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Record W2325389636 · doi:10.1158/1538-7445.am2012-2999

Abstract 2999: Gene expression signature of differential response to chemotherapy in sporadic pediatric osteosarcoma

2012· article· en· W2325389636 on OpenAlexaff
Jeff W. Martin, Madhuri Koti, Susan Chilton‐MacNeill, André J. van Wijnen, Jeremy A. Squire, Maria Zieleńska

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsKingston General HospitalSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsOsteosarcomaCancer researchChemotherapyCancerMalignancyPathologyBone cancerGene expressionMedicineGeneBiologyOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Osteosarcoma is the most common pediatric bone malignancy. Genomic instability, karyotypic heterogeneity and dysregulated gene expression are typical of osteosarcoma tumors, for which treatment is often multimodal and aggressive. Tumor necrosis in response to neoadjuvant chemotherapy is used to estimate outcome and survival, because reliable clinical diagnostic and prognostic biomarkers have not yet been described for this tumor. Our objective in the current study was to identify new molecular biomarkers for osteosarcoma based on differential mRNA expression levels of genes found to be consistently involved in the pathogenesis of osteosarcoma. The NanoString nCounter System utilizes color-coded probe pairs, which allow multiplexed expression analysis of specific genes in solution-phase hybridization with digital output. Advantages of the system are a higher sensitivity than microarrays and the utilization of small amounts of total RNA without requiring cDNA synthesis. We first determined the efficacy and consistency of the system using total RNA extracted from snap-frozen and formalin-fixed, paraffin-embedded osteosarcoma tissue specimens. We then verified that the NanoString assay was consistent with RT-PCR analysis of our samples, after which we subjected a cohort of osteosarcoma tumors to the expression assay. Seventeen genes, including many with well-described functions in osteosarcoma, were analyzed for expression in a cohort of tumors as well as normal human osteoblasts. Expression values of normal human osteoblasts were compared with those of tumors with more than 90% necrosis post-chemotherapy (an indicator of good prognosis) and of tumors with less than 90% necrosis post-chemotherapy (an indicator of poor prognosis). Of note, we found that tumors with poor response to chemotherapy had statistically significant up-regulation of CDKN1C, FOS, and RUNX2. The RUNX2 gene is located at chromosome 6p21.1, which frequently undergoes genetic amplification in osteosarcoma. Amplification-related overexpression of RUNX2 has been previously reported (Martin et al 2010, Sadikovic et al 2010) as a poor prognosticator for chemotherapy response, and the involvement of bone morphogenetic pathways downstream of RUNX2 are being addressed by multiplex analyses of differential gene expression. 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 2999. doi:1538-7445.AM2012-2999

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.379
Teacher spread0.353 · 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 designObservational
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 routes1
Has abstractyes

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