Cooperation and the common good

Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes

Background

Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of resistance.

Methods

In the study cohort, abiraterone was stopped when PSA was <50% of pretreatment value and resumed when PSA returned to baseline. Results are compared to a contemporaneous cohort who had >50% PSA decline after initial abiraterone administration and met trial eligibility requirements but chose standard of care (SOC) dosing.

Results

17 subjects were enrolled in the adaptive therapy group and 16 in the SOC group. All SOC subjects have progressed, but four patients in the study cohort remain stably cycling (range 53-70 months). The study cohort had significantly improved median time to progression (TTP; 33.5 months; p<0.001) and median overall survival (OS; 58.5 months; hazard ratio, 0.41, 95% confidence interval (CI), 0.20-0.83, p<0.001) compared to 14.3 and 31.3 months in the SOC cohort. On average, study subjects received no abiraterone during 46% of time on trial. Longitudinal trial data demonstrated the competition coefficient ratio (αRS/αSR) of sensitive and resistant populations, a critical factor in intratumoral evolution, was two- to threefold higher than pre-trial estimates. Computer simulations of intratumoral evolutionary dynamics in the four long-term survivors found that, due to the larger value for αRS/αSR, cycled therapy significantly decreased the resistant population. Simulations in subjects who progressed predicted further increases in OS could be achieved with prompt abiraterone withdrawal after achieving 50% PSA reduction.

Conclusions

Incorporation of evolution-based mathematical models into abiraterone monotherapy for mCRPC significantly increases TTP and OS. Computer simulations with updated parameters from longitudinal trial data can estimate intratumoral evolutionary dynamics in each subject and identify strategies to improve outcomes.

Funding

Moffitt internal grants and NIH/NCI U54CA143970-05 (Physical Science Oncology Network).

Evolution of strategic cooperation

Group‐beneficial behaviors have presented a long‐standing challenge for evolutionary theory because, although their benefits are available to all group members, their costs are borne by individuals. Consequently, an individual could benefit from “cheating” their group mates by not paying the costs while still reaping the benefits. There have been many proposed evolutionary mechanisms that could favor cooperation (and disfavor cheating) in particular circumstances. However, if cooperation is still favored in some circumstances, then we might expect evolution to favor strategic cooperation, where the level of contribution toward group‐beneficial behavior is varied in response to the social context. To uncover how and why individuals should contribute toward group‐beneficial behavior across social contexts, we model strategic cooperation as an evolutionary game where players can quantitatively adjust the amount they contribute toward group‐beneficial behavior. We find that the evolutionarily stable strategy (ESS) predicts, unsurprisingly, that players should contribute in relation to their relatedness to the group. However, we surprisingly find that players often contribute to cooperation in such a way that their fitness is inverse to their relatedness to the group such that those that contribute to cooperation end up with the same return from group‐beneficial behavior, essentially removing any potential advantage of higher relatedness. These results bring to light a paradox of group‐beneficial cooperation: groups do best when they contain highly related individuals, but those with the highest relatedness to the group will often have the lowest fitness within the group.

Correlated pay-offs are key to cooperation

The general belief that cooperation and altruism in social groups result primarily from kin selection has recently been challenged, not least because results from cooperatively breeding insects and vertebrates have shown that groups may be composed mainly of non-relatives. This allows testing predictions of reciprocity theory without the confounding effect of relatedness. Here, we review complementary and alternative evolutionary mechanisms to kin selection theory and provide empirical examples of cooperative behaviour among unrelated individuals in a wide range of taxa. In particular, we focus on the different forms of reciprocity and on their underlying decision rules, asking about evolutionary stability, the conditions selecting for reciprocity and the factors constraining reciprocal cooperation. We find that neither the cognitive requirements of reciprocal cooperation nor the often sequential nature of interactions are insuperable stumbling blocks for the evolution of reciprocity. We argue that simple decision rules such as 'help anyone if helped by someone' should get more attention in future research, because empirical studies show that animals apply such rules, and theoretical models find that they can create stable levels of cooperation under a wide range of conditions. Owing to its simplicity, behaviour based on such a heuristic may in fact be ubiquitous. Finally, we argue that the evolution of exchange and trading of service and commodities among social partners needs greater scientific focus.