Abstract 538: Identification and validation of novel prostate cancer biomarkers using the Berg Interrogative Biology™ platform
Bibliographic record
Abstract
Abstract Prostate cancer is the most frequent cancer diagnosis among men and the second leading cause of cancer-related death. Despite the widespread use of digital rectal exam (DRE) and blood-based screening of prostate-specific antigen (PSA) for prostate cancer screening, there are significant limitations in their specificity and prognostic value. Biomarkers which distinguish i) PSA-low prostate cancer from benign prostatic hyperplasia (BPH), and ii) indolent versus aggressive disease course represent unmet clinical needs. Experimentally, a panel of prostate cancer cell lines and non-tumorigenic, human primary cells were exposed to in vitro conditions designed to simulate poor oxygenation, low pH, diminished nutrient microenvironments, and metabolic perturbations (24-48 h) followed by iTRAQ proteomic analysis of cell lysates. Using the Berg Interrogative Biology™ platform, proteomic data were then subjected to Bayesian network learning to map molecular interactions, with cytoskeletal and scaffolding proteins Filamin A (FLNA), Filamin B (FLNB), and Keratin 19 (KRT19) identified as candidate prostate cancer biomarkers. To validate biomarker expression, mRNA and protein was quantified in panel of primary human prostate epithelial cells (HPrEC) and androgen-sensitive (LnCAP) or refractory (DU145, PC-3) prostate cancer cells, and each was differentially detected in one or more prostate cancer cell lines compared to HPrEC. Using proteomic analysis, peptides from FLNA, FLNB, and KRT19 were also detected cell culture media conditioned by prostate cancer cells (24 h), indicating they can be secreted. Importantly, unlike PSA expression, global regulation of FLNA, FLNB, and KRT19 expression remained unaltered after treatment with multiple prostate-cancer relevant stimuli (e.g., hypoxia, androgens, and inflammatory stimuli). In vivo validation was next conducted in sera from men (N = 447) with confirmed prostate cancer, benign prostate tumors, or BPH using LDT ELISA assays in a CLIA-certified laboratory. To assess the sensitivity and specificity of FLNA, FLNB, and KRT19 compared to PSA, ROC curve analysis was performed. The individual predictive power of each biomarker alone was comparable to that of PSA. However, the combination of age, levels of FLNA, FLNB, and KRT19, and PSA out-performed PSA alone in identification of patients with prostate cancer stratified compared to benign status, gleason scores and incidence of BPH. Together, these data validate the use of the Berg Interrogative Biology™ platform for biomarker discovery and indicate that FLNA, FLNB, and KRT19 can be used in conjunction with PSA for more sensitive and specific prostate cancer screening, a critical unmet need in the field. Citation Format: Niven R. Narain, Anne Diers, Rakibou Ouro-Djobo, Joyce Chan, Leonardo O. Rodrigues, Vivek K. Vishnudas, Eleftherios P. Diamandis, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan. Identification and validation of novel prostate cancer biomarkers using the Berg Interrogative Biology™ platform. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 538. doi:10.1158/1538-7445.AM2015-538
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".