Comparison of CellSearch versus Parsortix circulating tumor cell enumeration and molecular characterization: A pilot study in metastatic prostate cancer patients
Bibliographic record
Abstract
Introduction Prostate cancer is a leading cause of cancer death in men. Although early-stage prostate cancers can be effectively managed by surgery, radiation and/or androgen-deprivation therapies, many tumors eventually become castrate-resistant, leading to disease progression, metastasis and death. The goal of this pilot study was to gain insight into the biology of prostate cancer progression by assessing circulating tumor cells (CTCs) from 3 patient cohorts: low-volume metastatic hormone-sensitive prostate cancer (LV-mHSPC); high-volume metastatic hormone-sensitive prostate cancer (HV-mHSPC); and metastatic castrate-resistant prostate cancer (mCRPC). Materials & Methods CTCs were assessed using the epithelial-based CellSearch assay versus an epithelial-to-mesenchymal transition (EMT)-independent Parsortix assay. CTCs were also harvested from Parsortix and assessed by downstream molecular analysis using the HyCEAD mRNA multiplex assay. Specific molecular characteristics identified through HyCEAD were compared to prostate cancer data from The Cancer Genome Atlas (TCGA). Results Although no significant enumeration differences were observed between the two technologies, CellSearch was able to identify a greater number of CTCs in HV-mHSPC versus LV-mHSPC patients (p≤0.05). Between the 3 patient cohorts, 17 differentially expressed genes were identified that may contribute to prostate cancer disease progression. Conclusions Taken together, our findings provide a promising panel of potential biomarkers for further investigation in order to develop a comprehensive, real-time CTC liquid biopsy strategy for the personalized clinical management of metastatic prostate cancer patients in the future.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".