Abstract
Simple SummaryBrain cancers, such as gliomas, are very difficult to detect because of their localization and late onset of symptoms. Here, we have developed a novel cancer detection method based on plasma denaturation profiles obtained by a non-conventional use of Differential Scanning Fluorimetry. Using blood samples from glioma patients and healthy controls, we show that their denaturation profiles can be automatically distinguished with the help of machine learning algorithms with 92% accuracy. This promising approach can now be extended to other types of cancers and could become a powerful pan-cancer diagnostic and monitoring tool requiring only a simple blood test.Glioblastoma is the most frequent and aggressive primary brain tumor. Its diagnosis is based on resection or biopsy that could be especially difficult and dangerous in the case of deep location or patient comorbidities. Monitoring disease evolution and progression also requires repeated biopsies that are often not feasible. Therefore, there is an urgent need to develop biomarkers to diagnose and follow glioblastoma evolution in a minimally invasive way. In the present study, we described a novel cancer detection method based on plasma denaturation profiles obtained by a non-conventional use of differential scanning fluorimetry. Using blood samples from 84 glioma patients and 63 healthy controls, we showed that their denaturation profiles can be automatically distinguished with the help of machine learning algorithms with 92% accuracy. Proposed high throughput workflow can be applied to any type of cancer and could become a powerful pan-cancer diagnostic and monitoring tool requiring only a simple blood test.
Highlights
Diffuse gliomas are the most frequent and aggressive primary brain tumors in adults
Our results show that the detection of glioma based on the denaturation profile of plasma can be efficiently automated
A number of different cancer detection methods have been tested over the past ten years, which are based on biophysical methods such as differential scanning calorimetry (DSC) [11], infrared technology (ATR-FTIR) [13] as well as on the detection and isolation of cell-free nucleic acids, extracellular vesicles and circulating tumor cells [14,15,16,17,18]
Summary
No curative treatment is available despite the association of surgical resection, radiotherapy and chemotherapy as first-line treatment [1] Another major challenge in glioma patient management is obtaining timely and precise histological and molecular characterization of the tumor in order to establish diagnosis and orient treatment. Biopsies of these tumors could be impossible due to their deep or diffuse location or due to patient comorbidities. In these cases, treatment would be chosen based on MRI neuroimaging characteristics that are often insufficient. There is an urgent need in the neuro-oncology field to design new easy-to-use methods that are less invasive than histological examination and more efficient than neuroimaging in order to help patient diagnosis and to follow disease progression
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