This project is about finding novel biomarkers in ALS that can be used as surrogate for prognostication, target engagement and treatment response alongside clinical variables. ALS remains an incurable disorder with a significant impact on patients, family members and the society. Progress towards novel therapeutics to slow down or arrest the disease process is hampered by the lack of biological markers measurable in biological fluid that can be used to stratify patients based on phenotype while enabling an early diagnosis and more affordable, smaller size clinical trials. The use of neurofilaments has been a step change towards better designed clinical studies but other biological redouts reflecting the complexity of the pathological process are needed for early diagnosis in pre-symptomatic individuals, disease monitoring and treatment response. Discovery of new biomarkers hinges on in depth understanding of the disease pathobiology from onset and on the availability of analytical tools that make it possible to screen multiple targets in the same experiment. To this end, whilst transcriptomic and genetics still offers the most granular biological profile of a pathological state, proteomics allows investigation into the end-products of the pathological process. Our goal is to generate a database inclusive of all available proteomic data originated from tissues, fluids and from the ever-increasing cell lines modelling the disease. The aim is to bioinformatically define an ALS proteomic space derived from the biological signatures of the disease in a variety of matrices from patients and cell lines. Once regulated proteins are identified, we will build a custom panel using high-throughput, sensitive and large-scale proteomic techniques like SOMASCAN and Olink to screen cross-sectionally and longitudinally plasma samples from large cohorts of ALS patients including the ALS Biomarkers. This investigation aims primarily at easing the burden of complex clinical trial design, creating a new pipeline for faster and more effective screening of a larger number of compounds based on a new set of informative biomarkers. It wants to create tools of disease stratification that can inform on prognosis and in care planning. Most importantly, given the importance for early recognition of the disease when diagnostic delay world-wide still averages 12 months, novel biomarkers will help widening the window of therapeutic opportunities in ALS with an early diagnosis. This project will also feed future data science and machine learning experiments creating a resource that can be used by the ALS community for the common good.
