Abstract P28: Comprehensive Molecular Phenotyping of <i>ARID1A</i>-deficient Gastric Cancer Reveals Pervasive Epigenomic Reprogramming and Therapeutic Opportunities
Bibliographic record
Abstract
Abstract Objective Gastric cancer (GC) is a leading cause of cancer mortality, with ARID1A being the second most frequently mutated driver gene in GC. We sought to decipher ARID1A-specific GC regulatory networks and examine therapeutic vulnerabilities arising from ARID1A loss. Design Genomic profiling of GC patients including a Singapore cohort (>200 patients) was performed to derive mutational signatures of ARID1A inactivation across molecular subtypes. Single-cell transcriptomic profiles of ARID1A-mutated GCs were analyzed to examine tumor microenvironmental changes arising from ARID1A loss. Genome-wide ARID1A binding and chromatin profiles (H3K27ac, H3K4me3, H3K4me1, ATAC-seq) of gastric cell lines were generated to identify gastric-specific epigenetic landscapes regulated by ARID1A. Distinct cancer hallmarks of ARID1A-mutated GCs were converged at the genomic, single-cell, and epigenomic level, and targeted by pharmacological inhibition. Results We observed prevalent ARID1A inactivation across GC molecular subtypes, with distinct mutational signatures and linked to a NFKB-driven pro-inflammatory tumour microenvironment. ARID1A-depletion caused loss of H3K27ac activation signals at ARID1A-occupied distal enhancers, but unexpectedly gain of H3K27ac at ARID1A-occupied promoters in genes such as NFKB1 and NFKB2. Promoter activation in ARID1A-mutated GCs was associated with enhanced gene expression, increased BRD4 binding, and reduced HDAC1 and CTCF occupancy. Combined targeting of promoter activation and tumour inflammation via bromodomain and NFKB inhibitors confirmed therapeutic synergy specific to ARID1A-genomic status. Conclusion Our results suggest a therapeutic strategy for ARID1A-mutated GCs targeting both tumour-intrinsic (BRD4-assocatiated promoter activation) and extrinsic (NFKB immunomodulation) cancer phenotypes. Citation Format: Chang Xu, Kie Kyon Huang, Jia Hao Law, Joy Shijia Chua, Taotao Sheng, Natasha M. Flores, Melissa Pool Pizzi, Atsushi Okabe, Angie Lay Keng Tan, Feng Zhu, Vikrant Kumar, Xiaoyin Lu, Ana Morales Benitez, Benedict Shi Xiang Lian, Haoran Ma, Shamaine Wei Ting Ho, Kalpana Ramnarayanan, Chukwuemeka George Anene-Nzelu, Milad Razavi-Mohseni, Siti Aishah Binte Abdul Ghani, Su Ting Tay, Xuewen Ong, Ming Hui Lee, Yu Amanda Guo, Hassan Ashktorab, Duane Smoot, Shang Li, Anders Jacobsen Skanderup, Michael A. Beer, Roger Sik Yin Foo, Joel Shi Hao Wong, Kaushal Sanghvi, Wei Peng Yong, Raghav Sundar, Atsushi Kaneda, Shyam Prabhakar, Pawel Karol Mazur, Jaffer A. Ajani, Khay Guan Yeoh, Jimmy Bok-Yan So, Patrick Tan, Singapore Gastric Cancer Consortium. Comprehensive Molecular Phenotyping of ARID1A-deficient Gastric Cancer Reveals Pervasive Epigenomic Reprogramming and Therapeutic Opportunities [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P28.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".