PO-401 Tumour stem cell characteristics are negatively associated with anti-cancer immunity in diverse solid cancers
Bibliographic record
Abstract
Introduction The majority of clinical responses to immunotherapies appear to be restricted to tumours displaying a pre-existing T cell-infiltrated tumour microenvironment. Consequently, understanding the molecular mechanisms leading to a T cell-poor microenvironment will be crucial for the development of novel treatments to increase the number of patients benefiting from immunotherapy. Increasing evidence has suggested that tumours are organised in a hierarchical structure of phenotypically heterogeneous cell populations. Cancer stem cells (CSCs) are at the top of this tumour cell hierarchy and sustain the long-term maintenance of neoplasms. The ability of CSCs, as well as physiological stem cells, like embryonic stem cells and mesenchymal stem cells, to resist immune-mediated destruction is unrivalled by more differentiated cells. Despite this, the potential impact of a cancer stem-like tumour phenotype on the ability to drive immune exclusion and avoid immune rejection has not been systematically explored. Material and methods We calculated an RNA-based metric of stemness for >8000 TCGA solid tumour samples. We assessed the association of this metric with transcriptomic signatures of immune cell infiltration and other genomic, transcriptomic, and clinical parameters. Results and discussions Tumour stemness varied strongly across cancers and negatively associated with patient survival both within and across cancers. We found that high stemness tumours show reduced inferred infiltration of multiple immune cell types, particularly of anti-tumour effector cells such as CD8 +T cells, B-cells, and NK cells. Within well-defined cancer molecular subtypes, we observed recurrent negative associations between stemness and immunity. We also detect negative correlations between immunity and defined stem cell regulatory pathways that reflect the activity of specific stemness transcription factors. Screening for potential stemness associated axes of immunosuppression, we found that enrichment of extracellular matrix organisation process could be a possibly mechanism of stemness-immune interference. Using published data from clinical trials of immune checkpoint blockade therapy, we showed that tumour stem cell transcriptional programs negative correlates with patient survival. Conclusion Our findings reveal the landscape of stemness across human solid cancers, show that tumour stemness can predict anti-cancer immunity in diverse settings, and may help to identify patients that will have improved response to immunotherapy.
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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 | 0.000 |
| 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".