<i>Wnt</i> Signaling Prognosticates Outcome in Breast Cancer Cohorts.
Bibliographic record
Abstract
Abstract Background: Wnt signaling is highly conserved in Metazoan species. Wnt activation and inactivation have been well studied for their roles in development and tumorigenesis, and has emerged as one the most important core pathways in cancer biology. Wnt activation has long been associated with breast cancer, but has only recently been shown to be specifically upregulated in triple negative (TN) breast cancers.Material and Methods: FFPE specimens were obtained from St. Mary's Hospital, Montreal, QC (Quebec cohort) and Grady Hospital, Atlanta, GA (Georgia cohort). RNA was extracted from FFPE sections or cores using the RNA High Pure Kit (Roche) and quality analyzed as described previously (Abramovitz et al. Biotechniques, 2008). Quebec and Georgia cohort mRNAs were quantified using DASL assays, including a 502-gene panel of cancer related genes and a custom 512-gene panel targeting breast cancer pathologies. After quality control the Quebec cohort composed 97 patients and the Georgia cohort 142. Differential mRNA regulation was assessed by Significance Analysis of Microarrays. Meta-analysis included a cohort from Memorial Sloan-Kettering Cancer Center (MSKCC; GEO Series GSE2603), two cohorts from the UNC Lineberger Cancer Center (UNCCC; GSE6128 and GSE10886) and a study from the Netherlands Cancer Institute (NKI). Survival analysis utilized Kaplan-Meier curves and p-values derived from log-rank tests.Results: In the annotation of differentially expressed genes between TN tumors and other subtypes, a significant number of Wnt transcriptional targets and transducers were overexpressed, whereas Wnt attenuators were downregulated in the TN tumors. Meta-analysis in MSKCC and UNCCC cohorts yielded 9 Wnt-related mRNAs differentially regulated between TN (basal-like in the UNCCC case) and other subtypes. To determine if this 9-gene Wnt signature was a driver of breast cancer pathology, we developed a bioinformatic discriminator to bifurcate patients into high (Wnt+) and low (Wnt-) Wnt-expressing categories and applied this to MSKCC, UNCCC, and NKI published data sets composing 625 tumor profiles. Patients classified as Wnt+ exhibited worse outcome in bone metastasis free survival (BMFS), lung metastasis free survival (LMFS), recurrence free survival (RFS) and overall survival (OS). LMFS (p ≤ 0.001), RFS (p ≤ 0.001), and OS (p ≤ 0.01) were statistically significant, whereas BMFS was not. Furthermore, reapplying this bioinformatic discriminator on ER+ patients yielded similar results with Wnt+ patients experiencing worse metastasis-free survival (MFS) and OS (p ≤ 0.025).Discussion: Characterization of mRNAs in breast tumors from 864 patients across multiple microarray platforms indicated Wnt signaling as upregulated in TN breast cancers. A 9-gene Wnt signature was found to be indicative of outcome with respect to LMFS, RFS, and OS. A separate analysis of luminal patients will be presented which indicates that Wnt signaling, as a key mediator of breast cancer pathogenesis, segregates ER+ patients into good and poor prognoses. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 109.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".