Biomaterials’ enhancement of immunotherapy for breast cancer by targeting functional cells in the tumor micro-environment
Bibliographic record
Abstract
Immunotherapy for breast cancer is now being considered clinically, and more recently, the number of investigations aimed specifically at nano-biomaterials-assisted immunotherapy for breast cancer treatment is growing. Alterations of the breast cancer micro-environment can play a critical role in anti-tumor immunity and cancer development, progression and metastasis. The improvement and rearrangement of tumor micro-environment (TME) may enhance the permeability of anti-tumor drugs. Therefore, targeting the TME is also an ideal and promising option during the selection of effective nano-biomaterial-based immuno-therapeutic strategies excepted for targeting intrinsic resistant mechanisms of the breast tumor. Although nano-biomaterials designed to specifically release loaded anti-tumor drugs in response to tumor hypoxia and low pH conditions have shown promises and the diversity of the TME components also supports a broad targeting potential for anti-tumor drug designs, yet the applications of nano-biomaterials for targeting immunosuppressive cells/immune cells in the TME for improving the breast cancer treating outcomes, have scarcely been addressed in a scientific review. This review provides a thorough discussion for the application of the different forms of nano-biomaterials, as carrier vehicles for breast cancer immunotherapy, targeting specific types of immune cells in the breast tumor microenvironment. In parallel, the paper provides a critical analysis of current advances/challenges with leading nano-biomaterial-mediated breast cancer immunotherapeutic strategies. The current review is timely and important to the cancer research field and will provide a critical tool for nano-biomaterial design and research groups pushing the clinical translation of new nano-biomaterial-based immuno-strategies targeting breast cancer TME, to further open new avenues for the understanding, prevention, diagnosis and treatment of breast cancer, as well as other cancer types.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".