Combined Transcriptome and Circulating Tumor DNA Longitudinal Biomarker Analysis Associates With Clinical Outcomes in Advanced Solid Tumors Treated With Pembrolizumab
Bibliographic record
Abstract
PURPOSE: Immune gene expression signatures are emerging as potential biomarkers for immunotherapy (IO). VIGex is a 12-gene expression classifier developed in both nCounter (Nanostring) and RNA sequencing (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 IO treatment outcomes. Here, we investigated the performance of VIGex and other IO biomarkers in an independent data set of patients treated with pembrolizumab in the INSPIRE phase II clinical trial (ClinicalTrials.gov identifier: NCT02644369). MATERIALS AND METHODS: Patients with advanced solid tumors were treated with pembrolizumab 200 mg IV once 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 with VIGex I-Cold + Cold and four groups were defined on the basis of the combination of VIGex subgroups and the change in ctDNA at cycle 3 from baseline (ΔctDNA). RESULTS: 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 overall survival (OS) and progression-free survival (PFS) when included in a multivariable model adjusted for tumor type, tumor mutation burden, and PD-L1 immunohistochemistry. The addition of ΔctDNA improved the predictive performance of the baseline VIGex classification for both OS and PFS. CONCLUSION: 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 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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".