1) seconds, whereas the manual reviewer required a mean (SD) 11.9 (2.2), 22.6 (4.6), and 57.6 (7.1) minutes, for 3 reviewers, respectively., Conclusions and Relevance: The CONSORT-NLP tool is designed to assist in the reporting of randomized clinical trials. Potential users of CONSORT-NLP include clinicians, researchers, and scientists who plan to publish a randomized trial study in a peer-reviewed journal. The use of CONSORT-NLP may help them save substantial time when generating the CONSORT checklist. This tool may also be useful for manuscript reviewers and journal editors who review these articles.

In hepatocellular carcinoma (HCC) patients with extrahepatic metastasis, the lung is the most frequent site of metastasis. However, how the lung microenvironment favors disseminated cells remains unclear. Here, it is found that nidogen 1 (NID1) in metastatic HCC cell-derived extracellular vesicles (EVs) promotes pre-metastatic niche formation in the lung by enhancing angiogenesis and pulmonary endothelial permeability to facilitate colonization of tumor cells and extrahepatic metastasis. EV-NID1 also activates fibroblasts, which secrete tumor necrosis factor receptor 1 (TNFR1), facilitate lung colonization of tumor cells, and augment HCC cell growth and motility. Administration of anti-TNFR1 antibody effectively diminishes lung metastasis induced by the metastatic HCC cell-derived EVs in mice. In the clinical perspective, analysis of serum EV-NID1 and TNFR1 in HCC patients reveals their positive correlation and association with tumor stages suggesting the potential of these molecules as noninvasive biomarkers for the early detection of HCC. In conclusion, these results demonstrate the interplay of HCC EVs and activated fibroblasts in pre-metastatic niche formation and how blockage of their functions inhibits distant metastasis to the lungs. This study offers promise for the new direction of HCC treatment by targeting oncogenic EV components and their mediated pathways., Competing Interests: The authors declare no conflict of interest., (© 2020 The Authors. Published by Wiley‐VCH GmbH.)

Dysbiosis of the human gut microbiome has been linked to various health conditions, including respiratory tract infections (RTIs) through the gut-lung axis. Several trials have reported that synbiotic therapy could help prevent RTIs or relieve symptoms of some diseases. This meta-analysis comprehensively evaluates the clinical effects of synbiotic supplements for preventing RTIs. PubMed and Google Scholar were searched by keywords for eligible clinical trials until April 2019. Sixty-two studies were retrieved, and 16 studies were selected for meta-analysis. The primary outcomes were defined as the proportion of participants with RTIs at least once or the times of RTI episodes during follow-up based on the intention-to-treat approach. Overall, synbiotic interventions reduced the incidence rate of RTIs by 16% (95% CI: 4%, 27%) and the proportion of participants experiencing RTIs by 16% (95% CI: 5%, 26%). There was no significant evidence of publication bias. A subgroup analysis suggested more prominent effects of synbiotics among adults than infants and children for RTI prevention. The sensitivity analysis excluding trials with prebiotics or probiotics as controls was consistent with our primary analysis. This meta-analysis of clinical trials involving >10,000 individuals showed that synbiotic interventions could be an alternative nutrition strategy for conferring human health and preventing RTIs. Future investigations on the clinical efficacy and safety of synbiotic interventions are warranted with strain-specific and dose-specific approaches., (Copyright © The Author(s) 2020.)

Single-cell RNA-Sequencing (scRNA-Seq), an advanced sequencing technique, enables biomedical researchers to characterize cell-specific gene expression profiles. Although studies have adapted machine learning algorithms to cluster different cell populations for scRNA-Seq data, few existing methods have utilized machine learning techniques to investigate functional pathways in classifying heterogeneous cell populations. As genes often work interactively at the pathway level, studying the cellular heterogeneity based on pathways can facilitate the interpretation of biological functions of different cell populations. In this paper, we propose a pathway-based analytic framework using Random Forests (RF) to identify discriminative functional pathways related to cellular heterogeneity as well as to cluster cell populations for scRNA-Seq data. We further propose a novel method to construct gene-gene interactions (GGIs) networks using RF that illustrates important GGIs in differentiating cell populations. The co-occurrence of genes in different discriminative pathways and 'cross-talk' genes connecting those pathways are also illustrated in our networks. Our novel pathway-based framework clusters cell populations, prioritizes important pathways, highlights GGIs and pivotal genes bridging cross-talked pathways, and groups co-functional genes in networks. These features allow biomedical researchers to better understand the functional heterogeneity of different cell populations and to pinpoint important genes driving heterogeneous cellular functions.

