Gravity microfiltration for enriching circulating tumour cells and clusters
Bibliographic record
Abstract
Cancer is a leading cause of death worldwide, and metastatic lesions the primary mortal complication in solid tumour cancers. Tumour characterization by tissue biopsy is an invasive process that fails to fully account for widely prevalent spatiotemporal heterogeneity due to being assessed on a limited fraction of tissue taken at a single site and timepoint. Biopsy is often taken only during surgical resection, this can deny patients improved treatment informed by understanding tumour phenotype or worse, subject patients to costly and even harmful unnecessary treatment. This is true of colorectal cancer liver metastasis (CRCLM) in Canada, where patients receive pre-operative bevacizumab, which worsens outcome in a histological growth pattern (HGP) that is only diagnosed after the surgery.Circulating tumour cells (CTCs) in blood have a demonstrated clinical significance and are used for prognosis and monitoring in a wide range of cancers. They are much more rarely found as clusters (cCTCs), which have increased metastatic potential and may contain tumour-associated stromal and immune cells. However, most of the commonly used CTC isolation technologies are designed for isolating single cells and do not capture many cCTCs. The Juncker Lab has developed a gravity-driven microfiltration (GµF) platform suitable for the enrichment of single cell CTCs (scCTCs) and CTC clusters from blood samples on the basis of their size and mechanical properties. The platform has demonstrated excellent capture efficiency with spiked cells and has lead to exciting findings of cCTCs in ovarian cancer patients. However, bottlenecks in sample and analysis throughput need to be overcome to take advantage of the platform's strengths in large-scale studies. This works goes towards increasing the scalability of the GµF platform. First, high open-ratio membranes are designed and their fabrication optimized. These membranes allow for a five-fold increase in flow rate while maintaining the same shear-stress conditions. Next, a programmable confocal microscope routine is implemented to facilitate scalable data acquisition. Finally, we demonstrated the applicability of the improved platform in isolating CTCs from CRCLM patients. scCTCs and cCTCs were found in 13/13 patients. To the best of our knowledge, this is an unprecedented cCTC-positive rate (100%) in a set of CRCLM samples. This finding suggests that cCTC counts might be under-reported in literature due to the common use of CTC enrichment technologies that are incompatible with cCTC isolation or have low cCTC sensitivity. The outcome of this work improves the throughput of the GµF platform, which will facilitate its use in the pursuit of clinical translation research. Future studies are likely to investigate the particular role of cCTCs in the metastatic cascade, and assess their value as liquid analytes for cancer diagnosis, prognosis, and monitoring
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".