A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.

Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

Considerations for master protocols using external controls.

There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.

Duration of and time to response in oncology clinical trials from the perspective of the estimand framework.

Duration of response (DOR) and time to response (TTR) are typically evaluated as secondary endpoints in early-stage clinical studies in oncology when efficacy is assessed by the best overall response and presented as the overall response rate. Despite common use of DOR and TTR in particular in single-arm studies, the definition of these endpoints and the questions they are intended to answer remain unclear. Motivated by the estimand framework, we present relevant scientific questions of interest for DOR and TTR and propose corresponding estimand definitions. We elaborate on how to deal with relevant intercurrent events which should follow the same considerations as implemented for the primary response estimand. A case study in mantle cell lymphoma illustrates the implementation of relevant estimands of DOR and TTR. We close the paper with practical recommendations to implement DOR and TTR in clinical study protocols.

Oral ixazomib, lenalidomide, and dexamethasone for transplant-ineligible patients with newly diagnosed multiple myeloma.

Continuous lenalidomide-dexamethasone (Rd)-based regimens are among the standards of care in transplant-ineligible newly diagnosed multiple myeloma (NDMM) patients. The oral proteasome inhibitor ixazomib is suitable for continuous dosing, with predictable, manageable toxicities. In the double-blind, placebo-controlled TOURMALINE-MM2 trial, transplant-ineligible NDMM patients were randomized to ixazomib 4 mg (n = 351) or placebo (n = 354) plus Rd. After 18 cycles, dexamethasone was discontinued and treatment was continued using reduced-dose ixazomib (3 mg) and lenalidomide (10 mg) until progression/toxicity. The primary endpoint was progression-free survival (PFS). Median PFS was 35.3 vs 21.8 months with ixazomib-Rd vs placebo-Rd, respectively (hazard ratio [HR], 0.830; 95% confidence interval, 0.676-1.018; P = .073; median follow-up, 53.3 and 55.8 months). Complete (26% vs 14%; odds ratio [OR], 2.10; P < .001) and ≥ very good partial response (63% vs 48%; OR, 1.87; P < .001) rates were higher with ixazomib-Rd vs placebo-Rd. In a prespecified high-risk cytogenetics subgroup, median PFS was 23.8 vs 18.0 months (HR, 0.690; P = .019). Overall, treatment-emergent adverse events (TEAEs) were mostly grade 1/2. With ixazomib-Rd vs placebo-Rd, 88% vs 81% of patients experienced grade ≥3 TEAEs, 66% vs 62% serious TEAEs, and 35% vs 27% TEAEs resulting in regimen discontinuation; 8% vs 6% died on study. Addition of ixazomib to Rd was tolerable with no new safety signals and led to a clinically meaningful PFS benefit of 13.5 months. Ixazomib-Rd is a feasible option for certain patients who can benefit from an all-oral triplet combination. This trial was registered at www.clinicaltrials.gov as #NCT01850524.

Estimation of conditional power in the presence of auxiliary data.

Conditional power (CP) is a commonly used tool to inform interim decision-making in clinical trials, but the conventional approach using only primary endpoint data to calculate CP may not perform well when the primary endpoint requires a long follow-up period, or the treatment effect size changes during the trial. Several methods have been proposed to use additional short term auxiliary data observed at the interim analysis to improve the CP estimation in these situations, however, they may rely on strong assumptions, have limited applications, or use ad hoc choices of information fraction. In this paper we propose a general framework where the true CP formula is first derived in the presence of auxiliary data, and CP estimation is obtained by substituting the unknown parameters with consistent estimators. We conducted extensive simulations to examine the performance of both proposed and conventional approaches using the true CP as the benchmark. As the proposed approach is based on the true underlying CP, the simulations confirmed its superiority over the conventional approach in terms of efficiency and accuracy, especially if observed auxiliary data reflect the change of treatment effect size. The simulations also indicate that the magnitude of improvement in CP estimation is associated with the correlation between auxiliary and primary endpoints and/or the magnitude of the effect size change during the trial.

Estimands for overall survival in clinical trials with treatment switching in oncology.

