Autoimmune PaneLs as PrEdictors of Toxicity in Patients TReated with Immune Checkpoint InhibiTors (ALERT)
Journal of Experimental & Clinical Cancer Research volume 42, Article number: 276 (2023)
Immune-checkpoint inhibitors (ICI) can lead to immune-related adverse events (irAEs) in a significant proportion of patients. The mechanisms underlying irAEs development are mostly unknown and might involve multiple immune effectors, such as T cells, B cells and autoantibodies (AutoAb).
We used custom autoantigen (AutoAg) microarrays to profile AutoAb related to irAEs in patients receiving ICI. Plasma was collected before and after ICI from cancer patients participating in two clinical trials (NCT03686202, NCT02644369). A one-time collection was obtained from healthy controls for comparison. Custom arrays with 162 autoAg were used to detect IgG and IgM reactivities. Differences of median fluorescent intensity (MFI) were analyzed with Wilcoxon sign rank test and Kruskal–Wallis test. MFI 500 was used as threshold to define autoAb reactivity.
A total of 114 patients and 14 healthy controls were included in this study. irAEs of grade (G) ≥ 2 occurred in 37/114 patients (32%). We observed a greater number of IgG and IgM reactivities in pre-ICI collections from patients versus healthy controls (62 vs 32 p < 0.001). Patients experiencing irAEs G ≥ 2 demonstrated pre-ICI IgG reactivity to a greater number of AutoAg than patients who did not develop irAEs (39 vs 33 p = 0.040). We observed post-treatment increase of IgM reactivities in subjects experiencing irAEs G ≥ 2 (29 vs 35, p = 0.021) and a decrease of IgG levels after steroids (38 vs 28, p = 0.009).
Overall, these results support the potential role of autoAb in irAEs etiology and evolution. A prospective study is ongoing to validate our findings (NCT04107311).
Immune checkpoint inhibitors (ICI) targeting the PD1/PD-L1 and CTLA4 pathways have rapidly become one of the main treatment option for cancer patients . In a significant proportion of recipients, ICI can lead to life-threatening or disabling immune-related adverse events (irAEs) [2,3,4]. As compared to side effects from cytotoxic treatments, the type, severity and the timing of irAEs is less predictable . The majority of irAEs occur within the first few months of treatment . However, it is not uncommon to observe delayed and long lasting toxicities that fluctuate over time . Although some organs such as skin, thyroid and the gastrointestinal tract are more frequently involved, all body systems can be affected by immune-mediated toxicity . While endocrine toxicities can be managed with hormone replacement, irAEs involving other organs often require immunosuppressive therapies, ranging from systemic steroids to biological agents . Importantly, irAEs can result in death in up to 1.3% of patients . A deeper understanding the etiology of irAEs and the identification of reliable predictive biomarkers of toxicity are unmet clinical needs to appropriately implement novel therapies and reduce morbidity in cancer patients [8, 9].
The correlation between clinical benefit from ICI and immune-mediated toxicities may be linked to T cell activation [10,11,12,13,14,15]. The observation of organ-infiltrating T-cell during irAEs including myocarditis , colitis , nephritis , pneumonitis  and sicca syndrome  supports this hypothesis. The antitumor activity of ICI however, does not solely rely on T lymphocytes and can be deeply influenced by other elements of the tumor microenvironment . Similarly, irAEs might be the result of a complex network of interactions between tumors, healthy cells and multiple immune system effectors [5, 22,23,24]. Several reports indicate that ICI-induced de-regulation of B-cells and altered production of autoantibodies (AutoAb) may contribute to the onset of irAEs [25,26,27,28,29,30,31,32,33,34]. The ability to detect AutoAb in patients at risk to develop irAEs even prior ICI exposure has been explored in multiple studies [26, 28, 35,36,37,38,39,40,41]. While several studies support the use of pre-ICI anti-thyroid antibodies to predict the risk of thyroiditis [42, 43], no clear correlation has been identified between other types of tissue specific AutoAb and organ toxicity.
Our group has developed a large, customized panel for AutoAb profiling to enable the simultaneous detection of reactivity against multiple tissue-specific antigens . In the current study, we used the customized AutoAb array to interrogate the levels of AutoAb at baseline and throughout the course of ICI therapy in cancer patients who developed irAEs versus patients who did not experience immune-mediated toxicities.
Key eligibility criteria for this study included a diagnosis of solid tumor and history of treatment with an anti-PD1 based regimen. We identified a cohort of 114 patients with metastatic solid cancers who received treatment with ICI in two clinical trials (INSPIRE, NCT02644369 and MET4-IO, NCT03686202) . INSPIRE was a phase 2 study investigating novel immune biomarkers in patients with solid tumors receiving pembrolizumab [46, 47] while MET4-IO evaluated an orally-delivered microbiome intervention in cancer patients receiving ICI [48, 49]. Both were investigator-initiated studies, approved by the Princess Margaret Cancer Centre Research Ethics Board (#18–5950 and 15–9828). Samples obtained from the participants were used for this correlative study without interfering with the primary objectives of the two clinical trials. Plasma samples obtained from patients before and after ICI were retrospectively analyzed. Collection time points in the MET4-IO study included baseline (within 14 days before the start of ICI), 3–4 weeks, 6–8 weeks, 24 weeks and at the end of treatment, while time-points for the patients enrolled in the INSPIRE trial included baseline (within 10 days from the start of ICI) and 6 weeks (cycle 3). Plasma samples collected from 14 healthy controls at a single time point were used for comparison. Treatment emergent adverse events with potential immune related etiology were prospectively recorded for all the patients and graded according to the Common Terminology Criteria for Adverse Events (CTCAE), version 5.0. For the purpose of this study, we considered irAEs of grade (G) ≥ 2 requiring medical intervention including steroids or other immunosuppressive agents or hormone replacement. Information regarding pharmacological interventions for the treatment of irAEs, including the type, dose and duration of treatment were collected for all the participants.
AutoAb detection and measurement with antigen microarray
A total of 162 antigens, customized based on tissues or organs most frequently affected by irAEs were selected (a list of the antigens used for this study is provided in Supplementary Table 1). The antigens included in the panel are associated with multiple autoimmune disorders and have been used to screen for autoantibodies in a variety of conditions such as heart failure/heart transplantation [44, 50], kidney transplantation , liver transplantation , lung transplantation , systemic autoimmune rheumatic diseases , and post-covid vaccination . Antigens were diluted in PBS to 0.2 mg/ml and subsequently stored at -80 °C. Customized antigen microarrays were generated as described previously [56, 57]. Briefly, antigens were spotted in duplicate onto nitrocellulose coated slides (GVS, Sanford, ME, USA) using a VersArray Chipwriter Pro (Virtek, Waterloo, Canada) with solid pins (Arrayit, Sunnyvale, CA, USA). Slides were blocked overnight in blocking buffer (PBS with 0.1% Tween and 5% FBS). The slides were then probed with human serum (diluted 1:100) in blocking buffer for one hour at 4 °C. After washing, the slides were probed with a Cy3-labelled goat anti-human IgG Fc antibody (Jackson ImmunoResearch, Westgrove, PA, USA) and an Alexa Fluor 647-labelled goat anti-human IgM antibody (Jackson ImmunoResearch) for 45 min at 4 °C in blocking buffer. After additional washing, the slides were dried by centrifugation. Fluorescence was quantified using an Axon 4200A scanner (Molecular Devices, Sunnyvale, CA). Median fluorescence intensity (MFI) on both the IgG and IgM channels was calculated for each antigen by subtracting the local background fluorescence and then averaging duplicate features.
