Genome and Transcriptome Biomarkers of Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors
Bibliographic record
Abstract
Abstract Purpose: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of solid tumors with dramatic and durable responses seen across multiple tumor types. However, identifying patients who will respond to these drugs remains challenging, particularly in the context of advanced and previously treated cancers. Experimental Design: We characterized fresh tumor biopsies from a heterogeneous pan-cancer cohort of 98 patients with metastatic predominantly pretreated disease through the Personalized OncoGenomics program at BC Cancer (Vancouver, Canada) using whole genome and transcriptome analysis (WGTA). Baseline characteristics and follow-up data were collected retrospectively. Results: We found that tumor mutation burden, independent of mismatch repair status, was the most predictive marker of time to progression (P = 0.007), but immune-related CD8+ T-cell and M1-M2 macrophage ratio scores were more predictive for overall survival (OS; P = 0.0014 and 0.0012, respectively). While CD274 [programmed death-ligand 1 (PD-L1)] gene expression is comparable with protein levels detected by IHC, we did not observe a clinical benefit for patients with this marker. We demonstrate that a combination of markers based on WGTA provides the best stratification of patients (P = 0.00071, OS), and also present a case study of possible acquired resistance to pembrolizumab in a patient with non–small cell lung cancer. Conclusions: Interpreting the tumor-immune interface to predict ICI efficacy remains challenging. WGTA allows for identification of multiple biomarkers simultaneously that in combination may help to identify responders, particularly in the context of a heterogeneous population of advanced and previously treated cancers, thus precluding tumor type–specific testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".