Estrogen Receptor Blockade Potentiates Immunotherapy for Liver Metastases by Altering the Liver Immunosuppressive Microenvironment
Bibliographic record
Abstract
Liver metastases (LM) remain a major cause of cancer-related death and are a major clinical challenge. LM and the female sex are predictors of a poorer response to immunotherapy but the underlying mechanisms remain unclear. We previously reported on a sexual dimorphism in the control of the tumor microenvironment (TME) of colorectal carcinoma liver metastases (CRCLM) and identified estrogen as a regulator of an immunosuppressive TME in the liver. Here we aimed to assess the effect of estrogen deprivation on the cytokine/chemokine profile associated with CRCLM, using a multiplex cytokine array and the RNAscope technology, and its effects on the innate and adaptive immune responses in the liver. We also evaluated the benefit of combining the selective estrogen-receptor degrader Fulvestrant with immune checkpoint blockade for the treatment of CRCLM. We show that estrogen depletion altered the cytokine/chemokine repertoire of the liver, decreased macrophage polarization, as reflected in reduced accumulation of tumor infiltrating M2 macrophages and increased the accumulation of CCL5+/CCR5+ CD8+ T and NKT cells in the liver TME. Similar results were obtained in a murine pancreatic ductal adenocarcinoma model. Importantly, treatment with Fulvestrant also increased the accumulation of CD8+CCL5+, CD8+CCR5+ T and NK cells in the liver TME and enhanced the therapeutic benefit of anti-PD1 immunotherapy, resulting in a significant reduction in the outgrowth of LM. Taken together, our results show that estrogen regulates immune cell recruitment to the liver and suggest that inhibition of estrogen action could potentiate the tumor-inhibitory effect of immunotherapy in hormone-independent and immunotherapy-resistant metastatic cancer. SIGNIFICANCE: The immune microenvironment of the liver plays a major role in controlling the expansion of hepatic metastases and is regulated by estrogen. We show that treatment of tumor-bearing mice with an estrogen receptor degrader potentiated an anti-metastatic effect of immunotherapy. Our results provide mechanistic insight into clinical findings and a rationale for evaluating the efficacy of combination anti-estrogen and immunotherapy for prevention and/or treatment of hepatic metastases in female patients.
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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".