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Clonal Evolution Models of Tumor Heterogeneity

2015· review· en· W1589268924 on OpenAlexaff
Liran I. Shlush, Dov Hershkovitz

Bibliographic record

VenueAmerican Society of Clinical Oncology Educational Book · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsSomatic evolution in cancerEpigeneticsBiologyMulticellular organismSomatic cellClonal selectionPopulationCancerEpigenesisEvolutionary biologyGeneticsCellImmunologyGeneDNA methylationMedicine

Abstract

fetched live from OpenAlex

Somatic/clonal evolution is the process of sequential acquisition of vertically transmittable genetic/epigenetic elements in multicellular organisms. Cancer is the result of somatic evolution. Understanding the processes that shape the evolution of individual tumors might help us to treat cancer more efficiently. The initiating genetic/epigenetic events occur in functional cells and provide the cell of origin a selective advantage under a changing environment. The initiating genetic events tend to be enriched in specific tissues (and are sometimes specific for those tissues), as different tissues undergo different changes in the environment that will activate selective forces on different cells of origin. For the initial clonal expansion to occur premalignant clones need to have a relative fitness advantage over their competitors. It is estimated that the premalignant phase can take several years. Once the premalignant clonal expansion is established, the premalignant cells will contribute to the changing environment and will start competing among themselves. In late stages of cancer evolution the environmental changes might be similar across different tissues, including a lack of physical space, a shortage of energy, and activation of the immune system, and more and more of the hallmarks of cancer will evolve. In this review we will explore the possible clinical relevance of the heterogeneity that evolves during this long somatic evolution. Above all, it should be stressed that the earlier the clonal expansion is recognized, the less diverse and less fit for survival the cells in the population are.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.475
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2015
Admission routes1
Has abstractyes

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