Front. Immunol. Frontiers in Immunology Front. Immunol. 1664-3224 Frontiers Media S.A. 10.3389/fimmu.2025.1612287 Immunology Original Research Bimodal onset and pan-cancer uniformity of immune-mediated liver injury: a retrospective cohort study Song Jiaojiao 1 2 † Xu Biying 1 2 † Yu Lan 2 † Fu Haiwei 3 Wang Binliang 3 Zhou Mo 2 Hu Yumin 1 Xia Yang 2 * 1 Department of Respiratory and Critical Care Medicine, The First People’s Hospital of Linhai, Taizhou, Zhejiang, China 2 Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China 3 Department of Respiratory and Critical Care Medicine, Taizhou First People’s Hospital, Taizhou, Zhejiang, China

Edited by: Wenxue Ma, University of California, San Diego, United States

Reviewed by: Hao Sun, Dana–Farber Cancer Institute, United States

Xiaoqiang Gao, Guizhou Medical University, China

*Correspondence: Yang Xia, yxia@zju.edu.cn

†These authors have contributed equally to this work and share first authorship

 02 07 2025 2025 16 1612287 15 04 2025 20 06 2025 Copyright © 2025 Song, Xu, Yu, Fu, Wang, Zhou, Hu and Xia 2025 Song, Xu, Yu, Fu, Wang, Zhou, Hu and Xia

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

 Background

Immune-mediated liver injury (IMLI) is a critical adverse event in patients treated with PD-1/PD-L1 inhibitors. The study aims to characterize the clinical heterogeneity, temporal dynamics, and immunological drivers of PD-1/PD-L1 inhibitor-associated IMLI and optimize surveillance and management strategies.

 Methods

We retrospectively recruited 373 IMLI patients. We evaluated clinical data, including liver injury patterns, severity, temporal trends, and immune cell subsets. Statistical analyses identified risk factors for severe IMLI and temporal dynamics.

 Results

Among 373 patients (median age: 65 years; male: 74.8%), IMLI severity was graded as G1 (53.9%), G2 (25.2%), G3 (17.9%), and G4 (2.7%), with hepatocellular (17.2%), mixed (42.6%), and cholestatic (40.2%) patterns observed. The median time to onset was 106–115 days across severity groups. In contrast, recovery time was significantly prolonged (G1/2: 14 days vs. G3/4: 23 days, P<0.05), and recovery-phase CD8+ T cells (524.9 vs. 270.68 cells/μL, P=0.026) were higher in severe cases. Bimodal onset peaks occurred at 1–2 months and 3–4 months, with 88% recovering within 100 days. No tumor-type differences existed in patterns (P=0.427) or severity (P=0.054). Elevated baseline NK cells (OR=1.004, P=0.036) predicted severe IMLI.

 Conclusions

IMLI demonstrates bimodal onset and pan-cancer uniformity, driven by systemic immune dysregulation. Baseline NK cells are potential predictors of severity. Risk-adapted monitoring within 4 months post-ICI and standardized protocols are recommended.

 immune-mediated liver injury PD-1/PD-L1 inhibitor clinical characteristic immune dysregulation management section-in-acceptance Cancer Immunity and Immunotherapy Introduction

The advent of programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors has revolutionized cancer treatment by reinvigorating antitumor immunity (1, 2), yet their clinical utility is constrained by immune-related adverse events (irAEs) (3), particularly immune-mediated liver injury (IMLI), due to its heterogeneous presentation and potentially life-threatening consequences. Cumulative evidence indicates that IMLI occurs in 2-25% of patients receiving immune checkpoint inhibitors (ICIs), with mortality rates reaching 22% in severe cases (4, 5), which often necessitates treatment interruption and compromises antitumor efficacy and deteriorates patient-reported quality of life (6). The risk of IMLI exhibits significant variation depending on ICI types (7). Clinical trial data demonstrate a substantially higher incidence of CTLA-4 inhibitor-associated IMLI (2%-15%) compared to PD-1 inhibitors (0%-3%) and PD-L1 inhibitors (0%-6%) (8, 9). Importantly, real-world evidence reveals an amplified risk profile, particularly in combination therapy cohorts, which shows both increased frequency and severity of hepatic adverse events (10).

While current guidelines (ESMO/ASCO) provide a framework for managing irAEs based on Common Terminology Criteria for Adverse Events (CTCAE) grading (4, 11), critical knowledge gaps impede risk-benefit optimization. First, the diagnosis of IMLI relies on nonspecific biochemical markers and the exclusion of alternative etiologies, lacking validated biomarkers for early detection (12). Second, the temporal heterogeneity of IMLI onset remains poorly characterized, with prior studies reporting a unimodal distribution (median 6–14 weeks, but with significant variation-latencies of as much as 93 weeks have been reported (13), failing to capture nuanced temporal dynamics such as bimodal peaks or recovery trajectories. Third, emerging evidence indicates that hepatocellular carcinoma (HCC) patients exhibit a modestly higher incidence of IMLI following ICI therapy compared to other cancer types (14, 15). This observed disparity may be associated with underlying chronic liver disease or prior hepatic interventions. Besides, clinical observations have identified similarly elevated IMLI incidence rates and greater disease severity in melanoma and renal cell carcinoma (RCC) cohorts (10). Debates persist regarding whether IMLI features are tumor-agnostic or modulated by cancer-specific microenvironments, with implications for universal versus tailored management strategies (16–18).

To address these gaps, we aimed to delineate the temporal dynamics and immunological drivers of IMLI, evaluate its pan-cancer uniformity, and identify biomarkers for risk stratification. By integrating clinical profiling with peripheral immune monitoring, this study provides a roadmap for optimizing surveillance and therapeutic protocols.

 Materials and methods Study population

This retrospective cohort study enrolled consecutive patients admitted to the Second Affiliated Hospital of Zhejiang University School of Medicine between January, 2020 and July, 2024. Inclusion criteria were as follows: (i) adult patients (≥18 years) with pathologically confirmed malignancies; (ii) receipt of ICI therapy; (iii) Patients with normal baseline liver function who developed abnormalities during treatment, or patients with baseline liver function exceeding the normal range who exhibited significant biochemical abnormalities during treatment; (iv) Patients receiving antiviral therapy with hepatitis B virus DNA titers <100 IU/mL (if co-infected with hepatitis B) (19). Exclusion criteria: (i) Liver function abnormalities attributed to other causes (e.g., active viral hepatitis, tumor liver metastasis progression, hepatic hypoperfusion); (ii) Lack of clinical data.

