A liquid biopsy for detecting circulating mesothelial precursor cells: A new biomarker for diagnosis and prognosis in mesothelioma
Bibliographic record
Abstract
BACKGROUND: Malignant pleural mesothelioma (MPM) is an aggressive cancer related to asbestos exposure. Early diagnosis is challenging due to generic symptoms and a lack of biomarkers. We previously demonstrated that mesothelial precursor cells (MPC) characterized by mesothelin (MSLN)+CD90+CD34+ could be implicated in the development of mesothelioma after asbestos exposure. Here, we aimed to determine the clinical significance of detecting MPC in blood for early-stage diagnosis and prognosis of mesothelioma. METHODS: Due to the rarity of MPC in blood, it is challenging to identify this cell population using conventional techniques. Hence, we have developed a microfluidic liquid biopsy platform called MesoFind that utilizes an immunomagnetic, mesothelin capture strategy coupled with immunofluorescence to identify rare populations of cells at high sensitivity and precision. To validate our technique, we compared this approach to flow cytometry for the detection of MPC in murine blood and lavage samples. Upon successful validation of the murine samples, we then proceeded to examine circulating MPC in 23 patients with MPM, 23 asbestos-exposed individuals (ASB), and 10 healthy donors (HD) to evaluate their prognostic and diagnostic value. FINDING: MPC were successfully detected in the blood of murine samples using MesoFind but were undetectable with flow cytometry. Circulating MPC were significantly higher in patients with epithelioid MPM compared to HD and ASB. The MPC subpopulation, MSLN+ and CD90+, were upregulated in ASB compared to HD suggesting an early role in pleural damage from asbestos. The MPC subpopulation, MSLN+ and CD34+, in contrast, were detected in advanced MPM and associated with markers of poor prognosis, suggesting a predominant role during cancer progression. INTERPRETATION: The identification of circulating MPC presents an attractive solution for screening and early diagnosis of epithelioid mesothelioma. The presence of different subtypes of MPC have a prognostic value that could be of assistance with clinical decisions in patients with MPM. FUNDING: Princess Margaret Hospital Foundation Mesothelioma Research Fund, Toronto General & Western Hospital Foundation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".