Examining the Relationship between Pre-Malignant Breast Lesions, Carcinogenesis and Tumor Evolution in the Mammary Epithelium Using an Agent-Based Model

Agent-based approaches for biological modeling in oncology: A literature review

CONTEXT

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions.

METHODOLOGY

This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023.

RESULTS

Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

Gene-Environment Interaction between Arg72Pro SNP and Selected Environmental Exposures among Brazilian Women Diagnosed with Benign Breast Disease

Background:

Benign breast disease (BBD) is a factor strongly associated with breast cancer worldwide. Arg72Pro SNP association with breast cancer is controversial due to the suggestion that environmental factors are required to modulate such risk. There are no studies evaluating these environmental interactions of the aforementioned SNP within BBD.

Aim:

To determine the frequency of SNP Arg72Pro in a cohort of women diagnosed with BBD; and to investigate gene-environmental interactions with environmental factors.

Results:

The genotype frequency was 44.6% for Arg/Pro, 39.3% for Arg/Arg genotype, and 16.3% for Pro/Pro homozygote. Gene-environment interaction analysis shows that when Arg/Arg is considered as reference, there is an ORinteraction with Arg/Pro and fabric exposure (OR=1.90;95%CI:1.04,3.48), solvents (OR=2.21;95%CI:1.01,4.83) and chlorine, bleaches, disinfectants, and liquid wax exposure (OR=2.52;95%CI:1.07,5.91). Analysis with Pro/Pro genotype as the reference showed an interaction between alcohol consumption and recessive model (OR=1.58;95%CI:1.00,2.51). Gene-environmental interactions were observed too between exposure to hair dyes, straighteners or relaxers and Arg/Arg (OR=3.26;95%CI:1.21,8.82).

Conclusion:

The Arg/Pro genotype was the most frequent in the BBD cohort. When compared with the Arg/Arg genotype, the presence of Arg/Pro genotype and solvents, fabric and cleaning products exposure increased the risk of BBD. When compared with Pro/Pro genotype, there were interactions between recessive model with alcohol consumption and exposure to hair products on the risk of BBD.

Applying optimization algorithms to tuberculosis antibiotic treatment regimens

Introduction

Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens.

Methods

We define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments.

Results

We found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens.

Conclusions

Our results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.

Molecular Portrait of the Normal Human Breast Tissue and Its Influence on Breast Carcinogenesis

Normal human breast tissue consists of epithelial and nonepithelial cells with different molecular profiles and differentiation grades. This molecular heterogeneity is known to yield abnormal clones that may contribute to the development of breast carcinomas. Stem cells that are found in developing and mature breast tissue are either positive or negative for cytokeratin 19 depending on their subtype. These cells are able to generate carcinogenesis along with mature cells. However, scientific data remains controversial regarding the monoclonal or polyclonal origin of breast carcinomas. The majority of breast carcinomas originate from epithelial cells that normally express BRCA1. The consecutive loss of the BRCA1 gene leads to various abnormalities in epithelial cells. Normal breast epithelial cells also express hypoxia inducible factor (HIF) 1α and HIF-2α that are associated with a high metastatic rate and a poor prognosis for malignant lesions. The nuclear expression of estrogen receptor (ER) and progesterone receptor (PR) in normal human breast tissue is maintained in malignant tissue as well. Several controversies regarding the ability of ER and PR status to predict breast cancer outcome remain. Both ER and PR act as modulators of cell activity in normal human breast tissue. Ki-67 positivity is strongly correlated with tumor grade although its specific role in applied therapy requires further studies. Human epidermal growth factor receptor 2 (HER2) oncoprotein is less expressed in normal human breast specimens but is highly expressed in certain malignant lesions of the breast. Unlike HER2, epidermal growth factor receptor expression is similar in both normal and malignant tissues. Molecular heterogeneity is not only found in breast carcinomas but also in normal breast tissue. Therefore, the molecular mapping of normal human breast tissue might represent a key research area to fully elucidate the mechanisms of breast carcinogenesis.