The genomic and transcriptomic landscape of anaplastic thyroid cancer: implications for therapy
Bibliographic record
Abstract
BACKGROUND: Anaplastic thyroid carcinoma is the most undifferentiated form of thyroid cancer and one of the deadliest of all adult solid malignancies. Here we report the first genomic and transcriptomic profile of anaplastic thyroid cancer including those of several unique cell lines and outline novel potential drivers of malignancy and targets of therapy. METHODS: We describe whole genomic and transcriptomic profiles of 1 primary anaplastic thyroid tumor and 3 authenticated cell lines. Those profiles augmented by the transcriptomes of 4 additional and unique cell lines were compared to 58 pairs of papillary thyroid carcinoma and matched normal tissue transcriptomes from The Cancer Genome Atlas study. RESULTS: The most prevalent mutations were those of TP53 and BRAF; repeated alterations of the epigenetic machinery such as frame-shift deletions of HDAC10 and EP300, loss of SMARCA2 and fusions of MECP2, BCL11A and SS18 were observed. Sequence data displayed aneuploidy and large regions of copy loss and gain in all genomes. Common regions of gain were however evident encompassing chromosomes 5p and 20q. We found novel anaplastic gene fusions including MKRN1-BRAF, FGFR2-OGDH and SS18-SLC5A11, all expressed in-frame fusions involving a known proto-oncogene. Comparison of the anaplastic thyroid cancer expression datasets with the papillary thyroid cancer and normal thyroid tissue transcriptomes suggested several known drug targets such as FGFRs, VEGFRs, KIT and RET to have lower expression levels in anaplastic specimens compared with both papillary thyroid cancers and normal tissues, confirming the observed lack of response to therapies targeting these pathways. Further integrative data analysis identified the mTOR signaling pathway as a potential therapeutic target in this disease. CONCLUSIONS: Anaplastic thyroid carcinoma possessed heterogeneous and unique profiles revealing the significance of detailed molecular profiling of individual tumors and the treatment of each as a unique entity; the cell line sequence data promises to facilitate the more accurate and intentional drug screening studies for anaplastic thyroid cancer.
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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) | 0.000 | 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".