Abstract:
Classification of soft tissue tumors is expanding to include new entities of which the ermeging ones are sometimes described solely by their molecular recurrent characteristics. Diagnostic tools development are also on the rise with « multi-omics » systems directed at achieving high performance in precision medicine based on the therapeutic stratification of patients this in turn based on the biologic stratification of their tumors. The overall strategy of adequate patient care is to neither overtreat patients to prevent unnecessary side effects nor to undertreat them to avert recurrence, but to offer a calibrated therapy which addresses as much as possible the intrinsic biological challenges and evolution potential of the tumor. Mass spectrometry can be applied to soft tissue tumors and sarcomas for fast reliable and precise diagnosis as a point of care system. In a collaborative scheme, an ambient ionisation system in mass spectrometry called Spidermass ( acronym for Speed Profiling Innovative Diagnostic Endoscopy Real-time Mass spectrometry Amazing Straightforward Sensitivity) has been conceived, made operational and patented in 2014 by a french national health and medical research body laboratory, and represents a novel molecular diagnostic tool. The proof of concept has potentially been established in a work based on a series of 33 dog sarcoma biopsies. With the project MESSIDORE more than 200 human tumors are now being assessed at the Oscar Lambret Cancer Center and the French National Network of Soft Tissue Sarcomas for real-time ex vivo molecular diagnoses for real-time molecular lipid-based subtyping based on developed spectral libraries and their corresponding histological diagnoses in the form of a data base. Artificial intelligence technology with CNN (Convoluted neural Network ) such as deep learning and transfer learning will be integrated to permit the system to interrogate the data base for tumor identity. The next step is in vivo assays as an aid to intraoperative margin status determination of sarcomas.
Keywords: sarcoma, mass spectrometry, ambient ionization, artificial intelligence
