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Record W2398400369 · doi:10.1158/1538-7445.am2015-4320

Abstract 4320: Genomic change in residual triple-negative breast cancers after neoadjuvant chemotherapy

2015· article· en· W2398400369 on OpenAlexaff
Adriana Aguilar‐Mahecha, Ewa Przybytkowski, Josiane Lafleur, Cathy Lan, Stéphanie Légaré, Najmeh Alirezaie, Carole Seguin‐Devaux, Federico Discepola, Bojan Kovacina, Catalin Mihalcioiu, André Robidoux, Elizabeth Marcus, J.A. Roy, Manuela Pelmus, Olga Aleynikova, Sheida Nabavi, Jacek Majewski, Mark Basik

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsRoyal Victoria HospitalJewish General HospitalHôpital du Sacré-Cœur de MontréalConcordia UniversityCentre Hospitalier de l’Université de MontréalMcGill University
Fundersnot available
KeywordsBreast cancerTriple-negative breast cancerChemotherapyExome sequencingComparative genomic hybridizationMedicineCancerOncologyCopy-number variationCancer researchExomeChromosome instabilityInternal medicineGeneBiologyPhenotypeGeneticsChromosomeGenome

Abstract

fetched live from OpenAlex

Abstract Background: Triple negative breast cancer (TNBC) is characterized by its aggressive phenotype and its genomic instability. TNBC patients who do not respond to neoadjuvant chemotherapy have a very poor prognosis. Currently, little is known about the mechanisms of drug resistance and how to overcome it in TNBC. Our study aims at identifying molecular factors enriched for in residual TNBC tumors after standard neoadjuvant chemotherapy. Methods: We obtained specimens from 60 TNBC patients participating in a clinical trial (Q-CROC-03). Biopsies were collected prior to and after standard neoadjuvant chemotherapy and residual cancer was collected at the time of surgery. Matched tumor specimens (pre and post) from 9 patients were analyzed by array comparative genomic hybridization (CGH), gene expression microarrays and whole exome sequencing. All samples contained >50% tumor cellularity. Results: Gene expression data was used to identify the different TNBC subtypes (TNBCtype). Six of the 7 subtypes were represented in at least one sample from our cohort. In the post-chemo samples, we observed a change in TNBC subtype compared to the pre-chemo samples in 6 pairs. The most common switch was to the Immuno Modulatory subtype (IM). aCGH analysis showed relatively few differences in copy number variants (CNV) following chemotherapy in 3 out of 8 patients. Whole exome sequencing revealed increased allele frequency or appearance of de novo mutations in TP53 gene in the residual cancers of 2 of the 3 patients presenting differences in CNVs post treatment. Interestingly, pathway analyses revealed that genes involved in DNA binding, chromosomal organization and nucleosome organization were differentially expressed (>2fold) in 2 of the patients with CNV changes. Our results suggest that increased levels of TP53 mutations and altered transcriptional expression of genes involved in chromosomal functions could be associated with the presence of CNV changes in drug resistant tumors. Conclusion: In summary, the genome of TNBCs does not undergo major changes during neoadjuvant chemotherapy; however, enrichment for or de novo TP53 mutations is associated with the appearance of novel CNVs in drug resistant residual tumors. Citation Format: Adriana Aguilar-Mahecha, Ewa Przybytkowski, Josiane Lafleur, Cathy Lan, Stephanie Légaré, Najmeh Alirezaie, Carole Séguin, Federico Discepola, Bojan Kovacina, Catalin Mihalcioiu, André Robidoux, Elizabeth Marcus, Josée Anne Roy, Manuela Pelmus, Olga Aleynikova, Sheida Nabavi, Jacek Majewski, Mark Basik. Genomic change in residual triple-negative breast cancers after neoadjuvant chemotherapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4320. doi:10.1158/1538-7445.AM2015-4320

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.156
GPT teacher head0.440
Teacher spread0.284 · 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.

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
Published2015
Admission routes1
Has abstractyes

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