In the personalized medicine era, there is essential demand of creating a universal user-friendly as well as cost-effective detection test-system for breast cancer, the most prevalent cancer in female population, which would combine non-invasiveness with high-precision performance. Since mammary glands do not produce any specific molecular markers, we studied several molecular biomarkers that are involved in immunoregulation. One of such molecules are caspases, a family of intracellular enzymes that can play protective role in tumorigenesis by inducing apoptotic cell death in lymphocytes.
Activity of aspases-3, -6, -8, and -9 was assessed in the peripheral blood lymphocytes in patients at different breast cancer stage of breast benign disease (BBD) and healthy controls. The caspase activity was measured using fluorogenic substrate while cellular apoptosis was evaluated by means of cytofluorometric assay. In addition, the ratio of T-cell subsets was compared using antibodies to CD3, CD4, CD8, CD16, CD20, CD25, CD95 antigens. Discriminant function analysis and artificial neuronal networks (ANNs) method were used to create test-system.
We obtained statistically significant data in all groups of 138 analyzed samples. Discriminant analysis revealed significance for all 11 biomarkers. Using the biomarkers, we were able to differentiate correctly 100% of cases without pathology, 87% – BBD, I breast cancer stage – 90%, II stage – 100% and III stage – 100%. By introducing permutation in expanded to 3464 samples size, we were able to increase the sensitivity of the test system that is 100% control samples, 97% – BDD, 92% - stage I of breast cancer, 99% - stage II, 100% - stage III. On the basis of ANNs analysis software was developed in R-statistics. Network produced 100% correct result both on the original selection and on the artificially increased.
Studies have shown that combining of biomarkers with the used algorithms can be successfully used to differentiate pathological blood from controls, classify breast cancer by the stage and separate benign and malignancy breast tumors. Further, the diagnostic system must be blindly tested with new clinical data.
Clinical trial identification
Legal entity responsible for the study
Petrozavodsk State University
The Ministry of Education and Science of Russia, grant No 2014/154 – 1713.
All authors have declared no conflicts of interest.