Gravity-based microfiltration reveals unexpected prevalence of circulating tumor cell clusters in ovarian and colorectal cancer
Bibliographic record
Abstract
Circulating tumor cells (CTCs) are rare (a few cells per milliliter of blood) and mostly isolated as single-cell CTCs (scCTCs). CTC clusters (cCTCs), even rarer, are of growing interest, notably because of their higher metastatic potential, but very difficult to isolate. We introduce gravity-based microfiltration (GµF) for facile isolation of cCTCs using in-house fabricated microfilters and 3D printed cartridges. Optimal flow rate and pore size for cCTC isolation are determined by GµF of cultured ovarian single cells and cell clusters spiked in healthy blood. We perform GµF of blood from orthotopic ovarian cancer mouse models and characterize the morphological features of scCTCs and cCTCs, and the expression of molecular markers for aggressiveness. Finally, we analyze blood from 17 epithelial ovarian cancer patients with either localized or metastatic disease, and from 13 colorectal cancer liver metastasis patients. Here, we show that GµF optimized for cell cluster isolation captures cCTCs from blood while minimizing unwanted cluster disaggregation, with ~85% capture efficiency. We detect cCTCs in every patient, with between 2–100+ cells. We find cCTCs represent between 5–30% of all CTC capture events, and 10-80% of the CTCs are clustered; remarkably, in 10 patients, most CTCs are circulating not as scCTCs, but as cCTCs. GµF uncovers the unexpected prevalence and frequency of cCTCs including sometimes very large ones in epithelial ovarian cancer patients, and motivates additional studies to uncover their properties and role in disease progression. Circulating tumor cells (CTCs) are cancer cells that have moved from the cancer tumor itself into the bloodstream. CTCs are considered responsible for cancers spreading from the place they initially start to grow to other parts of the body, a process called metastasis. CTCs can be found as single cells or as clusters of cells, but clusters have remained elusive. We developed a way to capture clusters of CTCs and use it to isolate them from 17 people with ovarian cancer and 13 people with colorectal cancer which had spread to their liver. We find clusters of CTCs in all the cancer patients. Our study highlights the prevalence of these cell clusters, and our isolation method could be used to study them further, as well as potentially diagnose and monitor the impact of therapy in the future. Meunier et al. explore circulating tumor cell (CTC) population identification in ovarian and colorectal cancer. Utilization of optimized gravity-based microfiltration captures both single and clustered CTCs in animal models and patient samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".