Lymphokine-activated killer cell susceptibility and adhesion molecule expression of multidrug resistant breast carcinoma
Bibliographic record
Abstract
Reports showing susceptibility of multidrug resistant (MDR) cancer cells to immune effectors, together with P-glycoprotein (P-gp) expression in immune effector subsets, including immature natural killer (NK) cells, and some activated T cells, suggest P-gp or some changes associated with it, have implications in immune-mediated mechanisms. A series of experiments were done to determine the nature of alterations associated with susceptibility to immune effector cells of MDR tumor cells. A cell line isolated from the malignant pleural effusion of a breast cancer patient was transfected with human and murine MDR1 genes, and four variants with different levels of MDR were obtained. Lymphokine-activated killer (LAK) activity was measured by a 51Chromium release, and conjugate formation assays. MDR1 transfectant P-gp+ breast carcinoma lines had increased LAK susceptibility compared to their parent line. Some part of the increased LAK susceptibility of drug-resistant cell lines was at the binding/recognition level as shown by conjugate formation assays. This suggests that differences may exist between paired cell lines with respect to the expression of cell adhesion molecules (CAMs). Monoclonal antibodies (mAbs) to CAMs and flow cytometry were used to quantitate these antigens. The CAMs studied were those previously found to be upregulated by stimulating NK cells with (interleukin-2) IL-2; ICAM-1 (CD54), LFA-3 (CD58), N-CAM (CD56), and the beta chain of LFA-1 (CD18). Although no differences in these CAMs were found between the breast carcinoma line and its MDR1-transfected variants, the target susceptibility results given above suggest that IL-2 treatment could be effective in combination with current protocols using chemotherapeutics, monoclonal antibodies (mAbs) and stem cell transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".