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Retrospective Analysis of Lymphomas in the Setting of Autoimmune Disease and the Impact of Immunosuppression

2015· article· en· W2579680972 on OpenAlexaff
Adam M. Petrich, Stefan K. Barta, Frederick Lansigan, Trent Wang, Ananta Bhatt, Garrett T. Wasp, Addie Hill, Frank Passero, Amrit Kahalon, Roopesh Kansara, Mitul Gandhi, Graham W. Slack, Tatyana Feldman, Andrew M. Evens, Kerry J. Savage

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineRituximabImmunosuppressionInternal medicineLymphomaOncologyUnivariate analysisLymphoproliferative disordersProportional hazards modelRetrospective cohort studyInternational Prognostic IndexImmunologyMultivariate analysis

Abstract

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Abstract Introduction: Post-transplant lymphoproliferative disease (PTLD) encompasses a heterogeneous array of cases of lymphoma/lymphoma-like conditions arising in the setting of immunosuppression (IS) for prior organ or marrow transplant. Such pts face heightened risk of toxicity from exposure to cytotoxic chemotherapy, and may be best treated in the frontline with reduction of IS (RI) and anti-CD20 monoclonal antibody treatment (Trappe, 2012). Autoimmune (AI) disease has been associated with an increased risk of developing lymphoma; however, the relative impact of baseline clinical features, including prior IS, is unknown. Methods: We conducted a multicenter, retrospective analysis of adult pts with pre-existing AI conditions who were diagnosed with lymphoma since 1997. Baseline clinical features at diagnosis of lymphoid malignancy, including International Prognostic Index (IPI) risk factors; underlying AI disease; duration and type of IS; EBV status (by EBER in-situ hybridization); and primary therapy received (RI, rituximab [R] monotherapy, chemotherapy [+/- R]); were collected. Survival analyses were performed using Kaplan-Meier method. We then focused on those who had A) received IS other than corticosteroids (CS) alone; and B) those diagnosed with DLBCL. Those variables found to have significant correlation with OS by univariate analyses (UVA) were used to construct Cox proportional hazards model (multivariate analysis [MVA]) in order to determine which might have the strongest association with OS. Lastly, we sought to evaluate a potential role for RI and/or R as frontline therapy for those with DLBCL. Results: A total of 130 pts were included (Table 1). The most frequent AI disease was rheumatoid arthritis and for all cases, 76% had documented exposure to IS, for a median duration of 4.5 years (range 0.17-57 years) prior to diagnosis of lymphoma. The most common histologic subtype was DLBCL (52%). EBV status was reported for only 34% of pts, but was positive in 68% (25/37), all of whom had received prior exposure to IS beyond CS, and 80% of whom (20/25) were diagnosed with DLBCL. EBV status was infrequently tested in pts not previously exposed to IS (3/31). At a median follow-up of 61 months for the entire cohort, 2-year PFS and OS were 79% and 91%, respectively (Figure 1, Panel A). By UVA, age>60; PS>1; LDH> upper limit of normal (ULN); DLBCL (vs all other histologies); underlying rheumatoid arthritis (RA; vs all other AI diseases); and prior exposure to IS, each correlated with inferior OS (Table 1). By MVA, PS>1 and prior IS maintained significance (p<0.05). If those receiving only CS are grouped with those not previously exposed to IS, the correlation of this factor with OS was strengthened (p 0.008), and by MVA, PS>1 (p 0.002) and prior IS (p 0.010) maintain significance (data not shown). Among 67 pts with DLBCL, median age was 61 (range 26-90), 60% had advanced stage disease, and 32% had IPI of 4 or 5. At a median follow-up of 32 months, the 2-year PFS and OS were 82% and 84%, respectively. There were no differences in frequency of any IPI factors between patients exposed to prior IS (n=53) and those who were naïve to prior IS (n=14). For those not exposed to prior IS, the 2-year OS was 100%, compared to 80% in those who received prior IS (p 0.24); corresponding 2-year PFS were 92% and 79%, respectively (p 0.41). Age>60 and PS>1 were associated with an inferior OS but use of IS was not associated with outcome (Table 2). The 2 year OS for those treated with R plus CHOP(like) chemotherapy, CHOP(like) chemotherapy (without R), R alone (+/- RI), and with RI alone were 92%, 75%, 90%, and 67%, respectively (Figure 1, Panel B; log-rank p value 0.55). Patients who received CHOP-like therapy +/- R, as compared to R and/or RI were more likely to be naïve to IS therapy (15/46 vs 0/22, p = 0.003) and have 2 or more EN sites of disease (15/46 vs 2/22, =0.041). These differences notwithstanding, the 2-year PFS for the two groups were 86% and 74% (p 0.16), and 2-year OS for the two groups were 88% and 82%, respectively (Figure 1, Panel C; p 0.91). Conclusions: Pts with immunosuppression-related lymphoma have high rates of 2-year OS and in DLBCL, IS does not appear to be associated with an inferior outcome. Similar to evolving treatment paradigms in PTLD, rituximab monotherapy and other cytotoxic chemotherapy-free regimens as well as risk-adapted approaches may warrant further evaluation in IS-related DLBCL. Disclosures Petrich: Seattle Genetics: Consultancy, Honoraria, Research Funding. Barta:Seattle Genetics: Research Funding. Feldman:Celgene: Honoraria, Speakers Bureau; Pharmacyclics/JNJ: Honoraria, Speakers Bureau; Seattle Genetics: Honoraria, Speakers Bureau. Savage:Seattle Genetics: Honoraria, Speakers Bureau; BMS: Honoraria; Infinity: Honoraria; Roche: Other: Institutional 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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.276
Teacher spread0.267 · 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
Published2015
Admission routes1
Has abstractyes

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