Cell-free Tumor Methylome Analysis of Small Cell Lung Cancer Patients Identifies Subgroups with Prognostic Associations
Bibliographic record
Abstract
Abstract Introduction Small cell lung cancer (SCLC) is a highly aggressive type of cancer with a high risk of recurrence. The SCLC methylome may yield biologic insight but is understudied due to difficulty in acquiring primary patient tissue. Here, we comprehensively profile the SCLC methylome using cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq). Methods cfDNA was extracted from plasma samples collected from 74 SCLC patients prior to initiation of first-line treatment and from 20 non-cancer smoker participants. Genomic DNA (gDNA) was also extracted from paired peripheral blood leukocytes from the 74 SCLC patients and 7 accompanying circulating-tumour-cell patient-derived xenografts (CDX). cfDNA and gDNA were used as input for cfMeDIP-seq. We developed PeRIpheral blood leukocyte MEthylation (PRIME) subtraction as an algorithm to improve tumour specificity of cell-free methylome. Results SCLC total plasma cfDNA methylation profiles obtained using cfMeDIP-seq are representative of CDX tumour methylation. SCLC cfDNA methylation is distinct from non-cancer plasma. Using PRIME and k-means consensus clustering, we identified two SCLC methylome clusters with prognostic associations. These clusters had methylated biological pathways related to axon guidance, neuroactive ligand−receptor interaction, pluripotency of stem cells, and were differentially methylated at long noncoding RNA, LINEs, SINEs, retrotransposons, and other repeats features. Conclusions We have comprehensively profiled the SCLC methylome using cfMeDIP-seq in a large patient cohort and identified methylome clusters with prognostic associations. Our work demonstrates the potential of liquid biopsies in examining SCLC biology encoded in the methylome.
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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) | 0.002 | 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".