Abstract PR13: Comprehensive detection of ctDNA in localized head and neck cancer by genome- and methylome-based analysis
Bibliographic record
Abstract
Abstract Head and neck squamous cell carcinoma (HNSCC) comprises 3% of all cancer cases worldwide. Despite intensive multimodal therapies, HNSCC patient outcomes remain heterogeneous with minimal improvements in survival. Utilization of fluid-based biomarkers for prognostication, risk stratification, and disease surveillance may improve patient outcomes by enabling more effective treatment decisions. Here, we describe the performance of comprehensive mutation and methylome analysis for highly sensitive detection of circulating tumor (ct)DNA in HNSCC plasma. To detect HNSCC-specific mutations and aberrant methylation in ctDNA, we conducted CAPP-Seq (CAncer Personalized Profiling by deep Sequencing) and cfMeDIP-seq (cell-free Methylated DNA ImmunoPrecipitation sequencing), respectively. For CAPP-Seq, we developed a HNSCC-specific hybrid capture panel and applied it to plasma DNA and peripheral blood leukocyte (PBL) genomic DNA from a cohort of HNSCC patients (n=32) and healthy controls (n=20). Single-nucleotide variants (SNVs) were identified by integrated digital error suppression (iDES). For cfMeDIP-seq, hypermethylated differentially methylated regions within regions of low PBL methylation were identified by DESeq2. A median of 3 mutations (range: 1-10) were detected by CAPP-Seq within plasma DNA of 20/32 (62.5%) HNSCC patients. Mean mutant allele frequency ranged from 0.1–5% (median: 0.92%) and correlated with tumor stage. Among these 20 patients, cfMeDIP-seq identified 860 DMRs that were enriched (hypermethylated) in HNSCC plasma DNA compared with healthy controls. These hypermethylated DMRs (hyper-DMRs) were over-represented by CpG islands and gene promoters and showed significant overlap with HNSCC-specific methylated regions in TCGA. When applied to all 32 HNSCC patients, hyper-DMR abundance was positively correlated with mutation-based ctDNA abundance (R=0.87; p=1e−16). Hyper-DMRs were capable of accurate discrimination of HNSCC patients and healthy controls (AUC=0.85). The median DNA fragment size within these hyperDMRs was lower in HNSCC patients compared to healthy controls—a characteristic of ctDNA described in previous studies—and correlated with both hyperDMR-based and mutation-based ctDNA abundance. We have conducted the first comparative analysis of genetic and epigenetic profiling approaches for ctDNA detection in HNSCC. Both CAPP-Seq and cfMeDIP-seq have the potential to detect ctDNA in patient plasma without prior knowledge of patient-specific tumor aberrations. With CAPP-seq, ctDNA was detectable at levels as low as 0.1%. Plasma methylome profiling using cfMeDIP-seq revealed hyper-DMRs with tumor-related features and that correlated with mutation-based ctDNA abundance. Future analysis will validate patient-specific mutations and methylation signals in tumor tissue and in longitudinally collected samples from this cohort. This abstract is also being presented as Poster B47. Citation Format: Justin M. Burgener, Jinfeng Zou, Zhen Zhao, Shu Y. Shen, Daniel D. De Carvalho, Scott V. Bratman. Comprehensive detection of ctDNA in localized head and neck cancer by genome- and methylome-based analysis [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr PR13.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".