Immune checkpoint blockade in triple negative breast cancer influenced by B cells through myeloid-derived suppressor cells
Bibliographic record
Abstract
Triple negative breast cancer holds a dismal clinical outcome and as such, patients routinely undergo aggressive, highly toxic treatment regimens. Clinical trials for TNBC employing immune checkpoint blockade in combination with chemotherapy show modest prognostic benefit, but the percentage of patients that respond to treatment is low, and patients often succumb to relapsed disease. Here, we show that a combination immunotherapy platform utilizing low dose chemotherapy (FEC) combined with oncolytic virotherapy (oHSV-1) increases tumor-infiltrating lymphocytes, in otherwise immune-bare tumors, allowing 60% of mice to achieve durable tumor regression when treated with immune checkpoint blockade. Whole-tumor RNA sequencing of mice treated with FEC + oHSV-1 shows an upregulation of B cell receptor signaling pathways and depletion of B cells prior to the start of treatment in mice results in complete loss of therapeutic efficacy and expansion of myeloid-derived suppressor cells. Additionally, RNA sequencing data shows that FEC + oHSV-1 suppresses genes associated with myeloid-derived suppressor cells, a key population of cells that drive immune escape and mediate therapeutic resistance. These findings highlight the importance of tumor-infiltrating B cells as drivers of antitumor immunity and their potential role in the regulation of myeloid-derived suppressor cells.
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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 | 0.000 |
| 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".