 Data collection

Demographic data (age, sex, BMI, comorbidities), tumor type, and treatment regimens (ICI type, dosage, duration) were systematically collected. IMLI profiles were also recorded, including time of onset, clinical manifestations, laboratory parameters, imaging/pathological findings, concomitant irAEs, treatment measures, and outcomes. Key laboratory parameters including absolute leukocyte/neutrophil/lymphocyte counts, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin (TBIL), direct bilirubin (DBIL), albumin, coagulation profile, virological serology (hepatitis viruses, CMV, HSV, EBV antibodies), autoimmune markers (antinuclear antibodies, immunoglobulins), and metabolic indicators (ceruloplasmin, ferritin). Two blinded investigators independently cross-validated all data and confirmed IMLI diagnosis through the systematic exclusion of alternative etiologies. Discrepancies were resolved through multidisciplinary consensus involving hepatologists. The R-value [(ALT/ULN)/(ALP/ULN)] was calculated based on the ratio of ALT to the upper limit of normal (ULN) and AST to ULN, categorizing liver injury patterns into hepatocellular (R≥5), mixed (2<R<5), and cholestatic (R ≤ 2). Disease severity was graded according to CTCAE V5.0 (20), with grades 1–2 defined as mild liver injury (G1/2), grades 3–4 as severe liver injury (G3/4), and grade 5 as fatal liver injury. Patients were followed until July, 2024, death, or liver function recovery (defined as ALT/AST ≤1×ULN and ALP ≤1.5×ULN, or return to baseline levels). Follow-up data were censored at the last documented contact for patients lost to follow-up (n=7).

 Peripheral blood immune cell subset analysis

Flow cytometry (BD FACSCanto™ II; antibody panel: CD3 FITC, CD4 PE-Cy7, CD8 APC-Cy7, CD16 PE + CD56 PE, CD19 APC, CD45 PerCP-Cy5.5) was performed to analyze peripheral blood immune cell subset counts at three time points (baseline, early phase, and recovery phase). As a routine clinical assay in our hospital, the standardized protocol was strictly followed according to the manufacturer’s instructions. Freshly collected samples were processed and analyzed within 24 hours to minimize the technical and operator-dependent variability. Evaluated populations included total T lymphocytes (CD3+), CD4+/CD8+ T-cell subsets, natural killer (NK) cells (CD3-CD16+CD56+), and B lymphocytes (CD19+). Baseline data were defined as laboratory results from the first hospitalization before initiating immunotherapy. The early phase was defined as the period after starting immunotherapy but before the onset of IMLI. The recovery phase was defined as the period when liver function returned to normal or baseline levels.

 Statistical analysis

Continuous variables were expressed as mean ± standard deviation (x¯ ± s) or median (interquartile range, IQR) [M(IQR)] following normality testing, while categorical variables were reported as frequency and percentage (n, %). Intergroup comparisons were performed using the Kruskal-Wallis H test (for non-normally distributed continuous variables) or the chi-square test/Fisher’s exact test (for categorical variables). Binary logistic regression analysis was used to identify risk factors for severe IMLI (G3/4). Statistical significance was defined as a two-tailed P < 0.05. Statistical analyses were performed using SPSS software version 22.0 (IBM Corporation, Armonk, NY, USA).

 Results Baseline characteristics and clinical parameters of liver injury pattern groups

A total of 373 patients were included in the study (As shown in Table 1 ), with a median age of 65 years (range: 56–71 years), and 74.8% were male. The Eastern Cooperative Oncology Group Performance Status (ECOG PS) score indicated that the majority of patients (98.9%) had a score of 0-1. Among the patients, 348 (93.3%) received programmed cell death protein 1 (PD-1) inhibitors, 24 (6.4%) received programmed cell death ligand 1 (PD-L1) inhibitors, and 1 (0.3%) received a PD-1/vascular endothelial growth factor (VEGF) inhibitor. The included tumor types were primarily lung cancer (182/373, 48.8%) and digestive system cancers (147/373, 39.4%), followed by head and neck cancers (22/373, 5.9%), renal and urinary tract cancers (14/373, 3.8%), and other tumor types (2.1%). Baseline liver metastases were present in 20.1% of patients. The median values of baseline white blood cell count, absolute neutrophil count, absolute lymphocyte count, and neutrophil-to-lymphocyte ratio (NLR) were 6.3×109/L, 4.1×109/L, 1.4×109/L, and 3.0, respectively. Median baseline values were as follows: ALT, 25 U/L; AST, 26 U/L; ALP, 90 U/L; GGT, 45 U/L; TBIL, 11.2 μmol/L; DBIL, 2.2 μmol/L.

Baseline characteristics and clinical parameters of liver injury pattern groups.