AutoAb detection and measurement by enzyme-linked immunosorbent assay (ELISA)
The concentrations of IgM and IgG antibodies in plasma samples were measured by commercial ELISA kits (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to manufacturer protocol. Briefly, 96-well plates were coated with anti-human IgM or IgG monoclonal antibodies overnight at 4 °C. After blocking, the plates were incubated with plasma samples (diluted 10,000-fold and 500,000-fold for IgM and IgG assays, respectively) and appropriate standards at room temperature for 2 h, followed by incubation with HRP-conjugated antibody at room temperature for 1 h. Tetramethylbenzidine substrate solution was then added to each well to incubate in the dark for 15 min at room temperature before the addition of stop solution (2N H2SO4). All samples were run in duplicate. Finally, absorbance at 450 nm was detected by a plate reader to calculate IgM and IgG concentrations via standard curve OD values.
Mann–Whitney U test and Fisher’s exact test or the Chi-squared test were used to compare continuous and categorical variables, respectively, across patients with and without irAEs G ≥ 2. The assessment of differences in IgG and IgM distribution incorporated the Benjamini–Hochberg correction for multiple comparisons. Receiver operating characteristics (ROC) analysis was used to determine the optimal cutoff for IgG and IgM MFI to differentiate patients with and without irAEs G ≥ 2. The optimal cutoff was selected using the Youden index. AutoAb with MFI greater than the optimal cutoff per individual, as well as greater than 500, were compared between healthy controls and patients with and without irAEs G ≥ 2 using the Kruskal–Wallis test. Amongst patients with irAEs G ≥ 2, change in MFI > optimal cutoff from screening to toxicity, and change from toxicity to steroid use were modelled using the Wilcoxon signed-rank test. The analysis was repeated with patients without irAEs to evaluate change from screening to next collection. To assess cumulative incidence of irAEs G ≥ 2 by MFI group, time to irAEs G ≥ 2 was calculated as the number of months between first cycle and date of irAEs G ≥ 2. Patients without irAEs G ≥ 2 were censored at date of last follow up. Death was considered a competing risk. For each group, patients were categorized into “high” vs. “low” groups based on the median number of AutoAb above the selected cutoff. Differences in the cumulative incidence by group were assessed using Gray’s test. Patients were stratified by age group (≤ 45 vs. > 45, ≤ 50 vs. > 50 and ≤ 60 vs. 60 years) and differences in AutoAb distribution were assessed using the Mann–Whitney U test.
A total of 14 healthy controls and 114 cancer patients were included in this analysis. Clinical and treatment characteristics of the study population are described in Table 1. The median age of the healthy controls was 35 years, and the majority were female (64%). The median age of the patients was 61 years and 51% were male. Thirty-seven patients (32%) developed irAEs G ≥ 2. The type of therapy received (anti-PD1 with or without anti-CTLA4 antibodies), the type of tumor, the number of prior lines and the sex of the patients were significantly associated with the risk of developing irAEs. A greater proportion of irAEs G ≥ 2 was observed in patients receiving anti-PD1 in combination with anti-CTLA4 agents (24% vs 4%, p = 0.0018). As compared with other tumor types, patients with melanoma had a higher proportion of irAEs G ≥ 2 (43% vs 12%, p = 0.022) and greater proportion of irAEs G ≥ 2 occurred in patients who received immunotherapy as their first line of anticancer treatment as compared to those who had one or more prior lines of treatment (51% vs 18%, p = 0.012). A higher risk of irAEs G ≥ 2 was observed in male vs female (41% vs 23%, p = 0.017). The association between tumor type, number of prior lines and sex of the patients and the probability to develop irAEs was confirmed after adjusting for the type of treatment received. Patients who experienced irAEs G ≥ 2 were older; however, this difference was not significant after adjusting for treatment type (65 vs 59 years, p = 0.07). Eight patients had a past history of autoimmune disorders (3 hypothyroidism, 1 Hashimoto syndrome, 1 psoriasis, 1 rheumatoid arthritis, 1 diabetes mellitus, 1 Schoenlein-Henoch purpura). No significant association between prior autoimmune conditions and development of irAEs G ≥ 2 was observed in this cohort (p = 0.90). We observed 18 different types of irAEs (Supplementary Fig. 1). The most frequently observed were hypothyroidism (13 patients, 11%), pneumonitis (10 patients, 9%), colitis/diarrhea (7 patients, 6%), skin toxicity (7 patients, 6%), hepatitis (5 patients, 4%), infusion reaction (2 patients, 2%), myocarditis/troponin increase (2 patients, 2%) and pancreatitis/lipase increase (2 patients, 2%). In 4/13 cases (31%) hypothyroidism was preceded by subclinical hyperthyroidism detected with routine blood tests. Nineteen patients (17%) developed more than one irAE G ≥ 2.
Cancer patients had a greater number of IgG and IgM reactivities before ICI administration than healthy controls
Autoimmune reactivity between patients with and without irAEs G ≥ 2 and healthy controls was compared using an MFI > 500 as a threshold. This level was previously identified as the threshold for signal identification by ELISA . We evaluated both IgG and IgM AutoAb with MFI > or ≤ 500 in plasma samples collected from 114 cancer patients before ICI administration and from 14 healthy controls and assessed differences using the Mann–Whitney U test. A statistically significant difference in AutoAb reactivities with MFI > 500 was observed in samples collected from the healthy controls (median 32, [IQR 29–37.5]) versus AutoAb with MFI > 500 samples collected from cancer patients before the start of immunotherapy (median 62; [IQR 50–90]; p < 0.001). The median number of IgM reactivities in samples from healthy controls was 11.5 (IQR 9–14) while in cancer patients was 25 (IQR 15–41; p < 0.001). The median number of IgG reactivities in healthy controls was 23 (IQR 18–26) while in cancer patients was 34.5 (IQR 29–45) (p < 0.001) (Fig. 1). The healthy control’s median age was lower as compared to patients (31 vs 61 years, see Table 1). To evaluate the potential impact of this variable we divided the patients into two groups according to their age and compared IgG and IgM reactivities before ICI exposure in the two groups. Multiple thresholds were explored. The only significant difference observed was between patients ≤ 50 years vs > 50 years. Using this threshold, a lower median number of IgM and IgG with MFI > 500 was observed in the elderly population (60 vs 73, p = 0.04, see Supplementary Table 2).
