Whole-genome sequencing uncovers the genomic determinants of therapeutic resistance to immune checkpoint blockade
Bibliographic record
Abstract
Abstract Checkpoint inhibitors (CPI), ameliorate the anti-tumour response by blocking inhibitory immune checkpoint receptors, and have revolutionised the treatment of advanced cancers. However, the prediction of treatment response is suboptimal, and there remains a strong reliance on tumour mutation burden (TMB). Studies to date are limited to whole exome sequencing (WES), with no data yet reported on the utility of whole genome sequencing (WGS) in a pan-cancer cohort. Here we report a pan-cancer cohort of 318 tumour/normal genomes from the Genomics England 100,000 Genomes Project cohort treated with CPIs. Pan-cancer biomarkers previously reported from WES such as clonal TMB, total neoantigen burden and TMB had continued utility in predicting treatment response. Clonal TMB remained the strongest univariate predictor of positive treatment outcome, followed by infiltrating T cell fraction, and tobacco/UV mutational signatures. using whole genome assay, we additionally detected novel signatures associated with poor outcomes, including markers reflecting chemotherapy-induced mutations. Patients treated with chemotherapy prior to CPI displayed reduced survival irrespective of tumour type and had more subclonal mutations. Structural variants (SVs) were also predictive of poor therapeutic response and were enriched with non-coding intronic breakpoints, generating significantly fewer neoantigens than expected by chance. Global genomic features such as telomere length were associated with poor survival following CPI treatment, particularly in renal and bladder cancers. Together, these validated and novel biomarkers showed collective utility when combined to predict CPI outcomes. Our results highlight the value of WGS in detecting biomarkers of treatment resistance and highlight the promise of WGS for use in clinical practice.
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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) | 0.001 | 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".