Abstract 3330: Development of a genome-wide multiomic atlas of early-stage lung cancer enables identification of novel methylation biomarkers for disease detection beyond TCGA
Bibliographic record
Abstract
Abstract Lung cancer is the leading cause of cancer mortality, mainly due to diagnosis at advanced stages. Early detection reduces mortality and screening by CT is recommended for a high-risk population, however, uptake is low. Biomarkers for early-stage detection may enhance screening. Liquid biopsy and blood circulating tumor DNA (ctDNA) are established plasma surrogates of tumor tissue but their use in early detection remains challenging due to the high noise level of current techniques. We constructed a novel multiomic atlas through whole genome sequencing (WGS) to map all genetic and epigenetic changes associated with early-stage lung cancer as a tool to identify markers. Biospecimens from 48 early-stage lung cancer cases (58% stage I) and 29 cancer-free controls, all high-risk by USPSTF, were acquired from academic (UBC, Vanderbilt, Cleveland Clinic) and commercial biobanks. Cases had tumor and normal lung tissue, whole blood (WB) and plasma samples. Controls had WB and plasma samples. EpiCheck sequencing (ECS) which combines methylation-sensitive restriction endonuclease digestion to detect differential methylation with WGS for multiomic analyses was performed on each sample. Extracted DNA was digested, underwent standard library preparation and sequenced at an average depth of 600x for cfDNA and 80x for tissue and WB. A subset also underwent WG bisulfite sequencing (BS) and standard WGS for comparison. Data analysis was performed using customized software. ECS outperformed BS. Mapping rate was 99.6%, 99.7% and 85.7% and unique mapping rate was 94.1%, 94.3% and 81.4% for WGS, ECS, and BS samples, respectively. Copy number integrity showed Pearson correlations of 0.9 for ECS and 0.67 for BS. Somatic mutation analysis in tissue identified a subset of cases with relatively high ctDNA shedding in plasma that were associated with larger tumors, older age and squamous cell carcinoma histology. This subset was further used to identify tumor derived plasma-based markers and assess fragmentation with high confidence. Shorter ctDNA fragments were observed in some, but not all cases. Additional analysis of WB and plasma identified host derived blood-based markers using methylation and copy number. This orthogonal approach enabled detection of small, low shedding tumors. A discovery panel of 87 markers showed 100% sensitivity and 97% specificity to discriminate cases vs controls. Only 11% of these markers are represented in the TCGA data. ECS generates high integrity sequencing data superior to BS, enabling genome-wide multiomic analyses (methylation, mutation, copy number, fragmentomics), with methylation signatures beyond the scope of TCGA. Results, which require validation, underscore the potential of the EpiCheck lung cancer atlas as a development platform of novel blood biomarkers for early-stage lung cancer detection. Citation Format: Revital Knirsh, Stephen Lam, Anna McGuire, Peter J. Mazzone, Stephen Deppen, Eric Grogan, Fabien Maldonado, Orna Savin, Shacade Danan, Sarah Zaouch, Nimrod Axelrad, Dvir Netanely, Aharona Shuali, Catherine A. Schnabel, Adam Wasserstrom, Danny Frumkin. Development of a genome-wide multiomic atlas of early-stage lung cancer enables identification of novel methylation biomarkers for disease detection beyond TCGA [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 3330.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".