Non-Invasive Characterization and Early Detection of Post-Transplant Lymphoproliferative Disorders
Bibliographic record
Abstract
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.
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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 | 0.001 | 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 | 0.001 | 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".