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Record W3048464089 · doi:10.1093/rheumatology/keaa412

Immune-checkpoint kinetics for T cells in anti-MDA5 positive interstitial lung disease

2020· article· en· W3048464089 on OpenAlexaff
Kunihiro Suzuki, Toyoshi Yanagihara, Koichiro Matsumoto, Sy Giin Chong, Hiroyuki Ando, Maako Ide, Masako Arimura‐Omori, Kentaro Hata, Naoki Hamada

Bibliographic record

VenueLara D. Veeken · 2020
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineInterstitial lung diseaseImmune checkpointImmune systemMDA5KineticsLungImmunologyCancer researchImmunotherapyInternal medicine

Abstract

fetched live from OpenAlex

Immune checkpoint kinetics can be monitored in anti-MDA5 positive interstitial lung disease. DEAR EDITOR, The anti-melanoma differentiation associated gene 5 (anti-MDA5) antibody is associated with rapidly progressive interstitial lung disease in patients with dermatomyositis. Although adaptive immunity is considered to be involved in pathogenesis, precise molecular pathways remain largely unknown. We report the case of a 71-year-old Japanese man who was referred to our department due to deterioration of his dyspnea and abnormal findings on his chest radiograph. A surgery for his pharyngeal and esophagus cancer was planned. Physical examination revealed Gottron papules, heliotrope rash and bilateral fine crackles on his lung bases, but no myalgia nor muscle weakness. High-resolution computed tomography of the chest showed a patchy distribution of consolidation predominantly in the lower lung (Fig. 1). Pulmonary function test showed a restrictive pattern with moderate DLco impairment. Examination of bronchoalveolar lavage fluid (BALF) showed lymphocytosis with decreased CD4/CD8 ratio (0.08). Immune checkpoint expression in T cells in a patient with anti-MDA5 positive interstitial lung disease (A) High-resolution computed tomography of the chest of the patient showed a patchy distribution of consolidation predominantly in the lower lung, suggestive of organizing pneumonia pattern. (B) A clinical course of the patient. The proportion of immune-checkpoint expression on CD3+CD4+ or CD3+CD8+ T cells in peripheral blood before and after immunosuppressive treatment. (C) Flow cytometric analysis of CD3+CD4+ or CD3+CD8+ T cells in BALF or peripheral blood at the initiation of treatment and during follow-up. BALF: bronchoalveolar lavage fluid; mPSL: methylprednisolone; PBMC: peripheral blood mononuclear cell; PSL: prednisolone; TAC: tacrolimus; Tx: therapy. Immune checkpoint expression in T cells in a patient with anti-MDA5 positive interstitial lung disease (A) High-resolution computed tomography of the chest of the patient showed a patchy distribution of consolidation predominantly in the lower lung, suggestive of organizing pneumonia pattern. (B) A clinical course of the patient. The proportion of immune-checkpoint expression on CD3+CD4+ or CD3+CD8+ T cells in peripheral blood before and after immunosuppressive treatment. (C) Flow cytometric analysis of CD3+CD4+ or CD3+CD8+ T cells in BALF or peripheral blood at the initiation of treatment and during follow-up. BALF: bronchoalveolar lavage fluid; mPSL: methylprednisolone; PBMC: peripheral blood mononuclear cell; PSL: prednisolone; TAC: tacrolimus; Tx: therapy. Based on the positivity of the anti-MDA5 antibody on admission along with the clinical information, he was diagnosed as clinically amyopathic dermatomyositis-associated interstitial lung disease (CADM-ILD). The patient was subsequently treated with methylprednisolone pulse therapy, followed by treatment with oral prednisolone and tacrolimus. Troughs of tacrolimus during treatment were around 8–10 ng/ml. Clinical remission was maintained during the tapering of steroid therapy. Because little is known about the role of immune-checkpoints in the pathogenesis of CADM-ILD, we analysed the expression of immune-checkpoints—PD-1, TIM-3, TIGIT and PD-L1—on T cells in BALF and peripheral blood. We detected PD-1, TIM-3, TIGIT and PD-L1 expression on T cells in BALF, while scant PD-1 expression was in peripheral blood (Fig. 1). Please see the methods in the previous study [1]. We were able to detect the kinetic changes of immune-checkpoint expression, especially TIM-3 and TIGIT, on T cells in peripheral blood during treatment. Specifically, TIM-3 expression on both CD4+ and CD8+ T cells substantially decreased immediately after methylprednisolone pulse, peaked transiently on day 19 but remained at a low level afterwards. In contrast, TIGIT expression on CD8+ T cells decreased to its bottom level on day 19 but gradually increased thereafter. As the expression levels of immune-checkpoints can be one of the markers of T-cell activation [2], we showed evidence of the effects of immunosuppressive treatment in T cells from a patient with CADM-ILD. The titer of anti-MDA5 antibody is reported to be a useful tool to monitor the disease activity in CADM-ILD [3]. Considering the central role of T cells in immunity, we predicted that the measurement of the T-cell activation could be a much faster and more sensitive predictor of the clinical course rather than the other markers. Future study is warranted to confirm our hypothesis. We previously reported that the proportion of PD-1+PD-L1+ cells among CD8+ T cells was correlated with the severity of immune-checkpoint inhibitor-related ILD (ICI-ILD) [1]. Given the recent findings on cis-PD-L1/CD80 or cis-PD-L1/PD-1 interactions for optimal T-cell responses [4, 5], we are of the opinion that augmented PD-1 and PD-L1 expressions on CD8+ T cells might act as one of the inhibitory mechanisms of the auto-reactivity and then interruption of this cis-PD-L1/PD-1 interaction by ICI (anti-PD-1 or anti-PD-L1 antibodies) for treatment of cancers might cause the progression of CADM-ILD. Interestingly, the expression pattern of PD-1, TIM-3 and TIGIT in this case was quite similar with the expression pattern found in ICI-ILD but different from other ILDs such as sarcoidosis, rheumatoid arthritis-related or Sjögren’s syndrome-related ILD [1]. Our patient has cancers, and the association between dermatomyositis and malignancies has been reported in several studies. We previously showed that the identical T-cell clones were found in BALF from ICI-ILD and peritumoral pleural effusion by sequencing of the complementarity-determining region of the T-cell receptor [6]. We speculate that the immune-checkpoint positive T cells in BALF from the patient could be related to tumor-infiltrating lymphocytes. The other possibility of these immune-checkpoint positive T cells could be follicular helper T cells (TFH, CXCR5+ PD-1+) [7] or peripheral helper T cells (TPH, CXCR5– PD-1+ CXCL13+) [8], which may help B cells generating autoantibodies including anti-MDA5 antibodies. Thus, our report illustrates a ‘bench to bedside’ approach and would provide a mechanistic insight into this life-threatening disease. Experimental conception and design: T.Y. and K.S. Performed experiments: K.S. Clinical data collection and patient follow-up: H.A., M. I. and K.S. Interpreted results and provided critical support: T.Y., K.S., K. M., S.G.C., M.A-O., K.H. and N.H. Prepared manuscript: T.Y. and S.G.C. Reviewed or edited, and approved manuscript: all authors. Funding: No specific funding was received from any funding bodies in the public, commercial or not-for-profit sectors to carry out the work described in this manuscript. Disclosure statement: the authors have declared no conflicts of interest.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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
Published2020
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