Comparison of Three Sequencing Panels Used for the Assessment of Tumor Mutational Burden in NSCLC Reveals Low Comparability.

INTRODUCTION: Tumor mutational burden (TMB) has been proposed as a novel predictive biomarker for the stratification of patients with NSCLC undergoing immune checkpoint inhibitor (ICI) treatment. The assessment of TMB has recently been established using large targeted sequencing panels, and numerous studies are ongoing to harmonize TMB assessment. "Correlation" or the coefficient of determination has generally been used to evaluate the association between different panels. We hypothesized that these metrics might overestimate the comparability, especially for lower TMB values. METHODS: A total of 30 samples from patients with NSCLC undergoing ICI treatment were consecutively sequenced using the following three large, targeted sequencing panels: FoundationOne, Oncomine TML, and QiaSeq TMB. The TMB values were compared in the whole patient population and in a subset of patients in which the TMB assessed by FoundationOne was between 5 and 25 mutations/Mb. Prediction of durable clinical benefit (>6 mo with no progression) was assessed using receiver operator characteristics, and optimal cutoff values were calculated using the Youden J statistic. RESULTS: Correlation between the three targeted sequencing panels was strong in the whole patient population (R2 > 0.79) but was dramatically reduced in the subset of patients with TMB of 5 to 25 mutations/Mb. The agreement assessed using the Bland-Altman method was also very low. All panels were able to predict durable clinical benefit in the TMB-high population. CONCLUSIONS: Assessment of TMB using the three targeted sequencing panels was possible and predictive of response to ICI treatment, but correlation was an inappropriate measurement to assess the association between the respective panels.

Quantifying Registration Uncertainty With Sparse Bayesian Modelling.

We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion. In earlier work, approximate inference under the sparse Bayesian model was tackled in an efficient Variational Bayes (VB) framework. In this paper we are interested in the theoretical and empirical quality of uncertainty estimates derived under this approximate scheme vs. under the exact model. We implement an (asymptotically) exact inference scheme based on reversible jump Markov Chain Monte Carlo (MCMC) sampling to characterize the posterior distribution of the transformation and compare the predictions of the VB and MCMC based methods. The true posterior distribution under the sparse Bayesian model is found to be meaningful: orders of magnitude for the estimated uncertainty are quantitatively reasonable, the uncertainty is higher in textureless regions and lower in the direction of strong intensity gradients.

MRI Based Bayesian Personalization of a Tumor Growth Model.

The mathematical modeling of brain tumor growth has been the topic of numerous research studies. Most of this work focuses on the reaction-diffusion model, which suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information. However, estimating the parameters of the reaction-diffusion model is difficult because of the lack of identifiability of the parameters, the uncertainty in the tumor segmentations, and the model approximation, which cannot perfectly capture the complex dynamics of the tumor evolution. Our approach aims at analyzing the uncertainty in the patient specific parameters of a tumor growth model, by sampling from the posterior probability of the parameters knowing the magnetic resonance images of a given patient. The estimation of the posterior probability is based on: 1) a highly parallelized implementation of the reaction-diffusion equation using the Lattice Boltzmann Method (LBM), and 2) a high acceptance rate Monte Carlo technique called Gaussian Process Hamiltonian Monte Carlo (GPHMC). We compare this personalization approach with two commonly used methods based on the spherical asymptotic analysis of the reaction-diffusion model, and on a derivative-free optimization algorithm. We demonstrate the performance of the method on synthetic data, and on seven patients with a glioblastoma, the most aggressive primary brain tumor. This Bayesian personalization produces more informative results. In particular, it provides samples from the regions of interest and highlights the presence of several modes for some patients. In contrast, previous approaches based on optimization strategies fail to reveal the presence of different modes, and correlation between parameters.