Single-Cell RNA Sequencing Reveals Stromal Evolution into LRRC15+ Myofibroblasts as a Determinant of Patient Response to Cancer Immunotherapy
Bibliographic record
Abstract
Abstract With only a fraction of patients responding to cancer immunotherapy, a better understanding of the entire tumor microenvironment is needed. Using single-cell transcriptomics, we chart the fibroblastic landscape during pancreatic ductal adenocarcinoma (PDAC) progression in animal models. We identify a population of carcinoma-associated fibroblasts (CAF) that are programmed by TGFβ and express the leucine-rich repeat containing 15 (LRRC15) protein. These LRRC15+ CAFs surround tumor islets and are absent from normal pancreatic tissue. The presence of LRRC15+ CAFs in human patients was confirmed in >80,000 single cells from 22 patients with PDAC as well as by using IHC on samples from 70 patients. Furthermore, immunotherapy clinical trials comprising more than 600 patients across six cancer types revealed elevated levels of the LRRC15+ CAF signature correlated with poor response to anti–PD-L1 therapy. This work has important implications for targeting nonimmune elements of the tumor microenvironment to boost responses of patients with cancer to immune checkpoint blockade therapy. Significance: This study describes the single-cell landscape of CAFs in pancreatic cancer during in vivo tumor evolution. A TGFβ-driven, LRRC15+ CAF lineage is associated with poor outcome in immunotherapy trial data comprising multiple solid-tumor entities and represents a target for combinatorial therapy. This article is highlighted in the In This Issue feature, p. 161
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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) | 0.002 | 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".