Cancer Cell International - Review Report

Background and Motivation

In recent decades, research into cancer evolution has gradually revealed that the evolutionary patterns of cancer cells share many similarities with species evolution. This notion has changed our understanding of tumor evolution and heterogeneity. Cancer is initiated by the mutation and proliferation of a single cell, and under the selection pressures of the tumor microenvironment and within the body, cancer cells continuously accumulate mutations through various evolutionary mechanisms, forming heterogeneous tumor populations. The evolutionary process of cancer cells is not only influenced by Darwinian natural selection but also involves non-Darwinian mechanisms such as genetic drift and neutral evolution. The emergence of tumor evolutionary biology has prompted scientists to re-evaluate the “life history dynamics” in cancer evolution—specifically, the resource allocation and trade-offs made by cancer cells in proliferation and metastasis during evolution. These trade-offs result in cancer cells exhibiting different characteristics throughout the tumor lifecycle, thereby deepening tumor heterogeneity.

The authors of this paper, Mahmoud Ahmed and Deok Ryong Kim, hail from The Institute of Cancer Research in the UK and Gyeongsang National University in South Korea, respectively. Their research focuses on cancer genetics and evolutionary biology. This review was published in 2024 in the journal “Cancer Cell International.” It primarily discusses the life history theory of cancer cell evolution and its implications for understanding multitasking, specialization, trade-offs, and heterogeneity in cancer. It also summarizes current advancements in inferring tumor tasks.

Summary of Research Content

1. Analogy Between Cancer Cell and Species Evolution: Non-Darwinian Mechanisms and Multilevel Selection

The evolution of cancer cells is considered a microcosm of species evolution. Cancer cells undergo clonal expansion through asexual reproduction and form dominant clones under body selection pressures. The evolution of cancer cells relies not only on Darwinian selection but also on a range of non-Darwinian mechanisms, such as neutral evolution and genetic drift. These mechanisms enable cancer cells to undergo rapid mutations in early evolutionary stages and influence the selection of cancer cell traits through changes in the microenvironment. For example, some mutations are inherited independently of cellular adaptability and propagate through randomness among cancer cells; these mutations may be transmitted via extrachromosomal genes or even epigenetically. Moreover, the multilevel selection process of tumors enables cancer cells to function cooperatively within and between clones. In processes such as angiogenesis and stromal remodeling, inter-clonal cooperation is crucial for tumor survival.

2. Life History Theory and Task Specialization in Cancer Cells

Life history theory is traditionally used to explain how organisms allocate resources to optimize survival and reproductive success. In cancer research, life history theory is applied to study how cancer cells allocate resources between proliferation and metastasis capabilities. The “life history” of cancer cells reflects their resource investment and selection for specific tasks, such as proliferation or metastasis, where cancer cells may exhibit different characteristics at different stages. The authors detail several simulation and experimental studies supporting this view. For instance, Hausser et al. (2019) used Pareto optimization to analyze specialization in gene expression of cancer cells, revealing that cancer cells can become “specialists” by optimizing gene expression for specific tasks or maintain generalized gene expression as “generalists.”

For example, studies on breast cancer cells show that they must sacrifice proliferation rates to cope with oxidative stress during the clearing of harmful oxygen free radicals. Similarly, there are distinct trade-offs between serine and glutamine metabolism in estrogen receptor-positive and negative cells, leading to task division among cells. Through “specialization” and “task trade-offs,” cancer cells can adapt to changes in the tumor microenvironment, enhancing their survival abilities.

3. Trade-offs Between Metastasis and Proliferation

Tumor metastasis is an inefficient process in cancer evolution. Although the primary selection pressure is not on metastasis, some cells gain enhanced metastatic potential by entering circulation and surviving in heterogeneous microenvironments. The trade-off between metastasis and proliferation varies widely across different tumor types. For instance, in hypoxic conditions, glioblastoma cells switch from proliferative to invasive phenotypes through changes in gene expression, a shift regulated by various internal and external signals, such as small molecule RNAs. Studies have found that this duality in cell phenotype drives tumor growth and progression.

