A multi‐omic single cell sequencing approach to develop a <scp>CD8</scp> T cell specific gene signature for <scp>anti‐PD1</scp> response in solid tumors
Bibliographic record
Abstract
Abstract Immune checkpoint blockade (ICB) has led to durable clinical responses in multiple cancer types. However, biomarkers that identify which patients are most likely to respond to ICB are not well defined. Many putative biomarkers developed from a small number of samples often fail to maintain their predictive status in larger validation cohorts. We show across multiple human malignancies and syngeneic murine tumor models that neither pretreatment T cell receptor (TCR) clonality nor changes in clonality after ICB correlate with response. Dissection of tumor infiltrating lymphocytes pre‐ and post‐ICB by paired single‐cell RNA sequencing and single‐cell TCR sequencing reveals conserved and distinct transcriptomic features in expanded TCR clonotypes between anti‐PD1 responder and nonresponder murine tumor models. Overall, our results indicate a productive anti‐tumor response is agnostic of TCR clonal expansion. Further, we used single‐cell transcriptomics to develop a CD8+ T cell specific gene signature for a productive anti‐tumor response and show the response signature to be associated with overall survival (OS) on nivolumab monotherapy in CheckMate‐067, a phase 3 clinical trial in metastatic melanoma. These results highlight the value of leveraging single‐cell assays to dissect heterogeneous tumor and immune subsets and define cell‐type specific transcriptomic biomarkers of ICB response.
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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 | 0.001 | 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".