Global circulating free DNA methylation and fragmentome deconvolution in patients with metastatic renal cell carcinoma treated with immunotherapy (GOLDEN).
Bibliographic record
Abstract
4544 Background: Metastatic renal cell carcinoma (mRCC) lacks molecular biomarkers. Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) is a new, non-invasive approach to the detection of RCC-derived DNA in the circulation. In the GOLDEN study (NCT03702309), we explored the role of cfMeDIP-seq and fragmentomic dynamics as a biomarker, during immune checkpoint inhibition (ICI)-based treatment in mRCC patients (pts). Methods: Blood samples from mRCC pts were collected before and during treatment. Response to treatment was defined by the treating oncologist as: response, any degree of radiological tumor regression; stable disease (SD), no radiological change; or progressive disease (PD), radiological/clinical progression. More than 10ng of plasma cfDNA was subject to cfMeDIP-seq. A prognostic pan-cancer methylation signature previously generated using TCGA tissue methylation arrays was applied to cfMeDIP-seq output and cancer-signal methylation (CSM) score was calculated. An RCC-specific methylation signature, previously generated by identifying differentially methylated regions between RCC pts and controls, was applied ( Nuzzo et al, Nat Med 2020). Genome-wide fragmentation profiles were generated using DELFI. Using medians as cut off, log-rank test was performed to assess overall survival (OS) and progression-free survival (PFS). Results: Thirty-five patients were enrolled and n=33 received systemic treatment. 31/35 pts (89%) had clear cell and 4/35 (11%) non-clear cell histology. Median age was 61 (30-81) years and the M:F ratio was 6:1. Patients’ IMDC prognostic risk score was favorable n=6/35 (17%), intermediate n=23/35 (65%) and poor n=6/35 (17%). 20/33 (61%) patients received ipilimumab/nivolumab, 10/33 (30%) nivolumab and n=3/33 (9%) pembrolizumab/axitinib. There were n=14/33 (42%) responders, n=4/33 (12%) pts with SD and 15/33 (45%) pts with PD. Patients with high pan-cancer CSM score at baseline trended towards worse OS (HR 3.86, p = 0.079). Fragmentomics signature, consisting of increased proportion of short cfDNA fragments, showed a trend for worse PFS (HR = 3.04, p = 0.075) in on-treatment samples. Using the RCC-specific methylation signature, we found that those with an increase in signature score from baseline to on-treatment had better PFS (HR=2.4x10-10, p=0.002, n=10) and a trend towards better OS (HR=0.17, p=0.074, n=10). We hypothesize that RCC tissue-specific cell death early during ICI treatment could underpin this increase in the methylation score. Conclusions: This small study is the first report on the role of cfMeDIPseq-derived methylation and fragmentomic signatures in patients with mRCC undergoing ICI-based treatment. We found several trends and one significant association between these signatures and pt outcomes, which support further validation in larger cohorts. Clinical trial information: NCT03702309 .
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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".