Multiple Immune-Suppressive Mechanisms in Fibrolamellar Carcinoma
Bibliographic record
Abstract
Abstract Fibrolamellar carcinoma (FLC) is a rare type of liver cancer that affects adolescents and young adults. The most effective treatment for FLC is surgical resection, but no standardized systemic therapy exists for patients with recurrent or unresectable FLC. As a first step to understand the immune microenvironment of FLC, we investigated targetable immune-checkpoint pathways, PD-1, PD-L1, B7-H3, IDO-1, and LAG3, in relation to CD8+ cytotoxic T-lymphocyte density. Thirty-two FLC tumor specimens were analyzed using IHC staining for PD-L1, CD8, PD-1, IDO, LAG3, and B7-H3. Sixty-three percent of FLC cases demonstrated membranous PD-L1 expression on tumor cells, and almost 70% of cases demonstrated PD-L1+ tumor-infiltrating lymphocytes and tumor-associated macrophages (TIL/TAM). Myeloid-derived cells appeared to be a major component of PD-L1+ tumor-infiltrating immune cells. Forty percent of the cases showed B7-H3 expression in the tumor zone, with 91% cases showing B7-H3 expression in TILs and TAMs. IDO and PD-1 expression was highest in the tumor interface zone. B7-H3 or IDO expression on tumor cells significantly correlated with higher CD8+ T-cell density. In conclusion, a high proportion of FLC cases showed robust expression of PD-1, PD-L1, B7-H3, and IDO in an adaptive immune-resistance pattern. Our findings provide further basis for targeting these different immune-checkpoint axes in FLC.
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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 | 0.001 | 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".