Model selection based on combined penalties for biomarker identification

The growing role of targeted medicine has led to an increased focus on the development of actionable biomarkers. Current penalized selection methods that are used to identify biomarker panels for classification in high-dimensional data, however, often result in highly complex panels that need careful pruning for practical use. In the framework of regularization methods, a penalty that is a weighted sum of the L1 and L0 norm has been proposed to account for the complexity of the resulting model. In practice, the limitation of this penalty is that the objective function is non-convex, non-smooth, the optimization is computationally intensive and the application to high-dimensional settings is challenging. In this paper, we propose a stepwise forward variable selection method which combines the L0 with L1 or L2 norms. The penalized likelihood criterion that is used in the stepwise selection procedure results in more parsimonious models, keeping only the most relevant features. Simulation results and a real application show that our approach exhibits a comparable performance with common selection methods with respect to the prediction performance while minimizing the number of variables in the selected model resulting in a more parsimonious model as desired

A Bayesian model to estimate the cutoff and the clinical utility of a biomarker assay

To enable targeted therapies and enhance medical decision-making, biomarkers are increasingly used as screening and diagnostic tests. When using quantitative biomarkers for classification purposes, this often implies that an appropriate cutoff for the biomarker has to be determined and its clinical utility must be assessed. In the context of drug development, it is of interest how the probability of response changes with increasing values of the biomarker. Unlike sensitivity and specificity, predictive values are functions of the accuracy of the test, depend on the prevalence of the disease and therefore are a useful tool in this setting. In this paper, we propose a Bayesian method to not only estimate the cutoff value using the negative and positive predictive values, but also estimate the uncertainty around this estimate. Using Bayesian inference allows us to incorporate prior information, and obtain posterior estimates and credible intervals for the cut-off and associated predictive values. The performance of the Bayesian approach is compared with alternative methods via simulation studies of bias, interval coverage and width and illustrations on real data with binary and time-to-event outcomes are provided

AKT1 (E17K) mutation profiling in breast cancer: prevalence, concurrent oncogenic alterations, and blood-based detection.

BACKGROUND: The single hotspot mutation AKT1 [G49A:E17K] has been described in several cancers, with the highest incidence observed in breast cancer. However, its precise role in disease etiology remains unknown. METHODS: We analyzed more than 600 breast cancer tumor samples and circulating tumor DNA for AKT1 (E17K) and alterations in other cancer-associated genes using Beads, Emulsions, Amplification, and Magnetics digital polymerase chain reaction technology and targeted exome sequencing. RESULTS: Overall AKT1 (E17K) mutation prevalence was 6.3 % and not correlated with age or menopausal stage. AKT1 (E17K) mutation frequency tended to be lower in patients with grade 3 disease (1.9 %) compared with those with grade 1 (11.1 %) or grade 2 (6 %) disease. In two cohorts of patients with advanced metastatic disease, 98.0 % (n = 50) and 97.1 % (n = 35) concordance was obtained between tissue and blood samples for the AKT1 (E17K) mutation, and mutation capture rates of 66.7 % (2/3) and 85.7 % (6/7) in blood versus tissue samples were observed. Although AKT1-mutant tumor specimens were often found to harbor concurrent alterations in other driver genes, a subset of specimens harboring AKT1 (E17K) as the only known driver alteration was also identified. Initial follow-up survival data suggest that AKT1 (E17K) could be associated with increased mortality. These findings warrant additional long-term follow-up. CONCLUSIONS: The data suggest that AKT1 (E17K) is the most likely disease driver in certain breast cancer patients. Blood-based mutation detection is achievable in advanced-stage disease. These findings underpin the need for a further enhanced-precision medicine paradigm in the treatment of breast cancer

Histology independent drug development - Is this the future for cancer drugs?

The Cancer Drug Development Forum (CDDF)’s ‘Histology independent drug development – is this the future for cancer drugs?’ workshop was set up to explore the current landscape of histology independent drug development, review the current regulatory landscape and propose recommendations for improving the conduct of future trials.The first session considered lessons learnt from previous trials, including innovative solutions for reimbursement. The session explored why overall survival represents the most valuable endpoint, and the importance of duration of response, which can be captured with swimmer and spider plots.The second session on biomarker development and treatment optimisation considered current regulations for companion diagnostics, FDA guidance on histology independent drug development in oncology, and the need to establish cut-offs for the biomarker of tumour mutational burden to identify the patients most likely to benefit from PDL1 treatment.The third session reviewed novel trial designs, including basket, umbrella and platform trials, and statistical approaches of hierarchical modelling where homogeneity between study cohorts enables information to be borrowed between cohorts. The discussion highlighted the need to agree ‘common assessment standards’ to facilitate pooling of data across studies.In the fourth session, the sharing of data sets was recognised as a key step for improving equity of access to precision medicines across Europe. The session considered how the European Health Data Space (EHDS) could streamline access to medical records, emphasizing the importance of introducing greater accountability into the digital space.In conclusion the workshop proposed 11 recommendations to facilitate histology agnostic drug development.</p

A proposal for a new PhD level curriculum on quantitative methods for drug development

This paper provides an overview of “Improving Design, Evaluation and Analysis of early drug development Studies” (IDEAS), a European Commission–funded network bringing together leading academic institutions and small‐ to large‐sized pharmaceutical companies to train a cohort of graduate‐level medical statisticians. The network is composed of a diverse mix of public and private sector partners spread across Europe, which will host 14 early‐stage researchers for 36 months. IDEAS training activities are composed of a well‐rounded mixture of specialist methodological components and generic transferable skills. Particular attention is paid to fostering collaborations between researchers and supervisors, which span academia and the private sector. Within this paper, we review existing medical statistics programmes (MSc and PhD) and highlight the training they provide on skills relevant to drug development. Motivated by this review and our experiences with the IDEAS project, we propose a concept for a joint, harmonised European PhD programme to train statisticians in quantitative methods for drug development

A dynamic model of circadian rhythms in rodent tail skin temperature for comparison of drug effects

<p>Abstract</p> <p>Menopause-associated thermoregulatory dysfunction can lead to symptoms such as hot flushes severely impairing quality of life of affected women. Treatment effects are often assessed by the ovariectomized rat model providing time series of tail skin temperature measurements in which circadian rhythms are a fundamental ingredient. In this work, a new statistical strategy is presented for analyzing such stochastic-dynamic data with the aim of detecting successful drugs in hot flush treatment. The circadian component is represented by a nonlinear dynamical system which is defined by the van der Pol equation and provides well-interpretable model parameters. Results regarding the statistical evaluation of these parameters are presented.</p

Supplemental material for A Bayesian model to estimate the cutoff and the clinical utility of a biomarker assay

<p>Supplemental material for A Bayesian model to estimate the cutoff and the clinical utility of a biomarker assay by Eleni Vradi, Thomas Jaki, Richardus Vonk and Werner Brannath in Statistical Methods in Medical Research</p