FKBPL: a marker of good prognosis in breast cancer
Bibliographic record
Abstract
// Laura Nelson 1, * , Hayley D. McKeen 1, * , Andrea Marshall 2, * , Laoighse Mulrane 3 , Jane Starczynski 4 , Sarah J. Storr 5 , Fiona Lanigan 3 , Christopher Byrne 6 , Ken Arthur 7 , Shauna Hegarty 8 , Ahlam Abdunnabi Ali 1 , Fiona Furlong 1 , Helen O. McCarthy 1 , Ian O. Ellis 5 , Andrew R. Green 5 , Emad Rakha 5 , Leonie Young 6 , Ian Kunkler 10 , Jeremy Thomas 10 , Wilma Jack 10 , David Cameron 10 , Karin Jirström 11 , Anita Yakkundi 1 , Lana McClements 1 , Stewart G. Martin 5 , William M. Gallagher 3 , Janet Dunn 2 , John Bartlett 4, 9 , Darran O’Connor 3 , Tracy Robson 1 1 School of Pharmacy, Queen's University Belfast, Belfast, United Kingdom 2 Warwick Clinical Trials Unit, University of Warwick, Coventry, United Kingdom 3 Conway Institute, University College Dublin, Dublin, Ireland 4 Ontario Institute for Cancer Research, Toronto, Canada 5 Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, United Kingdom 6 Royal College of Surgeons Ireland, Dublin, Ireland 7 Northern Ireland Molecular Pathology Laboratory, CCRCB, Queens University Belfast, Belfast, United Kingdom 8 Department of Pathology, Royal Group of Hospitals, Grosvenor Road, Belfast, United Kingdom 9 Edinburgh Cancer Research Centre, The University of Edinburgh, Edinburgh, United Kingdom 10 Edinburgh Breast Unit, The University of Edinburgh, Edinburgh, United Kingdom 11 Department of Clinical Sciences, Lund University, Sweden * These authors have contributed equally to this work Correspondence to: Tracy Robson, e-mail: t.robson@qub.ac.uk Keywords: FKBPL, breast cancer, biomarker, personalized medicine Received: January 27, 2015 Accepted: March 09, 2015 Published: April 03, 2015 ABSTRACT FK506-binding protein-like (FKBPL) has established roles as an anti-tumor protein, with a therapeutic peptide based on this protein, ALM201, shortly entering phase I/II clinical trials. Here, we evaluated FKBPL’s prognostic ability in primary breast cancer tissue, represented on tissue microarrays (TMA) from 3277 women recruited into five independent retrospective studies, using immunohistochemistry (IHC). In a meta-analysis, FKBPL levels were a significant predictor of BCSS; low FKBPL levels indicated poorer breast cancer specific survival (BCSS) (hazard ratio (HR) = 1.30, 95% confidence interval (CI) 1.14–1.49, p < 0.001). The prognostic impact of FKBPL remained significant after adjusting for other known prognostic factors (HR = 1.25, 95% CI 1.07–1.45, p = 0.004). For the sub-groups of 2365 estrogen receptor (ER) positive patients and 1649 tamoxifen treated patients, FKBPL was significantly associated with BCSS (HR = 1.34, 95% CI 1.13–1.58, p < 0.001, and HR = 1.25, 95% CI 1.04–1.49, p = 0.02, respectively). A univariate analysis revealed that FKBPL was also a significant predictor of relapse free interval (RFI) within the ER positive patient group, but it was only borderline significant within the smaller tamoxifen treated patient group (HR = 1.32 95% CI 1.05–1.65, p = 0.02 and HR = 1.23 95% CI 0.99–1.54, p = 0.06, respectively). The data suggests a role for FKBPL as a prognostic factor for BCSS, with the potential to be routinely evaluated within the clinic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".