Simulation studies have shown how tumor cells optimize adaptation in hypoxic environments through phenotypic transitions; cells tend to proliferate at high oxygen levels and shift towards metastasis or invasion under low oxygen conditions. Furthermore, the distribution of proliferative and migratory abilities differs between the tumor center and periphery, indicating specific task division and spatial distribution within the tumor.

4. Inferring Tumor Tasks Through Data

With the development of data-driven methods, inferring tumor cell tasks has become an essential approach. Hart et al. (2015) proposed a method using Pareto front analysis of high-dimensional data to infer cancer cell tasks. Cancer cells tend to optimize their gene expression levels for specialized tasks, represented as extreme points in gene expression profiles, known as “archetypes.” Ahmed et al. applied this method to study tumor tasks and trade-offs, finding that cancer cells could be classified into “specialists” and “generalists,” each exhibiting distinct biological characteristics in drug resistance, heterogeneity, and spatial organization.

By comparing gene expressions across various cancer cell lines, researchers can determine the distance of a cell’s gene expression from the “archetype,” representing the degree of task specialization. This analysis provides new perspectives for task inference and optimization in cancer cells, allowing better prediction of cancer evolution and therapeutic response.

5. Multitasking and Explaining Tumor Heterogeneity

Intratumor heterogeneity (ITH) is a key issue in cancer progression, where genetic and phenotypical heterogeneity in cancer cells determines patient prognosis and treatment outcomes. Life history theory helps explain the origins of ITH and its impact on tumor behavior. For instance, Hausser et al. found that gene mutations promote the specialization of cells towards specific tasks by regulating task-related gene expression. Additionally, studies on small cell lung cancer demonstrate that task trade-offs driven by microenvironmental changes explain cellular plasticity, leading to phenotype adaptation in various subclones.

Experimental studies show that cancer cells’ task plasticity and diversity are related to variations in gene expression. For example, gene expression studies on yeast indicate that variation induced by environmental changes is significantly higher than that caused by gene mutations, emphasizing the environment’s role in shaping cancer phenotypes. Thus, using life history theory and Pareto front analysis effectively reveals the role of ITH in tumor progression.

6. Drug Resistance, Spatial Organization, and Metabolic Reprogramming

The specialization of cancer cells makes them sensitive to drugs targeting their specific tasks. For instance, cells close to a genetic expression archetype are more sensitive to specific drugs; Ras pathway inhibitor Trametinib shows high efficacy on invasion task archetypes. Meanwhile, cancer cells adapt to different microenvironments through metabolic reprogramming, notably in the tumor lifecycle. Studies have found variances in metabolic tasks across different cancer types; for example, basal-type breast cancer leans towards rapid proliferation, whereas luminal B breast cancer relies on energy metabolic pathways.

Metabolic reprogramming allows cancer cells to handle oxidative stress and enhance metastatic capability. The investment of cancer cells in different tasks significantly correlates with their drug resistance. Previous studies indicate that specialized cancer cells exhibit higher sensitivity to drugs because they have invested heavily in certain tasks, and disrupting these tasks drastically reduces their survival capabilities.

Conclusion and Future Research Directions

This paper suggests that applying life history theory to cancer evolution research effectively reveals the trade-offs in resource allocation and task optimization by cancer cells, which have profound implications for tumor progression and heterogeneity. The review points out challenges in current tumor task inference research, such as the lack of clear task definitions and biases in gene expression data. Future studies should focus on more precise cancer task definitions and integrate phenotypic and genotypic data from various cancer models to overcome data bias.

The researchers also recommend using independent repeat experiments, different data types, and inference methods to validate current findings. Further development of multitasking theory and its application to clinical therapies will help reveal adaptive mechanisms in cancer cells. Understanding the specialization and trade-off mechanisms of cancer cell tasks will likely lead to the development of more targeted treatment strategies to inhibit tumor progression and metastasis.