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Record W3212004376 · doi:10.1182/blood-2021-149728

A Large Multicenter Real-World Evidence (RWE) Analysis of Autoimmune (AI) Diseases and Lymphoma: Histologic Associations, Disease Characteristics, Survival, and Prognostication

2021· article· en· W3212004376 on OpenAlexaff
Jordan Carter, Adam J. Olszewski, Tatyana Feldman, Reem Karmali, Jean L. Koff, Sabarish Ayyappan, Matthew A. Lunning, Shazia Nakohda, Nadia Khan, Aryeh Pelcovits, Jason Lofters, Timothy S. Oh, Colin B. O’Leary, Kittika Poonsombudlert, Carman Tong, Elaine P. Kuhn, Mingen Liu, Andrea Anampa‐Guzmán, Megha Shalavadi, Colin Thomas, Melissa Kives, Ning Dong, Alexander Hershey, Neil Palmisiano, Alexey V. Danilov, Francisco J. Hernandez‐Ilizaliturri, Stefan K. Barta, Frederick Lansigan, Kerry J. Savage, Andrew M. Evens

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsMedicineInternal medicineLymphomaUnivariate analysisDiffuse large B-cell lymphomaProportional hazards modelDiseaseRituximabRheumatoid arthritisSurvival analysisOncologyMultivariate analysis

