Whole exome sequencing reveals mutations in FAT1 tumor suppressor gene clinically impacting on peripheral T-cell lymphoma not otherwise specified
Bibliographic record
Abstract
Peripheral T-cell lymphoma not otherwise specified represents a diagnostic category comprising clinically, histologically, and molecularly heterogeneous neoplasms that are poorly understood. The genetic landscape of peripheral T-cell lymphoma not otherwise specified remains largely undefined, only a few sequencing studies having been conducted so far. In order to improve our understanding of the genetics of this neoplasm, we performed whole exome sequencing along with RNA-sequencing in a discovery set of 21 cases. According to whole exome sequencing results and mutations previously reported in other peripheral T-cell lymphomas, 137 genes were sequenced by a targeted deep approach in 71 tumor samples. In addition to epigenetic modifiers implicated in all subtypes of T-cell neoplasm (TET2, DNMT3A, KMT2D, KMT2C, SETD2), recurrent mutations of the FAT1 tumor suppressor gene were for the first time recorded in 39% of cases. Mutations of the tumor suppressor genes LATS1, STK3, ATM, TP53, and TP63 were also observed, although at a lower frequency. Patients with FAT1 mutations showed inferior overall survival compared to those with wild-type FAT1. Although peripheral T-cell lymphoma not otherwise specified remains a broad category also on molecular grounds, the present study highlights that FAT1 mutations occur in a significant proportion of cases, being provided with both pathogenetic and prognostic impact.
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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) | 0.001 | 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".