Immune-related adverse events associated with immune checkpoint inhibitors: a call to action for collecting and sharing clinical trial and real-world data

Reynolds, Kerry L.; Arora, Shaily; Elayavilli, Ravikumar Komandur; Louv, William C; Schaller, Teilo; Khandelwal, Aakanksha; Rothenberg, Mace L.; Khozin, Sean; Guidon, Amanda C.; Dougan, Michael; Zubiri, Leyre; Petrillo, Laura; Sise, Meghan E.; Villani, Alexandra‐Chloé; Johnson, Douglas B.; Rahma, Osama E.; Sharon, Elad

doi:10.1136/jitc-2021-002896

Cited by 26 publications

(23 citation statements)

References 30 publications

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“…Despite the promising efficacy of immune checkpoint inhibitors, the majority of treated patients have had immune-related adverse events (IAEs) to varying degrees (Reynolds et al, 2021). Commonly reported IAEs include rash or pruritus, gastrointestinal disorders, and endocrine disorders (Farolfi et al, 2012;Robert et al, 2015).…”

Section: Immune-related Adverse Eventsmentioning

confidence: 99%

PD-1/PD-L1 Checkpoint Inhibitors in Tumor Immunotherapy

et al. 2021

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Programmed death protein 1 (PD1) is a common immunosuppressive member on the surface of T cells and plays an imperative part in downregulating the immune system and advancing self-tolerance. Its ligand programmed cell death ligand 1 (PDL1) is overexpressed on the surface of malignant tumor cells, where it binds to PD1, inhibits the proliferation of PD1-positive cells, and participates in the immune evasion of tumors leading to treatment failure. The PD1/PDL1-based pathway is of great value in immunotherapy of cancer and has become an important immune checkpoint in recent years, so understanding the mechanism of PD1/PDL1 action is of great significance for combined immunotherapy and patient prognosis. The inhibitors of PD1/PDL1 have shown clinical efficacy in many tumors, for example, blockade of PD1 or PDL1 with specific antibodies enhances T cell responses and mediates antitumor activity. However, some patients are prone to develop drug resistance, resulting in poor treatment outcomes, which is rooted in the insensitivity of patients to targeted inhibitors. In this paper, we reviewed the mechanism and application of PD1/PDL1 checkpoint inhibitors in tumor immunotherapy. We hope that in the future, promising combination therapy regimens can be developed to allow immunotherapeutic tools to play an important role in tumor treatment. We also discuss the safety issues of immunotherapy and further reflect on the effectiveness of the treatment and the side effects it brings.

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Section: Immune-related Adverse Eventsmentioning

confidence: 99%

PD-1/PD-L1 Checkpoint Inhibitors in Tumor Immunotherapy

et al. 2021

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“… 4 This reflects how difficult it remains to diagnose and effectively treat ICI-pneumonitis, as a result of the scarcity of available pathophysiological knowledge. 5 6 …”

Section: Introductionmentioning

confidence: 99%

Single-cell transcriptomics identifies pathogenic T-helper 17.1 cells and pro-inflammatory monocytes in immune checkpoint inhibitor-related pneumonitis

