Detection of circulating tumor DNA in patients with metastatic clear cell renal cell carcinoma.
Bibliographic record
Abstract
e16105 Background: Clear cell renal cell carcinoma (ccRCC) represents the most common presentation of kidney cancer. Patient outcomes have significantly improved with the approval of several targeted agents over the past decade. However, the lack of predictive biomarkers has hampered treatment optimization. Circulating tumor DNA (ctDNA) has emerged as an effective, minimally-invasive alternative to tissue for profiling the tumor genome in other tumors. Methods: Blood samples were collected from patients (pts) with metastatic ccRCC for next-generation sequencing of cell free DNA and germline DNA. Targeted sequencing was performed using the Roche Human Oncology Design panel of 981 genes to a median depth of 937x unique reads. Matched FFPE tissue was available from 14 pts and profiled using the same assay. Results: Samples from 52 metastatic, treatment-naïve pts were analyzed. Germline mutations were detected in 6/52 (11%) of the pts with the most frequent abnormalities affecting ATM, PALB2, RAD51D, CHEK2, BRCA1. Median ctDNA fraction was 3.9% (2-40%) with a median of 2 mutations/pt. Somatic mutations were detected in 29/52 (56%) of the samples; of those, 15/29 (51%) of pts harbored RCC-related genes mutations, 3/29 (10%) non-coding mutations and 10/29 (34%) alterations in non RCC-associated genes. Median variant allele frequency was 1.9% (1-22%). Consistent with tissue-based reports, VHL, BAP1, PBRM1, and TP53 were the most frequently altered genes. Lastly, 50% of the patient-matched FFPE tissue samples shared a fully concordant mutation profile. Conclusions: We confirmed a high prevalence of germline mutations in pts with ccRCC. The rate of ctDNA detection in metastatic ccRCC appears to be lower than in other metastatic solid tumors. Furthermore, it is not yet clear whether all the detected somatic alterations are strictly ccRCC-ctDNA dependent. Nevertheless, a quarter of pts exhibited clinically-informative ccRCC associated alterations in their liquid biopsy. These findings suggest that ctDNA is a promising tool for genomic profiling in a subset of patients with metastatic ccRCC.
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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 | 0.001 | 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) | 0.002 | 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".