Analysis of patient reported outcomes included in the registrational clinical trials of nivolumab for advanced non-small cell lung cancer

In the era of value-based oncology care, stakeholders are increasingly using patient reported outcomes (PROs) to guide clinical and regulatory decisions. PROs are also included in health technology assessments to guide patient access, drug reimbursement and pricing. We reviewed PROs collected in the United States Food and Drug Administration approved indications of nivolumab in advanced NSCLC. We analyzed the PRO data reported in the CheckMate 9LA (NCT03215706), CheckMate 227 (NCT02477826), CheckMate 057 (NCT01673867), and CheckMate 017 (NCT01642004) registrational clinical trials, and concluded that nivolumab alleviated symptom burden and improved health status of patients in this setting. However, inability of the included PRO instruments to measure immune-related adverse events, differences in the timing of PRO evaluation between treatment groups, incomplete patient participation at all time points, limited patient participation in the later time points, and interpretation of the longitudinal data are key challenges that impede accurate analysis and validation of PROs.     

A bayesian approach to the design of phase II clinical trials

A new strategy for the design of Phase II clinical trials is presented which utilizes the information provided by the prior distribution of the response rate, the costs of treating a patient, and the losses or gains resulting from the decisions taken at the completion of the study. A risk function is derived from which one may determine the optimal Bayes sampling plan. The decision theoretic/Bayesian approach is shown to provide a formal justification for the sample sizes often used in practice and shows the conditions under which such sample sizes are clearly inappropriate.     

A mixed approach for proving non-inferiority in clinical trials with binary endpoints

When a new treatment is compared to an established one in a randomized clinical trial, it is standard practice to statistically test for non-inferiority rather than for superiority. When the endpoint is binary, one usually compares two treatments using either an odds-ratio or a difference of proportions. In this paper, we propose a mixed approach which uses both concepts. One first defines the non-inferiority margin using an odds-ratio and one ultimately proves non-inferiority statistically using a difference of proportions. The mixed approach is shown to be more powerful than the conventional odds-ratio approach when the efficacy of the established treatment is known (with good precision) and high (e.g. with more than 56% of success). The gain of power achieved may lead in turn to a substantial reduction in the sample size needed to prove non-inferiority. The mixed approach can be generalized to ordinal endpoints.     

An approach to confirmatory testing of subpopulations in clinical trials

In oncology studies with immunotherapies, populations of “super‐responders” (patients in whom the treatment works particularly well) are often suspected to be related to biomarkers. In this paper, we explore various ways of confirmatory statistical hypothesis testing for joint inference on the subpopulation of putative “super‐responders” and the full study population. A model‐based testing framework is proposed, which allows to define, up‐front, the strength of evidence required from both full and subpopulations in terms of clinical efficacy. This framework is based on a two‐way analysis of variance (ANOVA) model with an interaction in combination with multiple comparison procedures. The ease of implementation of this model‐based approach is emphasized and details are provided for the practitioner who would like to adopt this approach. The discussion is exemplified by a hypothetical trial that uses an immune‐marker in oncology to define the subpopulation and tumor growth as the primary endpoint.     

Endpoints,patient selection,and biomarkers in the design of clinical trials for cancer vaccines

Therapeutic cancer vaccines are an emerging and potentially effective treatment modality. Cancer vaccines are usually very well tolerated, with minimal toxicity compared with chemotherapy. Unlike conventional cytotoxic therapies, immunotherapy does not result in immediate tumor shrinkage but may alter growth rate and thus prolong survival. Multiple randomized controlled trials of various immunotherapeutic agents have shown a delayed separation in Kaplan–Meier survival curves, with no evidence of clinical benefit within the first 6–12 months of vaccine treatment. Overall survival benefit is seen in patients with lower disease burden who are not expected to die within those initial 6–12 months. The concept of improved overall survival without marked initial tumor reduction represents a significant shift from the current paradigms established by standard cytotoxic therapies. Future clinical studies of therapeutic vaccines should enroll patients with either lower tumor burden, more indolent disease or both, and must seek to identify early markers of clinical benefit that may correlate with survival. Until then, improved overall survival is the only clear, discriminatory endpoint for therapeutic vaccines as monotherapies.     

Older cancer patients in cancer clinical trials are underrepresented. Systematic literature review of almost 5000 meta- and pooled analyses of phase III randomized trials of survival from breast,prostate and lung cancer

BackgroundOlder people represent increasing proportions of the population with cancer. To understand the representivity of cancer treatments in older people, we performed a systematic literature review using PRISMA guidelines of the age distribution of clinical trial participants for three leading cancer types, namely breast, prostate, and lung.MethodsWe used PubMed to identify articles detailing meta or pooled-analyses of phase III, randomised controlled trials (RCTs) of survival for breast, prostate and lung cancer, published ≤5 years from 2016. We compared the age distribution of participants to that of these cancers for “More developed regions”.Results4993 potential papers were identified, but only three papers on breast cancer, three on lung cancer, and none on prostate cancer presented the age distribution of their participants. Except for one paper of breast cancer, participants ≥70 years in all other papers were underrepresented.ConclusionsWe recommend the age distribution of patients be clearly reported in all clinical trials, as per guidelines. Clinical trials ought to be more representative of the populations most affected by the disease for which treatments are being tested. This should lead to better knowledge of effectiveness of treatments and better translation of trial results to optimal care of older cancer patients.     

