Non-Invasive Characterization and Early Detection of Post-Transplant Lymphoproliferative Disorders
Notice bibliographique
Résumé
Background: Post-transplant lymphoproliferative disorder (PTLD) is a feared complication of solid organ transplantation with no standard surveillance strategy. Serial EBV titers provide limited sensitivity for EBV-negative PTLD. Cell-free DNA (cfDNA) is an effective biomarker in lymphomas, and we hypothesized that cfDNA could allow multimodal non-invasive characterization and early detection of PTLD. We evaluated cfDNA-based genotyping, viral detection, and T-cell receptor (TCR) repertoire to characterize and facilitate early detection of PTLD. Methods: We studied 265 plasma or serum samples from 75 lymphoma patients (pts) from our global consortium of 12 solid organ transplant centers (median 3.5 samples/pt). We profiled serial pre-diagnostic specimens obtained during routine post-transplant care to characterize the window for early non-invasive detection, and post-treatment samples to evaluate response kinetics. We additionally assessed healthy adults without transplant (n=35), and transplant pts in good health (n=13), during acute allograft rejection (n=25), CMV reactivation (n=25), and EBV reactivation without lymphoma (n=18). CfDNA was extracted and enriched via hybrid capture to evaluate 186 B-cell lymphoma related genes (Alig et al Nature 2024), 180 viral species (Garofalo et al Blood 2019), TCR for immune repertoire profiling (Shukla Blood 2020), and common SNPs to assess donor-derived cfDNA. Results: Clinical Characteristics: Pts underwent heart (53%), lung (29%), kidney (12%), or liver transplantation (6%), with median 810 days between transplant and PTLD diagnosis. Clinical tumor EBER status was 71% EBV+, 23% EBV-, and 6% unknown. Among pts with available treatment data, 93% received initial rituximab, followed by observation (37%) or R-CHOP-like chemotherapy (63%). Circulating Virome: Higher cell-free EBV levels were observed in PTLD pts as compared to healthy adults or healthy transplant pts. Heart and lung transplant recipients had greater EBV and Anellovirus burden compared to kidney or liver recipients, likely reflecting more intensive immunosuppression. Circulating EBV was higher in patients with EBER+ tumors (p=0.019) and with diagnosis ≤2 years post-transplant (p=0.04). Anellovirus levels were higher in the early post-transplant period with no chronologic association with PTLD, while EBV levels increased in proximity to clinical PTLD diagnosis (≤3 months, p=0.026). TCR Repertoire: We evaluated circulating TCR clonotypes using SABER and quantified TCR repertoire diversity via Chao1 index. TCR repertoire diversity was similar between PTLD and non-malignant EBV reactivation but lower diversity was seen in a subset of patients with CMV reactivation and acute allograft rejection. Mutational Profiling: At diagnosis or closest pre-diagnostic timepoint, PTLD pts had a higher burden of missense mutations detected in cfDNA as compared to healthy adults (p=0.0041). As previously observed in PTLD tumors, more coding mutations were observed in cfDNA from EBV- as compared to EBV+ PTLD. Considering mutational signatures, EBV- cases were enriched in SBS84, an AID/SHM signature, while EBV+ cases were enriched in the DNA mismatch repair signature SBS6. Response Assessment: Among pts receiving risk-stratified sequential treatment (RSST) with initial rituximab monotherapy and available post-rituximab sample, pts achieving durable CR had lower interim ctDNA concentration (n=6, median 6 HGE/mL) as compared to pts requiring chemotherapy consolidation based on radiographic response (n=8, median 53 HGE/mL). Early Detection: We considered pre-diagnostic samples from pts developing PTLD (n=45). Samples obtained ≤6 months before clinical diagnosis had higher EBV levels (p=0.028) and more frequent coding mutations as compared to more distant timepoints. Mutations were detected in 71% of samples (32/45) at median 58 days prior to diagnosis, suggesting a window of months for early detection. Conclusions: Multimodal characterization of cfDNA revealed patterns of virome dysregulation and oncogenic mutations preceding clinical lymphoma diagnosis. Non-invasive surveillance via cfDNA is a promising approach in solid organ transplant pts at risk for PTLD, and could also facilitate risk-stratified therapy approaches.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».