Characteristic Overall N=373 Hepatocellular n =64, 17.2% Mixed n =159, 42.6 % Cholestatic n =150, 40.2% P
Age at diagnosis (years), Median (IQR) 65 (56-71) 61.5 (52-68) 65 (56-71) 66 (56-72) 0.026*
Sex, n(%) 0.213
Male 279 (74.8%) 17 (26.6%) 33 (20.8%) 44 (29.3%)
Female 94 (25.2%) 47 (73.4%) 126 (79.2%) 106 (70.7%)
BMI (kg/m2), Median (IQR) 22.3 (20.3-24.4) 21.9 (20.3-24.6) 22.4 (20.5-24.4) 22.2 (20.0-24.3) 0.793
Medical history, n(%)
Thyroid diseases 22 (5.9%) 3 (4.7%) 12 (7.5%) 7 (4.7%) 0.565
Rheumatic connective tissue diseases 8 (2.1%) 0 (0%) 3 (1.9%) 5 (3.3%) 0.352
Tuberculosis 9 (2.4%) 1 (1.6%) 2 (1.3%) 6 (4.0%) 0.292
Chronic pulmonary diseases 27 (7.2%) 4 (6.3%) 11 (6.9%) 12 (8.0%) 0.897
Cardiovascular diseases 24 (6.4%) 2 (3.1%) 9 (5.7%) 13 (8.7%) 0.343
Hypertension 112 (30.0%) 14 (21.9%) 56 (35.2%) 42 (28.0%) 0.113
Diabetes mellitus 49 (13.1%) 5 (7.8%) 25 (15.7%) 19 (12.7%) 0.279
Chronic renal insufficiency 16 (4.3%) 2 (3.1%) 8 (5.0%) 6 (4.0%) 0.839
Pre-existing liver disease, n(%)
Liver metastasis 75 (20.1%) 9 (14.1%) 26 (16.4%) 40 (26.7%) 0.032*
Viral hepatitis 40 (10.7%) 8 (12.5%) 15 (9.4%) 17 (11.3%) 0.761
Fatty liver 30 (8.0%) 6 (9.4%) 12 (7.5%) 12 (8.0%) 0.902
Autoimmune liver diseases 1 (0.3%) 0 (0.0%) 0 (0.0%) 1 (0.7%) 0.574
ECOG PS score, n(%)
0 81 (21.7%) 18 (28.1%) 35 (22.0%) 28 (18.7%) 0.305
1 288 (77.2%) 46 (71.9%) 124 (78.0%) 118 (78.7%) 0.530
2 3 (0.8%) 0 (0.0%) 0 (0.0%) 3 (2.0%) 0.142
3 1 (0.3%) 0 (0.0%) 0 (0.0%) 1 (0.7%) 0.574
Baseline blood parameters, Median (IQR)
White blood cells (×109/L) 6.3 (5-7.9) 5.7 (4.5-7.7) 6.4 (5.0-7.7) 6.3 (5.0-8.4) 0.236
Neutrophils (×109/L) 4.1 (3.1-5.7) 3.8 (2.8-5.3) 4.1 (3.3-5.9) 4.3 (3.1-5.9) 0.244
Lymphocytes (×109/L)NLR 1.4 (1-1.7)3 (2.1-4.9) 1.3 (1.0-1.6)3.0 (2.1-4.7) 1.4 (1.0-1.8)3.0 (2.1-4.7) 1.4 (1.0-1.7)3.2 (2.3-5.0) 0.3870.647
Baseline hepatic biochemistries, Median (IQR)
ALT(U/L) 25 (16-38) 23.5 (15.3-36.8) 26 (17-41) 24 (16-36.3) 0.390
AST(U/L) 26 (21-35) 25.0 (21.3-33.8) 26 (21-35) 28 (21-37) 0.723
ALP(U/L) 90 (67-126) 86.5 (57.3-102.5) 81 (61-112) 110 (77.5-147) <0.001***
GGT(U/L) 45 (24-83) 39.5 (23.3-57.5) 43 (23-75) 55 (27.5-94.5) 0.004**
TBIL (μmol/L) 11.2 (8.4-14) 11.2 (8.2-14.4) 10.8 (8.3-13.9) 11.7 (8.7-14.8) 0.423
DBIL (μmol/L) 2.2 (1.5-3.1) 2.0 (1.6-2.8) 2.1 (1.3-3.1) 2.3 (1.7-3.2) 0.120
Type of ICI, n(%)
Anti-PD-1 348 (93.3%) 62 (96.9%) 147 (92.5%) 139 (92.7%) 0.495
Anti-PD-L1 24 (6.4%) 2 (3.1%) 11 (6.9%) 11 (7.3%) 0.547
Anti-PD-1/VEGF 1 (0.3%) 0 (0.0%) 1 (0.6%) 0 (0.0%) 1.000
Clinical Manifestations, n(%)
Fatigue 59 (15.8%) 25 (39.1%) 18 (11.3%) 16 (10.7%) <0.001***
Abdominal distension 22 (5.9%) 9 (14.1%) 7 (4.4%) 6 (4.0%) 0.010*
Nausea/Vomiting 17 (4.6%) 1 (1.6%) 7 (4.4%) 9 (6.0%) 0.407
Jaundice 23 (6.2%) 3 (4.7%) 10 (6.3%) 10 (6.7%) 0.920
Hepatic biochemistries during IMLI occurrence, Median (IQR)
ALT(U/L) 109 (71.5-189) 288.5 (185.8-510.8) 120 (84-174) 75 (55.8-109) <0.001***
AST(U/L) 88 (56-149.5) 182.5 (114.8-557.3) 87 (60-136) 66.5 (48-117.3) <0.001***
GGT (U/L) 134 (98.5-236) 109.5 (78.3-148.5) 117 (86-156) 202 (133-459.5) <0.001***
ALP(U/L) 115 (58.5-302) 102.5 (52.5-180.5) 88 (50-203) 192.5 (78-526.5) <0.001***
TBIL(μmol/L) 13.8 (9.6-21.3) 14.1 (9.3-22.3) 12.8 (8.8-19.4) 15.3 (10.8-26.3) 0.014*
DBIL(μmol/L) 3.1 (1.9-6.6) 3.1 (1.9-7.4) 2.7 (1.7-4.9) 3.7 (2.1-9.2) 0.002**
Severity (CTCAE), n(%)
1 201 (53.9%) 12 (18.8%) 99 (62.3%) 90 (60.0%) <0.001***
2 94 (25.2%) 20 (31.3%) 41 (25.8%) 33 (22.0%) 0.352
3 67 (18.0%) 26 (40.6%) 17 (10.7%) 24 (16.0%) <0.001***
4 10 (2.7%) 6 (9.4%) 2 (1.3%) 2 (1.3%) 0.005**
5 1 (0.3%) 0 (0.0%) 0 (0.0%) 1 (0.7%) 0.574
Other irAEs, n(%)
Interstitial pneumonia 12 (3.2%) 1 (1.6%) 6 (3.8%) 5 (3.4%) 0.859
Myocarditis 6 (1.6%) 1 (1.6%) 5 (3.1%) 0 (0.0%) 0.069
Diarrhea 19 (5.1%) 1 (1.6%) 8 (5.0%) 10 (6.7%) 0.327
Skin rash 11 (3.0%) 1 (1.6%) 2 (1.3%) 8 (5.4%) 0.090
Endocrine toxicities 27 (7.2%) 5 (7.8%) 14 (8.8%) 8 (5.4%) 0.500
Nephrotoxicity 16 (4.3%) 3 (4.7%) 7 (4.4%) 5 (3.4%) 0.829
All the other irAEs*
Time until onset (days), Median (IQR) 107 (46.5-231.5) 88.5 (35-173.5) 107 (47-209) 123.5 (51.8-259) 0.082
Cycles of ICI infusion, Median (IQR) 8 (4-15) 7 (4-16.8) 7 (4-16) 8 (4-13.3) 0.942
Treatment with corticoids, n(%) 33 (8.8%) 11 (17.2%) 12 (7.5%) 10 (6.7%) 0.034*
Cumulative cocorticoid dose†, n(%)
<0.5mg/kg 3 (0.8%) 1 (1.6%) 1 (0.6%) 1 (0.7%) 0.573
0.5-1mg/kg 9 (2.4%) 2 (3.1%) 3 (1.9%) 4 (2.7%) 0.743
1-2mg/kg 15 (4.0%) 6 (9.4%) 6 (3.8%) 3 (2.0%) 0.050
>2mg/kg 6 (1.6%) 2 (3.1%) 2 (1.3%) 2 (1.3%) 0.565
Immunosuppresant,n (%) 1 (0.3%) 0 (0.0%) 0 (0.0%) 1 (0.7%) 0.574
Intravenous immunoglobulin, n (%) 15 (4.0%) 2 (3.1%) 7 (4.4%) 6 (4.0%) 1.000
ICI rechallenge, n (%) 59 (15.8) 22 (34.4%) 20 (12.6%) 17 (11.3%) <0.001***
Days until resolution to normal or baseline, Median (IQR) 16 (9-33) 21 (10-37) 13 (8-24) 18 (9-35) 0.103

Data are presented as median (IQR) or n (%). Intergroup comparisons were performed using the Kruskal-Wallis H test (for non-normally distributed continuous variables) or the chi-square test/Fisher’s exact test (for categorical variables).

