ReviewCancers2026
The Evolution of Fitness Landscapes: From Visualizing Evolution to Controlling It.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Fitness landscapes have long served as a conceptual tool in evolutionary biology. In recent years, however, they have shifted from being viewed primarily as theoretical constructs to being used to interpret experimental data and, increasingly, to guide evolutionary outcomes in biomedical systems. In this review, we trace that progression across major landscape formalisms, including Fisher's geometric model, Wright's adaptive landscape, fitness seascapes, and empirical fitness landscapes. We aim to highlight the strengths of each model and its potential for use in cancer research. Rather than treating these frameworks as isolated models, we show how they collectively reflect the field's maturation: from describing adaptation in idealized settings to quantifying epistasis and evolutionary accessibility, to interpreting experimental genotype-fitness data, and finally to motivating strategies for steering evolution in contexts such as treatment resistance in cancer. We argue that the central promise of the fitness landscape framework now lies not only in explaining evolutionary dynamics, but also in enabling predictive interventions and overcoming cancer evolution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.