Abstract PO-065: Utilization of optimized extracellular matrix substratum for inclusive capture of circulating tumor cells in stage IV colorectal cancer
Bibliographic record
Abstract
Abstract Current methods to recover circulating tumour cells (CTCs) from the peripheral blood circulation of carcinoma patients rely principally on the expression of epithelial markers such as EpCAM, physical characteristics, and the absence of hematopoietic biomarkers. However, this strategy favours recovery of a homogeneous cell population with primarily differentiated epithelial characteristics. It fails to fully include those subpopulations that do not fit predictions and in particular does not properly capture cells that have stem-like characteristics or have undergone epithelial-mesenchymal transition (EMT). Yet these are often the cells that are most likely to give rise to successful metastasis and contribute to disease progression. We favour an approach that anticipates cellular heterogeneity, includes cells that have poor expression of differentiated characteristics, and relies on the very functional properties that are involved in successful negotiation of the route that culminates in metastasis. We therefore use a ‘tuned ECM’ (tECM) approach in which a complex ECM substratum is designed to extract and recover heterogeneous CTCs from the blood of patients with the cancer of interest, allowing subsequent single-cell analysis of cell behaviours to inform on features that have been shown to predict disease progression and metastasis. In the work described here we first used model colorectal cancer (CRC) cell lines, including those made resistant to drugs used in CRC regimens, to establish the best composition of ECM and full procedure to maximally capture cells for functional evaluation. For CRC we identified a defined mixture of collagen I and human plasma fibronectin as optimal for cell capture, providing a total recovery equivalent to complex ECM derived from tumor sources. In addition, this mixture accentuated the recovery of CRC cells made resistant to SN-38, the active metabolite of irinotecan. We then applied this approach to the recovery of CTCs from the blood from patients with stage IV CRC, using density centrifugation for prefractionation of nucleated blood cells followed by incubation on the tECM substratum. The captured cells could be immunostained in situ for both cytoplasmic and cell-surface markers and showed suitability for future assessment of cell behaviours in unfixed preparations. Optimisation of the capture-surface coating concentrations and subsequent fixation approach led to a 128-fold improvement in the CTC capture and identification from CRC patient blood. This optimised tECM approach offers promise for future inclusive recovery of heterogeneous CTC samples based on functional behaviours that relate to the metastastic process of the particular carcinoma, permitting further analyses including of chemokine pathways that are believed to be important in CRC metastasis. Citation Format: Deep Patel, Mala Bahl, Mario Valdes, Jonathan Blay. Utilization of optimized extracellular matrix substratum for inclusive capture of circulating tumor cells in stage IV colorectal cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-065.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".