Abstract 4087: Large intergenic noncoding RNAs associated with Ewing sarcoma family of tumors
Bibliographic record
Abstract
Abstract BACKGROUND: Ewing sarcoma family of tumors (EFT) represents the second most common primary malignant bone tumor in children and adolescents. The majority of EFTs harbor a translocation (t11;22)(q24;q12) resulting in the expression of the EWS-FLI1 chimeric oncoprotein. We hypothesized that microarray gene expression profiling, in combination with next-generation sequencing technology, could be used to identify EFT-specific genes and transcripts including large intergenic noncoding (linc)RNAs. Next-generation sequencing, coupled with target enrichment for validation, can also identify driver mutations of metastasis and treatment resistance in high-risk cases. RESULTS: Genome-wide expression profiles of childhood sarcoma and normal tissues were analyzed using both Partek Genomics Suite as well as our own customized software, Genetrix. These are compared with Helicos single molecule sequencing data. The results show that both known genes and candidate lincRNAs strongly associate with EFT and can be used to distinguish EFT from other childhood tumors. Moreover, many lincRNAs are seen on both the poly-A selected track and on the random hexamer primed total RNA expression profile, indicating that the transcripts are multi-exonic and poly-adenylated. Importantly, regions of EFT-associated lincRNA expression may indicate regions that are deregulated by EWS-FLI1 in these tumors. We also focused on lincRNAs with differential expression in an EFT cell line pair (CHLA-9 and −10) derived, respectively, from patient-matched primary and metastatic tumors. RNA-seq determined ∼160,000 regions representing ∼16MB of genomic sequence as being at least 3-fold differentially expressed between the two cell lines. A higher threshold of at least 10-fold expression difference still revealed ∼20,000 regions representing ∼2MB of genomic sequence. In addition, the proportion of differentially-expressed intergenic transcripts was higher in CHLA-9 (18.1%) versus CHLA-10 (11.2%). Annotated transcripts with at least 10-fold expression in CHLA-10 are enriched in plasma membrane and adhesion-related functions, and this is consistent with metastatic behavior. Genes most highly expressed in CHLA-10 include ANXA5, FABP3, and HIST1H1A, whereas genes with much lower expression include CXCL14, MCTP2, and TRIM22. These genes, along with lincRNAs, may play important roles in disease progression and drug resistance. CONCLUSIONS: We have identified several large intergenic noncoding (linc)RNAs that are highly and differentially expressed by EFT. We hypothesize that these lincRNAs may be novel therapeutic targets in EFT. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4087.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".