THSD4 is a novel mediator of T cell exclusion and anti-PD-1 resistance in hormone receptor-positive breast cancer
Bibliographic record
Abstract
Breast cancer remains the most prevalent cancer among women, with hormone receptor-positive (HR +) tumors accounting for approximately 70% of breast cancer cases. While the immune checkpoint inhibitor (ICI) anti-programmed cell death 1 (PD-1) pembrolizumab has demonstrated efficacy in triple-negative breast cancers (TNBCs), its benefit in HR + subtypes is limited. ICI resistance in breast cancer is largely due to a "cold" tumor immune microenvironment characterized by low tumor-infiltrating lymphocytes (TILs). To identify novel genetic determinants of immune exclusion and pembrolizumab resistance, we analyzed multi-omics and clinical datasets from the I-SPY2 clinical trial and The Cancer Genome Atlas (TCGA), focusing on genes associated with low T cell infiltration and poor response to pembrolizumab. We identified thrombospondin type-1 domain containing 4 (THSD4) as a top candidate. THSD4 expression was significantly elevated in breast tumors with low T cells and in breast cancer patients exhibiting resistance to pembrolizumab, particularly within the HR + subtype. THSD4 expression is enriched in HR + breast cancers. Validation in local patient cohorts using RNA sequencing and multiplex immunofluorescence confirmed that both high THSD4 expression and anti-THSD4 antibody staining correlated with reduced T cell infiltration in the tumor epithelium and associations with poorer clinical outcomes. Functional studies in a syngeneic mouse HR + tumor model demonstrated that THSD4 promotes an immunosuppressive tumor microenvironment, with reduced T cells, resistance to anti-PD-1, and altered collagen fiber abundance. Collectively, these findings establish THSD4 as a prognostic biomarker of pembrolizumab resistance and a potential therapeutic target to enhance immunotherapy efficacy in breast cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".