Patients who develop G ≥ 2 irAEs have higher number of AutoAb reactivities at baseline
We compared the number of IgG and IgM reactivities with MFI > 500 in plasma collected before ICI administration from patients who subsequently developed irAEs G ≥ 2 versus those who did not using the Mann–Whitney U test. Samples from 37/114 patients (32%), who developed irAEs and 77/114 patients (68%) who did not develop irAEs were available for this analysis. Patients who experienced irAEs G ≥ 2 had a median of 69 (IQR 54–96) autoAg reactivities in their pre-ICI collection while the median number in patients who did not develop irAEs G ≥ 2 was 59 (IQR 49–84) (p = 0.06). We observed a statistically significantly higher number of reactive IgG in plasma collected from patients who developed irAEs (median 39, IQR 32–52) than in samples from patients who did not develop irAEs G ≥ 2 (median 33, IQR 28–43, p = 0.04). This difference was confirmed in the subgroup of patients who received anti-PD1 single agent (median 33 [IQR 27–42] vs 38 [IQR 32–53], p = 0.04) but not in those who received the combination with anti-CTLA-4 (median 50 [IQR 40–52] vs 39 [IQR 33–44] p = 0.64). No differences in the number of IgM reactivities were observed in plasma samples collected before the start of ICI from patients who did develop irAEs G ≥ 2 (median 26, IQR 16–43) and those who did not experience irAEs G ≥ 2 (median 25, IQR 15–40) (p = 0.57).
Beside to using the a priori selected MFI threshold of 500, ROC analysis was performed to identify additional thresholds for IgG and IgM reactivity, to further explore whether there was a difference in pre-ICI IgM and IgG levels in patients with vs without irAEs G ≥ 2. Cut-offs ranging from 100 to 1500 (with interval width of 25) were considered. The selected cutoff for IgM and IgG, which maximized the differences between the two groups of patients, was found to be MFI 950. ROC analyses were conducted separately for IgG alone and IgM alone. The optimal cut-off for IgG alone was identified as MFI 200, and for IgM alone was identified as MFI 1500. Differences in elevated AutoAb between healthy controls and cancer patients with and without irAEs G ≥ 2 using different thresholds are reported in Fig. 1.
Patients with a greater number of IgG reactivities at baseline are at higher risk to develop irAEs
We used the cut-offs identified in the prior analysis to divide the patients into “high” and “low” groups based on the number of AutoAb reactivities per patient detected in their pre-ICI collection. For total antibody reactivities as well as IgG reactivities at both thresholds, the “high” group had greater incidence of irAEs than the “low” group (Fig. 2A-B). No differences were observed in patients with “high” vs “low” IgM reactivity using MFI 500 or 1500 as cut-off (Fig. 2C).
Patients who develop irAEs G ≥ 2 have an increase of IgM reactivities after ICI
Twenty-four of the 37 patients who developed irAEs (65%) had available plasma samples collected after ICI administration, within 8 weeks from the development of irAEs (median 21 days, range from -13 to 55 days after irAEs diagnosis) and prior to the start of any immunosuppressive therapy. The Wilcoxon signed-rank test was used to evaluate changes in samples. Using MFI 500 as threshold, the median number of total AutoAg reactivities before ICI initiation in this group was 78 while a median of 86 reactivities were detected in the post-ICI collection (p = 0.030) (Fig. 3). A significant increase in post-ICI samples was confirmed for IgM reactivities (median 35 vs 29, p = 0.021) while the number of IgG reactivities did not change (median 39.5 vs 39, p = 0.17). No significant changes were observed in the number of AutoAb reactivities pre- and post-ICI in patients who did not develop G ≥ 2 irAEs (median 60 vs 58, p = 0.12 Supplementary Fig. 2). The absence of significant difference was confirmed for IgM (24 vs 23, p = 0.72) while post-treatment samples had a higher level of IgG (median 37 vs 33, p = 0.006). Differences in the level of AutoAg reactivities from baseline to the first post ICI collection or to the closest collection to irAEs development, separated by IgG and IgM are reported in Supplementary Fig. 3.
Patients who receive immuno-suppressive medications have a decrease of the number of IgG reactivities
We evaluated changes in the number of AutoAg reactivities in patients who received systemic immune suppressive agents to treat irAEs G ≥ 2 using the Wilcoxon signed-rank test. Nine of the 24 patients (37.5%) with available plasma collection within 8 weeks from the onset of irAEs had also available samples obtained after the start of immunosuppressive treatment. We observed a decrease in reactivities after immune suppression (median number of IgG and IgM with MFI > 500 54 post vs 79 pre-immune suppression p = 0.097). The changes were mainly related to a decrease of IgG (median number of IgG with MFI > 500 28 vs 38 pre-immune suppression, p = 0.033) while the levels of IgM were overall stable (median number of IgM with MFI > 500 33 vs 35 pre-immune suppression, p = 0.40 Supplementary Fig. 4).
Patients who develop irAEs have different mean and median MFI levels of specific AutoAb at baseline (pre-ICI) as compared with those who do not develop irAEs
The mean and median MFI for each specific AutoAg at baseline in patients with and without G ≥ 2 irAEs is reported in the Supplementary Table 3. No significant difference was observed with multiple comparison. AutoAb with significant difference between patients with and without irAEs at univariate analysis are reported in Supplementary Table 4. Additionally, no significant differences in the median MFI for each specific AutoAg were observed between patients with and without history of autoimmune disorders (Supplementary Table 5). We did not observe a correlation between a specific toxicity and high reactivity against a certain AutoAg (Supplementary Table 6). However, in some patients who experienced organ-specific irAEs we observed pre-ICI high levels of AutoAb against the related tissue. For instance, we detected high levels of IgG anti cardiac myosin before ICI exposure in a patient who subsequently experienced immune related myocarditis and very high pre-ICI levels of IgG anti-desmin in a patient who experienced colitis. Baseline and post-ICI reactivity of organ-specific AutoAb in selected patients, compared with median values in healthy controls and in all the patients before ICI exposure are reported in Supplementary Table 7.
No difference in the level of IgG and IgM measured by ELISA is observed between patients with and without irAEs
We compared the value of total IgM and IgG measured by ELISA in the pre- and post-ICI samples from patients with and without G ≥ 2 irAEs. No differences were observed at baseline between patients with and without irAEs G ≥ 2 (median IgM value 3.47 g/L vs 2.88 g/L, p = 0.10; median IgG value 15.18 g/L vs 13.97 g/L p = 0.49). No significant changes from baseline were observed at the time of irAEs (median IgM value pre-ICI 3.63 g/L vs 3.87 g/L post ICI, p = 0.64; median IgG value pre-ICI 15.91 g/L vs 15.60 g/L post-ICI, p = 0.22) or between the time of toxicity and after steroids administration (median IgM value post-ICI 10.70 g/L vs post-steroid 8.73 g/L p = 0.58; median IgG value post-ICI 17.11 g/L vs post-steroid 17.33 g/L p = 0.33) (Supplementary Fig. 5).
The relationship between cancer and immune system is only partially understood. The identification of reliable factors to distinguish upfront subjects with a higher risk of developing irAEs could improve treatment outcomes. Our data indicate the potential role of antibody-mediated autoreactivity as a predictive marker and/or causal factor in irAEs.