An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention-to-treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment-policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant. However, the respective estimand only yields a clinically meaningful comparison of two treatment arms if subsequent therapies are already approved and reflect clinical practice. We illustrate different scenarios where subsequent therapies are not yet approved drugs and thus do not reflect clinical practice. In such situations the hypothetical strategy could be more meaningful from patient's and prescriber's perspective. The cross-industry Oncology Estimand Working Group (www.oncoestimand.org) was initiated to foster a common understanding and consistent implementation of the estimand framework in oncology clinical trials. This paper summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials. We also discuss how different choices of estimands may impact study design, data collection, trial conduct, analysis, and interpretation.

Sample size and power for the weighted log-rank test and Kaplan-Meier based tests with allowance for nonproportional hazards.

Asymptotic distributions under alternative hypotheses and their corresponding sample size and power equations are derived for nonparametric test statistics commonly used to compare two survival curves. Test statistics include the weighted log-rank test and the Wald test for difference in (or ratio of) Kaplan-Meier survival probability, percentile survival, and restricted mean survival time. Accrual, survival, and loss to follow-up are allowed to follow any arbitrary continuous distribution. We show that Schoenfeld's equation-often used by practitioners to calculate the required number of events for the unweighted log-rank test-can be inaccurate even when the proportional hazards (PH) assumption holds. In fact, it can mislead one to believe that 1:1 is the optimal randomization ratio (RR), when actually power can be gained by assigning more patients to the active arm. Meaningful improvements to Schoenfeld's equation are made. The present theory should be useful in designing clinical trials, particularly in immuno-oncology where nonproportional hazards are frequently encountered. We illustrate the application of our theory with an example exploring optimal RR under PH and a second example examining the impact of delayed treatment effect. A companion R package npsurvSS is available for download on CRAN.

Validity of using ad hoc methods to analyze secondary traits in case-control association studies.

Case-control association studies often collect from their subjects information on secondary phenotypes. Reusing the data and studying the association between genes and secondary phenotypes provide an attractive and cost-effective approach that can lead to discovery of new genetic associations. A number of approaches have been proposed, including simple and computationally efficient ad hoc methods that ignore ascertainment or stratify on case-control status. Justification for these approaches relies on the assumption of no covariates and the correct specification of the primary disease model as a logistic model. Both might not be true in practice, for example, in the presence of population stratification or the primary disease model following a probit model. In this paper, we investigate the validity of ad hoc methods in the presence of covariates and possible disease model misspecification. We show that in taking an ad hoc approach, it may be desirable to include covariates that affect the primary disease in the secondary phenotype model, even though these covariates are not necessarily associated with the secondary phenotype. We also show that when the disease is rare, ad hoc methods can lead to severely biased estimation and inference if the true disease model follows a probit model instead of a logistic model. Our results are justified theoretically and via simulations. Applied to real data analysis of genetic associations with cigarette smoking, ad hoc methods collectively identified as highly significant (P<10-5) single nucleotide polymorphisms from over 10 genes, genes that were identified in previous studies of smoking cessation.

Impact of concomitant dexamethasone dosing schedule on bortezomib-induced peripheral neuropathy in multiple myeloma.

Peripheral neuropathy (PN) is the most troublesome adverse event associated with the proteasome inhibitor bortezomib. Studies suggest an inflammatory aetiology for bortezomib-induced PN (BiPN) and it has been hypothesized that reducing inflammation with concomitant dexamethasone may reduce BiPN incidence and/or severity. We retrospectively analysed PN rates from 32 studies (2697 patients with previously untreated multiple myeloma) incorporating bortezomib and differing dexamethasone schedules: partnered dosing (days of and after bortezomib), weekly dosing, and other dosing schedules (e.g. days 1-4, 8-11). Pooled overall PN rates were 45·5%, 63·9%, and 47·5%, respectively, with 5·3%, 11·0%, and 9·6% grade ≥3. Adjusting for potential confounders (age, gender, presence of thalidomide, bortezomib treatment duration), PN rates in patients on partnered dosing schedules appeared lower than in patients on weekly or other dosing schedules. Analyses conducted using patient-level data suggest that cumulative dexamethasone dose, a potential confounding factor, is unlikely to have influenced the analyses. Findings were similar in a separate pooled analysis excluding data from regimens incorporating thalidomide, when pooled overall PN rates were 50·1%, 63·9%, and 48·3%, respectively, with 4·2%, 11·0%, and 8·6% grade ≥3. These findings suggest that partnered dexamethasone dosing may result in less severe BiPN compared with alternative dexamethasone dosing schedules.

