The network of antigen-antibody reactions in adult women with breast cancer or benign breast pathology or without breast pathology

Use and Comparison of Machine Learning Techniques to Discern the Protein Patterns of Autoantibodies Present in Women with and without Breast Pathology

Breast cancer (BC) has become a global health problem, ranking first in incidence and fifth in mortality in women around the world. Although there are some diagnostic methods for the disease, these are not sufficiently effective and are invasive. In this work, we discriminated between patients without breast pathology (BP), with benign BP, and with BC based on the band patterns obtained from Western blot strip images of the autoantibody response to antigens of the T47D tumor line using and comparing supervised machine learning techniques to have a sensitive and accurate method. When comparing the aforementioned machine learning techniques, it was found that by obtaining a convolutional neural network architecture from a neuroevolution algorithm, it is possible to automatically discriminate with a classification accuracy of 90.67% between patients with cancer and with/without BP. In the case of discrimination between patients with cancer and without BP, a classification accuracy of 96.67% was obtained with the K-NN algorithm and 95.13% with the convolutional neural network obtained using a neuroevolution algorithm, although these results are not statistically significant. It is concluded that the convolutional neural network obtained by neuroevolution is the method with the best performance with respect to those evaluated in this work.

Psychological suppressive profile and autoantibodies variability in women living with breast cancer: A prospective cross-sectional study

Breast cancer (BC) is a leading cause of women's morbimortality worldwide. Unfortunately, attempts to predict women's susceptibility to developing BC well before it becomes symptomatic, based on their genetic, family, and reproductive background have proved unsatisfactory. Here we analyze the matching of personality traits and protein serum profiles to predict women's susceptibility to developing cancer. We conducted a prospective study among 150 women (aged 18-70 years), who were distributed into three groups (n = 50): women without breast pathology and women diagnosed with BC or benign breast pathology. Psychological data were obtained through standardized psychological tests and serum protein samples were analyzed through semiquantitative protein immunoblotting. The matching for psychological and immunological profiles was constructed from these data using a mathematical generalized linear model.The model predicted that women who have stronger associations between high-intensity stress responses, emotional containment, and an increased number and reduced variability of serum proteins (detected by IgG autoantibodies) have the greatest susceptibility to develop BC before the disease has manifested clinically. Hence, the present study endorses the possibility of using psychological and biochemical tests in combination to increase the possibility of identifying women at risk of developing BC before the disease shows clinical manifestations. A longitudinal study must be instrumented to test the prediction ability of the instrument in real scenarios.

Trial registration

Committee of Ethical Research of the Hospital General de México "Dr. Eduardo Liceaga," Ministry of Health (DI/12/111/03/064).

The dawn of vaccines for cancer prevention

An important role of the immune system is in the surveillance for abnormal or transformed cells, which is known as cancer immunosurveillance. Through this process, the first changes to normal tissue homeostasis caused by infectious or other inflammatory insults can be detected by the immune system through the recognition of antigenic molecules (including tumour antigens) expressed by abnormal cells. However, as they develop, tumour cells can acquire antigenic and other changes that allow them to escape elimination by the immune system. To bias this process towards elimination, immunosurveillance can be improved by the administration of vaccines based on tumour antigens. Therapeutic cancer vaccines have been extensively tested in patients with advanced cancer but have had little clinical success, which has been attributed to the immunosuppressive tumour microenvironment. Thus, the administration of preventive vaccines at pre-malignant stages of the disease holds promise, as they function before tumour-associated immune suppression is established. Accordingly, immunological and clinical studies are yielding impressive results.

Qa-2 expression levels is related with tumor-infiltrating lymphocytes profile during solid Ehrlich tumor development

The Qa-2 has been described as Human Leucocyte Antigen G (HLA-G) murine homolog. This homology is well accepted to gene and protein structure, in different pathology process and embryos implantation. However, in some neoplasm, this homology is questioned, where Qa-2 has been proposed as an immunogenic molecule, associated to tumor rejection. In this way, the aim of this study was to describe the pattern of Qa-2 expression and its relationship with the profile of tumor-infiltrating lymphocytes in solid Ehrlich tumor. The Ehrlich tumor growth was evaluated in Balb/c female mice in different tumor stages. The inflammatory infiltration features were determined by histopathology and, both lymphocyte type and tissue Qa-2 expression by immunohistochemistry. ELISA kit was used to determine soluble Qa-2 in the serum from the animals. We observed that Qa-2 in neoplastic cells increases in intermediate tumor development stages, while, serum Qa-2 increases in the late stage. Qa-2 increasing is correlated with CD3+ increase. Our results suggest that Qa-2 has a role opposite to HLA-G in Ehrlich solid carcinoma, and may be modulating the immune response by attracting the inflammatory infiltrate, especially T CD8+ Lymphocytes.