IQR, interquartile range; BMI, body mass index; ECOG PS, Eastern Cooperative Oncology Group performance status. NLR, absolute lymphocyte count, and neutrophil-to-lymphocyte ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; TBIL, total bilirubin; DBIL, direct bilirubin; ICI, immune checkpoint inhibitor; PD-1, programmed cell death protein 1; PD-L1, programmed cell death ligand 1; VEGF, vascular endothelial growth factor. IMLI, immune-mediated liver injury; CTCAE, common terminology criteria for adverse events; irAEs, immune-related adverse events. *P<0.05, **P<0.01, ***P<0.001.

The predominant liver injury patterns were mixed type (42.6%) and cholestatic type (40.2%), with hepatocellular injury being the least common (17.2%) (As shown in Figure 1A ). No significant differences were observed among the three groups in terms of sex ratio, incidence of concurrent irAEs in other systems, time from immunotherapy initiation to liver injury onset, or cycles of ICI infusion. Patients with hepatocellular injury exhibited a significantly lower median age compared to those with cholestatic injury (61.5 vs. 66 years, P=0.026). Patients with elevated baseline ALP and GGT were more likely to develop cholestatic injury (P<0.05), and this group had a higher proportion of liver metastases compared to other types (P<0.05). Glucocorticoid treatment and ICI rechallenge were primarily applied in hepatocellular-type patients (P<0.05). The median liver function recovery time in hepatocellular injury patients (21 days) exceeded that of mixed-type (13 days) and cholestatic-type (18 days) groups; however, this difference lacked statistical significance (P=0.103). The severity of IMLI was associated with liver injury patterns, with hepatocellular-type patients more likely to develop severe liver injury compared to mixed-type and cholestatic-type patients (50.0% vs. 12.0% vs. 17.3%, P<0.001). Additionally, hepatocellular-type patients were more prone to experience clinical symptoms such as fatigue and abdominal distension (P<0.05) (As shown in Table 1 ).

(A) Distribution of patients by liver injury patterns (N=373). (B) Distribution of patients by severity grades (N=373). Data are presented as percentages of total cases. (C) Distribution of liver injury patterns across cancer types (n=365). (D) Distribution of severity grades across cancer types (n=365). Intergroup comparisons were performed using the chi-square test/Fisher’s exact test.

Pie charts A and B show proportions of patients with different liver conditions: mixed, cholestatic, and hepatocellular. Bar charts C and D display proportions of patients with various cancers: head and neck, renal and urinary tract, digestive system, and lung, categorized by these liver conditions. Chart C highlights a p-value of 0.427, while chart D shows 0.054.

Further analysis revealed that mixed-type injury predominated in lung cancer and head and neck cancer patients (44.5% and 45.5%, respectively), while cholestatic injury was more common in digestive system cancer patients (46.3%). Among renal and urinary tract cancer patients, mixed-type injury accounted for 57.1% of cases. Overall, no significant differences in liver injury patterns were observed across tumor types (P=0.427) (As shown in Figure 1C ).

 Clinical characteristics among patients with different grades of liver injury

In this study, the severity of IMLI was graded according to the CTCAE 5.0 criteria. The results showed that the proportions of patients with G1-G5 grades were 53.9%, 25.2%, 17.9%, 2.7%, and 0.3%, respectively (As shown in Figure 1B ). Among them, mild liver injury (G1/2) accounted for 79.3%, while severe liver injury (G3/4) accounted for 20.7% (As shown in Table 2 ). The G5 case was excluded from further analysis due to the small sample size (n=1). There were no significant differences in demographic characteristics (age, gender), immunotherapy regimens (PD-1/PD-L1), prior treatment history, or the incidence of other systemic irAEs between patients with mild liver injury (G1/2) and severe liver injury (G3/4) (P>0.05). The median time from immunotherapy initiation to IMLI onset (106 days vs. 115 days, P=0.069) and the median number of treatment cycles (7 cycles vs. 8 cycles, P=0.651) also showed no statistically significant differences between the two groups. However, the liver function recovery time was significantly longer in the severe liver injury group (14 days vs. 23 days, P<0.05). Patients with higher baseline absolute lymphocyte counts and lower NLR had a significantly increased risk of severe liver injury (P<0.05). Compared to the mild group, the severe group showed lower baseline DBIL levels (P<0.05) and significantly higher proportions of patients receiving glucocorticoids (2.4% vs. 32.5%, P<0.001), intravenous immunoglobulin therapy (2.7% vs. 7.8%, P<0.05), and ICI rechallenge (11.2% vs. 33.8%, P<0.001). In the entire cohort, 93.8% (350/373) of patients showed improvement in liver function, while unresolved cases included 4 patients who voluntarily discharged themselves, 1 death, and 7 were lost to follow-up.

Clinical and biological characteristics and treatment outcomes between the two study groups.