The link between generic pre-ICI elevation of AutoAb and subsequent development of irAEs is controversial . Most published studies assess on a limited number of AutoAb, including rheumatoid factor, anti-thyroid and antinuclear antibodies (ANA) [25, 28, 58,59,60]. Few case reports have indicated an overall elevation of total IgG in patients who developed severe irAEs from ICI, supporting the potential role of gammaglobulin in irAEs etiopathogenesis [61,62,63]. However, the autoantibody levels were tested only after the development of the toxicities. To our knowledge, this is the first study using a large, customized panel of AutoAg to test reactivity against multiple antigens before immunotherapy exposure. Interestingly we observed a correlation between high pre-treatment IgG reactivity and irAEs development in the subgroup of patients treated with anti-PD1 single agent but not in those who received combined treatment. This might be due to the sample size (only 12/114 patients received anti-PD1 + anti-CTLA4). Nevertheless, it is possible that other factors, independent from autoAbs, are predominant in driving irAEs from anti-CTLA4.
We did not observe a significant correlation between pre-ICI reactivity against a specific AutoAg and a particular irAE, such as pneumonitis or hepatitis. Different types of antibodies however can cause similar clinical manifestations. As an example, ANA, anti-smooth muscle antibodies, and anti-liver–kidney microsomal antibodies are only some of the autoAbs that can lead to autoimmune liver disease . Of note we observed some isolated cases of patients who developed toxicity of a particular organ such as the heart or the gut and had extremely high levels of specific AutoAb pre-ICI (Supplementary Table 7). These observations can be hypothesis-generating for future mechanistic studies.
In our study, a significant increase of IgM was detected near the time of ICI toxicity in patients who developed irAEs G ≥ 2. IgM are the first type of antibody generated during immune activation , therefore the increase of this subclass of immunoglobulins at the time of inflammation is not unexpected. The decrease in AutoAg reactive antibodies with immunosuppression is consistent with the historical data on the use of corticosteroids leading to IgG depletion without affecting the levels of other immunoglobulins subtypes [66,67,68,69]. Further research is needed to clarify if hypogammaglobulinemia following immunosuppressive treatment can represent a useful biomarker to predict early recovery from ICI-toxicities and select patients for rapid prednisone tapering.
Neoplastic processes are known to stimulate the production of AutoAb through different mechanisms, including rapid cancer cell turnover, altered protein expression and chronic inflammation [70, 71]. The median IgM levels detected with ELISA in pre-ICI collection from cancer patients included in this study were above the normal range established for the healthy population . Moreover, in our cohort, cancer patients had higher IgG and IgM reactivity as compared to healthy controls. The patients and the healthy controls were not matched and multiple factors such as age and sex might have contributed to this difference. Still, a connection between cancer and increased AutoAb production can not be excluded. Paraneoplastic syndromes are a well-known phenomenon where tumors lead to the production of AutoAb attacking different organs such as the nervous system . It is possible that there is a proportion of patients who, while developing a certain degree of autoreactivity, do not develop clinical manifestations. Anti-cancer treatments such as immunotherapy, can alter the equilibrium between cancer and the immune system by triggering AutoAb production and autoimmune phenomena through several mechanisms (e.g. cross-reactivity between cancer and healthy tissue antigens) . The involvement of tissue specific AutoAb has been demonstrated for some of the most lethal ICI-related toxicities, such as AutoAb against the acetylcholine receptor in myasthenia-like syndrome , and anti-HU AutoAb in immune-related encephalitis . These events could represent the effect of immunotherapy on an immune system already altered by cancer and prone to autoreactivity.
To assess the potential effect of senescence on AutoAb levels we compared IgG and IgM reactivity at baseline in patients, grouping them by age. Interestingly we observed a higher IgG and IgM reactivity in patients ≤ 50 years and no other significant differences. These results suggest that the greater reactivity observed in patients versus healthy controls might be related to the cancer rather than to the older age.
In our study, we observed a greater proportion of male patients developing G ≥ 2 irAE; however, a clear correlation between demographic characteristics, such as age or sex and probability of toxicity from ICI has not been established in large randomized clinical trials [35, 76, 77]. The female sex is known to have a greater propensity to develop some autoimmune conditions such as systemic lupus erythematosus and rheumatoid arthritis due to the effect of estrogen and prolactin on the immune system . ICI activate the immune system regardless of the presence of sex hormones. This can explain why there is no evidence of an increased risk of irAEs in the female sex.
We did not observe a correlation between pre-existent autoimmune disorders and irAEs development. The absence of an increased risk of irAEs in patients with a history of autoimmune disease has also been reported in other studies and prospective trials are ongoing to validate the safety of ICI in this population [79,80,81,82]. Moreover, we did not detect higher reactivity against a specific AutoAg in patients with prior autoimmune conditions. This is not unexpected, the decrease of AutoAbs titers overtime following the acute phase of an autoimmune disease is a common event [83, 84].
Our study has several limitations, including the heterogeneity of tumor types. The indications of approved ICI-based therapies however are constantly expanding. Therefore, a standardized test, able to identify upfront patients with a higher risk of irAEs, across different tumor types would be of great clinical value. We used a novel approach to measure global reactivity against a large number of antigens to assess the potential role of pre-existent AutoAg reactivity in irAEs development. Importantly this approach, going beyond the assessment of each single tissue-specific antibody, was able to predict the risk of irAEs irrespectively of the specific type of toxicity. Of note, the total levels of IgG/IgM tested with ELISA were not able to discriminate patients with a higher probability to develop irAEs, indicating the higher sensitivity in AutoAb detection of the array technology. The retrospective nature, the small sample size and the low proportion of patients with blood samples collected at the time of irAE represent additional limitations of our analysis. A prospective study is currently ongoing at our institution to validate these observations (NCT04107311). If validated in a prospective cohort, autoantibody profiling might represent a useful tool to identify upfront patients at high risk of clinically relevant irAEs through a simple blood draw.
Availability of data and materials
Data supporting this study are included in the article and/or in the supporting materials.
Common Terminology Criteria for Adverse Events
Cytotoxic T-lymphocyte-associated antigen 4
Enzyme-linked immunosorbent assay
Immune checkpoint inhibitor
Immune-related adverse events
Median fluorescence intensity
Programmed cell death 1
Programmed cell death 1 ligand
Squamous cell cancer of the head and neck
Tumor associated antigens
Akinleye A, Rasool Z. Immune checkpoint inhibitors of PD-L1 as cancer therapeutics. J Hematol Oncol. 2019;12(1):92. https://doi.org/10.1186/s13045-019-0779-5. published Online First:2019/09/07.
Puzanov I, Diab A, Abdallah K, et al. Managing toxicities associated with immune checkpoint inhibitors: consensus recommendations from the Society for Immunotherapy of Cancer (SITC) Toxicity Management Working Group. J Immunother Cancer. 2017;5(1):95. https://doi.org/10.1186/s40425-017-0300-z. published Online First:2017/11/23.
