Predictive value of plasma tumor mutation burden (TMB) in the CCTG PA.7 trial: Gemcitabine (GEM) and nab-paclitaxel (Nab-P) vs. GEM, nab-P, durvalumab (D) and tremelimumab (T) as first line therapy in metastatic pancreatic ductal adenocarcinoma (mPDAC).
Bibliographic record
Abstract
411 Background: PA.7 evaluated whether combining PD-L1 and CTLA-4 inhibition with GEM and Nab-P increases efficacy as first line therapy in mPDAC. High TMB is associated with immunotherapy sensitivity, with a threshold of ≥10 mut/Mb receiving FDA accelerated approved for pembrolizumab in a tissue agnostic setting. We assessed the predictive value of plasma TMB in the PA.7 trial. Methods: This randomized phase II study (ClinicalTrials.gov NCT02879318) assessed the efficacy and safety of GEM, Nab-P, D, and T (arm A) vs. GEM and Nab-P (arm B) in patients (pts) with mPDAC (n = 180). The primary endpoint was overall survival (OS). Pre-treatment plasma was sequenced with the CLIA-certified PredicineATLAS cfDNA next generation assay (600 genes, 2.4 Mb panel). A pre-specified cut point of 5 mut/MB was selected based on distribution of TMB in the trial. 2-sided alpha set at 0.1. Results: 180 pts were randomized (119 to arm A and 61 to arm B). There was no significant difference in OS (9.8 months in arm A vs. 8.8 months in arm B, p-value 0.72) or PFS (5.5 months and 5.4 months respectively, HR 0.98, p-value 0.91). Plasma TMB analysis was performed on 174/180 pts with available samples, and tumor derived variants were detected in 173/174 pts (99.4%). 172 pts were microsatellite stable and 1 pt was microsatellite high (MSI-H) (plasma TMB 52.9 muts/Mb). Using the pre-specified cut-point of 5 mut/Mb there was no significant predictive value from plasma TMB (interaction p = 0.91). Using a minimum p-value approach, a cut-point of 9 mut/MB appeared predictive (p-interaction = 0.064; significant at pre-specified p = 0.1). 8/174 (4.6%) pts had a plasma TMB ≥9 mut/Mb (5/115 (4.4%) in arm A and 3/59 (5%) in arm B). GEM, Nab-P, D+T was associated with improved OS in patients with plasma TMB ≥9 mut/Mb (HR 0.30, 90% CI 0.06-1.37) while no activity was seen in pts with < 9 mut/Mb, (HR 0.97, 90% CI 0.73-1.29). TMB cut-point analysis revealed a clear trend for a decreasing HR favoring the GEM, Nab-P, D and T arm above the selected cut point, with no benefit in the low TMB group. Conclusions: Plasma TMB analysis was successful in over 99% of pts with available samples. Plasma TMB ≥9 mut/Mb was predictive of benefit from the addition of dual immune checkpoint inhibitors (D and T) to Gem and Nab-P, with a significant interaction p-value. While only present in a subgroup of pts (4.6%), this data defines a group beyond MSI-H PDAC that may benefit from immunotherapy. The optimal cut-point for high TMB in this setting requires validation. A clinical trial specifically assessing the role of chemotherapy combined with immune checkpoint inhibition in high TMB mPDAC is warranted. Clinical trial information: NCT02879318.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".