Characterization of tumor cell dissemination patterns in preclinical models of cancer metastasis using flow cytometry and laser scanning cytometry
Bibliographic record
Abstract
The inability to sensitively detect metastatic cells in preclinical models of cancer has created challenges for studying metastasis in experimental systems. We previously developed a flow cytometry (FCM) method for quantifying circulating tumor cells (CTCs) in mouse models of breast cancer. We have adapted this methodology for analysis of tumor dissemination to bone marrow (BM) and lymph node (LN), and for analysis of these samples by laser scanning cytometry (LSC). Our objective was to implement these methodologies for characterization of tumor cell dissemination in preclinical models of cancer metastasis. Human cancer cells were injected into mice via mammary fat pad (MFP; spontaneous metastasis), tail vein (TV; targets lung), or intracardiac (IC; targets bone) routes. At several time points postinjection (4 h to 8 weeks), mice were sacrificed and blood, LNs, and BM were collected. Samples were immunomagnetically enriched and labeled with human leukocytic antigen-fluorescein isothiocyanate and CD45-PE antibodies (FCM/LSC), and propidium iodide (FCM) prior to quantitative analysis. Following MFP injection, CTCs increased over time, as did disseminated cells to the LN. Interestingly, tumor cells also spontaneously disseminated to BM, peaking at 2 weeks postinjection. Following TV injection, CTCs were initially high but decreased rapidly by 1 week before increasing to peak at endpoint. Combined with an observed concurrent increase in disseminated cells to LN and BM, this suggests that tumor cells may shed into the circulation from lung metastases that establish following initial cell delivery. Following IC injection, CTCs increased over time, peaking at 4 weeks. Tumor cells in the BM (most prevalent site of metastasis after IC injection) remained at moderate levels until peaking at endpoint. Combined use of FCM and LSC allows sensitive quantification of disseminated tumor cells in preclinical models of metastasis. These methods will be valuable for future studies aimed at testing new therapeutics in the metastatic setting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".