Circulating tumor DNA (ctDNA) and correlations with clinical prognostic factors in patients with metastatic castration-resistant prostate cancer (mCRPC).
Bibliographic record
Abstract
186 Background: Sequencing of ctDNA is a minimally invasive method to study somatic DNA alterations. Quantitation and characterization of ctDNA may correlate with tumor burden and poor risk clinical features, but this is unproven in mCRPC. Methods: We performed deep targeted sequencing of 73 mCRPC-related genes in plasma ctDNA from 136 treatment naïve mCRPC patients. Fraction of ctDNA was determined by quantifying somatic mutant allele frequency and the deviation from heterozygosity for germline single nucleotide polymorphisms at regions of somatic copy number alterations. Baseline clinical factors were correlated with ctDNA fraction and genomic aberrations. Results: ctDNA could be quantified in 86/136 (63%) patients. Increasing fraction correlated with elevated ALP (chi2, p < 0.001), LDH (p < 0.001) and presence of liver metastasis (p = 0.01). Patients with any one of these factors were more likely to have a ctDNA fraction ≥ 40% (67% vs 36% of patients with neither factor, p = 0.001) and less likely to have unquantifiable ctDNA (26% vs 49%, p = 0.006). A decreased ctDNA fraction was associated with metastases only to lymph nodes (p = 0.012). Among the most frequently aberrant genes were Androgen Receptor (AR), p53, RB1, and PTEN, present in 38%, 30%, 23% and 22% of patients, respectively. Aberrations were associated with poor prognosis factors, including: AR and p53 alterations, which both correlated with presence of liver metastasis (p = 0.020 and p = 0.004 respectively), elevated LDH (p = 0.001, p < 0.001), and presence of ≥ 10 bone metastasis (p = 0.018, p = 0.017); PTEN deletion which correlated with elevated ALP (p < 0.001), elevated LDH (p = 0.001), ≥ 10 bone metastasis (p = 0.009) and time to CPRC of < 12 months (p = 0.021); and RB1 correlated with ≥ 10 bone metastasis (p = 0.009). Conclusions: We found an association between poor prognostic factors and increasing ctDNA fraction in patients with mCRPC. Genomic aberrations, particularly alteration in AR, p53 and PTEN, correlated with poor prognostic factors. These data show that clinical factors may help predict ctDNA yield in patients with mCRPC, and also provide insight into the role of deleterious genetic alterations.
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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) | 0.001 | 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".