Tumor-Agnostic Approvals: Insights and Practical Considerations.

Since the first approval of a tumor-agnostic indication in 2017, a total of seven tumor-agnostic indications involving six drugs have received approval from the FDA. In this paper, the master protocol subteam of the Statistical Methods in Oncology Scientific Working Group, Biopharmaceutical Session, American Statistical Association, provides a comprehensive summary of these seven tumor-agnostic approvals, describing their mechanisms of action; biomarker prevalence; study design; companion diagnostics; regulatory aspects, including comparisons of global regulatory requirements; and health technology assessment approval. Also discussed are practical considerations relating to the regulatory approval of tumor-agnostic indications, specifically (i) recommendations for the design stage to mitigate the risk that exceptions may occur if a treatment is initially hypothesized to be effective for all tumor types and (ii) because drug development continues after approval of a tumor-agnostic indication, recommendations for further development of tumor-specific indications in first-line patients in the setting of a randomized confirmatory basket trial, acknowledging the challenges in this area. These recommendations and practical considerations may provide insights for the future development of drugs for tumor-agnostic indications.

Rolling continual reassessment method with overdose control: An efficient and safe dose escalation design.

In phase 1 dose escalation studies, dose limiting toxicities (DLTs) are defined as adverse events of concern occurring during a predefined time window after first dosing of patients. Standard dose escalation designs, such as the continual reassessment method (CRM), only utilize this binary DLT information. Thus, late-onset DLTs are usually not accounted for when CRM guiding the dose escalation and finally defining the maximum tolerated dose (MTD) of the drug, which brings safety concerns for patients. Previously, several extensions of CRMs, such as the time-to-event CRM (TITE-CRM), fractional CRM (fCRM) and the data augmented CRM (DA-CRM), have been proposed to handle this issue without prolonging trial duration. However, among the model-based designs, none of the designs have explicitly controlled the risk of overdosing as in the escalation with overdose control (EWOC) design. Here we propose a novel dose escalation with overdose control design using a two-parameter logistic regression model for the probability of DLT depending on the dose and a piecewise exponential model for the time to DLT distribution, which we call rolling-CRM design. A comprehensive simulation study has been conducted to compare the performance of the rolling-CRM design with other dose escalation designs. Of note, the trial duration is significantly shorter compared to traditional CRM designs. The proposed design also retains overdose control characteristics, but might require a larger sample size compared to traditional CRM designs.

Assessing the Impact of COVID-19 on the Clinical Trial Objective and Analysis of Oncology Clinical Trials-Application of the Estimand Framework.

Abstract-Coronavirus disease 2019 (COVID-19) outbreak has rapidly evolved into a global pandemic. The impact of COVID-19 on patient journeys in oncology represents a new risk to interpretation of trial results and its broad applicability for future clinical practice. We identify key intercurrent events (ICEs) that may occur due to COVID-19 in oncology clinical trials with a focus on time-to-event endpoints and discuss considerations pertaining to the other estimand attributes introduced in the ICH E9 addendum. We propose strategies to handle COVID-19 related ICEs, depending on their relationship with malignancy and treatment and the interpretability of data after them. We argue that the clinical trial objective from a world without COVID-19 pandemic remains valid. The estimand framework provides a common language to discuss the impact of COVID-19 in a structured and transparent manner. This demonstrates that the applicability of the framework may even go beyond what it was initially intended for.

Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia.

Co-expression analysis reveals useful dysregulation patterns of gene cooperativeness for understanding cancer biology and identifying new targets for treatment. We developed a structural strategy to identify co-expressed gene networks that are important for chronic myelogenous leukemia (CML). This strategy compared the distributions of expressional correlations between CML and normal states, and it identified a data-driven threshold to classify strongly co-expressed networks that had the best coherence with CML. Using this strategy, we found a transcriptome-wide reduction of co-expression connectivity in CML, reflecting potentially loosened molecular regulation. Conversely, when we focused on nucleophosmin 1 (NPM1) associated networks, NPM1 established more co-expression linkages with BCR-ABL pathways and ribosomal protein networks in CML than normal. This finding implicates a new role of NPM1 in conveying tumorigenic signals from the BCR-ABL oncoprotein to ribosome biogenesis, affecting cellular growth. Transcription factors may be regulators of the differential co-expression patterns between CML and normal.