Integrative Genomic and Transcriptomic Analysis Reveals Targetable Vulnerabilities in Angioimmunoblastic T-Cell Lymphoma
Bibliographic record
Abstract
Follicular helper T-cell lymphoma of the angioimmunoblastic type (AITL) is associated with dismal prognosis. We performed functional genomic approaches including whole-exome sequencing (WES; n=119), transcriptomic (n=78) and methylation (n=40) analysis. We identified recurrent mutations in known epigenetic drivers ( TET2, DNMT3A, IDH2 R172), and also identified novel ones (TET3, KMT2D). Somatic mutation of all three epigenetic drivers ( TET2, IDH2, and DNMT3A) was associated with poor prognosis (p<.001). Mutations in genes regulating T-cell receptor (TCR) signaling ( CD28 VAV1, FYN, PLCG1) or activation ( RHOA G17V), and regulators of the PI3K pathway (PIK(3)C members, PTEN,PHLPP-1/-2) were also found. Genome-wide DNA-methylation analysis integrated with mRNA expression profiling also revealed epigenetic alterations in genes regulating TCR-RHOA/B/C or PI3K-signaling. TET2 loss was noted in 85% AITLs and was significantly associated with RHOA G17V, CD28 and IDH2 R172mutations. AITLs lacking RHOA G17V tended to have mutations regulating the JAK-STAT pathway ( JAK2, JAK3, STAT1, STAT3, SOCS1). RNA-seq analysis identified fusion transcripts in genes regulating TCR activation (8%), revealed a restricted TCR repertoire in the majority of cases (a=87%, b=72%), and showed the presence of Epstein-Barr virus transcriptome (73%). GEP demonstrated association of B-cells in the tumor-milieu with better prognosis (p=.006), while dendritic cells were associated with worse prognosis (p=.001), which was further validated by immunohistochemistry using CD20, CD68, and CD163 antibodies. RNA-seq and corresponding WES analysis of 12 AITL patient-derived-xenografts (PDX) showed that bi-allelic TET2 mutations, DNMT3A mutations or sub-clonal mutations ( PLCG1 PHLPP2) werepropagated in sequential passages. Gene signatures related to T FH (follicular helper) and T CM (central memory) were also well-maintained in secondary passages in PDX models. Gene signatures of late PDX passages (3 rd-5 th) were enriched with genes related to proliferation and metabolic reprogramming, and in an independent cohort of AITLs, high expression of T3/T5 related signatures was associated with worse outcome (p=0.02/p=0.009). Low mRNA expression of PHLPP2 predicted poor prognosis (p=.03) and engineered PHLPP2 loss showed enhanced PI(3)K activation and FOXO1 inactivation in CD4+ T-cells in-vitro. Thus, we defined the genomic landscape for AITL, which is largely characterized by epigenetic alterations, TCR signaling and PI3K/AKT dysregulation, which may be amenable for therapeutic targeting.
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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 | 0.000 |
| 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".