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Aberrant nuclear p53 protein expression detected by immunohistochemistry is associated with hemizygous <i>P53</i> deletion and poor survival for multiple myeloma

2007· article· en· W2015701553 on OpenAlexafffund
Hong Chang, Joanna Yeung, Connie Qi, Wei Xu

Bibliographic record

VenueBritish Journal of Haematology · 2007
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersLeukemia and Lymphoma ResearchCancer Research Society
KeywordsImmunohistochemistryFluorescence in situ hybridizationMultiple myelomaBiologyNuclear proteinCancer researchPathologyInternal medicineGeneMedicineTranscription factorImmunologyGenetics

Abstract

fetched live from OpenAlex

Hemizygous TP53 deletion is an adverse risk factor in multiple myeloma (MM) but its relationship with p53 protein expression is unclear. We investigated 105 newly diagnosed myeloma patients and correlated nuclear p53 protein immunoreactivity with TP53 deletion status, myeloma-associated genetic risk factors and survival. Fluorescence in situ hybridisation (FISH) detected hemizygous TP53 deletions in 13 (12%) patients while immunohistochemistry detected nuclear p53 protein expression in 12 (11%). Ten (77%) of the 13 del(TP53) cases expressed nuclear p53 protein while 10 (83%) of the 12 nuclear p53 immunoreactive cases had hemizygous TP53 deletions. Hemizygous TP53 deletion and p53 protein expression were strongly correlated (P < 0.001). The overall survival of patients with p53 protein expression was significantly shorter than that of patients without p53 expression (P < 0.001). A multivariate analysis including other myeloma-associated genetic risk factors confirmed p53 expression as an independent risk factor for survival. Our data indicate that nuclear p53 protein expression, detected by a widely available immunohistochemical method, is strongly associated with TP53 deletion and an adverse clinical outcome for MM.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.207
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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 teacher head, 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

Citations45
Published2007
Admission routes2
Has abstractyes

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