BHI-PRO

Large efforts are dedicated worldwide to develop mass spectrometry based analytical chains for the discovery, the validation and the quantification of protein biomarkers in complex matrices like urine or blood. The challenge is to combine high sensitivity to detect very small amounts of proteins, and a large separation capacity to reject the high content of background proteins and separate the signature of the targeted ones. However, mastering the technological variability on these analytical chains is a critical point to get significant results with an acceptable cost, analytical time and number of samples. Adequate information processing is mandatory for data analysis to take into account the complexity of the analysed mixture, to improve the measurement reliability and to make the technology easier to use. A proteomic analytical chain is a cascade of molecular events which can be depicted by a graph structure, each node being associated to an analytical level within the chain. Each branch of the graph corresponds to a molecular decomposition. Looking to molecular quantities, this molecular graph defines a hierarchical mixture model. In this BHI-PRO project, we propose to introduce a relevant hierarchical modelling of the MALDI and MRM3 chains. The new Bayesian Hierarchical Inversion algorithms will rely on two advances: the first one is related to "proteomics and inverse problems". The challenge is to develop an instrument model including together physical phenomena involved in the measurement process. It yields the statement of direct model involving relevant parameters and organised in a hierarchical structure. The second challenging task is related to "inverse problems and stochastic sampling". It requires the development of a detection-estimation methodology for MALDI and an estimation methodology for MRM. The proposed strategy relies on Bayesian statistics and the exploration of the a posteriori law will be achieved thanks to Monte Carlo Markov Chain sampling algorithms. For biostatistics, among the advantages of using proteomics to discover and use biomarkers is the ability to test many biomarkers simultaneously to improve both sensitivity and specificity. However, as the number of variables increases, so does the likelihood of finding results that appear statistically significant by chance. Within this project, we propose to evaluate the statistical power of discrimination test in the developed Bayesian framework. This BHI-PRO project involves 3 signal processing research teams (CEA-LETI, CEA-LIST, IMS), 2 biostatistical research teams (LBS, CLIPP) and 2 proteomics platforms (CLIPP for MALDI and bioMérieux for MRM3). It is the first opportunity to combine in a single research project Bayesian inversion, biostatistics, and proteomics platforms in order to study the technological variability on 2 proteomics analytical chains: matrix assisted laser desorption ionization (MALDI) by CLIPP applied to a proteomic discovery model and Multiple Reaction Monitoring (MRM) mass spectrometry by bioMérieux on a colorectal cancer model including 8 candidate proteins for MRM validation mode. The main deliverables will be 2 versions of Bayesian Hierarchical Inversion software, the first dedicated to the MALDI platform in discovery mode, the second to the MRM3 platform in validation mode, and one biostatistical guideline report. Dissemination plan targets 4 relevant publications and participation to international conferences. Valorisation includes the diffusion of a Bayesian Hierarchical Inversion software package dedicated to MALDI MS acquisition available through free access, the transfer to bioMérieux of the Bayesian Hierarchical Inversion software package dedicated to MRM MS acquisition, the publication of biostatistical guidelines to use optimized protocols and to define Best Practices Rules in conjunction with the operation of the proposed Bayesian hierarchical inversion software for mass spectrometry data analysis.