Abstract
Cancer is typically spoken of as a “complex” disease. But, in what sense are cancers “complex”? Is there one sense, or several? What implications does this complexity have – both for how we study, and how we intervene upon cancers? The aim of this paper is first, to clarify the variety of senses in which cancer is spoken of as "complex" in the scientific literature, and second, to discover what explanatory and predictive roles such features play.
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Notes
For detailed discussion of the methodology of the search through the literature, please see notes to Table 1.
Complex systems theorists have argued that there are “higher-order laws” that govern complex systems, and evolved biological systems in particular (for a review, see Green, 2015a). Some argue that certain kinds of regulatory feedback, such as bi-stable switches, make such systems more resilient to insult, and more “evolvable.” (For further discussion, see, e.g., Green, 2015a, 2015b; Wolkenhauer and Green, 2013; Hooker, 2013; Kitano, 2004a, 2004b, 2007; Alon, 2007; Tyson et al. 2003).
The following are just a sample of the main contributors to this literature: Soto and Sonnenschein, 2005; Malaterre, 2007; Bertolaso, 2009, 2016; Green, et al., 2018; Green, 2021. The term “emergence” is, needless to say, contentious (see, e.g., VanGulick, 2001 for discussion). Addressing whether and how cancer per se exhibits emergent behavior deserves further discussion; this paper is intended as a preliminary first step to addressing such a question.
For a discussion of these measures, and their various merits, see Chapter 4 of Ladyman and Wiesner (2020). (While I am relying here on Ladyman and Wiesner’s list of features for the sake of economy (and their attempt at being comprehensive) in What is a Complex System?, there are of course other excellent reviews of this literature of complexity and complex systems (see, e.g., Wolkenhauer and Green, 2013; Green, 2015a, 2015b).
Quorum sensing is one of the simplest forms of feedback loop, found in a diverse array of bacteria, in service of everything from avoiding toxic environmental conditions to seeking food (see, e.g., Eickhoff, et al., 2018).
Of course, whether these features and their co-occurrence are representative of these general regularities is exactly the matter at issue. I do not here claim to settle these issues once and for all, as these are empirical questions.
As mentioned in footnote 2, above, in many cases, scientists are simply referring to cancers’ “complexity” in the subjective sense that cancers are difficult to investigate and understand. I have made an effort to exclude where possible any such uses in my analysis, and focus only on objective features of cancer itself taken to be indicative of, or contributing to, “complexity.”.
For, Ladyman et al., 2013, “numerosity” refers to many parts in interaction, not simply that a system has many parts. However, many cancer scientists simply use the term “complexity” more or less interchangeably with “heterogeneity,” which (as will be discussed further below) may refers to either the diversity of cancer types, diversity of parts or causal bases of a cancer or cancer type, or, both “inter- “ and “intra- “ tumor heterogeneity, and only sometimes the extent of interaction between these parts.
See Table 1 for methods.
For the purposes of this paper, I follow the majority of cancer researchers in typing cancers in light of the tissue or organ of origin (breast, lung, etc.).
Though, of course, not all mutations are “drivers” – or make a contribution to the behavior of a cancer cells; some are probably functionally irrelevant. This is of significance to the explanatory power of heterogeneity; some cancers of the same type (e.g., breast cancer) exhibit a long-tail distribution of mutations, with no mutation necessarily highly frequent, let alone present in all instances.
One might wonder whether this is different from “numerosity,” since it refers to multiple parts or processes. However, I place this in the category of “interactive” or “organizational” complexity because it refers to types of interactions that span multiple types of cell, or lead to activation of interactions between systems that typically exercise discrete functions. (Thanks for this question from Bechtel.) I take it that this may be assimilated roughly to what Mitchell identifies as “multilevel organization,” or “evolved contingency.”.
For a much fuller discussion than is possible here, see chapter 4 of Pradeu, 2020.
Highly “plastic” entities or mechanisms are ones that can be modified to perform different functions, or revert to different states. Functional pleiotropy is when same function (chemotherapy resistance) is performed by a variety of different entities or mechanisms. This may be assimilated (roughly) to what Mitchell calls “plasticity in relation to context variation.”.
I am not here taking these terms to be necessarily interchangeable, but simply listing or describing the “patchwork” of ways in which such features are described by cancer scientists. For a fuller discussion of this perspective, see, e.g., Boniolo (2019) for discussion of this “patchwork narrative” in cancer research.
As one reviewer helpfully points out, some cancer scientists are committed to this being a process of natural selection, but of course not all are.
