Current Landscape of Immunotherapy in Breast Cancer
Bibliographic record
Abstract
IMPORTANCE: There is tremendous interest in using immunotherapy to treat breast cancer, as evidenced by the more than 290 clinical trials ongoing at the time of this narrative review. The objective of this review is to describe the current status of immunotherapy in breast cancer, highlighting its potential in both early-stage and metastatic disease. OBSERVATIONS: After searching ClinicalTrials.gov on April 24, 2018, and PubMed up to June 30, 2018, to identify breast cancer immunotherapy trials, we found that immune checkpoint blockade (ICB) is the most investigated form of immunotherapy in breast cancer. Use of ICB as monotherapy has achieved objective responses in patients with breast cancer, with higher rates seen when administered in earlier lines of therapy. For responding patients, those responses are durable. More recent data suggest clinical efficacy when ICB is given in combination with chemotherapy. Ongoing studies are evaluating combination strategies pairing ICB with additional chemotherapeutic agents, targeted therapy, vaccines, and local ablative therapies to enhance response. To date, robust predictive biomarkers for response to ICB have not been established. CONCLUSIONS AND RELEVANCE: It is anticipated that combination therapy strategies will be the way forward for immunotherapy in breast cancer, with an improved understanding of tumor, microenvironment, and host factors informing treatment combination decisions. Thoughtful study design incorporating appropriate end points and correlative studies will be critical in identifying optimal strategies for enhancing the immune response against breast 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".