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. 2021 Mar 15;13(6):1294.
doi: 10.3390/cancers13061294.

An AI-Powered Blood Test to Detect Cancer Using NanoDSF

Philipp O Tsvetkov  1   2 , Rémi Eyraud  3 , Stéphane Ayache  4 , Anton A Bougaev  5 , Soazig Malesinski  1 , Hamed Benazha  4 , Svetlana Gorokhova  6   7 , Christophe Buffat  8   9 , Caroline Dehais  10   11 , Marc Sanson  10   11 , Franck Bielle  10   12 , Dominique Figarella Branger  1   13 , Olivier Chinot  1   14 , Emeline Tabouret  1   14 , François Devred  1   2
Affiliations

An AI-Powered Blood Test to Detect Cancer Using NanoDSF

Philipp O Tsvetkov et al. Cancers (Basel). .

Abstract

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.

Keywords: biomarker; diagnostic; glioma; liquid biopsy; nanoDSF.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Means of the first derivatives of the ratio F330/F350. The red curve corresponds to glioma patients while the blue one is the one of the controls. The color regions graphically show the variability of the data by indicating the standard deviation.
Figure 2
Figure 2
Patient-to-profile workflow/design of the study. Left panel corresponds with the training of machine learning. Denaturation profiles of plasma samples of healthy individuals and glioma patients generated by nanoDSF are added to a database (Atlas) along with their corresponding clinical status. Artificial intelligence (AI) algorithms are then trained using this Atlas to generate a model. Right panel corresponds with the use of the obtained model to identify whether the nanoDSF profile of a new sample tested corresponds with a glioma or not. Dotted line indicates that the same workflow can be applied to another cancer (Cancer X).
See this image and copyright information in PMC

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