Abstract P6-07-17: Proteomic screening of FFPE tissue identifies FKBP4 as an independent prognostic factor in hormone receptor positive breast cancers
Bibliographic record
Abstract
Abstract Background: Adjuvant endocrine therapy reduces the risk of recurrence and death in hormone receptor positive breast cancer patients; however, 40–50% of estrogen receptor (ER) positive tumors are resistant to endocrine therapy. Identifying prognostic and predictive biomarkers to detect these non-responsive breast cancers at time of diagnosis may allow for improved clinical outcome for these patients. FKBP4 (FKBP52) is a co-chaperone protein that has been shown to regulate progesterone but not estrogen receptor activity, and was recently found to be highly expressed in early stage breast cancer compared to benign breast tissue. This evidence suggests that FKBP4 may play an important role in endocrine-responsive breast cancers. Methods: Global proteomic screening for biomarkers of aggressive breast cancer was performed on tissue samples from 24 patients with lymph node negative (LN−) disease and 24 patients with lymph node positive (LN+) disease randomly selected from the Calgary Tamoxifen Cohort, a retrospective cohort of breast cancer patients treated with adjuvant tamoxifen [n=511] from 1985–2000 at the Tom Baker Cancer Centre (Calgary, Canada). Liquid tissue lysates from FFPE tissue were analyzed by mass spectrometry and differentially expressed proteins were identified by the semi-quantitative spectral count method. FKBP4 was identified as an upregulated target in LN+ patients. The mRNA expression database of Kao et al. (BMC Cancer, 2011) was used to assess the prognostic potential of targets identified in the proteomic screen. FKBP4 protein expression was evaluated in tissue microarrays built from FFPE samples from the Calgary Tamoxifen Cohort using quantitative fluorescence immunohistochemistry and HistoRx AQUA analysis. Ten-year overall survival (OS) or five-year disease free survival (DFS) were the primary outcomes. Continuous variable FKBP4 data was dichotomized at the top quartile for both mRNA and protein expression analysis. Results: Univariate analysis demonstrated a significant association between high levels of FKBP4 mRNA and worse OS in ER+HER2− patients [n=182, HR=2.118 (1.070–4.192), p = 0.031] within the Kao et al database. Similarly, univariate analysis demonstrated that high levels of FKBP4 protein expression was associated with significantly worse DFS in ER+ HER2− patients [n=358, HR=1.632 (1.001–2.659), p = 0.049] in the Calgary Tamoxifen Cohort. FKBP4 was found to be an independent prognostic factor in both mRNA and protein expression cohorts using multivariate analysis adjusted for age, T stage, and lymph node status [OS: HR=2.786 (1.394–5.570), p = 0.004], or age, tumor size, tumor grade, and lymph node status [DFS: HR=1.875 (1.021–3.442), p = 0.043], respectively. Conclusions: Proteomic screening from FFPE tissue can identify new breast cancer biomarker candidates. Using this technique we have identified FKBP4 as an independent prognostic factor in ER+HER2− breast cancers, measured either by mRNA expression analysis or by quantitative protein expression analysis. Further studies are required to determine if FKBP4 may also be a predictive biomarker for tamoxifen response in ER+HER2− patients. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P6-07-17.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".