The neoantigen landscape of mycosis fungoides
Bibliographic record
Abstract
Abstract Background Mycosis fungoides (MF), the most common type of cutaneous T-cell lymphoma, has a dismal prognosis in advanced stages. Treatments for advanced disease are mostly palliative and MF remains incurable. Although MF is a known immunogenic neoplasm, immunotherapies such as interferons and the immune checkpoint inhibitors yield inconsistent results. Since the number, HLA-binding strength and subclonality of neoantigens are correlated with the therapeutic responses, we aimed here to characterize the landscape of neoantigens in MF. Methods We conducted whole exome and whole transcriptome sequencing of 24 MF samples (16 plaque, 8 tumour) from 13 patients. Bioinformatic pipelines (Mutect2, OptiType, MuPeXi) were used for in silico mutation calling, HLA typing, and neoantigen prediction. Phylogenetic analysis was used to subdivide the malignant cell population into stem and clades (subclones). Clonality of neaontigens was determined by matching neoantigens to the stem and clades of the phylogenetic tree of each MF sample. Results MF has a high mutational load (median 3217 non synonymous mutations), resulting in a significant number of total neoantigens (median 1309 per sample) and high-affinity neoantigens (median 328). In stage I disease most neoantigens were clonal but with progression to stage II, subclonal neoantigens comprised >50% of the total. There was very little overlap in neoantigens across patients or between different lesions on the same patient, indicating a high degree of genetic heterogeneity. Conclusions Analysis of the neoantigen landscape of MF revealed a very high neoantigen load and thus a significant immunogenic potential of this lymphoma. However, neoantigenic heterogeneity and significant subclonality might limit the efficacy of immunotherapy. We hypothesize that neoantigen number and subclonality might be useful biomarkers determining sensitivity to immunotherapeutic strategies.
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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) | 0.001 | 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".