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Record W2061342795 · doi:10.2174/156800906777723976

Radiation-Induced Bystander and other Non-Targeted Effects: Novel Intervention Points in Cancer Therapy?

2006· review· en· W2061342795 on OpenAlexafffund
Carmel Mothersill, Colin Seymour

Bibliographic record

VenueCurrent Cancer Drug Targets · 2006
Typereview
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsMcMaster University
FundersCanada Research ChairsIrish Research CouncilScience Foundation IrelandTechnological University DublinNational Science Foundation
KeywordsBystander effectDNA damageRadiation therapyCancer researchCancerCancer cellMedicineApoptosisBiologyImmunologyDNAGeneticsInternal medicine

Abstract

fetched live from OpenAlex

A major problem in the search for new cancer drug targets is that the drugs are often toxic to normal tissues and require high doses to kill tumor cells. Therefore cellular targets which appear to involve low dose responses to cancer therapy are especially interesting since they could selectively target normal tissues which are not targeted by the treatment and thus may be responsible for unpleasant side effects or may be amenable to exploitation in order to improve the therapeutic ratio. One such target, which is the subject of this review, is radiation-induced bystander effects [RIBE], which result in the observation of radiation like responses in cells which have not been irradiated. RIBE is a novel phenomenon which indicates that at low doses, cell signaling is more important than direct DNA damage. Historically, DNA has always been considered to be the target for radiation therapy. The growing realization that signaling is important opens up several important therapeutic strategies which will be discussed in this review. RIBE appears to be the result of a generalized stress response in tissues or cells which is expressed at the level of the tissue, organ or organism rather than at the level of the individual cell. The signals may be produced by all exposed cells, but the response may require a quorum of cells in order to be expressed. The major response involving low LET (x- or gamma-ray) radiation exposure discussed in the existing literature is a death response. This has many characteristics of apoptosis but may be detected in cell lines without p53 expression, although the death response is suppressed in many tumor cell lines. While a death response in unirradiated normal cells around a tumor might appear to be adverse, it can in fact be protective and remove damaged cells from the population. If harnessed correctly, it could lead to the development of new drugs aimed not at tissue destruction but at enabling homeostatic mechanisms to control tumor expansion. In this scenario, the level of harmful or beneficial response will be related to the background damage, carried by the cell population, and the genetic programme determining response to damage. This focus may be important when attempting to predict the consequences of mixed therapies involving radiation and other cytotoxic agents. In this review, our current knowledge of the mechanisms underlying the induction of bystander effects by ionizing radiation is reviewed, and the question of how bystander effects may be harnessed to produce a new generation of anti-cancer drugs aimed at stabilization of tissue homeostasis rather than tissue destruction is considered.

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)0.000
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.041
GPT teacher head0.389
Teacher spread0.349 · 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

Citations54
Published2006
Admission routes2
Has abstractyes

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