Use of Molecular Biomarkers to Quantify the Spatial Distribution of Effects of Anticancer Drugs in Solid Tumors
Bibliographic record
Abstract
Poor distribution of anticancer drugs within solid tumors may limit their effectiveness. Here, we characterize the distribution within solid tumors of biomarkers of drug effect. γ-H2AX, cleaved-caspase-3 or -6, and Ki67 were quantified in tumor sections in relation to blood vessels (recognized by CD31) using monoclonal antibodies and immunohistochemistry. To validate their use, we compared their time-dependent distribution with that of (i) fluorescent doxorubicin and (ii) a monoclonal antibody that detects melphalan-induced DNA adducts. The biomarkers were then used to quantify the distribution of docetaxel in relation to tumor blood vessels. Activation of γ-H2AX was evaluated following in vitro exposure of tumor cells to multiple drugs. Distributions of doxorubicin in MDA-MB-231 and MCF-7 xenografts and of melphalan-induced DNA adducts in MCF-7 and EMT-6 tumors decreased with distance from blood vessels, similar to the distributions of (i) γ-H2AX at 10 minutes, (ii) cleaved caspase-3 or -6, and (iii) change in Ki67 at 24 hours following treatment. The distribution of these biomarkers following treatment with docetaxel also decreased with increasing distance from tumor blood vessels. Activation of γ-H2AX occurred within 1 hour after exposure to several drugs in culture. Multiple anticancer drugs show a decrease in activity with increasing distance from tumor blood vessels; poor drug distribution is an important cause of drug resistance. The above biomarkers may be used in designing strategies to overcome therapeutic resistance by modifying or complementing the limited spatial distribution of drug activity in solid tumors.
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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 | 0.001 |
| 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".