External validation of the VIGex gene-expression signature as a novel predictive biomarker for immune checkpoint treatment
Bibliographic record
Abstract
Abstract Immune gene expression signatures are emerging as potential biomarkers for immunotherapy. Yet, their limited predictive performance and complexity limit routine clinical implementation. VIGex is a 12-gene expression classifier developed in both nCounter (Nanostring) and RNA-Seq assays and analytically validated across laboratories. VIGex classifies tumor samples into Hot, Intermediate-Cold (I-Cold) and Cold subgroups. VIGex-Hot has been associated with better immunotherapy (IO) treatment outcomes. Here we investigated the performance of VIGex and other IO biomarkers in an independent dataset of patients treated with Pembrolizumab in the INSPIRE phase 2 clinical trial (NCT02644369). Patients with advanced solid tumors were treated with Pembrolizumab 200 mg IV every 3 weeks. Tumor RNA-seq data from baseline tumor samples were classified by the VIGex algorithm. Circulating tumor DNA (ctDNA) was measured at baseline and start of cycle 3 using the bespoke Signatera™ assay. VIGex-Hot was compared to VIGex Intermediate-Cold + Cold and 4 groups were defined based on the combination of VIGex subgroups and the change in ctDNA at cycle 3 from baseline (ΔctDNA). Seventy-six patients were enrolled including 16 ovarian, 12 breast, 12 head and neck cancers, 10 melanoma and 26 other tumor types. Objective response rate was 24% in VIGex-Hot and 10% in I-Cold/Cold. VIGex-Hot subgroup was associated with higher OS (HR: 0.43; p = 0.009) and PFS (HR: 0.49; p = 0.036) when included in a multivariable model adjusted for tumor type, tumor mutational burden (TMB) and PD-L1 immunohistochemistry. The addition of ΔctDNA improved the predictive performance of the baseline VIGex classification for both OS and PFS. Our data indicate that the addition of ΔctDNA to baseline VIGex may refine prediction for IO.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".