ReviewiScience2022
Cancer progression as a learning process.
Review in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Inflamed Yet Immune-Evasive? A Transcriptomic Meta-Analysis Identifies Conserved Inflammatory, Developmental, and Neuronal Signatures Associated with Polyploid Giant Cancer Cells.International journal of molecular sciences · 2026Pooled it
- Systemic Alterations of Cancer Cells and Their Boost by Polyploidization: Unicellular Attractor (UCA) Model.International journal of molecular sciences · 2023Pooled it
- Dynamic ecosystems of tumor drug resistance mechanisms: from molecular heterogeneity to systemic interventions.Apoptosis : an international journal on programmed cell death · 2026Review
- Review
- Modulation of Network Plasticity Opens Novel Therapeutic Possibilities in Cancer, Diabetes, and Neurodegeneration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Polyploid giant cancer cells (PGCC): short-term return to multicellularity.Biological research · 2025Article
- Review
- Article
- The link between the trans-Golgi network and tumour progression.Molecular biology reports · 2025Review
- Basal Xenobot transcriptomics reveals changes and novel control modality in cells freed from organismal influence.Communications biology · 2025Article
- Review
- AhR and STAT3: A Dangerous Duo in Chemical Carcinogenesis.International journal of molecular sciences · 2025Review
- Amyloids in bladder cancer hijack cancer-related proteins and are positive correlated to tumor stage.Scientific reports · 2025Article
- Unveiling the Promising Role of Substance P/Neurokinin 1 Receptor in Cancer Cell Proliferation and Cell Cycle Regulation in Human Malignancies.Current medicinal chemistry · 2025Review
- Metabolic adaptation pilots the differentiation of human hematopoietic cells.Life science alliance · 2024Article
- Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells.PLoS biology · 2022Article
- Extracellular Vesicles and Cell Pathways Involved in Cancer Chemoresistance.Life (Basel, Switzerland) · 2022Review
- Cancer Evolution in Precision Medicine Era.Cancers · 2022Article
- Life Entrapped in a Network of Atavistic Attractors: How to Find a Rescue.International journal of molecular sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug resistance and metastasis-the major complications in cancer-both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.