Analysis of risk factors for immune-related adverse events in various solid tumors using real-world data

Safety of immune checkpoint inhibitors in patients aged over 80 years: a retrospective cohort study

BACKGROUND

Immuno-oncology (IO) drugs are essential for treating various cancer types; however, safety concerns persist in older patients. Although the incidence of immune-related adverse events (irAEs) is similar among age groups, higher rates of hospitalization or discontinuation of IO therapy have been reported in older patients. Limited research exists on IO drug safety and risk factors in older adults. Our investigation aimed to assess the incidence of irAEs and identify the potential risk factors associated with their development.

METHODS

This retrospective analysis reviewed the clinical data extracted from the medical records of patients aged > 80 years who underwent IO treatment at our institution. Univariate and multivariate analyses were performed to assess the incidence of irAEs.

RESULTS

Our study included 181 patients (median age: 82 years, range: 80-94), mostly men (73%), with a performance status of 0-1 in 87% of the cases; 64% received IO monotherapy. irAEs occurred in 35% of patients, contributing to IO therapy discontinuation in 19%. Our analysis highlighted increased body mass index, eosinophil counts, and albumin levels in patients with irAEs. Eosinophil count emerged as a significant risk factor for any grade irAEs, particularly Grade 3 or higher, with a cutoff of 118 (/μL). The group with eosinophil counts > 118 had a higher frequency of irAEs, and Grade 3 or higher events than the group with counts ≤ 118.

CONCLUSION

IO therapy is a safe treatment option for patients > 80 years old. Furthermore, patients with elevated eosinophil counts at treatment initiation should be cautiously managed.

Biomarkers for immune-related adverse events in cancer patients treated with immune checkpoint inhibitors

Although immune checkpoint inhibitors have greatly improved cancer therapy, they also cause immune-related adverse events, including a wide range of inflammatory side effects resulting from excessive immune activation. Types of immune-related adverse events are diverse and can occur in almost any organ, with different frequencies and severities. Furthermore, immune-related adverse events may occur within the first few weeks after treatment or even several months after treatment discontinuation. Predictive biomarkers include blood cell counts and cell surface markers, serum proteins, autoantibodies, cytokines/chemokines, germline genetic variations and gene expression profiles, human leukocyte antigen genotype, microRNAs and the gut microbiome. Given the inconsistencies in research results and limited practical utility, there is to date no established biomarker that can be used in routine clinical practice, and additional investigations are essential to demonstrate efficacy and subsequently facilitate integration into routine clinical use.

Key Determinants of Immune-Mediated Adverse Reactions to Oncology Drugs

To overcome the epidemiological severity of cancer, developing effective treatments is urgently required. In response, immune checkpoint inhibitors (ICIs) have been revealed as a promising resolution for treatment-resistant cancers across the world. Yet, they have both advantages and disadvantages, bringing therapeutic benefits while simultaneously inducing toxicity, and in particular, immune-mediated adverse drug reactions (imADRs), to the human body. These imADRs can be pathogenic and sometimes lethal, hampering health prediction and monitoring following the provision of ICI treatment. Therefore, it is necessary to collectively identify the determinant factors that contribute to these imADRs induced by ICIs. This article evaluated treatment-, tumor-, and patient-related determinants, and indicated a research gap for future investigations on the pathogenic mechanism of imADRs and translational conversion of determinants into clinical biomarkers to aid pharmacovigilance and cancer therapies.

Mechanisms and biomarkers of immune-related adverse events in gastric cancer

Immune-checkpoint inhibitors (ICIs), different from traditional cancer treatment models, have shown unprecedented anti-tumor effects in the past decade, greatly improving the prognosis of many malignant tumors in clinical practice. At present, the most widely used ICIs in clinical immunotherapy for a variety of solid tumors are monoclonal antibodies against cytotoxic T lymphocyte antigen-4 (CTLA-4), programmed cell death protein 1 (PD-1) and their ligand PD-L1. However, tumor patients may induce immune-related adverse events (irAEs) while performing immunotherapy, and irAE is an obstacle to the prospect of ICI treatment. IrAE is a non-specific disease caused by immune system imbalance, which can occur in many tissues and organs. For example, skin, gastrointestinal tract, endocrine system and lung. Although the exact mechanism is not completely clear, related studies have shown that irAE may develop through many ways. Such as excessive activation of autoreactive T cells, excessive release of inflammatory cytokines, elevated levels of autoantibodies, and common antigens between tumors and normal tissues. Considering that the occurrence of severe IrAE not only causes irreversible damage to the patient's body, but also terminates immunotherapy due to immune intolerance. Therefore, accurate identification and screening of sensitive markers of irAE are the main beneficiaries of ICI treatment. Additionally, irAEs usually require specific management, the most common of which are steroids and immunomodulatory therapies. This review aims to summarize the current biomarkers for predicting irAE in gastric cancer and their possible mechanisms.

