Abstract 4099: Leveraging the cell-free DNA hydroxymethylome as a prognostic biomarker in small cell lung cancer
Bibliographic record
Abstract
Abstract Purpose: Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine carcinoma characterized by rapid growth, early treatment resistance, and a dismal 5-year overall survival rate of <10%. Despite a high mutation burden, identifying robust biomarkers for SCLC remain a challenge due to the lack of actionable driver mutations and the scarcity of tumor tissue. Exploring DNA 5-hydroxymethylcytosine (5hmC) in plasma cell-free DNA offers a promising, minimally invasive opportunity for genome-wide profiling, however this remains understudied in SCLC. In this study, we hypothesize that the SCLC cell-free hydroxymethylome is distinct from healthy non-cancer controls and that it controls biological pathways that mediate prognosis. Methods: Blood plasma samples were collected from a cohort of 49 de novo SCLC patients at pretreatment timepoints and 55 healthy non-cancer controls. Cell-free DNA (cfDNA) was isolated from plasma and examined using the 5hmC-selective chemical labeling (HMe-SEAL) assay, followed by next generation sequencing, to generate genome-wide 5hmC profiles. Global and gene-feature specific 5hmC patterns were compared between SCLC and control cfDNA to delineate SCLC-specific differentially hydroxymethylated regions (DhMRs). KEGG pathway analysis was performed on global DhMRs significantly enriched in SCLC. Kaplan-Meier and log-rank analyses were performed to determine the relationship between global DhMRs and survival outcomes. Overall survival (OS) was anchored from the time of SCLC diagnosis and progression-free survival (PFS) was anchored from the start of first-line treatment. Results: Among the 49 SCLC patients, 69% had extensive-stage (ES-SCLC, n=34) and 65% were male (n=32). Global enrichment of 5hmC was observed in SCLC cfDNA compared to controls, particularly at intronic regions (p<0.001). Differential analyses revealed that global and gene feature-specific DhMRs, such as enhancer regions, could distinguish between SCLC and controls by principal component analysis. Pathway analysis of global DhMRs enriched in SCLC highlighted genes involved in cell proliferation (e.g. cAMP, Hippo, Wnt signaling pathways), axonal guidance, and stemness, which are commonly altered in cancer (p<0.001 for all mentioned pathways). When correlating 5hmC levels with survival data, SCLC patients presenting with high global 5hmC levels trended towards worse OS and PFS compared to those with low global 5hmC levels (median OS of 10.5 months vs 15.3 months, p=0.13; median PFS of 5.27 months vs 7.18 months, p=0.07). Conclusion: Global cell-free 5hmC patterns distinguished between patients with SCLC and non-cancer controls and mapped to cancer-related pathways. Global 5hmC levels could also be leveraged for patient prognostic stratification, presenting a novel, minimally invasive biomarker for SCLC. Citation Format: Janice J. Li, Dangxiao Cheng, Danielle B. Sacdalan, Luna J. Zhan, Sami Ul Haq, Vivek Philip, Gregory Schwartz, Scott V. Bratman, Geoffrey Liu, Benjamin H. Lok. Leveraging the cell-free DNA hydroxymethylome as a prognostic biomarker in small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4099.
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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 | 0.001 |
| 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".