An immunology primer for computational modelers

MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach

Motivation

Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario.

Results

We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor-immune dynamics starting from high-throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model.The application to real data of human colorectal cancer tissue investigating the spatio-temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach.

Availability and implementation

MAST, implemented in Python language, is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast), and a Docker image is provided to ease its deployment. The submitted software version and test data are available in Zenodo at https://dx.doi.org/10.5281/zenodo.7267745.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Etiology of Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL): Current Directions in Research

Simple Summary

The first report of breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) was in 1997. Although BIA-ALCL develops around breast implants, it is considered a cancer of the immune system and not a cancer of the breast ducts or lobules. Nearly all confirmed cases to date have been associated with textured surface (versus smooth surface) breast implants. As physicians have become more aware of BIA-ALCL, so has the number of reported cases, although the number of cases remains low. In most instances, patients have an excellent prognosis following removal of the breast implant and its surrounding fibrous capsule. Many theories on factors that trigger the development of BIA-ALCL, such as the presence of bacteria, have been proposed. However, the sequence(s) of events that follow the initial triggering event(s) have not been fully determined. This article summarizes the current scientific knowledge on the development of BIA-ALCL.

Abstract

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a CD30-positive, anaplastic lymphoma kinase-negative T-cell lymphoma. Where implant history is known, all confirmed cases to date have occurred in patients with exposure to textured implants. There is a spectrum of disease presentation, with the most common occurring as a seroma with an indolent course. A less common presentation occurs as locally advanced or, rarely, as metastatic disease. Here we review the immunological characteristics of BIA-ALCL and potential triggers leading to its development. BIA-ALCL occurs in an inflammatory microenvironment with significant lymphocyte and plasma cell infiltration and a prominent Th1/Th17 phenotype in advanced disease. Genetic lesions affecting the JAK/STAT signaling pathway are commonly present. Proposed triggers for the development of malignancy include mechanical friction, silicone implant shell particulates, silicone leachables, and bacteria. Of these, the bacterial hypothesis has received significant attention, supported by a plausible biologic model. In this model, bacteria form an adherent biofilm in the favorable environment of the textured implant surface, producing a bacterial load that elicits a chronic inflammatory response. Bacterial antigens, primarily of Gram-negative origin, may trigger innate immunity and induce T-cell proliferation with subsequent malignant transformation in genetically susceptible individuals. Although much remains to be elucidated regarding the multifactorial origins of BIA-ALCL, future research should focus on prevention and treatment strategies, recognizing susceptible populations, and whether decreasing the risk of BIA-ALCL is possible.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.