Background: Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients., Methods: Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists., Results: Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model., Conclusion: IVIM is useful in determining PLN involvement but the added value decreases with reader experience.

Background: Although the World Health Organization (WHO) has defined health as a state of physical, mental and social well-being, public health strategies have primarily focused on one domain of well-being. We sought to systematically and simultaneously identify and validate associations of behavioural patterns, psychosocial factors, mental and physical health conditions, access to and utilization of health care and anthropometrics with physical, mental and social well-being., Methods: We conducted a longitudinal environment-wide association study (EWAS) with a training and testing set approach, accounting for multiple testing using a false discovery rate control. We used multivariate multilevel regression to examine the association of each exposure at wave 1 with the three outcomes at wave 2 in the Hong Kong FAMILY Cohort (n = 10 484)., Results: Out of 194 exposures, we identified and validated 14, 5 and 5 exposures that were individually associated with physical, mental and social well-being, respectively. We discovered three factors, namely depressive symptoms, life satisfaction and happiness, that were simultaneously associated with the three domains that define health., Conclusions: These associations, if verified to be causal, could become intervention targets to holistically improve population health. Our findings provide empirical support for placing mental health at the forefront of the public health agenda, and also support recent calls to use life satisfaction and happiness to guide public policy., (© The Author(s) 2019; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

We investigated whether genetic susceptibility to tuberculosis (TB) influences lung adenocarcinoma development among never-smokers using TB genome-wide association study (GWAS) results within the Female Lung Cancer Consortium in Asia. Pathway analysis with the adaptive rank truncated product method was used to assess the association between a TB-related gene-set and lung adenocarcinoma using GWAS data from 5512 lung adenocarcinoma cases and 6277 controls. The gene-set consisted of 31 genes containing known/suggestive associations with genetic variants from previous TB-GWAS. Subsequently, we followed-up with Mendelian Randomization to evaluate the association between TB and lung adenocarcinoma using three genome-wide significant variants from previous TB-GWAS in East Asians. The TB-related gene-set was associated with lung adenocarcinoma (p = 0.016). Additionally, the Mendelian Randomization showed an association between TB and lung adenocarcinoma (OR = 1.31, 95% CI: 1.03, 1.66, p = 0.027). Our findings support TB as a causal risk factor for lung cancer development among never-smoking Asian women., (Copyright © 2019. Published by Elsevier Inc.)

Objectives: Neutropenia is associated with the risk of life-threatening infections, chemotherapy dose reductions and delays that may compromise outcomes. This analysis was conducted to develop a prediction model for chemotherapy-induced severe neutropenia in lung cancer., Materials and Methods: Individual patient data from existing cooperative group phase II/III trials of stages III/IV non-small cell lung cancer or extensive small-cell lung cancer were included. The data were split into training and testing sets. In order to enhance the prediction accuracy and the reliability of the prediction model, lasso method was used for both variable selection and regularization on the training set. The selected variables was fit to a logistic model to obtain regression coefficients. The performance of the final prediction model was evaluated by the area under the ROC curve in both training and testing sets., Results: The dataset was randomly separated into training [7606 (67 %) patients] and testing [3746 (33 %) patients] sets. The final model included: age (>65 years), gender (male), weight (kg), BMI, insurance status (yes/unknown), stage (IIIB/IV/ESSCLC), number of metastatic sites (1, 2 or ≥3), individual drugs (gemcitabine, taxanes), number of chemotherapy agents (2 or ≥3), planned use of growth factors, associated radiation therapy, previous therapy (chemotherapy, radiation, surgery), duration of planned treatment, pleural effusion (yes/unknown), performance status (1, ≥2) and presence of symptoms (yes/unknown)., Conclusions: We have developed a relatively simple model with routinely available pre-treatment variables, to predict for neutropenia. This model should be independently validated prospectively., Competing Interests: Declaration of Competing Interest Dr. Wong has reported a conflict of interest outside of the submitted work (immediate family member is an employee of Genentech with stock ownership). The remaining authors have no conflicts to report., (Published by Elsevier B.V.)

