Chromosomal Instability in Cell-Free DNA Is a Serum Biomarker for Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Genomic instability resulting in copy number variation is a hallmark of malignant transformation and may be identified through massive parallel sequencing. Tumor-specific cell free DNA (cfDNA) present in serum and plasma provides a real-time, easily accessible surrogate. METHODS: DNA was extracted from serum of 204 patients with prostate cancer (Gleason score 2-10), 207 male controls, and patients with benign hyperplasia (n = 10) and prostatitis (n = 10). DNA was amplified by use of random primers, tagged with molecular identifiers, sequenced on a SOLID system, and aligned to the human genome. We evaluated the number of sequence reads of cfDNA in sliding 100-kbp intervals for variation from controls. We used chromosomal regions with significant variations in alignment hits for their ability to segregate patients and matched controls. RESULTS: Using ROC curves to assess diagnostic performance, we evaluated the number of regions in a first subset (n = 177), with variations in alignment hits alone, provided an area under the curve (AUC) of 0.81 (95% CI 0.7-0.9, P < 0.001). Using 5 rounds of 10-fold cross-validation with the full data set, we established a final model that discriminated prostate cancer from controls with an AUC of 0.92 (0.87-0.95), reaching a diagnostic accuracy of 83%. Both benign prostatic hypertrophy and prostatitis could be distinguished from prostate cancer by use of cfDNA, with an accuracy of 90%. CONCLUSIONS: Assessment of a limited number of chromosomal structural instabilities by use of massive parallel sequencing of cfDNA was sufficient to distinguish between prostate cancer and controls. This large cohort demonstrates the utility of cfDNA in prostate cancer recently established in other malignant neoplasms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".