Patrinely JR Jr, Johnson R, Lawless AR, et al. Chronic Immune-Related Adverse Events Following Adjuvant Anti-PD-1 Therapy for High-risk Resected Melanoma. JAMA Oncol. 2021;7(5):744–8. https://doi.org/10.1001/jamaoncol.2021.0051. published Online First:2021/03/26.
Conroy M, Naidoo J. Immune-related adverse events and the balancing act of immunotherapy. Nat Commun. 2022;13(1):392. https://doi.org/10.1038/s41467-022-27960-2. published Online First:2022/01/21.
Esfahani K, Elkrief A, Calabrese C, et al. Moving towards personalized treatments of immune-related adverse events. Nat Rev Clin Oncol. 2020;17(8):504–15. https://doi.org/10.1038/s41571-020-0352-8. published Online First:2020/04/05].
Khan S, Gerber DE. Autoimmunity, checkpoint inhibitor therapy and immune-related adverse events: A review. Semin Cancer Biol. 2020;64:93–101. https://doi.org/10.1016/j.semcancer.2019.06.012. published Online First:2019/07/23.
Kennedy LB, Salama AKS. A review of cancer immunotherapy toxicity. CA Cancer J Clin. 2020;70(2):86–104. https://doi.org/10.3322/caac.21596. published Online First:2020/01/17.
Postow MA, Sidlow R, Hellmann MD. Immune-Related Adverse Events Associated with Immune Checkpoint Blockade. N Engl J Med. 2018;378(2):158–68. https://doi.org/10.1056/NEJMra1703481. published Online First:2018/01/11.
Weinmann SC, Pisetsky DS. Mechanisms of immune-related adverse events during the treatment of cancer with immune checkpoint inhibitors. Rheumatology (Oxford). 2019;58(Suppl 7):vii59–67. https://doi.org/10.1093/rheumatology/kez308. published Online First: 2019/12/10.
Hsiehchen D, Naqash AR, Espinoza M, et al. Association between immune-related adverse event timing and treatment outcomes. Oncoimmunology. 2022;11(1):2017162. https://doi.org/10.1080/2162402X.2021.2017162. published Online First:2022/01/11.
Ricciuti B, Genova C, De Giglio A, et al. J Cancer Res Clin Oncol. 2019;145(2):479–85. https://doi.org/10.1007/s00432-018-2805-3. published Online First:2018/12/07.
Genova C, Rossi G, Rijavec E, et al. Releasing the brake: safety profile of immune check-point inhibitors in non-small cell lung cancer. Expert Opin Drug Saf. 2017;16(5):573–85. https://doi.org/10.1080/14740338.2017.1313228. published Online First:2017/03/30.
Shankar B, Zhang J, Naqash AR, et al. Multisystem Immune-Related Adverse Events Associated With Immune Checkpoint Inhibitors for Treatment of Non-Small Cell Lung Cancer. JAMA Oncol. 2020;6(12):1952–6. https://doi.org/10.1001/jamaoncol.2020.5012. published Online First:2020/10/30.
Eggermont AMM, Kicinski M, Blank CU, et al. Association Between Immune-Related Adverse Events and Recurrence-Free Survival Among Patients With Stage III Melanoma Randomized to Receive Pembrolizumab or Placebo: A Secondary Analysis of a Randomized Clinical Trial. JAMA Oncol. 2020;6(4):519–27. https://doi.org/10.1001/jamaoncol.2019.5570. published Online First:2020/01/03.
Maher VE, Fernandes LL, Weinstock C, et al. Analysis of the Association Between Adverse Events and Outcome in Patients Receiving a Programmed Death Protein 1 or Programmed Death Ligand 1 Antibody. J Clin Oncol. 2019;37(30):2730–7. https://doi.org/10.1200/JCO.19.00318. published Online First:2019/05/23.
Johnson DB, Balko JM, Compton ML, et al. Fulminant Myocarditis with Combination Immune Checkpoint Blockade. N Engl J Med. 2016;375(18):1749–55. https://doi.org/10.1056/NEJMoa1609214. published Online First:2016/11/03.
Coutzac C, Adam J, Soularue E, et al. Colon Immune-Related Adverse Events: Anti-CTLA-4 and Anti-PD-1 Blockade Induce Distinct Immunopathological Entities. J Crohns Colitis. 2017;11(10):1238–46. https://doi.org/10.1093/ecco-jcc/jjx081. published Online First:2017/10/03.
Mamlouk O, Selamet U, Machado S, et al. Nephrotoxicity of immune checkpoint inhibitors beyond tubulointerstitial nephritis: single-center experience. J Immunother Cancer. 2019;7(1):2. https://doi.org/10.1186/s40425-018-0478-8. published Online First:2019/01/08.
Suresh K, Naidoo J, Zhong Q, et al. The alveolar immune cell landscape is dysregulated in checkpoint inhibitor pneumonitis. J Clin Invest. 2019;129(10):4305–15. https://doi.org/10.1172/JCI128654. published Online First:2019/07/17.
Pringle S, Wang X, Vissink A, et al. Checkpoint inhibition-induced sicca: a type II interferonopathy? Clin Exp Rheumatol. 2020;38 Suppl 126(4):253–60 published Online First: 2020/10/08.
Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19(3):133–50. https://doi.org/10.1038/s41568-019-0116-x. published Online First:2019/02/14.
Siwicki M, Gort-Freitas NA, Messemaker M, et al. Resident Kupffer cells and neutrophils drive liver toxicity in cancer immunotherapy. Sci Immunol 2021;6(61). https://doi.org/10.1126/sciimmunol.abi7083. published Online First: 2021/07/04.
Walsh MJ, Dougan M. Checkpoint blockade toxicities: Insights into autoimmunity and treatment. Semin Immunol. 2021;52:101473. https://doi.org/10.1016/j.smim.2021.101473. published Online First:2021/03/18.
Lozano AX, Chaudhuri AA, Nene A, et al. T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma. Nat Med. 2022;28(2):353–62. https://doi.org/10.1038/s41591-021-01623-z. published Online First:2022/01/15.
Les I, Martinez M, Narro A, et al. Association of immune-related adverse events induced by nivolumab with a battery of autoantibodies. Ann Med. 2021;53(1):762–9. https://doi.org/10.1080/07853890.2021.1931956. published Online First:2021/06/02.
Castel-Ajgal Z, Goulvestre C, Zaibet S, et al. Preexisting Autoantibodies and Immune Related Adverse Events in Metastatic Urothelial Carcinoma Patients Treated by Pembrolizumab. Clin Genitourin Cancer. 2022. https://doi.org/10.1016/j.clgc.2022.04.002. published Online First:2022/05/17.
Velasco R, Villagran M, Jove M, et al. Encephalitis Induced by Immune Checkpoint Inhibitors: A Systematic Review. JAMA Neurol. 2021;78(7):864–73. https://doi.org/10.1001/jamaneurol.2021.0249. published Online First:2021/03/16.
Toi Y, Sugawara S, Sugisaka J, et al. Profiling Preexisting Antibodies in Patients Treated With Anti-PD-1 Therapy for Advanced Non-Small Cell Lung Cancer. JAMA Oncol. 2019;5(3):376–83. https://doi.org/10.1001/jamaoncol.2018.5860. published Online First:2018/12/28.