The bacteriophage (phage) component of the skin microbiome in patients with psoriasis has not been systematically explored. The purpose of this study is to investigate phage and bacterial components of the skin microbiome in patients with psoriasis and in healthy family controls. Lesional skin swabs of four different locations (elbow, forearm, knee, and scalp) were taken from patients with psoriasis. Healthy skin swabs of matched locations were taken from contralateral non-lesional skin and healthy family controls. Skin microbiomes were investigated using next-generation shotgun metagenomics sequencing. 81 skin microbiome samples (27 lesional skin samples and 54 healthy skin samples from contralateral non-lesional skin and family controls) obtained from 16 subjects with psoriasis and 16 matched family controls were sequenced and analyzed. Among phage species with abundant host bacteria, two significantly differential abundant phage species, Acinetobacter phage Presley and Pseudomonas phage O4 (adjusted P < 0.05), between psoriasis lesional skin and healthy skin were identified. Samples with high levels of these phage species had their host bacteria abundance suppressed (P = 0.03 and P < 0.001). Differential phage composition between lesional skin in patients with psoriasis and healthy skin from contralateral non-lesional sites and family controls, as well as the suppression of bacteria host of the respective phage, suggest possible avenues for probiotic phage therapeutics., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Pathway-based differential expression analysis allows the incorporation of biological domain knowledge into transcriptomics analysis to enhance our understanding of disease mechanisms. To integrate information among multiple studies at the pathway level, pathway-based meta-analysis can be performed. Paired or partially paired samples are common in biomedical research. However, there are currently no existing pathway-based meta-analysis methods appropriate for paired or partially paired study designs. In this study, we developed a pathway-based meta-analysis approach for paired or partially paired samples. Meta-analysis on the transcriptomics profiles were conducted using p-value-based, rank-based, and effect size-based algorithms. The application of our approach was demonstrated using partially paired data from psoriasis transcriptomics studies. Upon combining six transcriptomics studies, genes related to the cell cycle and DNA replication pathways are found to be highly perturbed in psoriatic lesional skin samples. Results were validated externally with independent RNA-Seq data. Comparison with existing pathway meta-analysis methods revealed consistent results, with our method showing higher detection power. This study demonstrated the utility of our newly developed pathway-based meta-analysis that allows the incorporation of partially paired or paired samples. The proposed framework can be applied to omics data including but not limited to transcriptomics data., (© 2019 John Wiley & Sons, Ltd.)

Background: Mendelian randomization (MR) analyses have been increasingly used to seek evidence of causal associations. This systematic review aims at characterizing and evaluating the reporting of MR analyses in oncological studies., Methods: The PubMed database was searched to identify MR cancer studies until December 31, 2017. Two of the authors independently selected and evaluated reporting quality of the studies. Reporting quality in MR studies before 2016 and in 2016/17 was compared., Results: Cancer studies with MR analyses in 2016 and 2017 accounted for 55.8% of the total number of studies identified. In the 77 eligible articles, 39 (50.6%) did not report subjects' characteristics, 53 (68.8%) did not conduct power estimation, 40 (51.9%) did not state all of the first three MR assumptions (i.e., genetic instrument is associated with exposure, is not associated with confounders, and acts on outcome only through exposure), and 31 (40.3%) did not exclude SNPs that diverged from Hardy-Weinberg equilibrium. More studies estimated power in 2016/2017 than before 2016 (p = 0.028)., Conclusions: Some MR cancer studies did not sufficiently report essential information, posing obstacles for critical appraisal. This study proposes for MR analysis a guideline/checklist for future publications in cancer and other biomedical research., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Although the association of moderate alcohol consumption with specific disorders, such as cardiovascular disease and cancers, has been well documented, the evidence of the broader impact of alcohol consumption on health-related quality of life is less clear. Our objective was to examine the association of drinking patterns with changes in physical and mental well-being across populations., Methods: We conducted a multilevel analysis with multivariate responses in the population-representative FAMILY Cohort in the Hong Kong Special Administrative Region, China, to examine the association between alcohol drinking patterns across 2 waves (2009-2013) (i.e., quitters, initiators, persistent drinkers, persistent former drinkers and lifetime abstainers) and changes in physical and mental well-being (Physical and Mental Component Summary of the 12-Item Short Form Health Survey [SF-12]). Analyses were stratified by sex. We validated findings using a nationally representative cohort in the United States, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, 2001-2005)., Results: In the FAMILY Cohort ( n = 10 386; median follow-up 2.3 yr), the change in mental well-being was more favourable in female quitters than in lifetime abstainers (β = 1.44, 95% confidence interval [CI] 0.43 to 2.45; mean score change of +2.0 for quitters and +0.02 for lifetime abstainers). This association was validated in the NESARC ( n = 31 079; median follow-up 3.1 yr) (β = 0.83, 95% CI 0.08 to 1.58; mean score change of -1.1 for quitters and -1.6 for lifetime abstainers)., Interpretation: The change in mental well-being was more favourable in female quitters, approaching the level of mental well-being of lifetime abstainers within 4 years of quitting in both Chinese and American populations., Competing Interests: Competing interests: None declared., (© 2019 Joule Inc. or its licensors.)

