Abstract 6442: Investigation of custom biomarkers on circulating tumor cells from clinical samples using RarePlex® Developer Kits
Bibliographic record
Abstract
Abstract Enumeration and phenotypic profiling of circulating tumor cells (CTCs) can give important information about tumor progression, presence of therapeutic targets, and metastatic potential. New and informative cancer-specific biomarkers are being discovered at a rapid pace, so there is a strong need for tools that enable investigator driven assays to best study and utilize these biomarkers. Through the RareCyte platform, we provide sensitive and specific assays that are optimized and validated for CTC enumeration and biomarker expression. RarePlex Developer Kits enable the addition of user-selected antibodies against biomarkers of interest to a CTC detection assay. Here we demonstrate the application of RarePlex Developer Kits to study the presence of a variety of cancer related biomarkers. Using the Developer strategy, we present results for several biomarkers, including HER2, ER, PR, EGFR, Ki67, AR, ARv7, PDL1, and PSMA. We also characterized clinical samples from prostate (AR and ARv7) and breast (HER2 and ER) cancer patients. The biomarkers demonstrated proper localization on or within model CTC control cells when using default antigen retrieval and fixation conditions. For each biomarker, fluorescence intensity cut-offs that segregated negative and positive cell lines were statistically defined to maximize classification accuracy. For clinical samples, breast and prostate cancer sample staining showed expected localization based on available clinical information. In conclusion, RarePlex Developer Kits provide a flexible tool for custom CTC assay development that enables researchers to develop assays in their own lab for characterization of phenotypic heterogeneity. Citation Format: Edward Lo, Daniel Campton, Arturo Ramirez, Lillian Costandy, Brady Gardner, Ryan Houston, Heather Itamoto, Jeffery L. Werbin, VK Gadi, Tanisha Mojica, Alisa Clein, Celestia Higano, Daniel E. Sabath, Eric P. Kaldjian, Tad George. Investigation of custom biomarkers on circulating tumor cells from clinical samples using RarePlex® Developer Kits [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 6442.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".