Assessment of a 4-chemokine signature in prediction of T-cell inflammation and response to immune checkpoint inhibition across tumor types.
Bibliographic record
Abstract
2558 Background: Immune checkpoint inhibitors (ICI) are highly effective in select cancers. Novel predictors of T cell-inflammation may identify a broader subset of tumors with ICI responsiveness. Our group has identified four chemokines (CCL4, CCL5, CXCL9, CXLC10) able to predict a T cell-inflamed phenotype in primary and metastatic pancreatic tumors. Here, we test whether this 4-chemokine signature can predict T cell-inflammation across additional tumor types and response to ICI. Methods: Using matched genomic and transcriptomic data from 6,455 patients spanning 25 tumor types from The Cancer Genome Atlas, we searched for associations between the 4-chemokine signature and metrics of antitumor immunity. Further, we tested the association of this signature with markers of DNA damage repair deficiency. We also investigated the ability of this signature to predict response to immunotherapy using real-world data from a pan-cancer cohort of 82 patients in the Personalized OncoGenomics Program who had received ICI. Results: The majority of tumor types displayed sub-populations with high expression of the 4-chemokines (4-chemokinehi) and transcriptional hallmarks of the cancer-immunity cycle. Testicular germ cell tumors, cervical squamous cell carcinomas, and head and neck squamous cell carcinomas were the strongest expressors of the signature. Immunomodulatory genes, including PD-L1, PD-1, TIM3, LAG3, TIGIT, CTLA-4, and FASLG, were significantly overexpressed (p<0.05) in the 4-chemokinehi cohorts. Genesets of processes involved in the cancer-immunity cycle, including MHC I expression and cytolytic activity, were upregulated in the 4-chemokinehi cohorts (p<0.05). While a global relationship between tumor mutation burden (TMB) and 4-chemokine expression across tumor histological type was seen (rho=0.42, p=0.02), high TMB was associated with only a subset of 4-chemokinehi tumors. Among patients treated with ICIs, those with 4-chemokinehi tumors had a longer median time to progression (104 versus 71 days, p=0.013) and overall survival (391 versus 195 days, p=0.016). The 4-chemokine signature outperformed TMB for overall survival prediction. Conclusions: Sub-populations of T cell-inflamed patients exist across tumor types and may therefore respond favourably to ICI. The 4-chemokine signature has the potential to select a wider spectrum of patients that may benefit from ICIs. [Table: see text]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".