Medullary sinus macrophages limit cancer therapy efficacy by an efferocytosis-induced IL-33/ST2 axis in the tumor-draining lymph node
Bibliographic record
Abstract
Abstract Cell death is a fundamental aspect of tissue homeostasis that can expose potentially immunogenic cell components that must be removed to prevent inflammatory autoimmunity. We have shown immune suppression in response to apoptotic cell phagocytosis (a process called efferocytosis) is dependent on tissue-resident macrophages (Mf). Indeed, if we disrupt Mf regulatory responses post-efferocytosis, the same apoptotic cells induce inflammation suggesting Mf responses to dying cells are a key determinant of tolerance. We predict that resident macrophages lining the lymphatic sinus inside the lymph nodes are responsible for tolerance against tumor apoptotic cells. Using our mouse melanoma model, we observed that after either chemotherapy or targeted therapy a subpopulation of macrophages of the TDLN, called medullary sinus Mf (MSM), avidly phagocytosed dying tumor cells and acquired a tolerogenic phenotype. RNA sequencing analysis revealed MSM rapidly and exclusively induced expression of the alarmin IL-33 as opposed to other Mf or dendritic cell populations in the TDLN. Importantly, genetic deletion of Il33 in MSM using a novel MSM-specific deletion model (MSM-IL33ko) generated by our laboratory, or blockade of the IL-33 receptor ST2 with IgG, transformed responses to both chemotherapy or targeted therapy with prolonged, enhanced tumor control and reduced cancer reoccurrence. Functionally, MSM-derived IL-33 triggered accumulation and activation of regulatory T cells in the TDLN which then migrated to the tumor limiting intratumoral CD8+ T cell function. Thus, our data revealed a previously undescribed tumor cell death-induced mechanism limiting anti-cancer immunity and therapy efficacy. CIHR, NIH
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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 | 0.001 |
| 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".