Abstract B103: Molecular heterogeneity of gastric cancer explained by methylation-driven key regulators
Bibliographic record
Abstract
Abstract Gastric cancer (GC) is a heterogeneous disease in which diverse genetic, genomic, and epigenetic alterations can accumulate in different molecular and histologic subtypes. Tumor microenvironment (TME) also contributes to the heterogeneity of GC. To investigate what molecular features of tumor cells drive GC heterogeneity, we developed an integrative causal model, called Integrative Sequential Causality Test (ISCT), to identify key regulators of GC by integrating DNA methylation, copy number variation, and transcriptomic data. Applying ISCT to three GC cohorts that contain methylation, CNV, and gene expression data, 11 common methylation-driven key regulators were identified: ADHFE1, CDO1, CRYAB, FSTL1, GTP, PKP3, PTPRCAP, RAB25, RHOH, SFN, and SORD. Based on these 11 genes, gastric tumors resolved into three groups that were associated with known molecular subtypes, Lauren classification, tumor stage, and patient survival, suggesting significance of the methylation-driven key regulators in molecular and histologic heterogeneity of GC. We also investigated the relationship between TME and the methylation-driven key regulators and showed that both immune/stromal proportions in TME and tumor cell genomics variations contributed to expression variations of the methylation-driven key regulators. Especially, FSTL1, significantly associated with patient survival and tumor progression as well as stromal proportion in TME, was expressed at high level in both stromal and cancer cells, indicating its potential role in mediating tumor-stroma interactions. In summary, this study suggests that genetic, genomic, and epigenetic alterations as well as their interactions with TME contribute to heterogeneity of GC. Citation Format: Seungyeul Yoo, Quan Chen, Li Wang, Wenhui Wang, Ankur Chakravarthy, Rita Busuttil, Alex Boussioutas, Tim R. Fenton, Jiangwen Zhang, Xiaodan Fan, Seut-Yi Leung, Jun Zhu. Molecular heterogeneity of gastric cancer explained by methylation-driven key regulators [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr B103.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".