Abstract 2976: Comprehensive Pan-Genomic characterization of adrenocortical carcinoma
Bibliographic record
Abstract
Abstract Adrenocortical carcinoma (ACC) is a rare neoplasm with a heterogeneous outcome and limited treatment options. To understand its molecular and genomic landscape as a part of The Cancer Genome Atlas (TCGA) project, we performed the genomic, transcriptomic, epigenomic and proteomic profiling of 91 ACCs. We identified potential driving alterations including amplifications (TERT, TERF2 and CDK4), deletions (ZNRF3, CDKN2A and RB1) and point mutations in genes unknown to participate in adrenal disease (RPL22) and genes known to initiate familial syndromes that occasionally include adrenocortical neoplasms (TP53, CTNNB1, PRKAR1A, MEN1). The finding of PRKAR1A expands the catalogue of pathogenic pathways underlying ACC, suggesting of the protein kinase alpha signaling pathway as a potential target for molecular interventions. Novel transcript fusions potentially leading to overactive kinases included EXOSC10-MTOR and PPP1CB-BRE. DNA copy number analysis unveiled prevalent DNA losses leading to hypodiploidy as well as whole genome doubling (WGD) in 51% of ACC. The similar penetrance of loss of heterozygosity before and after WGD suggests a sequential development from hypodiploidy to polyploidy along the doubling in a subset of ACCs, which was endorsed by the worse outcome for WGD samples relative to nonWGD ACCs. An association between TERT expression and WGD was observed, suggesting a role for telomere regulation. These findings present ACC as a model disease for studies of WGD which is a frequent event in many tumor types. Unsupervised clustering of DNA methylation, copy number, gene expression, miRNA expression and protein abundance converged into three classes with specific biological characteristics and a respective median event free survival of 8, 38 and >100 months (p-value 1.7e-13). Comparison of the subtypes suggested additional drivers such as protein kinase C (PKC) phosphorylation and upregulation of a miRNA cluster at chromosome Xq27.3, which complemented the genomic alterations identified in these subtypes. To gain more insights into this rare cancer type, we placed ACC in a broader context of cancer genomic profiles including an array of other cancer types. These analyses revealed interesting shared features, including beta-catenin activation with a subset of endometroid cancer, DNA mismatch repair gene mutational signature with gastrointestinal cancers and a smoking signature with lung cancer. These findings highlight the commonalities between ACC and other lineages of cancer. Taken together, we found Wnt signaling pathway and p53/Rb signaling pathway were the most frequently altered pathways in ACC. Meanwhile, new players surfaced from our analyses including the PKA and PKC pathways. Our results present a comprehensive genomic landscape and refined molecular classification of ACC improve our understanding of its pathogenesis, and will ultimately improve the care of patients. Citation Format: Siyuan Zheng, Andrew D. Cherniack, Ninad Dewal, Richard A. Moffitt, Ludmila Danilova, Bradley A. Murray, Antonio M. Lerario, Tobias Else, Theo A. Knijnenburg, Giovanni Ciriello, Seungchan Kim, Guillaume Assie, Olena Morozova, Rehan Akbani, Juliann Shih, Katherine A. Hoadley, Toni K. Choueiri, Jens Waldmann, Ozgur Mete, Gordon A. Robertson, Matthew Meyerson, Michael J. Demeure, Felix Beuschlein, Anthony Gill, Ana C. Latronico, Maria C. Fragosa, Leslie Cope, Electron Kebebew, Mouhammed A. Habra, Timothy G. Whitsett, Kimberly J. Bussey, William E. Rainey, Sylvia Asa, Jérôme Bertherat, Martin Fassnacht, David A. Wheeler, The Cancer Genome Atlas Research Network, Gary D. Hammer, Thomas J. Giordano, Roel Verhaak. Comprehensive Pan-Genomic characterization of adrenocortical carcinoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2976. doi:10.1158/1538-7445.AM2015-2976
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".