Abstract 1030: Development of a genome-wide methylome enrichment platform for multi-cancer early detection (MCED)
Bibliographic record
Abstract
Abstract Background: Plasma cell-free DNA (cfDNA) tests represent a promising approach for cancer screening. cfDNA methylome approaches are well-suited for MCED; however, different methodologies vary in performance and many tests show decreased performance for early-stage or low-shedding tumors. Here we present a retrospective case-control study evaluating the performance of a novel genome-wide methylome enrichment platform for MCED. Methods: The full cohort (N=4,322) includes cancer cases (individuals with newly diagnosed treatment-naïve cancer) and age- and gender-matched non-cancer controls. Samples were analyzed with a bisulfite-free, non-degradative genome-wide DNA methylation enrichment platform using cfDNA isolated from plasma. Samples were split into distinct sets to train and test a machine learning classifier consisting of differentially methylated regions to distinguish cases from controls. Initial training in 1,536 samples across 8 cancer types is reported here (100 iterations of random splits (80:20) for cross-validation). The area under the receiver operating characteristic curves (AUC) and 95% confidence intervals for the median probabilities are reported in the cross-validation. Results: Cancer cases were distinguished from controls with an AUC of 0.94 (0.93, 0.96), with AUCs for individual cancer types ranging from 0.91 to 0.97. The AUC was 0.94 (0.92, 0.95) for stage I/II cancers and 0.95 (0.94, 0.96) for stage III/IV cancers. The AUC was 0.92 (0.91, 0.94) in the subset of cancers typically considered low shedding, with similar performance for stage I/II (0.91; 0.89, 0.93) and stage III/IV (0.93; 0.91, 0.95) in that subset. Conclusions: Initial analysis of case-control data demonstrates feasibility of a genome-wide methylome enrichment platform for MCED. The high detection of low-shedding and early-stage cancers is promising for MCED applications, as this will be critical for screening to identify cancers for which treatment may be more effective. Overall AUC (95% CI) for the full cohort and by cancer type. Cancer Type All Stages* Stage I/II Stage III/IV All Cancers N 931 461 437 AUC (95% CI) 0.94 (0.93, 0.96) 0.94 (0.92, 0.95) 0.95 (0.94, 0.96) Bladder Cancer** N 75 52 16 AUC (95% CI) 0.93 (0.91, 0.96) 0.93 (0.90, 0.96) 0.97 (0.95, 0.99) Breast Cancer** N 131 76 37 AUC (95% CI) 0.94 (0.91, 0.96) 0.92 (0.88, 0.95) 0.96 (0.94, 0.98) Colorectal Cancer N 182 94 88 AUC (95% CI) 0.97 (0.96, 0.98) 0.97 (0.96, 0.98) 0.97 (0.96, 0.99) Head & Neck Cancer N 75 20 55 AUC (95% CI) 0.96 (0.94, 0.98) 0.93 (0.86, 1) 0.97 (0.96, 0.99) Lung Cancer N 147 72 75 AUC (95% CI) 0.96 (0.95, 0.98) 0.96 (0.95, 0.98) 0.96 (0.95, 0.98) Ovarian Cancer N 46 12 34 AUC (95% CI) 0.96 (0.93, 0.98) 0.98 (0.96, 0.99) 0.95 (0.92, 0.98) Prostate Cancer** N 145 84 59 AUC (95% CI) 0.91 (0.88, 0.93) 0.92 (0.89, 0.95) 0.90 (0.87, 0.93) Renal Cancer** N 130 51 73 AUC (95% CI) 0.91 (0.89, 0.94) 0.89 (0.84, 0.94) 0.93 (0.89, 0.96) *Samples without stage information are included in this category **Typically considered low-shedding tumor Citation Format: Ben H. Park, Shu Yi Shen, Jun Min, Neil Fleshner, Jennifer Knox, Taymaa May, Laurie Ailles, Yulia Newton, Junjun Zhang, Rajat Singhania, Morgan Weichert, Justin Burgener, Iulia Cirlan, Jing Zhang, Yarong Wang, Eduardo Sosa, Angelica Polio, Owen Hall, Sarah Goldberg, Peter Mazzone, Brian Rini, Scott Bratman, Brian Allen, Krystal Brown, Abel Licon, Anne-Renee Hartman, Daniel D. De Carvalho, Geoffrey Liu. Development of a genome-wide methylome enrichment platform for multi-cancer early detection (MCED) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1030.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".