Franken

Mol

Vanmassenhove

et al. 2022

J Immunother Cancer

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BackgroundImmune checkpoint inhibitor (ICI)-related pneumonitis is the most frequent fatal immune-related adverse event associated with programmed cell death protein-1/programmed death ligand-1 blockade. The pathophysiology however remains largely unknown, owing to limited and contradictory findings in existing literature pointing at either T-helper 1 or T-helper 17-mediated autoimmunity. In this study, we aimed to gain novel insights into the mechanisms of ICI-related pneumonitis, thereby identifying potential therapeutic targets.MethodsIn this prospective observational study, single-cell RNA and T-cell receptor sequencing was performed on bronchoalveolar lavage fluid of 11 patients with ICI-related pneumonitis and 6 demographically-matched patients with cancer without ICI-related pneumonitis. Single-cell transcriptomic immunophenotyping and cell fate mapping coupled to T-cell receptor repertoire analyses were performed.ResultsWe observed enrichment of both CD4+ and CD8+ T cells in ICI-pneumonitis bronchoalveolar lavage fluid. The CD4+ T-cell compartment showed an increase of pathogenic T-helper 17.1 cells, characterized by high co-expression of TBX21 (encoding T-bet) and RORC (ROR-γ), IFN-G (IFN-γ), IL-17A, CSF2 (GM-CSF), and cytotoxicity genes. Type 1 regulatory T cells and naïve-like CD4+ T cells were also enriched. Within the CD8+ T-cell compartment, mainly effector memory T cells were increased. Correspondingly, myeloid cells in ICI-pneumonitis bronchoalveolar lavage fluid were relatively depleted of anti-inflammatory resident alveolar macrophages while pro-inflammatory ‘M1-like’ monocytes (expressing TNF, IL-1B, IL-6, IL-23A, and GM-CSF receptor CSF2RA, CSF2RB) were enriched compared with control samples. Importantly, a feedforward loop, in which GM-CSF production by pathogenic T-helper 17.1 cells promotes tissue inflammation and IL-23 production by pro-inflammatory monocytes and vice versa, has been well characterized in multiple autoimmune disorders but has never been identified in ICI-related pneumonitis.ConclusionsUsing single-cell transcriptomics, we identified accumulation of pathogenic T-helper 17.1 cells in ICI-pneumonitis bronchoalveolar lavage fluid—a phenotype explaining previous divergent findings on T-helper 1 versus T-helper 17 involvement in ICI-pneumonitis—,putatively engaging in detrimental crosstalk with pro-inflammatory ‘M1-like’ monocytes. This finding yields several novel potential therapeutic targets for the treatment of ICI-pneumonitis. Most notably repurposing anti-IL-23 merits further research as a potential efficacious and safe treatment for ICI-pneumonitis.

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“…This creates a dataset where associations between diseases and comorbidities can be identified which might have not been captured in the predesigned data collections. For instance, studying the association between checkpoint inhibitors and the diverse manifestations for immune related adverse events would have been difficult to design a priori 12. Particularly for complex autoimmune diseases, where both the risk factors and the disease classifications are uncertain, the high dimensional EHR data allows for wide data exploration to detect unknown patterns.…”

Section: Introductionmentioning

confidence: 99%

From real-world electronic health record data to real-world results using artificial intelligence

Knevel

Liao

2022

Ann Rheum Dis

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With the worldwide digitalisation of medical records, electronic health records (EHRs) have become an increasingly important source of real-world data (RWD). RWD can complement traditional study designs because it captures almost the complete variety of patients, leading to more generalisable results. For rheumatology, these data are particularly interesting as our diseases are uncommon and often take years to develop. In this review, we discuss the following concepts related to the use of EHR for research and considerations for translation into clinical care: EHR data contain a broad collection of healthcare data covering the multitude of real-life patients and the healthcare processes related to their care. Machine learning (ML) is a powerful method that allows us to leverage a large amount of heterogeneous clinical data for clinical algorithms, but requires extensive training, testing, and validation. Patterns discovered in EHR data using ML are applicable to real life settings, however, are also prone to capturing the local EHR structure and limiting generalisability outside the EHR(s) from which they were developed. Population studies on EHR necessitates knowledge on the factors influencing the data available in the EHR to circumvent biases, for example, access to medical care, insurance status. In summary, EHR data represent a rapidly growing and key resource for real-world studies. However, transforming RWD EHR data for research and for real-world evidence using ML requires knowledge of the EHR system and their differences from existing observational data to ensure that studies incorporate rigorous methods that acknowledge or address factors such as access to care, noise in the data, missingness and indication bias.

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Immune-related adverse events associated with immune checkpoint inhibitors: a call to action for collecting and sharing clinical trial and real-world data

Cited by 26 publications

References 30 publications

PD-1/PD-L1 Checkpoint Inhibitors in Tumor Immunotherapy

PD-1/PD-L1 Checkpoint Inhibitors in Tumor Immunotherapy

Single-cell transcriptomics identifies pathogenic T-helper 17.1 cells and pro-inflammatory monocytes in immune checkpoint inhibitor-related pneumonitis

From real-world electronic health record data to real-world results using artificial intelligence

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