Green (2015b) distinguishes two kinds of underdetermination that make reverse-engineering complex systems – and thus testing hypotheses about their properties – difficult. She calls these “synchronic and diachronic underdetermination.” Inferring to what systems theorists call “design principles” of complex systems is difficult, in other words, because “synchronic relations between lower-level processes and higher-level systems capacities are many-to-many,” and because “relations between system capacities and lower-level mechanisms are changing over time.” Both forms of underdetermination, arguably, are at work in the case of cancer. Perhaps related especially to this second sense, we might characterize a third type of underdetermination at work in the case of cancer: historical underdetermination. Namely, inferring facts about causal processes is made difficult because they have already occurred, and so we only have traces of past events as evidence.
Kim’s difficulties with the what he calls the “classical” conception of emergence are that the emergentist cannot consistently assert both that some macrostate supervenes on a microstate, and that it and its “emergent” features are irreducible to a microstate. For, whatever a supervenient property explains, its supervenience base could also explain, because whatever causal and explanatory work a purportedly emergent property can do, its supervenience base could equally well do.
See Green (2015b) for a detailed discussion of underdetermination in the context of “reverse engineering” and testing various hypotheses about network and system-wide structural properties of complex systems.
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Glossary
- Robustness
-
Sometimes used interchangeably with “resilience,” “robustness’ typically refers to the greater or lesser capacity of a cancer (both as a whole, or, particular cancer cells) to grow, survive and/or seed metastases, develop resistance to treatment, or to “resist attacks” from the immune system.
- Heterogeneity
-
This typically refers to extent of genetic or genomic variability across or within particular types or subtypes of cancer (ranging from the sheer number of mutations, to varieties of types of mutations and chromosomal alterations – inversions, deletions, etc.), but may also refer to genomic variability within a population of cancer cells (“intra-tumor heterogeneity”: co-evolving lineages, or genetically diverse populations or subpopulations). “Heterogeneity” may (confusingly) also refer to diversity of parts, entities, or properties of entities that play a causal role in a cancer, such as: microRNA, epigenomic features (hyper- or hypo-methylation), or co-factors in the tissue microenvironment, such as fibroblasts, or leukocytes.
- Network
-
A “network” typically refers to a relationship among signaling molecules or gene products, in ways that promote or halt various activity, or maintain or disrupt various functions. Thus, e.g., various products of oncogenes and tumour suppressors may be said to form networks or “pathways” involved in various processes (e.g., metastasis). A “complex regulatory network” may be a set of signaling proteins that modulates the expression of others, in service of some function (e.g., the epithelial-mesenchymal transition). Networks can span several spatiotempral levels, and stages of gene expression or modification of expression: e.g., including transcription, post-transcription, epigenetic modification, alternative splicing, protein stability and subcellular localization. Sometimes parts of networks are also referred to as “signaling cascades,” where some proteins (e.g., insulin-like growth factor (IGF)) promote the activity of others. Typically a “network” will involve some form of feedback, insofar as several tend to act together in service of maintaining or promoting function (or, in the case of cancer, malfunction).
- Biomarker (Sometimes used to refer to instances of bearers of what Ladyman and Wiesner call “memory” or “information.”)
-
This may refer to any protein, gene variant, or other biological feature (RNA sequence variant) associated with a particular outcome of interest, such as responsiveness to a drug, or association with high mortality. Such markers can form parts of signaling cascades or networks.
- Scale-free
-
This refers to a type of network organization where there are a few highly connected nodes. It is sometimes also referred to as a highly “modular” network.
- Modularity
-
This is type of organization of a network with several nodes that are highly connected. Such nodes tend to play important regulatory roles in key functions in the cell, or may regulate or co-regulate several signaling pathways associated with such roles.
- Feedback mechanisms
-
Pathways of regulating functions in the cell that have a “looped” organization, such that outputs of a pathway are “fed” back into that same pathway, carrying “information” about downstream effects. An example of “feedback” is, e.g., a double-negative feedback loop, which serves to maintain homeostasis (cf. Zhang & Weinberg, 2018).
- Complexity
-
As discussed in Table 1, this term is used to refer to everything from genomic heterogeneity, to heterogeneity of many other parts and processes involved in cancer progression (mRNA, etc.), to functional pleiotropy, to organizational features of signaling pathways in the cell and across the cell and tissue microenvironment.
- Adaptation
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In the context of cancer research, the “adaptive” features of cancer cells (or, for that matter, whole tumors) are those that tend to promote either cell or tumor growth, invasiveness, or metastasis, or enable resistance, e.g., to chemotherapy. This may refer to “adaptation” in the sense of “plastic response to environment,” or adaptation in the sense of traits that have been effectively subject to selection over the course of the tumor’s progression.
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Plutynski, A. How is cancer complex?. Euro Jnl Phil Sci 11, 55 (2021). https://doi.org/10.1007/s13194-021-00371-8
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DOI: https://doi.org/10.1007/s13194-021-00371-8