Abstract 4512: The landscape of hypoxia-driven alternative splicing in breast cancer
Bibliographic record
Abstract
Abstract Tumor hypoxia is generally associated with poor patient outcome and resistance to therapy. Hypoxia has an impact on multiple pathways inside the cell and a widespread effect on phenotype. Here we determined the changes in transcript architecture that arise as result of alternative splicing in hypoxic cells. A panel of breast cancer cell lines that include the Luminal A and B, HER2+, Triple-Negative subcategories grown in hypoxic and normoxic conditions were subjected for deep RNA-sequencing. Depending on the cell lines we found between 23- 927 splicing events with 3-45% of them also present in the differentially expressed fraction of genes. This points to an extensive isoform switching control independently of the transcriptional control of the genes themselves. The splicing events appear on genes responsible for mRNA processing (splicing, nuclear export and catabolism) as well as initiation of translation. Members of the SR family of proteins involved in RNA splicing, appear to be potential regulators of the biogenesis of the hypoxia specific splicing events. The differentially spliced exons are frequently part of both the coding and noncoding isoforms of a given gene potentially indicating a switch between these two types under hypoxia. Ten of the hypoxia specific splicing events were also found to be associated with the hypoxia status of primary breast cancer samples taken from The Cancer Genome Atlas (TCGA) database. Altogether, these data demonstrate the important role of hypoxia in driving alternative splicing events in breast cancer. Citation Format: Hani Choudhry, Spyridon Oikonomopoulos, Peng Yu, Cristina Ivan, Mircea Ivan, Adrian L. Harris, Jiannis Ragoussis. The landscape of hypoxia-driven alternative splicing in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4512. doi:10.1158/1538-7445.AM2017-4512
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".