Reuniting philosophy and science to advance cancer research.

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

Towards evolutionary predictions: Current promises and challenges.

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

On geometric mean fitness: a reply to Takacs and Bourrat.

In a recent paper, Takacs and Bourrat (Biol Philos 37:12, 2022) examine the use of geometric mean reproductive output as a measure of biological fitness. We welcome Takacs and Bourrat's scrutiny of a fitness definition that some philosophers have adopted uncritically. We also welcome Takacs and Bourrat's attempt to marry the philosophical literature on fitness with the biological literature on mathematical measures of fitness. However, some of the main claims made by Takacs and Bourrat are not correct, while others are correct but not for the reasons they give.

The Price equation and the causal analysis of evolutionary change.

Though the Price equation in itself is simply a statistical identity, biologists have often adopted a 'causal interpretation' of the equation, in the sense that its component terms have been supposed to correspond to distinct causal processes in evolution, such as natural selection and transmission bias. In this paper, we bring the issue of causal interpretation to the fore, by studying the conditions under which it is legitimate to read causal meaning into the Price equation. We argue that only if substantive assumptions about causal structure are made, which can be represented in the form of a causal model, can the component terms of the Price equation be interpreted as causally meaningful. We conclude with a reflection on the epistemic uses of the Price equation, emphasizing the difference between the description, explanation and prediction of evolutionary change. This article is part of the theme issue 'Fifty years of the Price equation'.

The causal meaning of Hamilton's rule.

Hamilton's original derivation of his rule for the spread of an altruistic gene (rb>c) assumed additivity of costs and benefits. Recently, it has been argued that an exact version of the rule holds under non-additive pay-offs, so long as the cost and benefit terms are suitably defined, as partial regression coefficients. However, critics have questioned both the biological significance and the causal meaning of the resulting rule. This paper examines the causal meaning of the generalized Hamilton's rule in a simple model, by computing the effect of a hypothetical experiment to assess the cost of a social action and comparing it to the partial regression definition. The two do not agree. A possible way of salvaging the causal meaning of Hamilton's rule is explored, by appeal to R. A. Fisher's 'average effect of a gene substitution'.

Emergent group traits, reproduction, and levels of selection.

All group traits, "emergent" or otherwise, are ultimately dependent on the traits and behaviours of the individuals that constitute the group. Unless a process of "group reproduction" is envisaged, this means that the evolution of group traits can in principle be studied in an individualistic way, by studying the dynamics of the underlying individual traits on which they depend.

Inclusive fitness maximization: An axiomatic approach.

Kin selection theorists argue that evolution in social contexts will lead organisms to behave as if maximizing their inclusive, as opposed to personal, fitness. The inclusive fitness concept allows biologists to treat organisms as akin to rational agents seeking to maximize a utility function. Here we develop this idea and place it on a firm footing by employing a standard decision-theoretic methodology. We show how the principle of inclusive fitness maximization and a related principle of quasi-inclusive fitness maximization can be derived from axioms on an individual׳s 'as if preferences' (binary choices) for the case in which phenotypic effects are additive. Our results help integrate evolutionary theory and rational choice theory, help draw out the behavioural implications of inclusive fitness maximization, and point to a possible way in which evolution could lead organisms to implement it.

Emergence, hierarchy and top-down causation in evolutionary biology.

The concept of emergence and the related notion of 'downward causation' have arisen in numerous branches of science, and have also been extensively discussed in philosophy. Here, I examine emergence and downward causation in relation to evolutionary biology. I focus on the old, but ongoing discussion in evolutionary biology over the 'levels of selection' question: which level(s) of the biological hierarchy natural selection acts at, e.g. the gene, individual, group or species level? The concept of emergence has arisen in the levels-of-selection literature as a putative way of distinguishing between 'true' selection at a higher level from cases where selection acts solely at the lower level but has effects that percolate up the biological hierarchy, generating the appearance of higher level selection. At first blush, this problem seems to share a common structure with debates about emergence in other areas, but closer examination shows that it turns on issues that are sui generis to biology.

Multilevel selection and the partitioning of covariance: a comparison of three approaches.

Where the evolution of a trait is affected by selection at more than one hierarchical level, it is often useful to compare the magnitude of selection at each level by asking how much of the total evolutionary change is attributable to each level of selection. Three statistical partitioning techniques, each designed to answer this question, are compared, in relation to a simple multilevel selection model in which a trait's evolution is affected by both individual and group selection. None of the three techniques is wholly satisfactory: one implies that group selection can operate even if individual fitness is determined by individual phenotype alone, whereas the other two imply that group selection can operate even if there is no variance in group fitness. This has significant implications both for our understanding of what the term "multilevel selection" means and for the traditional concept of group selection.