G1/2 n=295, 79.3% G3/4 n=77, 20.7% P
Age at diagnosis (years),Median (IQR) 64 (56-70) 66 (56-71.5) 0.199
Sex, n (%) 0.873
Male 221 (74.9%) 57 (74.0%)
Female 74 (25.1%) 20 (26.0%)
BMI (kg/m2), Median (IQR) 22.41 (20.18-24.46) 21.91 (20.73-23.775) 0.574
Medical history, n (%)
Viral hepatitis 32 (10.8%) 8 (10.4%) 0.908
Hypertension 93 (31.5%) 19 (24.7%) 0.243
Diabetes mellitus 42 (14.2%) 7 (9.1%) 0.234
Liver metastasis, n (%) 60 (20.3%) 14 (18.2%) 0.673
Baseline blood parameters,Median (IQR)
White blood cells (×109/L) 6.2 (5-7.8) 6.3 (4.7-8.05) 0.656
Neutrophils (×109/L) 4.18 (3.14-5.7) 4.07 (3.02-5.220) 0.368
Lymphocytes (×109/L) 1.33 (0.97-1.68) 1.47 (1.13-1.815) 0.031*
NLR 3.18 (2.18-5.01) 2.77 (1.88-4.015) 0.035*
Baseline hepatic biochemistries,Median (IQR)
ALT (U/L) 26 (17-37) 22 (14.5-39) 0.393
AST (U/L) 27 (21-35) 25 (21-34) 0.751
ALP (U/L) 89 (67-128) 95 (72.5-122) 0.527
GGT (U/L) 46 (24-85) 44.5 (25.25-63.75) 0.464
TBIL (μmol/L) 11.3 (8.6-14.6) 10 (8.05-13.5) 0.092
DBIL (μmol/L) 2.3 (1.6-3.2) 1.9 (1.2-2.4) 0.006**
Type of ICI, n (%)
Anti-PD-1 274 (92.9%) 73 (94.8%) 0.730
Anti-PD-L1 20 (6.8%) 4 (5.2%) 0.808
Previous treatment regimens,n (%)
Chemotherapy 194 (65.8%) 56 (72.7%) 0.246
Chemotherapy plus radiotherapy 12 (4.1%) 5 (6.5%) 0.364
Chemotherapy plus targeted therapy 14 (4.7%) 5 (6.5%) 0.535
Chemotherapy plus anti-angiogenic therapy 16 (5.4%) 1 (1.3%) 0.216
None 24 (8.1%) 6 (7.8%) 0.921
Clinical Manifestations,n (%)
Fatigue 22 (7.5%) 37 (48.1%) <0.001***
Abdominal distension 6 (2.0%) 16 (20.8%) <0.001***
Nausea/Vomiting 12 (4.1%) 5 (6.5%) 0.364
Jaundice 9 (3.1%) 13 (16.9%) <0.001***
Hepatic biochemistries during IMLI occurrence,Median (IQR)
ALT (U/L) 94 (67-140) 331 (190.5-533) <0.001***
AST (U/L) 72 (54-117) 271 (144-569) <0.001***
GGT (U/L) 123 (90-182) 275 (151-578) <0.001***
ALP (U/L) 89 (50-202) 361 (165.5-646.5) <0.001***
TBIL (μmol/L) 12.7 (8.8-18.3) 20.1 (14.65-44.2) <0.001***
DBIL (μmol/L) 2.7 (1.7-4.80) 7.5 (3.3-28.35) <0.001***
Pattern of liver injury, n (%)
Hepatocellular 32 (50.0%) 32 (50.0%) 1.000
Mixed 140 (88.1%) 19 (11.9%) <0.001***
Cholestatic 123 (82.6%) 26 (17.4%) <0.001***
Other irAEs, n (%)
Interstitial pneumonia 9 (3.1%) 3 (3.9%) 0.991
Myocarditis 5 (1.7%) 1 (1.3%) 1.000
Diarrhea 15 (5.1%) 4 (5.2%) 1.000
Skin rash 7 (2.4%) 4 (5.2%) 0.355
Endocrine toxicities 20 (6.8%) 7 (9.1%) 0.486
Nephrotoxicity 13 (4.4%) 2 (2.6%) 0.694
Time until onset (days),Median (IQR) 106 (41-220) 115 (70-287) 0.069
Cycles of ICI infusion,Median (IQR) 7 (4-15) 8 (3-16.5) 0.651
Treatment with corticoids, n (%) 7 (2.4%) 25 (32.5%) <0.001***
Cumulative corticoid dose†,n (%)
<0.5mg/kg 2 (0.7%) 1 (1.3%) 0.502
0.5-1mg/kg 0 (0%) 9 (11.7%) <0.001***
1-2mg/kg 5 (1.7%) 10 (13.0%) <0.001***
>2mg/kg 0 (0%) 5 (6.5%) <0.001***
Immunosuppressant, n (%) 0 (0%) 1 (1.3%) 0.207
Intravenous immunoglobulin,n (%) 8 (2.7%) 6 (7.8%) 0.037*
ICI rechallenge, n (%) 33 (11.2%) 26 (33.8%) <0.001***
Days until resolution to normal or baseline,Median (IQR) 14 (8-29) 23 (12-39) 0.007**

†All glucocorticoid doses are expressed as methylprednisolone equivalents. Data are presented as median (IQR) or n (%). Intergroup comparisons were performed using the Kruskal-Wallis H test (for non-normally distributed continuous variables) or the chi-square test/Fisher’s exact test (for categorical variables).

IQR, interquartile range; BMI, body mass index; NLR, absolute lymphocyte count, and neutrophil-to-lymphocyte ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; TBIL, total bilirubin; DBIL, direct bilirubin; ICI, immune checkpoint inhibitor; PD-1, programmed cell death protein 1; PD-L1, programmed cell death ligand 1. IMLI, immune-mediated liver injury; irAEs, immune-related adverse events. * P<0.05, ** P<0.01, *** P<0.001.

Further analysis revealed no significant differences in the severity of IMLI across cancer types (P=0.054) (As shown in Figure 1D ).

 Correlation analysis between peripheral blood immune cell subsets and IMLI severity

This study monitored peripheral blood immune cell subset counts at baseline, early ICI treatment phase, and recovery phase. Patients with severe IMLI (G3/4) exhibited significantly higher baseline NK cell counts (median [IQR]: 347.2 [210-524.1] vs. 219.7 [107.6-362.6] cells/μL, P=0.030) and elevated recovery-phase total T cells (CD3+ median [IQR]: 1121.28 [793.271-1454.594] vs. 781.87 [523.44-1140.36] cells/μL, P=0.043) and CD8+ T cells (median [IQR]: 524.9 [272.56-835.751]vs. 270.68 [195.63-448.79] cells/μL, P=0.026) compared to mild cases (G1/2) (As shown in Table 3 ).

Peripheral blood immunological parameters between the two study groups.

G1/2 n=67, 84.8% G3/4 n=12, 15.2% P
Baseline immune cell counts, Median (IQR)
Total CD3+ T cells (/μL) 897.1 (522.3-1185.6) 971.4 (792.7-1134.7) 0.521
CD4+ T cells (/μL) 547.7 (250.2-667.9) 530.9 (440.9-706.3) 0.662
CD8+ T cells (/μL) 313 (191.3-483.8) 341.5 (257.9-510.6) 0.495
CD4/CD8 1.5 (0.9-2.4) 1.4 (0.8-2.1) 0.764
NK cells (/μL) 219.7 (107.6-362.6) 347.2 (210-524.1) 0.030*
B cells (/μL) 121.5 (74.9-186.6) 120.8 (95.1-160.3) 0.557
Early phase immune cell counts, Median (IQR) G1/2n=24, 77.4% G3/4n=7, 22.6% P
Total CD3+ T cells (/μL) 717.545 (518.238-1198.799) 944.36 (791.252-1265.75) 0.216
CD4+ T cells (/μL) 449.68 (278.028-642.2515) 621.57 (382.228-728) 0.253
CD8+ T cells (/μL) 278.293 (209.28-430.6005) 348.04 (281.93-427.5) 0.317
CD4/CD8 1.52 (1.035-2.2625) 1.32 (1.08-2.2) 0.878
NK cells (/μL) 223.86 (136.749-445.618) 449.96 (101.25-819.49) 0.473
B cells (/μL) 84.749 (48.0060-142.7135) 149.25 (59.5-241.7) 0.234
Recovery phase immune cell counts, Median (IQR) G1/2n=35, 72.9% G3/4n=13, 27.1% P
Total CD3+ T cells (/μL) 781.87 (523.44-1140.36) 1121.28 (793.271-1454.594) 0.043*
CD4+ T cells (/μL) 378.19 (220.85-714.72) 431.683 (378.66-602.330 0.521
CD8+ T cells (/μL) 270.68 (195.63-448.79) 524.9 (272.56-835.751) 0.026*
CD4/CD8 1.3 (0.83-1.96) 1.06 (0.435-1.885) 0.164
NK cells (/μL) 149.76 (83.85-297.5) 212.553 (101.336-654.591) 0.133
B cells (/μL) 92.752 (45.13-145.92) 95.238 (71.9-157.72) 0.521

Intergroup comparisons were performed using the Kruskal-Wallis H test. IQR, interquartile range; NK, natural killer. * P<0.05.

