Abstract 3396: Identification of small cell lung cancer stage-specific DNA methylation in patients using liquid biopsies
Bibliographic record
Abstract
Abstract Introduction: Small cell lung cancer (SCLC) is a deadly disease and patients often suffer from recurrent disease. Biologic mechanisms of recurrence are unclear. Epigenetic mechanisms, like DNA methylation, may be operant. SCLC is rarely resected; therefore, the SCLC methylome is understudied due to scarce tumor tissue. Methods: In this study, we examined the pre-treatment methylome of 72 SCLC patients at our institution through cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) on plasma cell-free DNA (cfDNA) and on sheared genomic DNA from paired peripheral blood leukocytes (PBLs) (n = 72) to increase tumor signal specificity. cfMeDIP-seq was also performed on an independent cohort of healthy non-cancer controls (n = 20) and on tumors from circulating tumor cell-derived xenograft (CDX) models (n = 12 CDXs of 72 patients). cfMeDIP-seq libraries were sequenced at a depth of 100 million reads, paired-end, on the NovaSeq 6000. For all bioinformatic analyses, chromosomes 1-22 were binned into 300-bp windows (n = 9.6e6 genome-wide windows); reads from cfMeDIP-seq were tallied per bin. To filter out noise from non-tumor cells, ENCODE blacklist regions (n = 1e6 windows) were removed and CG-rich (>5 CGs per window), PBL-associated windows with MeDEStrand-converted beta-values < 0.2 were kept (n = 190,769 windows). Subsequent cfDNA analyses were performed using these 190,769 windows. Results: 33 (45.8%) and 39 (54.2%) of the 72 patients had limited-stage (LS) and extensive-stage (ES) SCLC, respectively. Most were current or former smokers (65/72, 90.3%). Using filtered windows (n = 190,769), methylated cfDNA from SCLC were distinguished from healthy controls: SCLC cfDNA was enriched for hypermethylated CpG islands and shore regions, whereas controls had more methylated open-sea regions. SCLC cohort by consensus clustering of the top 5000 most variant windows revealed two distinct cfDNA methylation patterns. Cluster A (9 ES, 22 LS) and Cluster B (33 ES, 11 LS) were significantly different by stage (X2(1) = 13.79, p < 0.001). There was no significant difference in sex (p = 0.81). Patients in Cluster A had a lower concentration of cfDNA (median = 7.2ng/ml) compared to Cluster B (median = 15.9ng/ml) but not statistically significant (p = 0.33). High concordance between genome-wide methylome profiles was found between paired patient cfDNA and CDX tumor, demonstrating that cfDNA is representative of tumor methylation (Pearson's correlation median r = 0.86). Conclusion: We identified stage-specific methylation patterns in the plasma of SCLC patients using the cfMeDIP-seq assay that may reveal novel epigenetic and biologic mechanisms of SCLC disease progression. Moreover, CDXs recapitulate the methylome of SCLC cell-free patient samples highlighting their utility in future work as representative models for methylome analysis. Citation Format: Sami Ul Haq, Sabine Schmid, Mansi K. Aparnathi, Katrina Hueniken, Luna J. Zhan, Danielle Sacdalan, Janice J.N. Li, Devalben Patel, Dangxiao Cheng, Vivek Philip, Geoffrey Liu, Scott V. Bratman, Benjamin H. Lok. Identification of small cell lung cancer stage-specific DNA methylation in patients using liquid biopsies [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 3396.
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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) | 0.004 | 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".