Abstract 3413: A pan-cancer atlas of genomic, epigenomic and transcriptomic alterations in the TGF-β pathway
Bibliographic record
Abstract
Abstract The TGF-β pathway is a multifunctional signaling cascade with context-dependent roles in diverse biologic processes, including tumor promotion or suppression, metastasis, stem cell homeostasis, and immune suppression. Due to its highly context-dependent nature, decoding functional outcomes of the TGF-β pathway in specific tissues is highly challenging. Here, we present comprehensive genomic, transcriptomic and epigenomic analyses of the TGF-β pathway identified by 44 core pathway genes across 33 TCGA tumor types and 9125 samples. The core pathway genes involve TGF-β like ligands, receptors, intracellular SMAD molecules and adaptors. Although individual core pathway genes were rarely mutated or copy number altered in different cancer types, 41% of all samples have at least one genomic alteration in the TGF-β pathway, predominantly in the form of mutations. We identified a highly conserved TGF-β downstream gene expression signature associated with alterations in core pathway genes, suggesting that the alterations in the pathway have shared functional consequences. We observed a significant enrichment of the genomic alterations in gastrointestinal cancers (GI) with a distinct gene expression signature. The newly identified gene expression signature (over- or downregulation of key TGF-β downstream genes) in pan-cancer cohort was associated with significantly poor prognosis, particularly when it co-occurred with genomic alterations in the core pathway. Analysis of mutational hotspot sites revealed 6 genes with hotspots recurring in at least 9 (up to 78) mutational incidences. The hotspot mutations were also highly enriched in GI cancers. We identified previously characterized cancer mutation sites on SMAD4 and SMAD2 as hotspots mainly in GI cancers. We hypothesized novel functions to two of the newly identified hotpot sites through structural and trancriptomic analyses, and two other novel hotspot sites in the pathway await functional characterization. miRNA and epigenomic analyses revealed that TGF-β pathway activity is limited by epigenetic silencing or miRNA expression, especially in cancers with very low pathway gene expression levels. This multidimensional study provides the multifacefed landscape of TGF-β signaling in both individual disease and pan-cancer settings to guide future functional and therapeutic studies of this key cancer pathway. Citation Format: Anil Korkut, Sobia Zaidi, Rupa Kanchi, Ashton C. Berger, Gordon Robertson, Lawrence N. Kwong, Mike Datto, Jason Roszik, Shiyun Ling, Andre Schultz, Visweswaran Ravikumar, Ganiraju Manyam, Arvind Rao, Simon Shelley, Yuexin Liu, Zhenlin Ju, Donna Hansel, Guillermo de Velasco, Arjun Pennathur, Jesper B. Andersen, Colm J. O'Rourke, Kazufumi Ohshiro, Wilma Jogunoori, Nancy Gough, Shulin Li, Hatice Osmanbeyoglu, Andres Houseman, Shuyun Rao, Maciej Wiznerowicz, Jian Chen, Shoujun Gu, Wencai Ma, Jiexin Zhang, Pan Tong, Andrew D. Cherniack, Chuxia Deng, Linda Resar-Smith, Jaffer Ajani, The Cancer Genome Atlas Research Network, John N. Weinstein, Lopa Mishra, Rehan Akbani. A pan-cancer atlas of genomic, epigenomic and transcriptomic alterations in the TGF-β pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3413.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".