An AI-Powered Blood Test to Detect Cancer Using NanoDSF

We describe a novel cancer diagnostic method based on plasma denaturation profiles obtained by a non-conventional use of Differential Scanning Fluorimetry. We show that 84 glioma patients and 63 healthy controls can be automatically classified using denaturation profiles 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 from a simple blood test.

[1]  Kun Zhang,et al.  Non-invasive early detection of cancer four years before conventional diagnosis using a blood test , 2020, Nature Communications.

[2]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[3]  P. Tsvetkov,et al.  Functional Status of Neuronal Calcium Sensor-1 Is Modulated by Zinc Binding , 2018, Front. Mol. Neurosci..

[4]  Raffaele Palmirotta,et al.  Liquid biopsy of cancer: a multimodal diagnostic tool in clinical oncology , 2018, Therapeutic advances in medical oncology.

[5]  R. Mirimanoff,et al.  Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. , 2005, The New England journal of medicine.

[6]  J. Qazi,et al.  ATR-FTIR spectroscopy as the future of diagnostics: a systematic review of the approach using bio-fluids , 2020 .

[7]  C. Punyadeera,et al.  Circulating biomarkers in patients with glioblastoma , 2019, British Journal of Cancer.

[8]  A. Matheu,et al.  Liquid Biopsy in Glioblastoma: Opportunities, Applications and Challenges , 2019, Cancers.

[9]  Georg Langs,et al.  The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space , 2017, Nature Medicine.

[10]  P. Tsvetkov,et al.  Differential scanning calorimetry of plasma in glioblastoma: toward a new prognostic / monitoring tool , 2018, Oncotarget.

[11]  L. Hansen,et al.  Differential scanning calorimetry of gliomas: a new tool in brain cancer diagnostics? , 2013, Neurosurgery.

[12]  L. Recht,et al.  Preliminary use of differential scanning calorimetry of cerebrospinal fluid for the diagnosis of glioblastoma multiforme , 2011, Journal of Neuro-Oncology.

[13]  J. Boxerman,et al.  Pseudoprogression, radionecrosis, inflammation or true tumor progression? challenges associated with glioblastoma response assessment in an evolving therapeutic landscape , 2017, Journal of Neuro-Oncology.

[14]  David J. Anderson,et al.  Biofluid Diagnostics by FTIR Spectroscopy: A Platform Technology for Cancer Detection. , 2020, Cancer letters.

[15]  P. Tsvetkov,et al.  Plasmatic Signature of Disease by Differential Scanning Calorimetry (DSC). , 2019, Methods in molecular biology.

[16]  J. Chaires,et al.  Differential scanning calorimetry of blood plasma for clinical diagnosis and monitoring. , 2009, Experimental and molecular pathology.

[17]  Susan M. Chang,et al.  Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  P. Tsvetkov,et al.  Zinc binding to RNA recognition motif of TDP-43 induces the formation of amyloid-like aggregates , 2017, Scientific Reports.