Measurement of Signaling Pathway Activities in Solid Tumor Fine-needle Biopsies by Slide-based Cytometry
Bibliographic record
Abstract
The application of molecular targeted therapies is expected to cause a modulation of cellular signaling pathway(s) that can be monitored by sequential biopsies. Fine-needle sampling (FNS) is an atraumatic and safe technique that can be repeated at numerous points during the clinical or experimental administration of a drug. However, small volume and paucicellularity of fine-needle samples may preclude a comprehensive analysis. We describe here the image-based detection of phosphorylated signaling proteins, an approach for the measurement of pathway activities and preliminary concepts for a multiplexed analysis in these specimens. Fine-needle samples were obtained from xenograft tumors and used for cell block preparations. Preanalytical parameters for the detection of phosphorylated Stat3 and nuclear factor kappaB were determined. A cytometric approach for the measurement of pathway activities was tested using 2 different slide-based analysis techniques applied to immunofluorescence and immunohistochemistry. Changes in the phosphorylation state of Stat3 and nuclear factor kappaB were observed due to delayed fixation and reproducibly quantified. Data obtained from xenografts after drug treatment suggest that slide-based cytometry gives results that are comparable to conventional analysis methods. The applicability of quantum dot nanocrystals for the detection of phosphorylated Stat3 and the combination of different labeling techniques suggest a potential for a multiplexed analysis. We propose here that FNS of solid tumors may be useful in anatomic sites where core-needle biopsies are not possible or not well tolerated. FNS can be used for biomarkers with a homogeneous distribution throughout the tumor, and slide-based analysis techniques may be applied to quantify pathway activities.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".