Complementary detection of genomic alterations in metastatic castration-resistant prostate cancer (mCRPC) from CheckMate 9KD through analyses of tumor tissue and plasma DNA.
Bibliographic record
Abstract
5038 Background: Accurate analysis of the genomic alteration landscape within tissue- and plasma-derived tumor DNA using next-generation sequencing (NGS) may provide insights into specific patient populations that benefit from different therapies. The interchangeable use of tissue- and plasma-based assessments may benefit patients when tissue availability is limited, a common occurrence in individuals with mCRPC. To understand the potential sources of technical and biological variability in this setting, we performed comprehensive comparative analyses across 3 NGS platforms, using samples from patients enrolled in CheckMate 9KD, a phase 2 study of nivolumab combined with docetaxel, rucaparib, or enzalutamide for patients with confirmed mCRPC (NCT03338790). Methods: We performed retrospective integrated analyses of sequence and structural alterations identified through comprehensive genomic profiling (CGP) of DNA obtained from formalin-fixed, paraffin-embedded tissue specimens and cell-free DNA obtained from plasma. Tissue-based analysis was performed using the FoundationOne assay (F1, 395 genes), while the FoundationACT (FACT, 70 genes) and GuardantOMNI (OMNI, 500 genes) assays were used for plasma-based analysis. Analysis was performed on samples from 103 patients for which datasets from all 3 assays were available. Inter-platform analysis considered variants with ≥ 0.50% variant allele fraction and common to the shared pairwise targeted regions, while excluding synonymous variants. Results: Through broad profiling of DNA obtained from tissue and plasma, we uncovered previously identified recurrent alteration of AR, TP53, PTEN, and TMPRSS2 fusion with ETS genes. Additionally, we found that 42% (F1), 45% (FACT), and 34% (OMNI) of patients harbored a combination of germline and somatic mutations in homologous recombination repair pathway genes. Across all samples, median tumor mutational burden was 3.5 mutations per megabase (mut/Mb) by F1 and 8.6 mut/Mb by OMNI. Inter-platform variant analyses demonstrated concordance of 52% for F1 vs FACT, 40% for F1 vs OMNI, and 75% for FACT vs OMNI. Conclusions: Overall, these data demonstrate the value of integrated tissue and liquid biopsy profiling in mCRPC. Both technical and biological sources of variation, including panel size, mutation detection algorithms, variant annotation and reporting, analytical performance, circulating tumor DNA levels, and tumor heterogeneity, may be captured differently by tissue- and plasma-based techniques, accounting for the discordance in reported results. Clinical trial information: NCT03338790.
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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 | 0.001 |
| 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".