Bibliographic record
Abstract
Besides limited success in treatment of melanoma and renal cell carcinoma, immune treatments of cancer (cancer immunotherapy) had not until recently met the great expectations associated with them over the years. This failure appears now to be reversed with the introduction of checkpoint (immune blockade) inhibitors. Two receptor-ligand checkpoint inhibition pairs, the one based on the inhibition of inhibitory receptor cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and the other based on the inhibition of the programmed death-ligand 1/programmed cell death-1 (PD-L1/PD-1) pair have entered the clinical arena. Melanoma leads the way followed by non-small cell lung cancer (NSCLC) in both of which such drugs are already approved for clinical use. Several other cancer types will follow as trials data accumulate. Breast cancer clinical data are mixed so far and the arising picture is one of efficacy dependent on sub-types and sub-sets. This article will review available data on checkpoint molecules expression in breast cancer cells that may be one determinant factor of effective inhibition, as well as other possible biomarkers of immune blockade inhibitors effectiveness in breast cancer. Emerging data of clinical trials of immune checkpoint inhibitors in breast cancer will also be presented. Development and validation of reliable predictive markers of response to this new category of anti-cancer drugs will help optimize results and spare patients not expected to respond the toxicity and cost of the drugs. Moreover, predictive markers may advance the understanding of resistance to these therapies in order to reverse it.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.002 |
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".