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Abstract S2-04: Comprehensive molecular characterization of invasive lobular breast tumors

2015· article· en· W2164320457 on OpenAlexaff
Giovanni Ciriello, Michael L. Gatza, Katherine A. Hoadley, Hailei Zhang, Suhn K. Rhie, Reanne Bowlby, Matthew D. Wilkerson, Cyriac Kandoth, Michael D. McLellan, Andrew D. Cherniack, Peter W. Laird, Chris Sander, Tari A. King, Charles M. Perou

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsCDH1PTENInvasive lobular carcinomaCancer researchCarcinogenesisBreast cancerFOXA1BiologyEpigeneticsProtein kinase BPI3K/AKT/mTOR pathwayCancerPhosphorylationGeneGeneticsCadherinInvasive ductal carcinomaSignal transduction

Abstract

fetched live from OpenAlex

Abstract Invasive lobular breast cancer (ILC) is the second most common histological subtype of breast cancer accounting for 10-15% of invasive breast tumors. ILC is typically ER+ and beyond the known mutation and/or loss of E-cadherin function, which contributes to a highly discohesive morphology, little is known about the additional mechanisms driving ILC tumorigenesis, or alterations that differentiate ILC from invasive ductal carcinomas (IDC). Methods A dataset of 817 breast tumors from the TCGA Project, including 490 IDC, 127 ILC and 88 samples with a mixed IDC-ILC histology, were profiled on six genomic platforms to develop a comprehensive atlas of mutational, epigenetic, transcriptional and proteomic data. Integrative genomic analyses, both supervised and unsupervised, of ILC tumors and across histological subtypes were performed to identify genomic drivers of ILC oncogenesis. Results Comprehensive multi-platform analyses identified distinct molecular events associated with ILC tumors. As expected, lack of E-cadherin protein, as determined by Reverse Phase Protein Array (RPPA), and CDH1 mRNA expression was uniformly observed in ILC cases associated with distinct alterations targeting CDH1. In addition to previously reported CDH1 and PIK3CA mutations, we identified a number of novel ILC-enriched recurrent mutations targeting PTEN, RUNX1, TBX3, and FOXA1. An increased incidence of PTEN inactivating events, both mutations and copy number changes, were identified in ILC (13%) compared to IDC ER+ (7%), which corresponded with altered PTEN protein expression. These alterations were largely mutually exclusive with PIK3CA mutations and correlate with increased Akt activation as evident by increased Akt phosphorylation (pS473 and pT308), thus identifying a potential therapeutic opportunity for ILC patients. GATA3 signaling, which regulates epithelial cell differentiation, is frequently altered in luminal/ER+ breast cancers. Our analyses determined GATA3 mutations are more frequent in IDC luminal tumors as compared to ILC (19 % vs 5%). ILC luminal tumors show significantly lower GATA3 protein expression, but a higher frequency of mutations in FOXA1 (9% vs 2% in Luminal IDC), a transcription factor required to promote ER transcriptional programs. Within ILC tumors, FOXA1 mutations were found to cluster into a specific region of the Forkhead (FK) DNA binding domain. A broader analysis of FOXA1 mutations in breast and prostate cancer confirm two specific hotspots in the FK domain and the C-terminal transactivation domain. Interestingly, these mutational classes are associated with distinct transcriptional changes suggesting different functional effects. Finally, mRNA-seq analyses identified three robust molecular subclasses that are characterized by distinct genetic, genomic and proteomic patterns, including an increased immune-related group (Class 2), as well as differences in prognosis. Conclusions In this study, we developed a comprehensive atlas of genomic alterations that reveals key molecular differences differentiating ILC (FOXA1) from IDC (GATA3) tumorigenesis, a potential therapeutic target for ILC (Akt), and novel ILC subclasses based on underlying biological events. These findings provide further insight into the molecular heterogeneity of ER+ breast cancer. Citation Format: Giovanni Ciriello, Michael L Gatza, Katherine A Hoadley, Hailei Zhang, Suhn K Rhie, Reanne Bowlby, Matthew D Wilkerson, Cyriac Kandoth, Michael McLellan, Andrew Cherniack, Peter W Laird, Chris Sander, Tari A King, Charles M Perou. Comprehensive molecular characterization of invasive lobular breast tumors [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr S2-04.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.051
GPT teacher head0.341
Teacher spread0.290 · 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".

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Citations9
Published2015
Admission routes1
Has abstractyes

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