Analysis of Clinical Prognostic Variables for Triple Negative Breast Cancer Histological Grading and Lymph Node Metastasis

Abstract

Background: Triple Negative Breast Cancer (TNBC) is a type of breast cancer with very bad prognosis. Predicting the histological grade (HG) and the lymph nodes metastasis is crucial for developing more suitable treatment strategies. Methods: We present the main clinical and pathological variables to predict the histological grade and lymph nodes metastasis via novel machine learning techniques. These variables are currently being used for prognosis and treatment in medical practice. This analysis was performed using a database of 102 Caucasian women diagnosed with TNBC. The results were cross-validated using random simulations of this dataset. Results: HG was predicted with an accuracy of 93.8% using a list of 6 prognostic variables with significant implications: Ki67 expression, use of Oral contraceptives, Col11A1 expression, Col11A1 score, E-cad truncated and Tumor size. The lymph nodes metastasis was predicted with an accuracy of almost 85% using only 6 prognostic variables: Vascular invasion, Tumor size, Perineural invasion, Age at diagnosis, Ki67 expression, and Col11A1 score. This analysis also served to establish the median signatures of the groups with and without lymph node metastasis, and proved the existence of a kind of small-size tumors (around 2.15 cm) with lymph node metastasis but not showing vascular and perineural invasions and higher protein Col11A1 score. Besides, these signatures proved to be very stable. Conclusions: The additional information conveyed by the prognostic variables found in these two classification problems provides new insight about the genesis and progression of this disease and can be used in medical practice to improve decisions in patient diagnosis and further treatment.

Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning

“…To deal with this uncertainty problem we use bootstrapping methodology to sample the genes that are discriminatory in different holdouts and perform posterior analysis of these discriminatory networks to unravel the biological pathways that are involved in the disease development. This algorithm has been named holdout sampler [19] and has been used to sample the uncertainty space in various inverse problems [60,61]. It has been also successfully applied in phenotype prediction and drug design [62][63][64][65].…”

Đặc Điểm Mô Bệnh Học Ung Thư Vú Có Bộ Ba Dấu Ấn Hóa Mô Miễn Dịch Âm Tính Tại Bệnh Viện Ung Bướu Đà Nẵng

“…Đặc điểm tuổi mắc bệnh của bệnh nhân trong nghiên cứu của chúng tôi, tuổi trung bình là 54,29±14,32 tuổi; tuổi thấp nhất là 24; tuổi cao nhất là 101. Kết quả này hấp hơn so với kết quả của Ana Cernea và cộng sự (2018) khi nghiên cứu 102 bệnh nhân da trắng mắc TNBC với độ tuổi trung bình là 59 tuổi, tuổi nhỏ nhất là 30 và lớn nhất là 94 [4]. Trong một nghiên cứu khác trên 647 bệnh nhân, Pruneri và cộng sự (2016) nhận thấy độ tuổi trung bình mắc TNBC là 52 tuổi, tương tự độ tuổi trung bình trong nghiên cứu của chúng tôi đồng thời có sự tương đồng về nhóm tuổi có tỷ lệ mắc cao nhất là 50-59 tuổi [8].…”

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

“…Different interesting methods were proposed by Cernea et al 8 and successfully applied in the analysis of Triple Negative Breast Cancer metastasis, comparing the results obtained with Bayesian networks. 78 Bayesian networks are utilized to model the genetic signatures' distribution related to the phenotype prediction, P g=c obs À Á , according to Bayes' rule: [79][80][81] P g=c obs À Á , P g ð ÞP c obs =g À Á…”

“…We illustrated the importance of genomic robust sampling in precision medicine and uncertainty analysis with the analysis of the Triple Negative Breast Cancers (TNBC) phenotype. The algorithms utilized have been successfully utilized in the analysis of breast cancer and lymph node metastasis, 8,90 in Sarcopenia, 91 Multiple Sclerosis, 92 Multiple Myeloma 93 and Inclusion Body Myositis. 94 More specifically, we performed a robust sampling in order to find out the altered genetic pathways by the metastasis events.…”