CAR-T cell therapy in triple-negative breast cancer: Hunting the invisible devil
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is known as the most intricate and hard-to-treat subtype of breast cancer. TNBC cells do not express the well-known estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (HER2) expressed by other breast cancer subtypes. This phenomenon leaves no room for novel treatment approaches including endocrine and HER2-specific antibody therapies. To date, surgery, radiotherapy, and systemic chemotherapy remain the principal therapy options for TNBC treatment. However, in numerous cases, these approaches either result in minimal clinical benefit or are nonfunctional, resulting in disease recurrence and poor prognosis. Nowadays, chimeric antigen receptor T cell (CAR-T) therapy is becoming more established as an option for the treatment of various types of hematologic malignancies. CAR-Ts are genetically engineered T lymphocytes that employ the body's immune system mechanisms to selectively recognize cancer cells expressing tumor-associated antigens (TAAs) of interest and efficiently eliminate them. However, despite the clinical triumph of CAR-T therapy in hematologic neoplasms, CAR-T therapy of solid tumors, including TNBC, has been much more challenging. In this review, we will discuss the success of CAR-T therapy in hematological neoplasms and its caveats in solid tumors, and then we summarize the potential CAR-T targetable TAAs in TNBC studied in different investigational stages.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".