 Risk factors for IMLI

The hepatocellular injury was identified as an independent predictor of severe IMLI (OR=7.368, 95% CI: 3.713-14.622; P<0.001), while baseline high NK cell counts showed a weaker predictive effect (OR=1.004, 95% CI: 1.000-1.007, P=0.036). Although univariate analysis revealed associations between recovery-phase total T cell counts (CD3+), CD8+ T cell counts, and the severity of liver injury, these associations were not significant in multivariate regression analysis (OR=1.000, P=0.916; OR=1.002, P=0.350) (As shown in Figure 2 ).

Risk Factors for Severe Liver Injury. Binary logistic regression analysis was used to identify risk factors for severe IMLI (G 3/4).

Forest plots illustrating the odds ratios and ninety-five percent confidence intervals for various factors. Top plot: Hepatocellular shows a significant association (red marker) with an OR over 5, while Cholestatic, NLR, DBIL, and Lymphocyte have ORs around 1. Bottom plot: Baseline NK cell (red marker) shows a slight increase above 1, while Recovery-phase total T cell and CD8+ T cell hover around 1. Temporal analysis of IMLI

This study found that the risk of IMLI in patients across various cancer types during the follow-up period exhibited a bimodal distribution. The first peak occurred within 1–2 months after the initiation of immunotherapy, followed by a secondary peak at 3–4 months. The risk of new-onset IMLI was extremely low after 12 months (As shown in Figure 3A ). The cumulative recovery trends of IMLI were generally similar across cancer types, with most cases recovering within 50 days. Delayed recovery (>100 days) was rare (As shown in Figure 3B ).

(A) Incidence of New-onset IMLI Across Cancer Types. (B) Cumulative Recovery Rate of IMLI Across Cancer Types. Patients were followed until July, 2024. Patients lost to follow-up or who died were censored at the last contact date. Incidence of new-onset IMLI = Number of new IMLI cases within a specific time interval/Total study population (N=373). Cumulative recovery rate = Cumulative number of recovered IMLI cases within a specific time interval/Total study population (N=373). IMLI, immune-mediated liver injury.

Two graphs show cancer data over time. Graph A depicts the incidence of new-onset IMI over 900 follow-up days, with lines showing varying trends for digestive system, lung, renal and urinary tract, and head and neck cancers. Graph B shows cumulative IMI recovery rates over 500 follow-up days, with different lines indicating trends for the same cancer types, highlighting faster recovery in lung and renal cancers than in digestive and head and neck cancers. Discussion

By analyzing 373 IMLI patients treated with PD-1/PD-L1 inhibitors across different tumor types, we revealed a bimodal onset, uniform injury patterns and severity across tumor types, and NK cells as a potential predictive marker, which helps to optimize advancing personalized monitoring and universal therapeutic decision-making.

This study reveals that IMLI exhibits a bimodal temporal distribution (1–2 months vs. 3–4 months), which differs significantly from the previously reported (12) unimodal distribution (e.g., 1.4 months vs. 4.7 months). However, the overall onset window (1–4 months) remains consistent with the high-risk period for IMLI (within the first 6 months of treatment) reported in most studies (21). The observed bimodal dynamic of IMLI progression closely parallels the established mechanism of epitope spreading (ES) (22). During the initial phase, clonal expansion of tumor antigen-specific T cells (corresponding to the first IMLI peak) (23, 24) progressively evolves through ES into systemic autoreactivity against hepatic abundant self-antigens, including cytochrome P450 and mitochondrial antigens, ultimately manifesting as the second peak of liver injury (22). The elevated recovery-phase CD8+ T cells in severe cases (524.9 vs. 270.68 cells/μL, P=0.026) further support this mechanism, corroborating single-cell evidence of accelerated effector T cell differentiation post-PD-1 blockade (25). Early diversification of the T-cell repertoire after ICI initiation indirectly supports this autoimmune cascade process (26). This temporal stratification advocates for risk-adapted monitoring within the first 4 months, aligning surveillance intensity with peak hazard periods. Despite the significant acute-phase injury, the disease exhibits self-limiting characteristics due to CD8+ T cell exhaustion (Tex) in the chronic antigen environment (27) and liver parenchymal regeneration mediated by hepatic progenitor cells (28). Tex constitutes a complex dynamic process spanning from precursor exhausted T cells with stem-like proliferative capacity to terminally exhausted T cells that completely lose effector functions and proliferative potential. This process not only represents a crucial immunoregulatory mechanism but also serves as a major pathway for tumor resistance to immune checkpoint blockade therapy. As established immune checkpoint receptors, PD-1 and T-cell immunoglobulin and mucin-domain containing-3 (TIM-3) represent reliable indicators of exhausted T cells (29). Their persistent overexpression on T cells results in the suppression of effector functions, diminished proliferative capacity, and attenuated cytokine production, which are consistently observed in both tumor microenvironments and chronic inflammatory states (30, 31). A comprehensive understanding of the multidimensional features of Tex holds significant implications for developing novel immunotherapeutic strategies.

Our study observed pan-cancer uniformity in liver injury patterns (P=0.427) and severity (P=0.054), suggesting a systemic immune dysregulation rather than tumor-driven pathology. Mechanistically, the PD-1/PD-L1 signaling pathway is pivotal to hepatic immune homeostasis (32, 33). Under physiological conditions, constitutive PD-L1 expression on liver sinusoidal endothelial cells and hepatocytes suppresses CD8+ T cell activation via TCR-peptide-major histocompatibility complex (TCR-p-MHC) signaling blockade, maintaining peripheral tolerance through T cell exhaustion and apoptosis (34). ICI-mediated disruption of this pathway unleashes autoreactive CD8+ T cells, which infiltrate the liver parenchyma and drive injury via cytotoxic effector gene activation, including perforin and granzyme (23, 24) while secreting pro-inflammatory cytokines like IFN-γ and TNF-α to recruit Kupffer cells and amplify inflammation through a self-reinforcing loop (35–37). The liver’s rich self-antigen repertoire, including cytochrome P450 and mitochondrial antigens, likely facilitates cross-cancer T cell targeting, explaining the observed clinical homogeneity (38). These findings align with the review demonstrating tumor-agnostic irAE profiles for ICIs (39), contrasting with emerging tumor-specific irAE prediction models (18) and meta-analysis (17). The homogeneous sample composition (predominantly lung and digestive system cancers) and treatment protocols likely contributed to the absence of observed differences. Future multicenter prospective studies with diverse cancer types are warranted for validation.