Background: Older patients with non-small cell lung cancer (NSCLC) are often not prescribed standard therapy. It is important to know which older patients would be candidates for aggressive therapy based on their prognosis, and to develop a model that can help determine prognosis., Methods: Data on older patients (≥70 years) enrolled on 38 NCI cooperative group trials of advanced NSCLC from 1991 to 2011 were analyzed. Multivariable Cox PH model was built with a stepwise selection. We derived a prognostic score using the estimated Cox PH regression coefficient. We then calculated the area under receiver operating characteristic (ROC) curve of survival in the testing set., Results: The final analysis included 1467 patients, who were randomly divided into a training (n = 963) and a testing set (n = 504). The prognostic risk score was calculated as: 3 (if male) + 3 (if PS = 1) + 8 (if PS = 2) + 11 (if initial stage = IV) + 4 (if weight loss). Patients were classified into two prognostic groups: good (0-8) and poor (≥9). The median survival in the two groups in the testing set were 13.15 (95% CI, 10.82-15.91) and 8.52 months (95% CI, 7.5-9.63), respectively. The model had area under the 1-year and 2-year ROCs (0.6 and 0.65, respectively) that were higher than existing models., Conclusions: Male gender, poor performance status, distant metastases and recent weight loss predict for poor overall survival (OS) in older patients with advanced NSCLC. This study proposes a simple prognostic model for older adults with advanced NSCLC., (Copyright © 2019. Published by Elsevier Ltd.)

Background: Platinum and etoposide with thoracic radiation followed by prophylactic cranial irradiation constitute the standard treatment for limited-stage small cell lung cancer (LS-SCLC). Many patients with LS-SCLC are elderly with comorbidities., Methods: Individual patient data were collected from 11 phase 2 or 3 trials for LS-SCLC conducted by the National Clinical Trials Network and activated from 1990 to 2010. The primary endpoint was overall survival (OS); the secondary endpoints were progression-free survival (PFS), the rate of severe adverse events, and off-treatment reasons. The outcomes were compared for patients 70 years old or older (elderly patients) and patients younger than 70 years (younger patients)., Results: Individual patient data from 1049 younger patients (81%) and 254 elderly patients (19%) were analyzed. In the multivariate model, elderly patients, in comparison with younger patients, had worse OS (hazard ratio [HR], 1.38; 95% confidence interval [CI], 1.18-1.63; median OS for elderly patients, 17.8 months; OS for younger patients, 23.5 months) and worse PFS (HR, 1.19; 95% CI, 1.03-1.39; median PFS for elderly patients, 10.6 months; median PFS for younger patients, 12.3 months). Elderly patients, in comparison with younger patients, experienced more grade 5 adverse events (8% vs 3%; P < .01) and more grade 3 or higher dyspnea (11% vs 7%; P = .03) but less grade 3 or higher esophagitis/dysphagia (14% vs 19%; P = .04) and less grade 3 or higher vomiting (11% vs 17%; P = .01). Elderly patients completed treatment less often, discontinued treatment because of adverse events and patient refusal more frequently, and died during treatment more frequently., Conclusions: Elderly patients with LS-SCLC have worse PFS and OS and more difficulty in tolerating therapy. Future trials should incorporate assessments of elderly patients, novel monitoring of adverse events, and more tolerable radiation and systemic therapies., (© 2018 American Cancer Society.)

For time-to-event outcomes, the Kaplan-Meier estimator is commonly used to estimate survival functions of treatment groups and to compute marginal treatment effects, such as the difference in survival rates between treatments at a landmark time. The derived estimates of the marginal treatment effect are uniformly consistent under general conditions when data are from randomized clinical trials. For data from observational studies, however, these statistical quantities are often biased due to treatment-selection bias. Propensity score-based methods estimate the survival function by adjusting for the disparity of propensity scores between treatment groups. Unfortunately, misspecification of the regression model can lead to biased estimates. Using an empirical likelihood (EL) method in which the moments of the covariate distribution of treatment groups are constrained to equality, we obtain consistent estimates of the survival functions and the marginal treatment effect. Equating moments of the covariate distribution between treatment groups simulate the covariate distribution that would have been obtained if the patients had been randomized to these treatment groups. We establish the consistency and the asymptotic limiting distribution of the proposed EL estimators. We demonstrate that the proposed estimator is robust to model misspecification. Simulation is used to study the finite sample properties of the proposed estimator. The proposed estimator is applied to a lung cancer observational study to compare two surgical procedures in treating early-stage lung cancer patients.

Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis., Competing Interests: The authors have declared that no competing interests exist.

TRC105 is an anti-endoglin antibody currently being tested in combination with VEGF inhibitors. In the phase Ib trial, 38 patients were treated with both TRC105 and bevacizumab (BEV), and improved clinical outcomes were observed, despite the fact that 30 patients (79%) were refractory to prior anti-VEGF therapy. Plasma samples were tested for angiogenic and inflammatory biomarkers at baseline and on-treatment. To provide broader context of this combination biomarker study, direct cross-study comparisons were made to biomarker studies previously conducted in patients treated with either BEV or TRC105 monotherapy. Upon treatment with BEV and TRC105, pharmacodynamic changes in response to both BEV (PlGF increase) and TRC105 (soluble endoglin increase) were noted. In addition, distinct patterns of change were identified (similar, opposing, neutralizing). Similar patterns were observed when the combination elicited similar effects to those observed with monotherapy treatment (i.e., decreases of Ang-2, increases of IL6 and VCAM-1). Opposing patterns were observed when the combination led to opposing effects compared with monotherapy treatment (i.e., TGFβ1, PDGF-AA and PDGF-BB, PAI-1). Lastly, neutralizing patterns were observed when one drug led to increase, whereas the other drug led to decrease, and the combination elicited no overall effect on the marker (i.e., VEGF-A, VEGF-D, and IGFBP-3). Patients achieving partial responses or stable disease from the combination exhibited significantly lower expression of E-Cadherin, HGF, ICAM-1, and TSP-2 at baseline. Taken together, the novel biomarker modulations identified may deepen our understanding of the underlying biology in patients treated with BEV and TRC105 compared with either drug alone. Mol Cancer Ther; 17(10); 2248-56. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Purpose: This study evaluated the maximum tolerated dose or recommended phase II dose (RPTD) and safety and tolerability of the ganitumab and everolimus doublet regimen followed by the ganitumab, everolimus, and panitumumab triplet regimen., Materials and Methods: This was a standard 3 + 3 dose escalation trial. Doublet therapy consisted of ganitumab at 12 mg/kg every 2 weeks; doses of everolimus were adjusted according to dose-limiting toxicities (DLTs). Panitumumab at 4.8 mg/kg every 2 weeks was added to the RPTD of ganitumab and everolimus. DLTs were assessed in cycle 1; toxicity evaluation was closely monitored throughout treatment. Treatment continued until disease progression or undesirable toxicity. Pretreatment and on-treatment skin biopsies were collected to assess insulin-like growth factor 1 receptor and mammalian target of rapamycin (mTOR) target modulation., Results: Forty-three subjects were enrolled. In the doublet regimen, two DLTs were observed in cohort 1, no DLTs in cohort -1, and one in cohort -1B. The triplet combination was discontinued because of unacceptable toxicity. Common adverse events were thrombocytopenia/neutropenia, skin rash, mucositis, fatigue, and hyperglycemia. In the doublet regimen, two patients with refractory non-small cell lung cancer (NSCLC) achieved prolonged complete responses ranging from 18 to >60 months; one treatment-naïve patient with chondrosarcoma achieved prolonged stable disease >24 months. In dermal granulation tissue, the insulin-like growth factor receptor and mTOR pathways were potently and specifically inhibited by ganitumab and everolimus, respectively., Conclusion: The triplet regimen of ganitumab, everolimus, and panitumumab was associated with unacceptable toxicity. However, the doublet of ganitumab at 12 mg/kg every 2 weeks and everolimus five times weekly had an acceptable safety profile and demonstrated notable clinical activity in patients with refractory NSCLC and sarcoma., Implications for Practice: This trial evaluated the maximum tolerated dose or recommended phase II dose and safety and tolerability of the ganitumab and everolimus doublet regimen followed by the ganitumab, everolimus, and panitumumab triplet regimen. Although the triplet regimen of ganitumab, everolimus, and panitumumab was associated with unacceptable toxicity, the doublet of ganitumab at 12 mg/kg every 2 weeks and everolimus at five times weekly had an acceptable safety profile and demonstrated notable clinical activity in patients with refractory non-small cell lung cancer and sarcoma., Competing Interests: Disclosures of potential conflicts of interest may be found at the end of this article., (© AlphaMed Press 2018.)