Cancer cell SMAD4 loss promotes tumor progression by modulating the tumor immune microenvironment in pancreatic ductal adenocarcinoma 2803
Bibliographic record
Abstract
Abstract Description Pancreatic cancer’s dismal 12% five-year survival rate urgently calls for a better understanding of the underlying tumor biology. KRAS, TP53, and SMAD4 mutations are frequent and are thought to be key disease drivers in the most common pancreatic cancer type, pancreatic ductal adenocarcinoma (PDAC). Recent literature has suggested KRAS and TP53 mutations can drive cancer progression by enhancing suppressive myeloid cell recruitment to the tumor microenvironment (TME). Thus, this prompts the need to understand if additional SMAD4-inactivating mutations can further contribute to an immunosuppressive PDAC TME. We hypothesize SMAD4 loss modulates the TME myeloid compartment to support tumor progression. We used CRISPR-Cas9 to delete Smad4 in an immunogenic murine PDAC cell line to generate SMAD4 knockout (SKO) and SMAD4-intact control lines (mock). SKO orthotopic implantation in C57BL/6 mice resulted in greater tumor burden compared to mock, but not in immunodeficient NSG mice. Single cell RNA-sequencing of tumors prior to tumor weight divergence revealed the greatest differences in the macrophage and granulocyte clusters. Notably, lipid-associated macrophages were enriched in SKO tumors, and this was accompanied by an influx of exhausted CD8+ T cells, highlighting a more immunosuppressive TME overall. Collectively, these results suggest a critical role for cancer cell SMAD4 loss in modulating the TME to subvert anti-tumor immunity and promote PDAC progression. Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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) | 0.002 | 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".