Abstract 2598: <i>In silico</i> gene expression analysis of PTHrP and its association with molecular subtypes and organ-specific metastasis in human triple-negative breast cancer
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) represents 10-20% of all BC cases, and is characterized by an aggressive clinical course with high risk of metastasis and lack of targeted therapy. Gene expression profiling revealed the heterogeneity of TNBC enabling its classification into distinct molecular subtypes with distinct susceptibilities to chemotherapies; including basal-like (BL), mesenchymal (M), and luminal androgen receptor (LAR) subtypes. Overexpression of the parathyroid hormone-related protein (PTHrP) in breast cancer has been extensively linked to its progression with increased propensity for bone metastasis. However, its expression and implication in organ-specific metastasis in the TNBC subtype remains largely unknown. In this study, we conducted in silico analyses to examine the association of PTHrP with various molecular BC subgroups and with organ-specific metastasis in TNBC. Breast Cancer Gene-Expression Miner Version 4.0 (bc-GenExMiner v4.0) online database was used to evaluate the relative mRNA expression levels of the PTHLH gene (which encodes PTHrP) with respect to other markers. This microarray-based tool uses 36 public datasets and 5861 patients, and allows the expression, correlation, and prognostic analyses of genes in BC. Using the gene expression correlation analysis module of bc-GenExMiner v4.0, we assessed Pearson's correlation coefficient between PTHLH expression and gene signatures characteristic of TNBC molecular subtypes or representative of organ-specific metastasis in BC. We found that PTHLH expression displays significant positive correlations with components of signalling pathways enriched in the mesenchymal subtype, and with key luminal markers and AR signalling genes characteristic of the LAR subtype. While PTHLH expression presents significant positive correlations with signature genes involved in bone and lung metastases in all BC subtypes, we identified for the first time a correlation between PTHLH expression and brain metastasis specifically in TNBC patients. Interestingly, PTHLH correlates with the brain metastatic genes HBEGF (Heparin-binding EGF-like growth factor) and ANGPTL4 (Angiopoietin-like 4) selectively in both TNBC and BL subtypes as opposed to other BC subtypes, which is in line with studies reporting their common morphological and genetic features and increased rate of brain metastasis. In conclusion, our in silico analysis reveals for the first time a strong association between PTHrP expression and specific TNBC molecular subtypes' markers, as well as its potential role in the mechanisms of TNBC brain metastasis and the identification of a novel gene signature with a prognostic value for brain progression in TNBC. Funding: Department of Defense (DoD, USA) Award No. W81XWH-15-1-0723 Citation Format: Gloria Assaker, Anne Camirand, Siham Sabri, Richard Kremer. In silico gene expression analysis of PTHrP and its association with molecular subtypes and organ-specific metastasis in human triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2598.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".