Background and objective: Cancer Immunoediting (CI) describes the cellular-level interaction between tumor cells and the Immune System (IS) that takes place in the Tumor Micro-Environment (TME). CI is a highly dynamic and complex process comprising three distinct phases (Elimination, Equilibrium and Escape) wherein the IS can both protect against cancer development as well as, over time, promote the appearance of tumors with reduced immunogenicity. Herein we present an agent-based model for the simulation of CI in the TME, with the objective of promoting the understanding of this process. Methods: Our model includes agents for tumor cells and for elements of the IS. The actions of these agents are governed by probabilistic rules, and agent recruitment (including cancer growth) is modeled via logistic functions. The system is formalized as an analogue of the Ising model from statistical mechanics to facilitate its analysis. The model was implemented in the Netlogo modeling environment and simulations were performed to verify, illustrate and characterize its operation. Results: A main result from our simulations is the generation of emergent behavior in silico that is very difficult to observe directly in vivo or even in vitro. Our model is capable of generating the three phases of CI; it requires only a couple of control parameters and is robust to these. We demonstrate how our simulated system can be characterized through the Ising-model energy function, or Hamiltonian, which captures the “energy” involved in the interaction between agents and presents it in clear and distinct patterns for the different phases of CI. Conclusions: The presented model is very flexible and robust, captures well the behaviors of the target system and can be easily extended to incorporate more variables such as those pertaining to different anti-cancer therapies. System characterization via the Ising-model Hamiltonian is a novel and powerful tool for a better understanding of CI and the development of more effective treatments. Since data of CI at the cellular level is very hard to procure, our hope is that tools such as this may be adopted to shed light on CI and related developing theories.

This work aims for modeling and simulating the metastasis of cancer, via the analogy between the cancer process and the board game Go. In the game of Go, black stones that play first could correspond to a metaphor of the birth, growth, and metastasis of cancer. Moreover, playing white stones on the second turn could correspond the inhibition of cancer invasion. Mathematical modeling and algorithmic simulation of Go may therefore benefit the efforts to deploy therapies to surpass cancer illness by providing insight into the cellular growth and expansion over a tissue area. We use the Ising Hamiltonian, that models the energy exchange in interacting particles, for modeling the cancer dynamics. Parameters in the energy function refer the biochemical elements that induce cancer birth, growth, and metastasis; as well as the biochemical immune system process of defense.

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.

In the Tumor Micro-Environment, cancer progression and its relationship with the Immune System (IS) are described in terms of cancer immunoediting (CI) phases, each of which is characterized by different types and levels of interaction between the tumor cells and elements of the IS, such as CD8+T cells. Said interactions are governed by genotypical (Tumor Mutational Burden, TMB) and phenotypical aspects pertaining to the tumor, as well as by the strength of the IS. In this work, a computational model of CI is presented that incorporates the TMB and the biomarker Tumor Immunogenic Phenotype (TIP) as its control parameters, and which employs the Ising-model Hamiltonian to characterize the system with respect to the CI phases. Our model is a probabilistic multi-agent system with agents for tumor cells and for the IS. The computer implementation includes the parametrization of the TMB and the TIP, which is useful for identifying whether a tumor is hot or cold based on tumor immunogenicity and inflammation. For modeling the interactions between tumor and immune cells, the relevant elements are integrated under a Michaelis-Menten equation that regulates the recruitment rate of CD8+T cells and other IS elements. This novel quantification of immune cell recruitment encompasses the growth of neoantigen production, which in turn triggers the growth of CD8+T cells. Our model reliably captures the Elimination, Equilibrium, and Escape phases of tumor-immune cell interactions, modulating the observed behaviors through the introduced parametrization of TMB and TIP biomarkers. Notably, these results align well with the combination of genotypical and phenotypical biomarkers analyzed in recent literature. A remarkable instance is the appreciable inhibition of the tumor activity during the Escape phase, observed for phenotypically hot tumors with relatively high TMB, and pointing towards improved efficacy of the IS against such tumors. The Ising-Hamiltonian provides precise quantification of diverse tumor-immune interactions across different TMB and TIP value combinations. The presented model, formed by relatively simple agents, generates emergent behaviors through which the phases of CI are identified. The flexible choice of control parameters is robust enough and provides a plausible explanation for the mechanisms through which tumors with high TMB and high immunogenicity (i.e., hot tumors) exhibit a higher probability of responding to immunotherapy treatment. Characterization via the Ising-model Hamiltonian supports this explanation by summarizing the system's dynamics, which, in turn, facilitates its analysis and methodical improvements. The complex interplay of TMB, TIP, and individual physiology is finely captured.