Kurimoto C, Inaba H, Ariyasu H, et al. Predictive and sensitive biomarkers for thyroid dysfunctions during treatment with immune-checkpoint inhibitors. Cancer Sci. 2020;111(5):1468–77. https://doi.org/10.1111/cas.14363. published Online First:2020/02/23.
Muir CA, Wood CCG, Clifton-Bligh RJ, et al. Association of Antithyroid Antibodies in Checkpoint Inhibitor-Associated Thyroid Immune-Related Adverse Events. J Clin Endocrinol Metab. 2022;107(5):e1843–9. https://doi.org/10.1210/clinem/dgac059. published Online First:2022/02/02.
Da Gama DJ, Parakh S, Andrews MC, et al. Autoantibodies May Predict Immune-Related Toxicity: Results from a Phase I Study of Intralesional Bacillus Calmette-Guerin followed by Ipilimumab in Patients with Advanced Metastatic Melanoma. Front Immunol. 2018;9:411. https://doi.org/10.3389/fimmu.2018.00411. published Online First:2018/03/20.
Tahir SA, Gao J, Miura Y, et al. Autoimmune antibodies correlate with immune checkpoint therapy-induced toxicities. Proc Natl Acad Sci U S A. 2019;116(44):22246–51. https://doi.org/10.1073/pnas.1908079116. published Online First:2019/10/16.
Das R, Bar N, Ferreira M, et al. Early B cell changes predict autoimmunity following combination immune checkpoint blockade. J Clin Invest. 2018;128(2):715–20. https://doi.org/10.1172/JCI96798. published Online First:2018/01/09.
Patel AJ, Willsmore ZN, Khan N, et al. Regulatory B cell repertoire defects predispose lung cancer patients to immune-related toxicity following checkpoint blockade. Nat Commun. 2022;13(1):3148. https://doi.org/10.1038/s41467-022-30863-x. published Online First:2022/06/08.
Chennamadhavuni A, Abushahin L, Jin N, et al. Risk Factors and Biomarkers for Immune-Related Adverse Events: A Practical Guide to Identifying High-Risk Patients and Rechallenging Immune Checkpoint Inhibitors. Front Immunol. 2022;13:779691. https://doi.org/10.3389/fimmu.2022.779691. published Online First:2022/05/14.
Morad G, Helmink BA, Sharma P, et al. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell. 2022;185(3):576. https://doi.org/10.1016/j.cell.2022.01.008. published Online First:2022/02/06.
Olsson-Brown AC, Baxter M, Feeney L, et al. The association of pre-existing autoimmune disease and immune-related adverse events secondary to immune checkpoint inhibition therapy in a UK multicenter cohort. J Clin Oncol. 2022;40(16_suppl):2522–22. https://doi.org/10.1200/JCO.2022.40.16_suppl.2522.
Daban A, Gonnin C, Vano YA, et al. Preexisting autoantibodies as predictor of immune-related adverse events for advanced solid tumors treated with immune checkpoint inhibitors. J Clin Oncol. 2022;40(16_suppl):2523–23. https://doi.org/10.1200/JCO.2022.40.16_suppl.2523.
Altan M, Wang Q, Li Q-Z, et al. Auto-reactive antibodies as predictive markers for immune checkpoint–induced pneumonitis. J Clin Oncol. 2022;40(16_suppl):2554–54. https://doi.org/10.1200/JCO.2022.40.16_suppl.2554.
Music M, Iafolla M, Soosaipillai A, et al. Predicting response and toxicity to PD-1 inhibition using serum autoantibodies identified from immuno-mass spectrometry. F1000Res. 2020;9:337. https://doi.org/10.12688/f1000research.22715.1. published Online First: 2020/12/11.
Les I, Perez-Francisco I, Cabero M, et al. Prediction of Immune-Related Adverse Events Induced by Immune Checkpoint Inhibitors With a Panel of Autoantibodies: Protocol of a Multicenter, Prospective. Observational Cohort Study Front Pharmacol. 2022;13:894550. https://doi.org/10.3389/fphar.2022.894550. published Online First:2022/06/21.
Osorio JC, Ni A, Chaft JE, et al. Antibody-mediated thyroid dysfunction during T-cell checkpoint blockade in patients with non-small-cell lung cancer. Ann Oncol. 2017;28(3):583–9. https://doi.org/10.1093/annonc/mdw640. published Online First:2016/12/22.
Kobayashi T, Iwama S, Yasuda Y, et al. Patients With Antithyroid Antibodies Are Prone To Develop Destructive Thyroiditis by Nivolumab: A Prospective Study. J Endocr Soc. 2018;2(3):241–51. https://doi.org/10.1210/js.2017-00432. published Online First:2018/03/31.
Chruscinski A, Huang FY, Nguyen A, et al. Generation of Antigen Microarrays to Screen for Autoantibodies in Heart Failure and Heart Transplantation. PLoS ONE. 2016;11(3):e0151224. https://doi.org/10.1371/journal.pone.0151224. published Online First:2016/03/12.
Genta S, Keshavarzi S, Yee N, et al. Customized autoantibodies (autoAbs) profiling to predict and monitor immune-related adverse events (irAEs) in patients receiving immune checkpoint inhibitors (ICI). J Clin Oncol. 2022;40(16_suppl):2528–28. https://doi.org/10.1200/JCO.2022.40.16_suppl.2528.
Cindy Yang SY, Lien SC, Wang BX, et al. Pan-cancer analysis of longitudinal metastatic tumors reveals genomic alterations and immune landscape dynamics associated with pembrolizumab sensitivity. Nat Commun. 2021;12(1):5137. https://doi.org/10.1038/s41467-021-25432-7. published Online First:2021/08/28.
Bratman SV, Yang SYC, Iafolla MAJ, et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat Cancer. 2020;1(9):873–81. https://doi.org/10.1038/s43018-020-0096-5. published Online First:2020/09/01.
Araujo DV, Bernal MO, Tan TJY, et al. First-in-class microbial ecosystem therapeutics 4 (MET4) in metastatic solid cancer patients treated with immunotherapy: MET4-IO. J Clin Oncol. 2020;38(15_suppl):3098–98. https://doi.org/10.1200/JCO.2020.38.15_suppl.3098.
Spreafico A, Heirali AA, Araujo DV, et al. First-in-class Microbial Ecosystem Therapeutic 4 (MET4) in combination with immune checkpoint inhibitors in patients with advanced solid tumors (MET4-IO trial). Ann Oncol. 2023. https://doi.org/10.1016/j.annonc.2023.02.011. published Online First:2023/03/03.
Grosman-Rimon L, Ajrawat P, Lioe J, et al. Increases in Serum Autoantibodies After Left Ventricular Assist Device Implantation. J Card Fail. 2019;25(4):301–6. https://doi.org/10.1016/j.cardfail.2019.01.002. published Online First:2019/01/11.