Predictive Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Events

Immune-checkpoint inhibitors (ICIs) are antagonists of inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen-4, the programmed cell death protein-1 and its ligand PD-L1, and they are increasingly used in cancer treatment. By blocking certain suppressive pathways, ICIs promote T-cell activation and antitumor activity but may induce so-called immune-related adverse events (irAEs), which mimic traditional autoimmune disorders. With the approval of more ICIs, irAE prediction has become a key factor in improving patient survival and quality of life. Several biomarkers have been described as potential irAE predictors, some of them are already available for clinical use and others are under development; examples include circulating blood cell counts and ratios, T-cell expansion and diversification, cytokines, autoantibodies and autoantigens, serum and other biological fluid proteins, human leucocyte antigen genotypes, genetic variations and gene profiles, microRNAs, and the gastrointestinal microbiome. Nevertheless, it is difficult to generalize the application of irAE biomarkers based on the current evidence because most studies have been retrospective, time-limited and restricted to a specific type of cancer, irAE or ICI. Long-term prospective cohorts and real-life studies are needed to assess the predictive capacity of different potential irAE biomarkers, regardless of the ICI type, organ involved or cancer site.

Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients

PURPOSE

Clinical management of patients receiving immune checkpoint inhibitors (ICIs) could be informed using accurate predictive tools to identify patients at risk of short-term acute care utilization (ACU). We used routinely collected data to develop and assess machine learning (ML) algorithms to predict unplanned ACU within 90 days of ICI treatment initiation.

METHODS

We used aggregated electronic health record data from 7,960 patients receiving ICI treatments to train and assess eight ML algorithms. We developed the models using pre-SARS-COV-19 COVID-19 data generated between January 2016 and February 2020. We validated our algorithms using data collected between March 2020 and June 2022 (peri-COVID-19 sample). We assessed performance using area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, and calibration plots. We derived intuitive explanations of predictions using variable importance and Shapley additive explanation analyses. We assessed the marginal performance of ML models compared with that of univariate and multivariate logistic regression (LR) models.

RESULTS

Most algorithms significantly outperformed the univariate and multivariate LR models. The extreme gradient boosting trees (XGBT) algorithm demonstrated the best overall performance (AUROC, 0.70; sensitivity, 0.53; specificity, 0.74) on the peri-COVID-19 sample. The algorithm performance was stable across both pre- and peri-COVID-19 samples, as well as ICI regimen and cancer groups. Type of ICI agents, oxygen saturation, diastolic blood pressure, albumin level, platelet count, immature granulocytes, absolute monocyte, chloride level, red cell distribution width, and alcohol intake were the top 10 key predictors used by the XGBT algorithm.

CONCLUSION

Machine learning algorithms trained using routinely collected data outperformed traditional statistical models when predicting 90-day ACU. The XGBT algorithm has the potential to identify high-ACU risk patients and enable preventive interventions to avoid ACU.

Risk factors indicating immune-related adverse events with combination chemotherapy with immune checkpoint inhibitors and platinum agents in patients with non-small cell lung cancer: a multicenter retrospective study

PURPOSE

Immune checkpoint inhibitors (ICI) ushered in a new era for the treatment of non-small cell lung cancer (NSCLC). However, they carry the risk of immune-related adverse events (irAEs). Recently, various studies have been conducted on the predictive factors for irAEs, but there are no reports focusing only on ICI plus platinum agents. The present study aimed to identify the risk factors for irAEs due to ICI combined with platinum-based induction immunochemotherapy in NSCLC patients, focusing only on the period of combined therapy and excluding the period of ICI maintenance therapy.

METHODS

This retrospective study included 315 NSCLC patients who started ICI combined with platinum-based chemotherapy treatment at 14 hospitals between December 2018 and March 2021. A logistic regression analysis was used to explore the predictive factors.

RESULTS

Fifty patients (15.9%) experienced irAEs. A multivariate analysis revealed that squamous cell carcinoma (P = 0.021; odds ratio [OR]: 2.30; 95% confidence interval [Cl]: 1.14-4.65), anti-programmed death 1 antibody (anti-PD-1) plus anti-cytotoxic T-lymphocyte antigen-4 antibody (anti-CTLA-4) regimens (P < 0.01; OR: 22.10; 95% Cl: 5.60-87.20), and neutrophil-to-lymphocyte rate (NLR) < 3 (P < 0.01; OR: 2.91; 95% Cl: 1.35-6.27) were independent predictive factors for irAEs occurrence.

