Abstract 3826: Clinical validation of a serum protein panel (FLNA, FLNB and KRT19) for diagnosis and prognosis of prostate cancer
Bibliographic record
Abstract
Abstract Background: Prostate cancer (PrCa) is a leading cause of cancer deaths in males in the US. Current tests of prostate-specific antigen (PSA) screening and the diagnostic prostate biopsy are often inconclusive. Many patients with a PSA positive blood test often undergo invasive repeat biopsy procedures. Additionally, there is a need for diagnostic tests that differentiate between low and high-risk cancers. This study reports on the development of a novel serum protein panel of three PrCa biomarkers, Filamin A, Filamin B and Keratin-19 (FLNA, FLNB and KRT19) using multivariate models for disease screening and prognosis. Methods: ELISA and IPMRM (LC-MS/MS) based assays were developed and analytically validated by quantitative measurements of the biomarkers in serum. Retrospectively collected and clinically annotated serum samples with PSA values and Gleason scores (GS) were analyzed from 503 subjects who underwent prostate biopsy, and showed no evidence of cancer with or without indication of prostatic hyperplasia, or had a definitive pathology diagnosis of prostatic adenocarcinoma. Probit linear regression models were used to combine the analytes into score functions to address the following clinical questions: does the biomarker test augment PSA for population screening? Can aggressive disease be differentiated from lower risk disease, and can the panel discriminate between benign prostate hyperplasia (BPH) and PrCa? Results: Table 1: Berg PrCa Panel AUC summary for the four clinical indications. Conclusion: As shown in Table 1, modelling of the data demonstrated that the new PrCa biomarkers and PSA in combination were better than PSA alone in identifying PrCa, improved the prediction of high and low risk disease, and improved prediction of BPH versus PrCa. Citation Format: Shobha Ravipaty, Wenfang Wu, Aditee Dalvi, Nikunj Tanna, Joe Andreazi, Tracey Friss, Allison Klotz, Chenchen Liao, Jeonifer Garren, Sally Schofield, Eleftherios P. Diamandis, Eric A. Klein, Albert Dobi, Shiv Srivastava, Poornima Tekumalla, Michael A. Kiebish, Vivek K. Vishnudas, Rangaprasad Sarangarajan, Niven R. Narain, Viatcheslav Akmaev. Clinical validation of a serum protein panel (FLNA, FLNB and KRT19) for diagnosis and prognosis of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3826. doi:10.1158/1538-7445.AM2017-3826
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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 | 0.004 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".