Abstract 707: Combinatorial assessment of ctDNA release and mutational burden predicts clinical outcome from anti-PD-(L)1 therapies in non-small-cell lung cancer
Bibliographic record
Abstract
Abstract Background: Blood-based detection of circulating tumor DNA (ctDNA) is a non-invasive and clinical accessible source of biomarkers that enables selection of non-small-cell lung cancer (NSCLC) patients for treatment with anti-PD-(L)1 therapies. However, new approaches are necessary to improve enrichment of responders. Patients and Methods: Pretreatment plasma ctDNA from 97 advanced-stage NSCLC patients who underwent anti-PD-(L)1 therapy from December 2015 to August 2017, with matched tissue samples from 66 patients, were profiled using a targeted next-generation sequencing (NGS) panel (Geneseeq) encompassing 422 cancer-relevant genes. External validation was performed using two large randomized trials, POPLAR and OAK. Results: In the 72 patients with adequate ctDNA release (maximum somatic allele frequency, MSAF ≥2%), high blood tumor mutational burden (bTMB) was associated with significantly prolonged median PFS (110 vs 60 days, HR=0.54 [95%CI, 0.31-0.92]; log-rank p = 0.02) and a trend towards improved DCB (34.8% vs 22.5%, p=0.39). While a high concordance was observed between bTMB and tTMB (Spearman's ρ= 0.71), the greatest progression-free survival (PFS) improvement was achieved with concomitantly high bTMB and tTMB (mPFS, 156 vs 59 days, HR=0.47 [95%CI, 0.24-0.91], log-rank p=0.02). Remarkably, the subset of patients with low ctDNA release (MSAF<2%), and consequently non-evaluable bTMB, derived similar PFS benefit as those with high bTMB. The additive predictive value of MSAF and bTMB was externally validated by results from the two large randomized trials. Conclusions: Our findings validated panel-assessed bTMB as a promising biomarker that demonstrated added value over tissue testing alone for benefit with anti-PD-(L)-1 therapies. Our findings also provided the first clinical evidence suggesting that matched tissue and blood testing might be necessary to refine the predictive value of TMB. Moreover, we identified low ctDNA MSAF as an important and robust predictor of improved immunotherapy outcome. Citation Format: Wenfeng Fang, Yuxiang Ma, Jiani C. Yin, Huaqiang Zhou, Fufeng Wang, Hua Bao, Ao Wang, Xue Wu, Shaodong Hong, Yunpeng Yang, Yan Huang, Hongyun Zhao, Yang W. Shao, Li Zhang. Combinatorial assessment of ctDNA release and mutational burden predicts clinical outcome from anti-PD-(L)1 therapies in non-small-cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 707.
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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 | 0.002 |
| 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".