Abstract 3385: Pre-diagnosis plasma cell-free DNA methylation profiling reveals signatures of cancers up to7 years prior to clinical detection
Bibliographic record
Abstract
Abstract Cancer survival rates are significantly improved when detected at early stages, particularly when the tumour is still localised to the tissue of origin. However, effective screening tools for early cancer detection is currently limited to a subset of cancer types. Profiling cell-free DNA (cfDNA) patterns has emerged as a prominent non-invasive biomarker for detection and subtyping of cancers. However, owing to difficulties in observing the early development of human malignancies as cancers are often detected once they become symptomatic, most cancer biomarker and evolution studies to date have primarily examined the genomics from solid tumour or liquid biopsies following a diagnosis. Utilizing cfDNA as a screening tool for early cancer detection requires profiling of blood plasma samples collected from asymptomatic individuals prior to the diagnosis of cancers to enable assessment of the earliest detectability and predictive performance of potential biomarkers. Here, we leverage the Canadian Partnership for Tomorrow’s Health Project (CanPaTH), to profile blood plasma collected prior to the clinical detection of underlying cancers. Specifically, we use cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq), a highly sensitive assay for profiling cfDNA methylomes, to profile over 300 blood plasma samples collected up to seven years prior to the detection of a breast, prostate or pancreatic cancer, in addition to matched controls with no history of cancer free through follow-up. We identified differentially methylated signatures in pre-diagnosis cfDNA that discriminated cancer-free controls from pre-diagnosis cancer cases up to seven years before diagnosis, and demonstrated that these markers were reflective of differentially methylated in cancer tissue relative to normal tissue and peripheral blood leukocytes. Further, predictive modelling reveals that cfDNA methylation markers in blood are predictive of pre-diagnosis breast cancer cases, achieving an average test AUROC of 0.75 (95% CI: 0.70 - 0.80). Predictive models trained solely with pre-diagnosis cfDNA methylation samples were also predictive of prostate and pancreatic cancer samples collected following diagnosis, achieving an average test AUROCs of 0.95 (95% CI: 0.93-0.96) and 0.96 (95% CI: 0.94-0.97) respectively. In our current studies, we focus specifically on breast, prostate and pancreatic cancer cases, and are extending this to further pan-cancer applications in subsequent investigations. Citation Format: Nicholas Cheng, David Soave, Kimberly Skead, Tom Ouellette, Scott Bratman, Daniel De Carvalho, Philip Awadalla. Pre-diagnosis plasma cell-free DNA methylation profiling reveals signatures of cancers up to7 years prior to clinical detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3385.
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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 | 0.000 | 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) | 0.003 | 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".