Clotet-Freixas S, Kotlyar M, McEvoy CM, et al. Increased Autoantibodies Against Ro/SS-A, CENP-B, and La/SS-B in Patients With Kidney Allograft Antibody-mediated Rejection. Transplant Direct. 2021;7(10):e768. https://doi.org/10.1097/TXD.0000000000001215. published Online First:2021/09/25.
Chruscinski A, Juvet S, Moshkelgosha S, et al. Autologous Hematopoietic Stem Cell Transplantation for Liver Transplant Recipients With Recurrent Primary Sclerosing Cholangitis: A Pilot Study. Transplantation. 2022;106(3):562–74. https://doi.org/10.1097/TP.0000000000003829. published Online First:2021/05/29.
Watanabe T, Martinu T, Chruscinski A, et al. A B cell-dependent pathway drives chronic lung allograft rejection after ischemia-reperfusion injury in mice. Am J Transplant. 2019;19(12):3377–89. https://doi.org/10.1111/ajt.15550. published Online First:2019/08/01.
Munoz-Grajales C, Prokopec SD, Johnson SR, et al. Serological abnormalities that predict progression to systemic autoimmune rheumatic diseases in antinuclear antibody-positive individuals. Rheumatology (Oxford). 2022;61(3):1092–105. https://doi.org/10.1093/rheumatology/keab501. published Online First:2021/06/28.
Spiliopoulou P, Janse van Rensburg HJ, Avery L, et al. Longitudinal efficacy and toxicity of SARS-CoV-2 vaccination in cancer patients treated with immunotherapy. Cell Death Dis. 2023;14(1):49. https://doi.org/10.1038/s41419-022-05548-4. published Online First: 2023/01/21.
Balboni I, Limb C, Tenenbaum JD, et al. Evaluation of microarray surfaces and arraying parameters for autoantibody profiling. Proteomics. 2008;8(17):3443–9. https://doi.org/10.1002/pmic.200800146. published Online First:2008/08/30.
Singh H, Henry KA, Wu SS, et al. Reactivity profiles of broadly neutralizing anti-HIV-1 antibodies are distinct from those of pathogenic autoantibodies. AIDS. 2011;25(10):1247–57. https://doi.org/10.1097/QAD.0b013e32834785cf. published Online First:2011/04/22.
de Moel EC, Rozeman EA, Kapiteijn EH, et al. Autoantibody Development under Treatment with Immune-Checkpoint Inhibitors. Cancer Immunol Res. 2019;7(1):6–11. https://doi.org/10.1158/2326-6066.CIR-18-0245. published Online First:2018/11/15.
Yoneshima Y, Tanaka K, Shiraishi Y, et al. Safety and efficacy of PD-1 inhibitors in non-small cell lung cancer patients positive for antinuclear antibodies. Lung Cancer. 2019;130:5–9. https://doi.org/10.1016/j.lungcan.2019.01.014. published Online First:2019/03/20.
Sakakida T, Ishikawa T, Chihara Y, et al. Safety and efficacy of PD-1/PD-L1 blockade in patients with preexisting antinuclear antibodies. Clin Transl Oncol. 2020;22(6):919–27. https://doi.org/10.1007/s12094-019-02214-8. published Online First:2019/10/03.
Ghosn J, Vicino A, Michielin O, et al. A severe case of neuro-Sjogren’s syndrome induced by pembrolizumab. J Immunother Cancer. 2018;6(1):110. https://doi.org/10.1186/s40425-018-0429-4. published Online First:2018/10/24.
Tardy MP, Gastaud L, Boscagli A, et al. Autoimmune hemolytic anemia after nivolumab treatment in Hodgkin lymphoma responsive to immunosuppressive treatment. A case report. Hematol Oncol. 2017;35(4):875–7. https://doi.org/10.1002/hon.2338. published Online First:2016/08/20.
Sadik CD, Langan EA, Gratz V, et al. Checkpoint Inhibition May Trigger the Rare Variant of Anti-LAD-1 IgG-Positive, Anti-BP180 NC16A IgG-Negative Bullous Pemphigoid. Front Immunol. 2019;10:1934. https://doi.org/10.3389/fimmu.2019.01934. published Online First:2019/09/03.
Sebode M, Weiler-Normann C, Liwinski T, et al. Autoantibodies in Autoimmune Liver Disease-Clinical and Diagnostic Relevance. Front Immunol. 2018;9:609. https://doi.org/10.3389/fimmu.2018.00609. published Online First:2018/04/11.
Jones K, Savulescu AF, Brombacher F, et al. Immunoglobulin M in Health and Diseases: How Far Have We Come and What Next? Front Immunol. 2020;11:595535. https://doi.org/10.3389/fimmu.2020.595535. published Online First:2020/11/17.
Butler WT, Rossen RD. Effects of corticosteroids on immunity in man. I. Decreased serum IgG concentration caused by 3 or 5 days of high doses of methylprednisolone. J Clin Invest. 1973;52(10):2629–40. https://doi.org/10.1172/JCI107455. published Online First: 1973/10/01.
Fan PT, Yu DT, Clements PJ, et al. Effect of corticosteroids on the human immune response: comparison of one and three daily 1 gm intravenous pulses of methylprednisolone. J Lab Clin Med. 1978;91(4):625–34 published Online First: 1978/04/01.
Settipane GA, Pudupakkam RK, McGowan JH. Corticosteroid effect on immunoglobulins. J Allergy Clin Immunol. 1978;62(3):162–6. https://doi.org/10.1016/0091-6749(78)90101-x. published Online First:1978/09/01.
Wirsum C, Glaser C, Gutenberger S, et al. Secondary Antibody Deficiency in Glucocorticoid Therapy Clearly Differs from Primary Antibody Deficiency. J Clin Immunol. 2016;36(4):406–12. https://doi.org/10.1007/s10875-016-0264-7. published Online First:2016/03/17.
Zaenker P, Gray ES, Ziman MR. Autoantibody Production in Cancer-The Humoral Immune Response toward Autologous Antigens in Cancer Patients. Autoimmun Rev. 2016;15(5):477–83. https://doi.org/10.1016/j.autrev.2016.01.017. published Online First:2016/02/02.
Bei R, Masuelli L, Palumbo C, et al. A common repertoire of autoantibodies is shared by cancer and autoimmune disease patients: Inflammation in their induction and impact on tumor growth. Cancer Lett. 2009;281(1):8–23. https://doi.org/10.1016/j.canlet.2008.11.009. published Online First:2008/12/19.
Puissant-Lubrano B, Peres M, Apoil PA, et al. Immunoglobulin IgA, IgD, IgG, IgM and IgG subclass reference values in adults. Clin Chem Lab Med. 2015;53(12):e359–61. https://doi.org/10.1515/cclm-2014-1186. published Online First:2015/02/27.
Pelosof LC, Gerber DE. Paraneoplastic syndromes: an approach to diagnosis and treatment. Mayo Clin Proc. 2010;85(9):838–54. https://doi.org/10.4065/mcp.2010.0099. published Online First:2010/09/03.
Safa H, Johnson DH, Trinh VA, et al. Immune checkpoint inhibitor related myasthenia gravis: single center experience and systematic review of the literature. J Immunother Cancer. 2019;7(1):319. https://doi.org/10.1186/s40425-019-0774-y. published Online First:2019/11/23.