Predictive biomarkers are crucial for guiding treatment decisions (40). To our knowledge, this is the first study to identify baseline NK cell counts as a potential biomarker for severe IMLI (OR=1.004, P=0.036). Although statistically significant, the interpretation of this finding requires caution, given that the OR is close to 1, and the clinical significance of NK cells remains incompletely understood. Multiple studies have demonstrated the critical role of NK cells in antitumor immunity (3, 41–43). As key components of the innate immune system, NK cells can induce target cell apoptosis through the release of perforin, granzymes, and expression of Fas ligand (FasL) and TNF-related apoptosis-inducing ligand (TRAIL). Additionally, they can secrete chemokines such as TNF-α and CCL3/4/5, recruiting more immune cells to infiltrate the liver and exacerbate tissue damage. Thus, a high baseline NK cell population may be more prone to excessive activation, leading to severe IMLI. On the other hand, NK cells can be classified into CD56bright and CD56dim subsets based on specific surface markers. The CD56dim NK cell subset primarily exerts cytotoxic effects by directly killing hepatocytes, whereas the CD56bright NK cell subset not only secretes pro-inflammatory cytokines that aggravate liver injury but also transmits inhibitory signals through high expression of natural killer group 2A (NKG2A), potentially suppressing excessive inflammation via IL-10 secretion (44, 45). Thus, NK cells may play dual roles in IMLI progression by initially exacerbating tissue damage and subsequently suppressing excessive immune responses through regulatory functions. However, baseline total NK cell counts fail to distinguish functional subset differences, thereby attenuating the effect size. Future studies should further delineate the proportions of NK cell functional subsets and their specific roles in IMLI through larger prospective cohorts to validate these findings.

Despite the significant findings, this study has certain limitations. First, as a single-center retrospective study, it is inherently subject to selection and information biases, and some subgroups had limited sample sizes. Second, peripheral blood immune cell profiling fails to capture hepatic microenvironmental dynamics, compounded by the lack of large-scale histopathological data. The absence of longitudinal multi-timepoint immune monitoring further restricts the analysis of temporal immune cell evolution in IMLI pathogenesis. Third, the predominance of lung and digestive system cancers may mask tumor-specific differences. Future studies should integrate multi-omics approaches to clarify local immune dynamics further, providing references for developing targeted intervention strategies with specific regulatory mechanisms.

 Conclusions

Our findings advocate for risk-adapted monitoring with tailored surveillance windows focusing on the first 4 months post-IC, with intensified surveillance during bimodal peaks. The pan-cancer consistency of IMLI characteristics supports standardized management protocols across malignancies. Furthermore, baseline NK cell counts may serve as a potential biomarker to refine risk-stratification strategies. Future studies are warranted to establish clinically actionable cut-off values for optimized patient management.

 Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

 Ethics statement

This is a retrospective cohort analysis that utilized anonymized historical medical records. The study protocol was approved by the Institutional Review Board (Approval No.: (2023) Ethics Review Research, 0344).

 Author contributions

JS: Writing – original draft, Data curation, Formal Analysis. BX: Investigation, Methodology, Validation, Supervision, Writing – original draft. LY: Investigation, Validation, Supervision, Writing – original draft, Methodology. HF: Writing – review & editing, Data curation. BW: Writing – review & editing, Data curation. MZ: Visualization, Supervision, Writing – review & editing. YH: Supervision, Writing – review & editing, Visualization. YX: Conceptualization, Data curation, Supervision, Writing – review & editing.

 Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 References Sharma P Allison JP . Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell. (2015) 161:205–14. doi: 10.1016/j.cell.2015.03.030 Ledford H Else H Warren M . Cancer immunologists scoop medicine Nobel prize. Nature. (2018) 562:20–1. doi: 10.1038/d41586-018-06751-0 Yao L Jia G Lu L Bao Y Ma. Factors affecting tumor responders W . and predictive biomarkers of toxicities in cancer patients treated with immune checkpoint inhibitors. Int Immunopharmacol. (2020) 85:106628. doi: 10.1016/j.intimp.2020.106628 Haanen J Carbonnel F Robert C Kerr KM Peters S Larkin J . Management of toxicities from immunotherapy: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. (2017) 28:iv119–42. doi: 10.1093/annonc/mdx225 Wang DY Salem JE Cohen JV Chandra S Menzer C Ye F . Fatal toxic effects associated with immune checkpoint inhibitors: a systematic review and meta-analysis. JAMA Oncol. (2018) 4:1721–8. doi: 10.1001/jamaoncol.2018.3923 Regev A Avigan MI Kiazand A Vierling JM Lewis JH Omokaro SO . Best practices for detection, assessment and management of suspected immune-mediated liver injury caused by immune checkpoint inhibitors during drug development. J Autoimmun. (2020) 114:102514. doi: 10.016/j.jaut.2020.102514 Liu Z Zhu Y Xie H Zou Z . Immune-mediated hepatitis induced by immune checkpoint inhibitors: Current updates and future perspectives. Front Pharmacol. (2023) 13:1077468. doi: 10.3389/fphar.2022.1077468 Robert C Schachter J Long GV Arance A Grob JJ Mortier L . Pembrolizumab versus ipilimumab in advanced melanoma. N Eng J Med. (2015) 372:2521–32. doi: 10.1056/NEJMoa1503093 Choueiri TK Larkin J Oya M Thistlethwaite F Martignoni M Nathan P . Preliminary results for avelumab plus axitinib as first-line therapy in patients with advanced clear-cell renal-cell carcinoma (JAVELIN Renal 100): an open-label, dose-finding and dose-expansion, phase 1b trial. Lancet Oncol. (2018) 19:451–60. doi: 10.1016/S1470-2045(18)30107-4 Atallah E Welsh SJ O'Carrigan B Oshaughnessy A Dolapo I Kerr AS . Incidence, risk factors and outcomes of checkpoint inhibitor-induced liver injury: a 10-year real-world retrospective cohort study. JHEP Rep. (2023) 5:100851. doi: 10.1016/j.jhepr.2023.100851 Schneider BJ Naidoo J Santomasso BD Lacchetti C Adkins S Anadkat M . Management of immune-related adverse events in patients treated with immune checkpoint inhibitor therapy: ASCO guideline update. J Clin Oncol. (2021) 39:4073–126. doi: 10.1200/jco.21.01440 Ito T Takeuchi Y Mizuno K Imai M Yoshimaru Y Abe K . Diagnostic guide for immune checkpoint inhibitor-induced liver injury. Hepatol Res. (2024) 54:719–26. doi: 10.1111/hepr.14078 Cunningham M Gupta R Butler M . Checkpoint inhibitor hepatotoxicity: pathogenesis and management. Hepatology. (2024) 79:198–212. doi: 10.1097/HEP.0000000000000045 Sangro B Chan SL Meyer T Reig M El-Khoueiry A Galle PR . Diagnosis and management of toxicities of immune checkpoint inhibitors in hepatocellular carcinoma. J Hepatol. (2020) 72:320–41. doi: 10.1016/j.jhep.2019.10.021 Celsa C Cabibbo G Fulgenzi CAM Scheiner B D’Alessio A Manfredi GF . Characteristics and outcomes of immunotherapy-related liver injury in patients with hepatocellular carcinoma versus other advanced solid tumours. J Hepatol. (2024) 80:431–42. doi: 10.1016/j.jhep.2023.10.040 Pauken KE Dougan M Rose NR Lichtman AH . Adverse events following cancer immunotherapy: obstacles and opportunities. Trends Immunol. (2019) 40:511–23. doi: 10.1016/j.it.2019.04.002 Tong X Tang R Xu J Wang W Du Q Shi S . Cancer type-specific adverse events of immune checkpoint inhibitors: a systematic review and meta-analysis. Heliyon. (2025) 11:e41597. doi: 10.1016/j.heliyon.2024.e41597 Wang B Zhuang S Lin S Lin J Zeng W Du B . Analysis of risk factors for immune checkpoint inhibitor-associated liver injury: a retrospective analysis based on clinical study and real-world data. Hepatol Int. (2025). doi: 10.1007/s12072-025-10783-w Lee PC Chao Y Chen MH Lan KH Lee IC Hou MC . Risk of HBV reactivation in patients with immune checkpoint inhibitor-treated unresectable hepatocellular carcinoma. J Immunother Cancer. (2020) 8:e001072. doi: 10.1136/jitc-2020-001072 National Cancer Institute . Common Terminology Criteria for Adverse Events (CTCAE) v5.0 (2017). Available online at: https://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htmctc_50 (Accessed February 9 2025). De Martin E Michot JM Papouin B Champiat S Mateus C Lambotte O . Characterization of liver injury induced by cancer immunotherapy using immune checkpoint inhibitors. J Hepatol. (2018) 68:1181–90. doi: 10.1016/j.jhep.2018.01.033 Vanderlugt CL Miller SD . Epitope spreading in immune-mediated diseases: implications for immunotherapy. Nat Rev Immunol. (2022) 2:85–95. doi: 10.1038/nri724 Das R Verma R Sznol M Boddupalli CS Gettinger SN Kluger H . Combination therapy with anti-CTLA-4 and anti-PD-1 leads to distinct immunologic changes in vivo . J Immunol. (2015) 194:950–9. doi: 10.4049/jimmunol.1401686 Riaz N Havel JJ Makarov V Desrichard A Urba WJ Sims JS . Tumor and microenvironment evolution during immunotherapy with Nivolumab. Cell. (2017) 171:934–949. e16. doi: 10.1016/j.cell.2017.09.028 Okamura H Okazaki IM Shimizu K Maruhashi T Sugiura D Mizuno R . PD-1 aborts the activation trajectory of autoreactive CD8+ T cells to prohibit their acquisition of effector functions. J Autoimmun. (2019) 105:102296. doi: 10.1016/j.jaut.2019.06.007 Oh DY Cham J Zhang L Fong G Kwek SS Klinger M . Immune toxicities elicted by CTLA-4 blockade in cancer patients are associated with early diversification of the T-cell repertoire. Cancer Res. (2017) 77:1322–30. doi: 10.1158/0008-5472.can-16-2324 Fagerberg E Attanasio J Dien C Singh J Kessler EA Abdullah L . KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection. Science. (2025) 387:eadn2337. doi: 10.1126/science.adn2337 Wang T Yeh MM Avigan MI Pelosof L Feldman GM . Deciphering the dynamic complexities of the liver microenvironment - toward a better understanding of immune-mediated liver injury caused by immune checkpoint inhibitors (ILICI). AAPS J. (2021) 23:99. doi: 10.1208/s12248-021-00629-2 Wherry EJ Kurachi M . Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol. (2015) 15:486–99. doi: 10.1038/nri3862 Hsu CL Ou DL Bai LY Chen CW Lin L Huang SF . Exploring markers of exhausted CD8 T cells to predict response to immune checkpoint inhibitor therapy for hepatocellular carcinoma. Liver Cancer. (2021) 10:346–59. doi: 10.1159/000515305 Fan Q Wang Y Cheng J Pan B Zang X Liu R . Single-cell RNA-seq reveals T cell exhaustion and immune response landscape in osteosarcoma. Front Immunol. (2024) 15:1362970. doi: 10.3389/fimmu.2024.1362970 Keir ME Liang SC Guleria I Latchman YE Qipo A Albacker LA . Tissue expression of PD-L1 mediates peripheral T cell tolerance. J Exp Med. (2006) 203:883–95. doi: 10.1084/jem.20051776 Dong H Strome SE Salomao DR Tamura H Hirano F Files DB . Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat Med. (2002) 8:793–800. doi: 10.1038/nm730 Chen L Han X . Anti-PD-1/PD-L1 therapy of human cancer: past, present, and future. J Clin Invest. (2015) 125:3384–91. doi: 10.1172/jci80011 Kisseleva T Brenner DA . The crosstalk between hepatocytes, hepatic macrophages, and hepatic stellate cells facilitates alcoholic liver disease. Cell Metab. (2019) 30:850–2. doi: 10.1016/j.cmet.2019.10.010 Fujita T Narumiya S . Roles of hepatic stellate cells in liver inflammation: a new perspective. Inflammation Regen. (2016) 36:1. doi: 10.1186/s41232-016-0005-6 Pesce S Greppi M Grossi F Del Zotto G Moretta L Sivori S . PD/1-PD-Ls checkpoint: insight on the potential role of NK cells. Front Immunol. (2019) 10:1242. doi: 10.3389/fimmu.2019.01242 Horst AK Kumashie KG Neumann K Diehl L Tiegs G . Antigen presentation, autoantibody production, and therapeutic targets in autoimmune liver disease. Cell Mol Immunol. (2021) 18:92–111. doi: 10.1038/s41423-020-00568-6 Postow MA Sidlow R Hellmann MD . Immune-related adverse events associated with immune checkpoint blockade. N Engl J Med. (2018) 378:158–68. doi: 10.1056/NEJMra1703481 Yu B Ma W . Biomarker discovery in hepatocellular carcinoma (HCC) for personalized treatment and enhanced prognosis. Cytokine Growth Factor Rev. (2024) 79:29–38. doi: 10.1016/j.cytogfr.2024.08.006 Shimasaki N Jain A Campana D . NK cells for cancer immunotherapy. Nat Rev Drug Discov. (2020) 19:200–18. doi: 10.1038/s41573-019-0052-1 Tang F Li J Qi L Liu D Bo Y Qin S . A pan-cancer single-cell panorama of human natural killer cells. Cell. (2023) 186:4235–4251.e20. doi: 10.1016/j.cell.2023.07.034 Crinier A Narni-Mancinelli E Ugolini S Vivier E . Snapshot: natural killer cells. Cell. (2020) 180:1280. e1. doi: 10.1016/j.cell.2020.02.029 Sun C Xu J Huang Q Huang M Wen H Zhang C . High NKG2A expression contributes to NK cell exhaustion and predicts a poor prognosis of patients with liver cancer. Oncoimmunology. (2017) 6:e1264562. doi: 10.1080/2162402X.2016.1264562 Jenne CN Kubes P . Immune surveillance by the liver. Nat Immunol. (2013) 14:996–1006. doi: 10.1038/ni.2691