Abstract 563: cfDNA-based analysis of minimal residual disease and T-cell receptor clonality as predictors of relapse in stage 3 NSCLC treated with chemoradiotherapy and durvalumab
Bibliographic record
Abstract
Abstract Introduction: Durvalumab immunotherapy has rapidly emerged as standard treatment for stage 3 NSCLC patients following definitive chemoradiotherapy (CRT). Multiple novel immunotherapeutic strategies are in development to enhance the chance of cure in this setting as well. There exists a critical need to identify blood-based biomarkers capable of predicting clinical benefit from adjuvant immunotherapy as well selecting patients at high-risk of relapse for further drug development. Cell-free DNA (cfDNA)-based analysis of both minimal residual disease (MRD) and T-cell receptor (TCR) clonality have immense potential to predict and monitor response to adjuvant immunotherapy. In this study, we have combined innovative cfDNA measures of MRD (CAPPseq), TCR clonality (CapTCR-seq) and methylation (cfMeDIPseq) as potential predictive biomarkers of disease progression in stage 3 NSCLC patients treated with CRT and durvalumab. Methods: Stage 3 NSCLC patients undergoing CRT and durvalumab were recruited prospectively to undergo serial blood collections at baseline, pre- and post- durvalumab. CAPPseq and cfMeDIPseq were performed as measures of MRD. TCR repertoire analysis (CapTCR-seq) was performed on cfDNA using hybrid-capture TCR sequencing and TCR diversity/clonality was estimated using the Shannon's index. Correlations between MRD, TCR clonality, response and progression-free survival (PFS) were examined using logistic/cox regression. Results: 79 stage 3 NSCLC patients have been prospectively recruited and undergone serial blood collection. CAPPseq, cfMeDIPseq and capTCR-seq have been completed in 22 patients (5 primary progression on CRT, 17 received durvalumab). Tumor cfDNA was detectable by CAPPseq at baseline in 14 patients. High correlation between tumor cfDNA detected by CAPPseq and cfMeDIPseq was found (R=0.68, p<0.0001). Failure to clear MRD with CRT plus durvalumab was associated with significantly increased risk of recurrence with a median PFS of 5.0 vs 15.0 months (p<0.0001). Lower TCR clonality measured pre-durvalumab trended with lower likelihood of response (OR 0.82, p=0.09) and worse PFS (HR 1.16 P=0.10). Importantly, a decrease in TCR clonality compared to baseline, signaling the lack of clonal expansion on treatment, was significantly associated with a worse PFS (p=0.05). A decrease in TCR clonality of 50% after CRT was associated with a worse PFS (HR 3.5, p=0.14). CAPPseq, cfMeDIPseq and capTCR-seq analyses are ongoing in the full cohort. Conclusions: Failure to clear MRD and decreasing TCR clonality as assessed by cfDNA was highly correlated with increased risk of recurrence and reduced PFS with consolidation durvalumab. This innovative approach has significant potential to define a new biomarker for the use and development of adjuvant immunotherapy. Citation Format: Sally CM Lau, Shirin Soleimani, Jinfeng Zou, Justin Burgener, Shelley Kuang, Stephanie WY Wong, Malcolm Ryan, Ben X. Wang, Stephanie Pedersen, Devalben Patel, Penelope A. Bradbury, Geoffrey Liu, Natasha Leighl, Ming S. Tsao, Pamela S. Ohashi, Scott V. Bratman, Trevor Pugh, Frances A. Shepherd, Adrian G. Sacher. cfDNA-based analysis of minimal residual disease and T-cell receptor clonality as predictors of relapse in stage 3 NSCLC treated with chemoradiotherapy and durvalumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 563.
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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 | 0.000 | 0.001 |
| 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".