A novel approach to estimate the German-wide incidence of testicular cancer

Background: Currently, only 7 out of 16 Federal States of Germany provide testicular cancer incidence rates with an estimated completeness of at least 90% which complicates the regional comparison of incidence rates. The aim of this study was to provide a novel approach to estimate the testicular cancer incidence in Germany by using nationwide hospitalization data. Methods: We used the nationwide hospitalization data (DRG statistics) of the years 2005–2006 including 16,6 million hospitalizations among men. We identified incident testicular cancer cases by the combination of a diagnosis of testicular cancer and an orchiectomy during the same hospitalization and estimated the age-specific and age-standardized (World Standard Population) incidence of testicular cancer across Federal States. We also analyzed available cancer registry data from 2005 to 2006. Results: A total of 8544 hospitalizations indicated incident testicular cancer cases in 2005–2006. The nationwide crude incidence rate of testicular cancer was 10,6 per 100.000 person-years. The ratio of the number of registered cases (cancer registry) to the estimated number of cases based on the hospitalization statistics ranged between 79% and 100%. There was only little variation of the age-standardized DRG-based incidence estimates across Federal States (range: 8,2–10,6 per 100.000 person-years). Discussion: We provided testicular cancer incidence estimates for each of the 16 Federal States of Germany based on hospitalization data for the first time. The low within-population incidence variability in Germany and high between-population incidence variability in Europe may indicate that ecologic factors play a causal role in the European variation of testicular cancer.     

A human rights approach to low data reporting in clinical trials of psychiatric deep brain stimulation

The reporting of clinical trial data is necessary not only for doctors to determine treatment efficacy, but also to explore new questions without unnecessarily repeating trials, and to protect patients and the public from dangers when data are withheld. This issue is particularly salient in those trials involving invasive neurosurgical interventions, such as deep brain stimulation (DBS), for ‘treatment refractory’ psychiatric disorders. Using the federal database ClinicalTrials.gov, it was discovered that out of the completed or unknown‐status trials related to psychiatric DBS up to November 2018, only two had submitted results to ClinicalTrials.gov. These results suggest that, despite federal requirements to report clinical trial data, reporting on psychiatric DBS trials is problematically minimal. It is argued that a human rights approach to this problem establishes a legal and ethical foundation for the need to report clinical trial results in this area.     

Assessment of clinical outcomes in breast cancer patients treated with taxanes: multi-analytical approach

Polymorphisms in genes encoding CYPs (Phase I) and ABCB1 (Phase III) enzymes may attribute to variability of efficacy of taxanes. The present study aims to find the influence of CYP and ABCB1 gene polymorphisms on taxanes based clinical outcomes. 132 breast cancer patients treated with taxanes based chemotherapy were genotyped for CYP3A4*1B, CYP3A5*3, CYP1B1*3, CYP2C8*3, ABCB1 1236C>T, 2677G>T/A and 3435C>T polymorphisms using PCR-RFLP. Associations of genetic variants with clinical outcomes in terms of response in 58 patients receiving neo-adjuvant chemotherapy (NACT), and chemo-toxicity in 132 patients were studied. Multifactor dimensionality reduction (MDR) analysis was performed to evaluate higher order gene–gene interactions with clinical outcomes. Pathological response to taxane based NACT was associated with GA genotype as well as A allele of CYP3A5*3 polymorphism (P_corr = 0.0465, P_corr = 0.0465). Similarly, association was found in dominant model of CYP3A5*3 polymorphism with responders (P_corr = 0.0465). Haplotype analysis further revealed A_CYP3A4–A_CYP3A5 haplotype to be significantly associated with responders (P_corr = 0.048). In assessing toxicity, significant association of variant (TT) genotype and T allele of ABCB1 2677G>T/A polymorphism, was found with ‘grade 1 or no leucopenia’ (P_corr = 0.0465, P_corr = 0.048). On evaluating higher order gene–gene interaction models by MDR analysis, CYP3A5*3; ABCB11236C>T and ABCB1 2677G>T/A; ABCB1 3435C>T and CYP1B1*3 showed significant association with treatment response, grade 2–4 anemia and dose delay/reduction due to neutropenia (P = 0.024, P = 0.004, P = 0.026), respectively. Multi-analytical approaches may provide a better assessment of pharmacogenetic based treatment outcomes in breast cancer patients treated with taxanes.     

