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The Outcome Of Patients With Post Transplant and Immunodeficiency Associated Lymphoproliferative Disorder: The BCCA Experience

2013· article· en· W2262784706 on OpenAlexaffabout
Amrit Kahlon, Graham W. Slack, Yuhua Sun, Richard Klasa, Laurie H. Sehn, Diego Villa, Tamara Shenkier, Randy D. Gascoyne, Joseph M. Connors, Kerry J. Savage

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsRituximabImmunosuppressionMedicineLymphoproliferative disordersInternal medicineLymphomaGastroenterologyPost-transplant lymphoproliferative disorderDiffuse large B-cell lymphomaTransplantationOncologyImmunology

Abstract

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Abstract Background Post-transplant lymphoproliferative disorders (PTLD) develop in the setting of immunosuppression following solid organ transplant or allogeneic stem cell transplant (alloSCT). Other iatrogenic immunodeficiency-associated lymphoproliferative disorders (‘PTLD-like’) can occur with the use of immunosuppressive agents for autoimmune or related disorders. The optimal first-line treatment and risk stratification tool for PTLD remains unclear and rituximab (R) monotherapy is often used following a reduction of immunosuppression (RIS), reserving combination chemotherapy for those failing this initial approach. However, this approach may not be suitable in those with high risk disease. The aim of this study was to review the outcome of patients with PTLD and PTLD-like disease, Methods Using the British Columbia Cancer Agency (BCCA) Lymphoid Cancer Database, all patients diagnosed with PTLD and PTLD-like conditions from 1981-2013 were identified, excluding primary CNS lymphoma, low-grade lymphomas and HIV associated lymphomas. Results 104 patients were identified: 86 PTLD and 18 PTLD-like. PTLD: Median age 47 y (range 6-71); 64% male; 60% late onset (> 1 year from transplant); 71% extranodal (EN) disease; 14% graft involved; 49% elevated LDH (n=36, missing n=13); 59% PS > 2 (n=45, missing n=10); 60% prior renal transplant. Pathology: 57% EBV positive (n=43, missing n=11); 2% early lesions, 13% polymorphic, 79% monomorphic (83% DLBCL), 1.1% classical Hodgkin lymphoma (CHL) and 4.7% unknown. All patients underwent initial RIS, followed by: observation (n=23, 27%), single agent rituximab (n=37, 43%), anthracycline based combination chemotherapy + R (n= 9 (n=6 + R), 10%), single agent chemotherapy + R (n=8 (n=4 + R), 9%); 4 (5%) were too frail to treat. With a median follow-up for living patients of 4.0 y (range 0.8-16.2 y), the 3-y OS and 3-y TTP were 50% and 48%, respectively. Polymorphic cases had a more favorable outcome compared to monomorphic cases with a 3-y OS of 91% vs. 45% (p=0.029) and a 3-y TTP of 73% vs. 44% (p=0.037). Monomorphic PTLD prognostic factors (n=63): In univariate analysis for TTP, an elevated LDH (p=0.006), poor PS >2 (p=.0012), EN sites > 1 (p=0.005), B symptoms (p=0.0019) and GI (p=0.022), kidney (p=0.009) and hepatic (p<0.00001) involvement were all associated with an inferior TTP. Similarly, elevated LDH (p= 0.028), poor PS (p=0.0001) and kidney involvement (p=0.027) were associated with a poor OS. A high IPI score was associated with an inferior outcome: 3-y OS and 3-y TTP for low (0,1) (n=20), intermediate (2,3) (n=34) and high risk (4,5) (n=14) groups were 69%, 40%, and 21% (p=0.03) and 83%, 28%, and 14% (p<0.0001), respectively. For monomorphic DLBCL cases receiving single agent rituximab (n=29), a higher IPI was also associated with a worse 3-y OS (76%, 64%, and 20%, p=0.018) and 3-y TTP (67%, 27%, and 20%, p=0.03) for the low, intermediate and high risk groups, respectively. PTLD-like: Median age 63 y (range 26-74); 50% male; 51% EN disease; 33% LDH elevated (n=6); 44% PS > 2 (n=7, missing n=2); 92% EBV positive (n= 12, missing n=5). Pathology: 1 polymorphic (6%); 15 monomorphic (83%) (DLBCL n=11, 73%, DLBCL/CHL grey zone lymphoma n=2, 13%), 1 CHL (6%), 1 Hodgkin-like (6%), 1 unknown (6%). The majority of patients had a history of rheumatoid arthritis (n=10, 56%) and had been on treatment with methotrexate (n=10, 56%). The median time from onset of autoimmune disorder to the development of PTLD-like disease was 11 y (0.16-50) and the median duration of drug exposure prior to the development of the PTLD-like disease was 5 y (0.16-15y). All patients underwent RIS, followed by observation (n=6, 32%); single agent rituximab (n=4, 21%); anthracycline based combination chemotherapy + R (n=6, 37%; R, n=4); or single agent chemotherapy + R (n=1, 5.3%); 1 was too frail. With a median follow-up of 4.9 y (range 0.3-17 y), the 3-y TTP and OS were 65% and 77%, respectively. Conclusion For PTLD, a select group of patients with a low IPI score appear to have high cure rates with single agent rituximab. For those with multiple IPI factors a risk-adapted strategy with earlier use of chemotherapy may be preferred. In the group with PTLD-like disease we observed Hodgkin lymphoma and Hodgkin-like proliferations but also grey zone lymphomas, the latter of which has not been previously reported. Patients with PTLD-like disorders have a more favorable outcome than those with PTLD. Disclosures: Klasa: Roche Canada: Research Funding. Sehn:Roche/ Genentech: Consultancy, Honoraria, Research Funding. Villa:Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria, Research Funding. Shenkier:Roche Canada: Research Funding. Connors:Roche Canada: Research Funding; F Hoffmann-La Roche: Research Funding. Savage:F Hoffmann-La Roche: Research Funding; Roche Canada: Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.212
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2013
Admission routes2
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