CONCLUSION

Squamous cell carcinoma, anti-PD-1 plus anti-CTLA-4 regimens, and NLR < 3 may be predictive factors for the occurrence of irAEs due to induction immunochemotherapy in patients with NSCLC. By focusing on the potential risk of irAEs in patients with these factors, irAEs can be appropriately managed from an early stage.

Prognostic Significance of the Severity of Immune-Related Adverse Events in Advanced Cancer Patients Treated with PD-1/PD-L1 Inhibitors: A Real-World Data Analysis

BACKGROUND

There is limited evidence regarding immune-related adverse events (irAEs) in Asian cancer patients treated with antibodies directed against programmed cell death-1 (PD-1) or programmed cell death-ligand 1 (PD-L1).

OBJECTIVE

This study aimed to investigate the clinical patterns and prognostic significance of grade 1-2 and grade ≥ 3 irAEs by PD-1/PD-L1 inhibitors in cancer patients using real-world clinical data.

PATIENTS AND METHODS

We conducted a retrospective study of cancer patients who received pembrolizumab, nivolumab, or atezolizumab at a tertiary hospital in South Korea. Incidence, time to onset, and grade 1-2 and grade ≥ 3 irAE risk factors were analyzed from medical records. The association of irAE severity with progression-free survival (PFS) and prognostic factors for PFS were evaluated.

RESULTS

Among a total of 431 patients, irAEs occurred in 45.2%, and 9.5% were grade ≥ 3 irAEs. There were no significant differences in the median time to onset based on severity. Risk factors for the development of grade ≥ 3 irAEs were the presence of autoimmune disorders or diabetes mellitus. The median PFS was significantly different at 13.20, 9.00 and 4.17 months for the grade 1-2, grade ≥ 3, and no irAE groups, respectively. An increase in administration cycles was associated with a reduced risk of progression in patients with grade 1-2 and grade ≥ 3 irAEs.

CONCLUSIONS

The development of grade ≥ 3 irAEs was affected by comorbidities and associated with improved PFS compared with those without irAEs. Our findings identified the real-world epidemiology, risk factors, and prognostic significance of irAEs, which may guide treatment decisions of PD-1/PD-L1 inhibitors.

PD-1 inhibitor-based adverse events in solid tumors: A retrospective real-world study

Background & Aims: Immune checkpoint inhibitors (ICIs) have transformed the landscape of cancer treatment, and ICI-related toxicities (i.e., immune-related adverse events (irAEs) have been reported in many clinical studies. However, the toxicity data of real-world have not been fully assessed. Methods: Patients with histologically confirmed solid tumors who had been treated with PD-1 inhibitors were included in the study. Patient data were collected from electronic medical records, including basic characteristics, data of irAEs, management and outcome. Incidences of irAEs were pooled and compared, and the risk of irAEs was also analyzed. Results: A total of 362 solid tumor patients treated with sintilimab (n = 171), camrelizumab (n = 60), toripalimab (n = 72), and pembrolizumab (n = 59) were included. In total, any grade irAEs, grade 1-2 irAEs, and grade ≥3 irAEs accounted for 47.24%, 38.67% and 8.56% of cases, reapectively. Further, 29.24% of patients discontinued immunotherapy due to irAEs, with pneumonitis being the main reason for discontinuation. By comparing the toxicity profiles between different ICIs, we found that reactive capillary haemangiomas were camrelizumab-specific. Additionally, the frequency of irAEs was association with ICIs type, the pooled incidence (standardized rate) of irAEs related to sintilimab, camrelizumab, toripalimab and pembrolizumab were 55.56% (52.81%), 48.33% (55.55%), 33.33% (29.23%) and 38.98% (38.29%), respectively. Sintilimab and camrelizumab had higher incidences of any grade and grade 1-2 than toripalimab (55.56% vs. 33.33%, p = 0.002; 48.54% vs. 25.00%, p = 0.0001) and pembrolizumab (55.56% vs. 38.98%, p = 0.0028; 48.54% vs. 25.42%, p = 0.002), while the grade ≥3 irAEs of pembrolizumab (13.56%) were approximately 1.63- to 1.93-fold higher than other ICIs, and the standardized grade ≥3 of pembrolizumab was significantly higher than that of sintilimab (13.21% vs. 7.12%, p = 0.026), especially for grade ≥3 pneumonitis. Multivariate analysis found that cumulative cycles of ICI (OR = 1.081; 95% CI: 1.023-1.142; p = 0.006), and lung cancer (OR = 1.765; 95% CI: 1.105-2.820; p = 0.017) were independent risk factors for irAEs. Conclusion: The frequency of irAEs is associated with ICI type. The pooled incidence of irAEs related to sintilimab and pneumonitis caused by pembrolizumab were higher. These data indicate the importance of having different monitoring priorities for different PD-1 inhibitors.

