Clonotypic heterogeneity in cutaneous T-cell lymphoma (mycosis fungoides) revealed by comprehensive whole-exome sequencing
Bibliographic record
Abstract
Mycosis fungoides (MF), the most common type of cutaneous T-cell lymphoma, is believed to represent a clonal expansion of a transformed skin-resident memory T cell. T-cell receptor (TCR) clonality (ie, identical sequences of rearranged TCRα, TCRβ, and TCRγ), the key premise of this hypothesis, has been difficult to document conclusively because malignant cells are not readily distinguishable from the tumor-infiltrating reactive lymphocytes that contribute to the TCR clonotypic repertoire of MF. Here, we have successfully adopted targeted whole-exome sequencing (WES) to identify the repertoire of rearranged TCR genes in tumor-enriched samples from patients with MF. Although some of the investigated MF biopsies had the expected frequency of monoclonal rearrangements of TCRγ corresponding to that of tumor cells, the majority of the samples presented multiple TCRγ, TCRα, and TCRβ clonotypes by WES. Our findings are compatible with the model in which the initial malignant transformation in MF does not occur in mature memory T cells but rather at the level of T-lymphocyte progenitors before TCRβ or TCRα rearrangements. We have also shown that WES can be combined with whole-transcriptome sequencing in the same sample, which enables comprehensive characterization of the TCR repertoire in relation to tumor content. WES/whole-transcriptome sequencing might be applicable to other types of T-cell lymphomas to determine clonal dominance and clonotypic heterogeneity in these malignancies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".