Abstract

fetched live from OpenAlex

Abstract Introduction: While an association between AI diseases and the development of lymphoma (LYM) is known, the impact of AI diseases on patient (pt) outcome across varying LYM subtypes, including the role of immunosuppressive medications (ISM), is not well understood. Methods: We conducted a large, multicenter, RWE retrospective analysis of adult pts with a diagnosis (dx) of lymphoma (LYM) between 1/2000 and 12/2020 and a pre-existing AI disease (see full list AI in Table 1). We examined baseline clinical features at LYM diagnosis, underlying AI disease characteristics, type/duration of ISM, LYM therapy received and survival outcomes. Multinomial regression models adjusted for age and sex identified associations between LYM subtypes, AI diseases, and ISM exposure, and were reported as relative risk ratio (RRR). Survival rates were estimated by Kaplan-Meier. Univariate associations of baseline factors with survival in cases with non-missing data were examined by Cox model and stratified by International Prognostic Index (IPI). Results: In total, 785 pts were identified across 14 North American institutions, of which 694 pts with complete data were included in the final analysis. Rheumatoid arthritis was the most common AI disease (30%) (Table 1). Diffuse large B-cell lymphoma (DLBCL) was the most common histologic subtype (n=303, 44%), and it was the most prevalent histology for nearly every AI disease. The median duration between AI disease and LYM diagnosis was 108 months (mo) (1-816 mo). Several associations were found between specific AI diseases and LYM subtypes, including: Hashimoto's thyroiditis with marginal zone lymphoma (MZL, RRR 2.78 (95% CI 1.14-6.78, P=0.024), Waldenstrom's macroglobulinemia (RRR 7.12 (95% CI 1.07-47.3, P=0.031), and follicular lymphoma (RRR, 2.71, 95% CI 1.01-7.24, P=0.047); Polymyalgia rheumatica with CLL/SLL (RRR 21.27 (95% CI 4.57-98.96), P=0.0001) and MZL (RRR 6.62 (95% CI 1.38-31.80), P=0.018); Psoriasis with peripheral T-cell lymphoma (PTCL, RRR 3.85 (95% CI 1.21-12.22), P=0.022); and Inflammatory bowel disease with CLL/SLL (HR 4.60 (95% CI 1.72-12.34), P=0.002). Overall, 402 (58%) pts had ISM exposure prior to LYM dx, with 279 (40%) pts on active ISM at time of LYM dx. The most commonly used ISM agent was methotrexate (26%); the median duration of ISM prior to LYM diagnosis was 60 mo (1-480). Compared with DLBCL pts, those diagnosed with PTCLs were more likely to have had exposure to TNF-α inhibitors (RR 1.89, 95% CI 1.23-2.89, P=0.004) or apremilast, a phosphodiesterase-4 inhibitor (RR 19.2, 95% CI 2.6-149.9, P=0.005). Rituximab +/- chemotherapy was used as frontline therapy in the majority of B-cell LYM pts, including 90% of DLBCL. Of LYM pts on ISM at initial dx, 41% had a reduction in immunosuppression (RIS), of whom 81% stopped ISM completely (Table 1). Only 10% of pts who underwent RIS were reported to have a flare of AI disease during initial therapy, compared with 7% of pts who had a flare despite no RIS (P=NS). Survival by histology is depicted in Figure A/B. For DLBCL pts (n=303), survival appeared overall comparable to historically reported outcomes in the general DLBCL population. Neither antecedent use of ISM nor duration of ISM were associated with pt outcomes (data not shown). However, a survival advantage was identified among DLBCL pts whose ISM was stopped completely during frontline therapy compared with pts who were not on ISM at diagnosis (Figure C/D). In addition, 10 DLBCL pts underwent 100% RIS +/- rituximab with therapeutic intent and did not receive cytotoxic chemotherapy as part of frontline therapy. Among these, 4 pts (mean age 62 years (26-84); ISM: n=2 MTX, and 1 each azathioprine and etanercept/MTX; EBV+ 2/4) achieved complete remission and remain disease-free at time of last follow-up at 5, 74, 105, and 221 months post-DLBCL dx. Conclusions: Altogether, we identified several novel histologic associations of AI diseases with LYM histologic subtypes. Furthermore, prior receipt of TNF-α inhibitor or a phosphoediesterase 4 inhibitor were associated with a diagnosis of PTCL. For DLBCL pts with antecedent AI disease, complete cessation of ISM during frontline LYM therapy appeared to be associated with improved survival. Finally, there were a small number of select DLBCL pts who garnered long-term disease-free survival using RIS +/- rituximab as frontline therapy, similar to the treatment paradigm in post-transplant lymphoproliferative disorders. Figure 1 Figure 1. Disclosures Olszewski: TG Therapeutics: Research Funding; PrecisionBio: Research Funding; Celldex Therapeutics: Research Funding; Acrotech Pharma: Research Funding; Genentech, Inc.: Research Funding; Genmab: Research Funding. Feldman: Alexion, AstraZeneca Rare Disease: Honoraria, Other: Study investigator. Karmali: Karyopharm: Consultancy; Epizyme: Consultancy; Morphosys: Consultancy, Speakers Bureau; Kite, a Gilead Company: Consultancy, Research Funding, Speakers Bureau; Genentech: Consultancy; AstraZeneca: Speakers Bureau; BeiGene: Consultancy, Speakers Bureau; BMS/Celgene/Juno: Consultancy, Research Funding; EUSA: Consultancy; Janssen/Pharmacyclics: Consultancy; Roche: Consultancy; Takeda: Research Funding. Lunning: TG Therapeutics: Consultancy; Janssen: Consultancy; Verastem: Consultancy; Spectrum: Consultancy; Myeloid Therapeutics: Consultancy; AstraZeneca: Consultancy; Morphosys: Consultancy; ADC Therapeutics: Consultancy; Beigene: Consultancy; Daiichi-Sankyo: Consultancy; Kyowa Kirin: Consultancy; Celgene, a Bristol Myers Squibb Co.: Consultancy; Legend: Consultancy; Acrotech: Consultancy; Novartis: Consultancy; Kite, a Gilead Company: Consultancy; Karyopharm: Consultancy; AbbVie: Consultancy. Palmisiano: Genentech: Research Funding; AbbVie: Consultancy, Research Funding; Takeda: Consultancy; Foundation One: Consultancy. Danilov: Bristol-Meyers-Squibb: Honoraria, Research Funding; Gilead Sciences: Research Funding; Pharmacyclics: Consultancy, Honoraria; Beigene: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; TG Therapeutics: Consultancy, Research Funding; Takeda Oncology: Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Bayer Oncology: Consultancy, Honoraria, Research Funding; Astra Zeneca: Consultancy, Honoraria, Research Funding; SecuraBio: Research Funding; Rigel Pharm: Honoraria. Barta: Kyowa Kirin: Honoraria; Acrotech: Honoraria; Daiichi Sankyo: Honoraria; Seagen: Honoraria. Lansigan: Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees. Savage: Astra-Zeneca: Consultancy, Honoraria; Takeda: Other: Institutional clinical trial funding; Roche: Research Funding; Merck: Consultancy, Honoraria, Other: Institutional clinical trial funding; BMS: Consultancy, Honoraria, Other: Institutional clinical trial funding; Seattle Genetics: Consultancy, Honoraria; Servier: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Beigene: Other: Institutional clinical trial funding; Genentech: 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)0.006
metaresearch head score (Gemma)0.012
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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.295
Teacher spread0.274 · 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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Citations1
Published2021
Admission routes1
Has abstractyes

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