Immune Checkpoint Inhibitor-Induced Myositis/Myocarditis with Myasthenia Gravis-like Misleading Presentation: A Case Series in Intensive Care Unit

Introduction: Immune checkpoint inhibitors (ICIs) are a major breakthrough in cancer treatment. Their increasingly frequent use leads to an uprising incidence of immune-related adverse events (irAEs). Among those, myocarditis is the most reported fatal cardiovascular irAE, frequently associated with ICI-related myositis. Case series: Here, we report three cases of ICI-induced myocarditis/myositis with an extremely severe myasthenia gravis-like (MG-like) presentation, highlighting the main challenges in irAEs management. These patients were over 60 years old and presented an ongoing melanoma, either locally advanced or metastatic, treated with ICI combinations. Shortly after the first or second ICI infusion, they were admitted in an intensive care unit (ICU) for grade 3 ICI-induced MG-like symptoms leading to acute respiratory failure (ARF) requiring invasive mechanical ventilation (IMV). The initial misdiagnosis was later corrected to severe ICI-induced seronegative myocarditis/myositis upon biological results and histopathology from muscular/endomyocardial biopsies. All of them received urgent high-dose corticosteroids pulses. The oldest patient died prematurely, but the two others received targeted therapies leading to complete recovery for one of them. Discussion: These cases highlight the four main challenges of irAEs, encompassing the lack of knowledge among physicians, the risk of misdiagnosis due to numerous and non-specific symptoms, the frequent overlapping forms of irAEs, and the extremely rare MG-like misleading presentation of myocarditis/myositis. The exact pathophysiology of irAEs remains unclear, although a major involvement of the lymphoid compartment (specifically T lymphocytes) was evidenced. Therapeutic management is based on urgent high-dose corticosteroids. For the severest forms of irAEs, case-by-case targeted immunosuppressive therapies should be urgently administered upon multidisciplinary meetings. Conclusion: These cases highlight the lack of knowledge of irAEs among physicians, aggravated by misleading overlapping forms, requiring specific management in trained units and multidisciplinary care. Severe MG-like presentation of irAEs constitutes an absolute therapeutic emergency with high-dose corticosteroids and targeted immunosuppressive therapy.

Risk Factors and Biomarkers for Immune-Related Adverse Events: A Practical Guide to Identifying High-Risk Patients and Rechallenging Immune Checkpoint Inhibitors

Immune-related adverse events (irAEs) are a range of complications associated with the use of immune-checkpoint inhibitors (ICIs). Two major classes of ICIs widely used are Cytotoxic T-Lymphocyte Antigen 4 (CTLA4) and Programmed Cell death-1 (PD-1)/Programmed death-ligand 1 (PD-L1) inhibitors. High-grade irAEs are life-threatening and often cause a severe decline in performance status in such that patients do not qualify for any further anticancer treatments. It is difficult to generalize the evidence in the current literature on risk factors or biomarkers for the entire class of ICIs as the studies so far are either disease-specific (e.g., lung cancer or melanoma) or ICI agent-specific (e.g., pembrolizumab, ipilimumab) or irAE-specific (e.g., pneumonitis or gastritis). In this review, risk factors and biomarkers to consider before initiating or monitoring ICI are listed with a practical purpose in day-to-day practice. Risk factors are grouped into demographics and social history, medical history, and medication history, tumor-specific and agent-specific risk factors. A higher risk of irAE is associated with age <60 years, high body mass index, women on CTLA4 and men on PD-1/PD-L1 agents, and chronic smokers. Patients with significant kidney (Stage IV-V), cardiac (heart failure, coronary artery disease, myocardial infarction, hypertension), and lung (asthma, pulmonary fibrosis, and chronic obstructive pulmonary disease) are at a higher risk of respective organ-specific irAEs. Pre-existing autoimmune disease and chronic use of certain drugs (proton pump inhibitors, diuretics, anti-inflammatory drugs) also increase the irAE-risk. Biomarkers are categorized into circulating blood counts, cytokines, autoantibodies, HLA genotypes, microRNA, gene expression profiling, and serum proteins. The blood counts and certain protein markers (albumin and thyroid-stimulating hormone) are readily accessible in current practice. High neutrophil-lymphocyte ratio, eosinophil/monocyte/lymphocyte counts; TSH and troponins at diagnosis and drop in the white count and lymphocyte count can predict irAE. Other biomarkers with limited evidence are cytokines, autoantibodies, HLA genotypes, microRNA, and gene expression profiling. With fast-expanding approvals for ICIs in various cancer types, knowledge on risk factors and biomarkers can help providers assess the irAE-risk of their patients. Prospective disease and agent-specific studies are needed to provide further insight on this essential aspect of ICI therapy.