Abstract 5144: Exploring the potential of a novel multiplex proteomics technology to identify personalized biomarkers of tumor burden in ovarian cancer patients
Bibliographic record
Abstract
Abstract Objective: Owing to tumor heterogeneity, we hypothesize that each tumor secretes a unique mixture of tumor-related proteins into the bloodstream, which when quantified can serve as personalized markers of tumor load. We previously introduced the concept of "personalized tumor markers", which are highly sensitive for monitoring tumor load in a small subpopulation (5-30%) of patients. Screening against a robust panel of such personalized markers could identify the most effective markers of relapse in each patient. With the advent of multiplex proteomics technologies, for the first time we can screen serum for tumor-related proteins in individual patients. In this pilot study, we explored the potential of one of the leading new multiplex proteomics technologies for identifying personalized markers of tumor burden in the most common and deadliest type of ovarian cancer - high grade serous carcinoma (HGSC). Serum CA125 is the only widely used marker for HGSC but it is not informative of relapse in 30-60% of cases. With up-and-coming immunotherapies and precision medicines, there is a timely need for personalized markers to monitor for relapse and find the optimal timing for 2nd-line treatment in each patient. Methods: We leveraged the multiplex immuno-PCR assay, Proseek Panels (Olink, Sweden), to concurrently measure 1,196 proteins in sera obtained pre- and post-surgically from 10 HGSC patients. Sera from two healthy individuals, with blinded technical duplicates, were used as controls. To validate the multiplexed results, we used independent, clinical grade ELISA immunoassays to measure three candidate proteins in sera collected pre- and post-surgically from five of the same 10 HGSC patients. Results: Scatterplot analysis of 1,196 protein measurements in the technical duplicates showed high correlation and excellent assay precision. For candidate selection, we eliminated proteins that showed greater than two-fold change in the controls. We identified a panel of eight proteins that dropped more than 10-fold post-surgery in at least two patients. The panel as a whole was informative of tumor burden for 80% (8/10) of the patients tested. Using independent ELISAs, we observed high concordance in the post-surgical fold decrease between the Proseek panels and ELISA results, confirming the reliability of the novel platform. Further literature search demonstrated the relevance to tumorigenesis and ovarian cancer for the eight candidate personalized tumor markers. Significance: With future validation in larger, longitudinal studies, our goal is to develop a panel of personalized HGSC biomarkers of relapse to aid in individualizing patient monitoring and ameliorating survival outcome. Our study also showed the utility of an innovative high throughput biotechnology that is promising for measuring tumor-derived proteins, which can be adapted for a myriad of biomarker studies, particularly in prevalent cancers with no reliable serum biomarkers such as lung cancer. Citation Format: Annie Ren, Ioannis Prassas, Antoninus Soosaipillai, Marcus Bernardini, Vathany Kulasingam, Eleftherios Diamandis. Exploring the potential of a novel multiplex proteomics technology to identify personalized biomarkers of tumor burden in ovarian cancer patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5144.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".