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Record W2326818380 · doi:10.1158/1538-7445.am2011-479

Abstract 479: Targeting tumor initiating cells inhibits tumor growth and serial transplantation ability in soft-tissue sarcomas

2011· article· en· W2326818380 on OpenAlexaff
Qingxia Wei, Chang Ye Yale Wang, Feifei Zheng, Phil Zhang, Weishi Wang, Jay S. Wunder, Benjamin A. Alman

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMount Sinai HospitalQueen's UniversityCentre for Social InnovationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCancer researchBiologyCancer stem cellHedgehog signaling pathwaySide populationHedgehogPopulationNotch signaling pathwayStem cellGene expression profilingSignal transductionPathologyGene expressionGeneCell biologyMedicineGenetics

Abstract

fetched live from OpenAlex

Abstract Tumors contain heterogeneous cell populations. There is a subpopulation of cells enriched for tumor initiating potential in sarcomas, which excludes Hoechst dye, and resides in the “side population” when subjected to flow cytometry (SP cells). These cells possess multi-potent differentiation potential, and as such they behave as “cancer stem cells” (CSCs), while the remainder of tumor cells act as “transient amplifying” cells. Because traditional cancer therapies may not target these tumor initiating cells, the persistence of SP cells could be responsible for relapse or a failed response to therapy. To detect signaling pathways that are differentially regulated in SP cells versus the remainder (non-SP) of the cells, we used gene profiling by microarray to compare their differences. RNA expressions were compared between these two cell populations from six malignant fibrous histiocytoma (MFH) samples using microarray. Differentially regulated genes were then analyzed by compiling a list of genes that showed a fold change greater than 1.25 in at least five of the six samples all in the same direction, and identifying if the list is enriched for genes involved in common molecular pathways, using Genespring® analysis tool. Two of the differentially regulated pathways detected were the Hedgehog signaling pathway and the Notch signaling pathway. Differential target gene expression for both pathways was verified using quantitative PCR. Triparanol (an agent that inhibits hedgehog signaling) and DAPT (an agent that inhibits Notch signaling) were used to treat eight primary MFH xenografts established in NOD-SCID mice. The xenografts produced visible tumors six weeks after subcutaneous transplantation, after which the mice were treated with one of the agents or a carrier as a control. At the end of the treatment, the tumors were harvested; their growth and SP% were assessed and compared; and the cells harvested from the treated xenografts were again implanted into mice to study the rate of re-growth upon secondary transplantation. Both triparanol and the Notch blocker DAPT treatment suppressed these pathways in tumor cells, depleted the abundance of SP cells, and reduced tumour growth. Intriguingly, treatment substantially inhibited the tumour-initiating potential of the treated sarcoma cells upon secondary transplantation. The data provides support that SP cells act as tumour initiating cells in sarcomas and shows that targeting the SP (in this case by targeting the Hh and Notch pathways) is an enticing approach for sarcoma therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 479. doi:10.1158/1538-7445.AM2011-479

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 score0.003
Threshold uncertainty score0.009

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)0.0030.001

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 head0.087
GPT teacher head0.369
Teacher spread0.282 · 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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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