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Record W2221578897

Genomic alterations in primary breast cancers and their sentinel lymph node metastases detected by array CGH

2008· article· en· W2221578897 on OpenAlexaffabout
Chunjie Wang, Vladmir Vladmir, Vietty Wong, Stephanie Leung, Keisha Warren, Gaiane Iakovleva, Nona Arneson, Naomi Miller, Bruce Youngson, David R. McCready, Susan J. Done

Bibliographic record

VenueCancer Research · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineBreast cancerSentinel lymph nodeComparative genomic hybridizationLymph nodeMetastasisSentinel nodeBreast carcinomaAxillary lymph nodesLymphCancerOncologyPathologyInternal medicineBiologyChromosomeGene
DOInot available

Abstract

fetched live from OpenAlex

AACR Annual Meeting-- Apr 12-16, 2008; San Diego, CA 347 Breast cancer is a devastating disease afflicting millions of women and it is a major cause of premature mortality. It is estimated that one in eight women in North America will be diagnosed with breast cancer in their lifetime. Breast cancer kills by metastasizing. The presence and extent of axillary lymph nodes metastasis is one of the most important prognostic indicators in breast cancer. The sentinel lymph node is the first node that receives lymphatic drainage from a primary cancer within the breast. Identification of genomic alterations in sentinel lymph node metastases may allow identification of the earliest changes associated with metastatic spread. In this investigation, using array comparative genomic hybridization (aCGH), we compared the genomic profiles of primary breast carcinomas, their matched sentinel and more distal lymph node metastases and invasive breast carcinomas without nodal metastasis. All the cases studied were from a single institution, University Health Network, Toronto, ON. Prior to the study institutional Research Ethics Board approval was obtained. Nineteen cases of infiltrating duct carcinoma (IDC) were subjected to aCGH: 8 cases IDC without metastasis, 11 cases IDC with axillary lymph node metastases. Fourteen lymph node metastasis samples were studied: 9 sentinel and 5 distal. aCGH data was analyzed by correlation of raw data, cluster analysis, circular binary segmentation and moving average methods. The results showed that primary tumors and their matched sentinel lymph node metastases share high genomic similarity. However, gains at 2p24-13, 2q22-33, 9q21-31, 12q21-23, 17 q23-25 and loses at 11q23-ter, 14q23-31, 20p11-q12, 2q36-ter, 8q24-ter, 9q33-ter, 2p11-q11, and 12q13 were either preferentially or uniquely detected in IDC associated with lymph node metastasis. Two genes from each region of gain were selected for further validation by quantitative real-time PCR (Q-PCR). Amplification within the 60-63Mb region of chromosome 17 (17q23.3-24.2) was associated with large IDC size and lymph node metastasis ( p <0.05). Our findings indicate that genomic events associated with metastatic progression do occur and can be detected within primary breast cancer. Gain on 17q23-25 is a candidate region for further testing as a predictor of nodal metastasis. These results could be applied to develop novel targeted gene therapies and predict clinical prognosis.

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 score0.002
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
Bibliometrics0.0010.000
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 head0.023
GPT teacher head0.280
Teacher spread0.257 · 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
Published2008
Admission routes2
Has abstractyes

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