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Abstract B103: Molecular heterogeneity of gastric cancer explained by methylation-driven key regulators

2020· article· en· W3014079509 on OpenAlexaff
Seungyeul Yoo, Quan Chen, Li Wang, Wenhui Wang, Ankur Chakravarthy, Rita A. Busuttil, Alex Boussioutas, Tim R. Fenton, Jiangwen Zhang, Xiaodan Fan, Seut-Yi Leung, Jun Zhu

Bibliographic record

VenueCancer Immunology Research · 2020
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsEpigeneticsDNA methylationTumor microenvironmentBiologyMethylationStromal cellCancerCancer researchComputational biologyGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.082
GPT teacher head0.388
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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