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Record W2324916450 · doi:10.1158/1538-7445.am2012-5247

Abstract 5247: Use of two markers of hypoxia to study migration, re-oxygenation and repopulation of originally hypoxic cells in MCF-7 tumor xenografts following chemotherapy

2012· article· en· W2324916450 on OpenAlexaffabout
Jasdeep K. Saggar, Ian F. Tannock

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsHypoxia (environmental)Bone marrowBiologyPathologyRadiation therapyTumor hypoxiaCancer researchChemotherapyMedicineInternal medicineChemistryOxygen

Abstract

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Abstract Background: Hypoxia occurs in solid tumors: chronic diffusion-limited hypoxia occurs distal from functional blood vessels, while acute hypoxia may occur because of interruptions in blood flow. Well-nourished tumor cells close to blood vessels tend to be rapidly proliferating but hypoxic cells located farther away are slowly-proliferating. Chemotherapy may spare hypoxic cells because of poor drug distribution to them and because most drugs are selectively toxic to proliferating cells. Intervals between administration of chemotherapy allow for recovery of normal tissues (e.g. repopulation of bone marrow) but might allow re-oxygenation and resumed proliferation of formerly hypoxic cells due to better supply of nutrients to them, as is observed during radiotherapy. Therefore, it is important to establish a technique whereby hypoxia can be studied allowing for the assessment of different hypoxia-modulating therapies that can be used to prevent tumor relapse. Methods: Two specific markers of hypoxic cells (pimonidazole [pimo] and EF5) were injected into mice bearing MCF7 tumor xenografts, and tumor cells labeled with one or both markers were recognized in tumor sections (in relation to DioC7+ve functional tumor blood vessels) using appropriate fluorescence-labeled antibodies and imunohistochemistry. Proliferating cells were identified by an antibody to Ki67. Mice were treated with pimo and then either doxorubicin or saline one hour later; EF5 was given after a variable interval of 24, 48, 72, 96 or 120 hours; mice were killed two hours after the second injection. Changes in the location, proliferation and oxygen status of formerly hypoxic (pimo+ve) cells were quantified by their distance from blood vessels, Ki67 status and uptake of EF5 as a function of time. Results: Following treatment with doxorubicin, the proportion of hypoxic cells in the entire tumor decreased from1.5% (pimo+ve) prior to injection to 0.7% (EF5+ve) at 24 hours. The proportion of pimo+ve formerly hypoxic cells that are no longer hypoxic (i.e. EF5 - ve) at 24 hours was 75% after treatment compared to 18% in controls indicating rescue of previous hypoxic cells that would have died in the absence of treatment. The proportion of these pimo+ve cells that were cycling (Ki67+ve) increased from 4.7% to 15.0% at 24 Hours and then slowly decreased. Conclusions: There is a decrease in the proportion of hypoxic cells in MCF7 xenografts following treatment with doxorubicin. Originally hypoxic cells (that may have died in the absence of treatment) can move closer to blood vessels, re-oxygenate, and repopulate the tumor. Treatment directed to killing hypoxic cells (e.g. with hypoxia activated pro-drugs) has the potential to improve the outcome of chemotherapy by inhibiting tumor cell repopulation. Supported by are search grant from the Canadian Institutes for Health Research. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5247. doi:1538-7445.AM2012-5247

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.379
Teacher spread0.326 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2012
Admission routes2
Has abstractyes

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