A novel approach for enhancing the catalytic efficiency of a protease at low temperature: Reduction in substrate inhibition by chemical modification

The alkaline protease, savinase was chemically modified to enhance the productivity of the enzyme at low temperatures on a complex polymeric protein (azocasein) substrate. At 5 and 15°C, savinase modified with ficol or dextran hydrolyzed fivefold more azocasein than the unmodified savinase. Kinetic studies showed that the catalytic improvements are associated with changes in uncompetitive substrate inhibition with K_i values of modified savinases sixfold higher than the unmodified savinase. Modeling of small‐angle scattering data indicates that two substrate molecules bind on opposing sides of the enzyme. The combined kinetic and structural data indicate that the polysaccharide modifier sterically blocks the allosteric site and reduces substrate inhibition. In contrast to the properties of cold‐active enzymes that generally manifest as low activation enthalpy and high flexibility, this study shows that increased activity and productivity at low temperature can be achieved by reducing uncompetitive substrate inhibition, and that this can be achieved using chemical modification with an enzyme in a commercial enzyme‐formulation. Biotechnol. Bioeng. 2009;103: 676–686. © 2009 Wiley Periodicals, Inc.     

A shrinkage approach for estimating a treatment effect using intermediate biomarker data in clinical trials

In clinical trials, a biomarker (S ) that is measured after randomization and is strongly associated with the true endpoint (T) can often provide information about T and hence the effect of a treatment (Z ) on T. A useful biomarker can be measured earlier than T and cost less than T. In this article, we consider the use of S as an auxiliary variable and examine the information recovery from using S for estimating the treatment effect on T, when S is completely observed and T is partially observed. In an ideal but often unrealistic setting, when S satisfies Prentice's definition for perfect surrogacy, there is the potential for substantial gain in precision by using data from S to estimate the treatment effect on T. When S is not close to a perfect surrogate, it can provide substantial information only under particular circumstances. We propose to use a targeted shrinkage regression approach that data-adaptively takes advantage of the potential efficiency gain yet avoids the need to make a strong surrogacy assumption. Simulations show that this approach strikes a balance between bias and efficiency gain. Compared with competing methods, it has better mean squared error properties and can achieve substantial efficiency gain, particularly in a common practical setting when S captures much but not all of the treatment effect and the sample size is relatively small. We apply the proposed method to a glaucoma data example.     

A bi-ordering approach to linking gene expression with clinical annotations in gastric cancer

Background  

In the study of cancer genomics, gene expression microarrays, which measure thousands of genes in a single assay, provide abundant information for the investigation of interesting genes or biological pathways. However, in order to analyze the large number of noisy measurements in microarrays, effective and efficient bioinformatics techniques are needed to identify the associations between genes and relevant phenotypes. Moreover, systematic tests are needed to validate the statistical and biological significance of those discoveries.     

A novel approach for determining cancer genomic breakpoints in the presence of normal DNA

CDKN2A (encodes p16(INK4A) and p14(ARF)) deletion, which results in both Rb and p53 inactivation, is the most common chromosomal anomaly in human cancers. To precisely map the deletion breakpoints is important to understanding the molecular mechanism of genomic rearrangement and may also be useful for clinical applications. However, current methods for determining the breakpoint are either of low resolution or require the isolation of relatively pure cancer cells, which can be difficult for clinical samples that are typically contaminated with various amounts of normal host cells. To overcome this hurdle, we have developed a novel approach, designated Primer Approximation Multiplex PCR (PAMP), for enriching breakpoint sequences followed by genomic tiling array hybridization to locate the breakpoints. In a series of proof-of-concept experiments, we were able to identify cancer-derived CDKN2A genomic breakpoints when more than 99.9% of wild type genome was present in a model system. This design can be scaled up with bioinformatics support and can be applied to validate other candidate cancer-associated loci that are revealed by other more systemic but lower throughput assays.     

Identifying common prognostic factors in genomic cancer studies: A novel index for censored outcomes

Background  

With the growing number of public repositories for high-throughput genomic data, it is of great interest to combine the results produced by independent research groups. Such a combination allows the identification of common genomic factors across multiple cancer types and provides new insights into the disease process. In the framework of the proportional hazards model, classical procedures, which consist of ranking genes according to the estimated hazard ratio or the p-value obtained from a test statistic of no association between survival and gene expression level, are not suitable for gene selection across multiple genomic datasets with different sample sizes. We propose a novel index for identifying genes with a common effect across heterogeneous genomic studies designed to remain stable whatever the sample size and which has a straightforward interpretation in terms of the percentage of separability between patients according to their survival times and gene expression measurements.     

A novel statistical test for treatment differences in clinical trials using a response-adaptive forward-looking Gittins Index Rule

The most common objective for response-adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by altering the chance of allocation on the basis of accumulating data. Approaches that yield good patient benefit properties suffer from low power from a frequentist perspective when testing for a treatment difference at the end of the study due to the high imbalance in treatment allocations. In this work we develop an alternative pairwise test for treatment difference on the basis of allocation probabilities of the covariate-adjusted response-adaptive randomization with forward-looking Gittins Index (CARA-FLGI) Rule for binary responses. The performance of the novel test is evaluated in simulations for two-armed studies and then its applications to multiarmed studies are illustrated. The proposed test has markedly improved power over the traditional Fisher exact test when this class of nonmyopic response adaptation is used. We also find that the test's power is close to the power of a Fisher exact test under equal randomization.