Aplicação de espectroscopia de infravermelho com transformada de fourier na busca de marcadores diagnósticos em pacientes com leucemia mieloide aguda

Abstract

Acute myeloid leukemia (AML) is part of a group of hematologic neoplasms that are characterized by maturation blockade and exacerbated clonal proliferation of myeloid progenitors in the bone marrow or peripheral blood. Currently, the main diagnostic methods for this neoplasm depend on the presence of at least 20% of malignant cells in the bone marrow or peripheral blood, which generally coincides with the advanced stage of the disease. In addition, these methodologies require expensive equipment and supplies, as well as specialized labor, which makes the diagnosis of AML restricted only to large diagnostic or research centers. Therefore, in this work, we implement the use of Fourier Transform Infrared Spectroscopy and attenuated total reflection (ATR-FTIR) univariate and coupled with artificial intelligence algorithms, as a fast, inexpensive tool with high sensitivity and specificity for patient discrimination with clinical and laboratory diagnosis of acute myeloid leukemia. For this, we performed a quantitative spectral analysis of plasma (1μl) of 35 patients and 35 control subjects, with a resolution of 4 cm- 1 and 32 spectral scans in triplicate, using the air spectrum as the background of the analysis. As a result, ten vibrational modes were identified (3095 cm-1, 1648 cm-1, 1635 cm-1, 1540 cm-1, 1284 cm-1, 1170 cm-1, 1120 cm-1, 1078 cm-1, 1031 cm-1 and 850 cm-1) with sensitivity and specificity ≥80% (p<0.0001) in discriminating the pathological samples in relation to the control group. Furthermore, two regions of the medium spectrum (1333 cm-1 -1272 cm-1 and 1096 cm-1-996 cm-1) also presented sensitivity and specificity values equal to or greater than 80% (p<0.0001). Regarding the classification of spectra by artificial intelligence, logistic regression, linear discriminant analysis and random forest algorithms presented accuracy equal to or greater than 90% in at least one selected pre-processing set, with emphasis on the vector machine algorithm of support, which discriminated the control and LMA with 100% accuracy. In conclusion, univariate ATR-FTIR spectroscopy or coupled with artificial intelligence algorithms was able to discriminate the plasma of AML patients and controls, promising its use as a fast, low-cost test with high sensitivity and specificity for AML screening.