Therapeutic Targeting of LIF Overcomes Macrophage-mediated Immunosuppression of the Local Tumor Microenvironment
Bibliographic record
Abstract
PURPOSE: Leukemia inhibitory factor (LIF) is a multifunctional cytokine with numerous reported roles in cancer and is thought to drive tumor development and progression. Characterization of LIF and clinical-stage LIF inhibitors would increase our understanding of LIF as a therapeutic target. EXPERIMENTAL DESIGN: We first tested the association of LIF expression with transcript signatures representing multiple processes regulating tumor development and progression. Next, we developed MSC-1, a high-affinity therapeutic antibody that potently inhibits LIF signaling and tested it in immune competent animal models of cancer. RESULTS: LIF was associated with signatures of tumor-associated macrophages (TAM) across 7,769 tumor samples spanning 22 solid tumor indications. In human tumors, LIF receptor was highly expressed within the macrophage compartment and LIF treatment drove macrophages to acquire immunosuppressive capacity. MSC-1 potently inhibited LIF signaling by binding an epitope that overlaps with the gp130 receptor binding site on LIF. MSC-1 showed monotherapy efficacy in vivo and drove TAMs to acquire antitumor and proinflammatory function in syngeneic colon cancer mouse models. Combining MSC-1 with anti-PD1 leads to strong antitumor response and a long-term tumor-free survival in a significant proportion of treated mice. CONCLUSIONS: Overall, our findings highlight LIF as a therapeutic target for cancer immunotherapy.
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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".