Hottinger AF, de Micheli R, Guido V, et al. Natalizumab may control immune checkpoint inhibitor-induced limbic encephalitis. Neurol Neuroimmunol Neuroinflamm. 2018;5(2):e439. https://doi.org/10.1212/NXI.0000000000000439. published Online First:2018/11/23.
Samani A, Zhang S, Spiers L, et al. Impact of age on the toxicity of immune checkpoint inhibition. J Immunother Cancer 2020;8(2). https://doi.org/10.1136/jitc-2020-000871. published Online First: 2020/10/10.
Betof AS, Nipp RD, Giobbie-Hurder A, et al. Impact of Age on Outcomes with Immunotherapy for Patients with Melanoma. Oncologist. 2017;22(8):963–71. https://doi.org/10.1634/theoncologist.2016-0450. published Online First:2017/05/10.
Zandman-Goddard G, Peeva E, Shoenfeld Y. Gender and autoimmunity. Autoimmun Rev. 2007;6(6):366–72. https://doi.org/10.1016/j.autrev.2006.10.001. published Online First:2007/06/01.
Yeung C, Kartolo A, Holstead R, et al. Safety and Clinical Outcomes of Immune Checkpoint Inhibitors in Patients With Cancer and Preexisting Autoimmune Diseases. J Immunother. 2021;44(9):362–70. https://doi.org/10.1097/CJI.0000000000000377. published Online First:2021/06/15.
Dumbrava EE, Dougan ML, Gupta S, et al. A phase 1b study of nivolumab in patients with autoimmune disorders and advanced malignancies (AIM-NIVO). J Clin Oncol. 2021;39(15_suppl):TPS2676–TPS76. https://doi.org/10.1200/JCO.2021.39.15_suppl.TPS2676.
Abdel-Wahab N, Shah M, Lopez-Olivo MA, et al. Use of Immune Checkpoint Inhibitors in the Treatment of Patients With Cancer and Preexisting Autoimmune Disease: A Systematic Review. Ann Intern Med. 2018;168(2):121–30. https://doi.org/10.7326/M17-2073. published Online First:2018/01/04.
Tison A, Garaud S, Chiche L, et al. Immune-checkpoint inhibitor use in patients with cancer and pre-existing autoimmune diseases. Nat Rev Rheumatol. 2022;18(11):641–56. https://doi.org/10.1038/s41584-022-00841-0. published Online First:2022/10/06.
Schmidt M, Voell M, Rahlff I, et al. Long-term follow-up of antithyroid peroxidase antibodies in patients with chronic autoimmune thyroiditis (Hashimoto’s thyroiditis) treated with levothyroxine. Thyroid. 2008;18(7):755–60. https://doi.org/10.1089/thy.2008.0008. published Online First:2008/07/18.
Sanders JS, Huitma MG, Kallenberg CG, et al. Prediction of relapses in PR3-ANCA-associated vasculitis by assessing responses of ANCA titres to treatment. Rheumatology (Oxford). 2006;45(6):724–9. https://doi.org/10.1093/rheumatology/kei272. published Online First:2006/01/10.
We thank all the patients who participated in the INSPIRE and MET-IO studies and their families. We thank Merck and NuBiyota for providing the study drugs for the INSPIRE and MET4-IO studies.
This study was supported by Dr Spreafico's research funding and Dr. Genta's Novartis Young Canadian Investigator Award. Study drugs for the INSPIRE and MET4-IO studies were kindly provided by Merck and NuBiyota.
Ethics approval and consent to participate
The INSPIRE (NCT02644369) and MET4-IO (NCT03686202) trials were investigator-initiated studies, approved by the Princess Margaret Cancer Centre Research Ethics Board (#18–5950 and 15–9828). All the patients signed informed consent for the participation in the INSPIRE or MET4-IO studies. Samples obtained from the participants were used for this correlative study without interfering with the primary objectives of the two clinical trials.
Consent for publication
All the authors approved the current version of the manuscript and agree with publication.
The following authors have conflicts of interest to declare:
Consultant for (Advisory Board): Merck (compensated), Bristol-Myers Squibb (compensated), Oncorus (compensated), Janssen (compensated), Medison & Immunocore (compensated);
Grant/Research support from (Clinical Trials): Novartis, Bristol-Myers Squibb, Symphogen AstraZeneca/Medimmune, Merck, Bayer, Surface Oncology, Northern Biologics, Janssen Oncology/Johnson & Johnson, Roche, Regeneron, Alkermes, Array Biopharma/Pfizer, GSK, NuBiyota, Oncorus, Treadwell, Amgen.
Lillian L. Siu:
Consultant for (Advisory Board): Merck, Pfizer, AstraZeneca, Roche, GlaxoSmithKline, Voronoi, Arvinas, Tessa, Navire, Relay, Rubius, Daiichi Sanyko, Coherus, Marengo, InteRNA, Tubulis, LTZ Therapeutics, NGM Biotherapeutics.
Stock ownership: Agios (spouse);
Leadership position: Treadwell Therapeutics (spouse);
Grant/Research support from (Clinical Trials): Novartis, Bristol-Myers Squibb, Pfizer, Boerhinger-Ingelheim, GlaxoSmithKline, Roche/Genentech, AstraZeneca, Merck, Celgene, Astellas, Bayer, Abbvie, Amgen, Symphogen, Shattucks, BionTech, 23Me, EMD Serono.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Table 1. Summary of Antigens included in the Microarray.
Supplementary Table 2. Screening MFI Distribution by Age Group.
Supplementary Table 3. Baseline (pre-ICI) distribution of single AutoAb by irAEs Status.
Supplementary Table 4. Autoantibodies with significantly different mean MFI levels at baseline (pre-ICI) in patients with and without irAEs.
Supplementary Table 5. AutoAbs Distribution by History of Autoimmune Disease Status
Supplementary Table 6. AutoAb distrubution by specific types of irAEs.
Supplementary Table 7. Selected cases of patients in which a significant elevation of autoAb correlated with organ-related toxicity.
Supplementary Fig. 1. Frequencies of different irAEs G≥2.
Supplementary Fig. 2. Changes in IgM and IgG levels in 61 patients without irAEs from baseline (pre-ICI collection) to the first collection after ICI administration.
Supplementary Fig. 3. Dynamic changes of autoAbs in patients with and without irAEs G≥2.
Supplementary Fig. 4. Comparison of the level of IgM and IgG with MFI> 500 at the time of irAEs and post steroids administration in 9 patients.
Supplementary Fig. 5. Comparison of AutoAbs levels measured with ELISA in patients with and without irAEs G≥2 and dynamic changes in patients who developed irAEs G>2.
About this article
Cite this article
Genta, S., Lajkosz, K., Yee, N.R. et al. Autoimmune PaneLs as PrEdictors of Toxicity in Patients TReated with Immune Checkpoint InhibiTors (ALERT). J Exp Clin Cancer Res 42, 276 (2023). https://doi.org/10.1186/s13046-023-02851-6