Negative impact of steroids on the efficacy of immunotherapy in a multi- tumor cohort: time and dose-dependent

Negative impact of steroids on the efficacy of immunotherapy in a multi- tumor cohort: time and dose-dependent | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Negative impact of steroids on the efficacy of immunotherapy in a multi- tumor cohort: time and dose-dependent Víctor Albarrán, Patricia Guerrero, Coral García de Quevedo, Carlos González, and 22 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4159119/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 8 You are reading this latest preprint version Abstract Previous studies have suggested a negative impact of steroids on the efficacy of immune checkpoint inhibitors (ICI), but how this effect is modulated by the dosage and time of administration is yet to be clarified. We have performed a retrospective analysis of 475 patients with advanced solid tumors treated with ICI as monotherapy from 2015 to 2022. Data regarding immune-related adverse events (irAEs) and clinical outcomes were collected. For each patient, the daily steroid dose (in mg/kg of prednisone) was registered until disease progression or death. The impact of cumulative doses on response rates and survival outcomes was analyzed within different periods. The objective response rate (ORR) was significantly lower among patients exposed to steroids within 30 days before the first cycle of ICI (C1) (20.3% vs. 36.7%, p < 0.01) and within the first 90 days of treatment (25.7% vs. 37.7%, p = 0.01). This negative effect was confirmed by multivariable analysis. Higher mean steroid doses were observed among non-responders, and cumulative doses were inversely correlated with the disease control rate (DCR) around ICI initiation. Remarkably, poorer outcomes were observed even in patients belonging to the lowest dose quartile compared to the steroid-naïve population. The detrimental impact of steroids was not observed in long-term responders exposed to steroids after 6 months from C1. Our results suggest that the negative impact of steroids on ICI efficacy seems to be time-dependent, prevailing around ICI initiation, and may also be dose-dependent, with modulation of neutrophil-to-lymphocyte ratio as a potential underlying mechanism. steroids immunotherapy immune checkpoint inhibitors dose neutrophil-to-lymphocyte ratio solid tumors Figures Figure 1 Figure 2 Highlights This study confirms the detrimental impact of steroids on the efficacy of immunotherapy in a large multi-tumor cohort. Our data suggest a time-dependent effect, with a negative impact on response rates, PFS and OS around ICI initiation. This is the first study to show that steroids impact on immunotherapy outcomes may be dose-dependent. Our results suggest a modulation of neutrophil-to-lymphocyte ratio by steroid exposure as a potential underlying mechanism. 1. INTRODUCTION The activation and migration of T lymphocytes plays a key role in the immune response against solid tumors 1 . Necrosis of malignant cells following the attack of macrophages and natural killer (NK) cells, main components of our innate immunity, leads to the release of tumor-associated antigens (TAA), which are phagocytosed by dendritic and other antigen-presenting cells (APC). In lymph nodes, these antigens are presented to naïve CD8 + T cells through the interaction between T cell receptors (TCR) and major histocompatibility complex class I (MHC-I) molecules on the APC surface, unleashing adaptive immunity. This process requires co-stimulatory signals, such as the coupling between co-receptor B7 (CD80) and CD28, and is downregulated by competitive co-receptors on the T cell membrane (CTLA-4, LAG-3, TIM-3), which induce negative feedback 2 . Following T cell activation, a shift in membrane protein expression promotes their trafficking to the tumor site, giving rise to a population of tumor-infiltrating lymphocytes (TILs) capable of recognizing TAA on the surface of tumor cells, thus inducing a cytotoxic cascade. As in the priming phase, this process is downregulated by inhibitory T-cell co-receptors such as programmed cell death receptor 1 (PD-1), which is activated by PD-1 ligand PD-L1 and other immunosuppressive molecules released by tumor cells and cells from the tumor microenvironment (TME). Tumor immune escape through pathological upregulation of these checkpoints provides a rationale for the use of immune checkpoint inhibitors (ICI) 3 . ICI have changed the therapeutic landscape of many solid tumors. However, huge variability in immunogenicity and responsiveness to treatment has been observed among different patients. This is a highly complex phenomenon influenced by multiple biomarkers, including tumor mutational burden (TMB) 4 , PD-L1 expression 5 , quantity and characteristics of infiltrating T lymphocytes 6 , B cells phenotype 7 and the interaction between the immune system and the TME 8 , among others. The presence of immunosuppressive cells in the TME, such as myeloid-derived suppressor cells (MDSCs 9 ), tumor-associated macrophages (TAMs 10 ) and regulatory T cells (T-reg 11 ), strongly interferes with the efficacy of the anti-tumor response, leading to immune-resistant ‘cold’ tumors and promoting tumor progression. Steroids have anti-inflammatory and immunosuppressive effects, which imply both innate and adaptive immunity. Glucocorticoid receptor (GR) activation alters the transcription of numerous genes encoding pro-inflammatory cytokines (NF-kB, AP-1) and genes related to T-cell survival, trafficking, and cytotoxic activity 12 . Steroids have been proven to reduce immune cell infiltration 13 , facilitate MDSCs expansion 14 , promote T-reg proliferation, and enhance the production of T-reg-activating cytokines (such as TGF-β) 15 . Increased GR signaling has been correlated with CD8 + TILs dysfunction and upregulation of several immune checkpoints 16 . Steroids are frequently prescribed for patients with advanced solid tumors owing to a wide range of clinical scenarios, including symptomatic brain metastasis, chemotherapy-induced emesis, cancer-related complications, and additional chronic comorbidities. In addition, except for endocrine toxicity, steroids are the mainstay of management for grade ≥ 2 immune-related adverse events (irAEs), usually starting at 0.5-2 mg/kg/day of prednisone until clinical improvement, and subsequent slow tapering over 4–6 weeks 20 . Given their immunosuppressive properties, there is growing concern regarding their detrimental effects on the clinical outcomes of immunotherapy. Although the negative impact of steroid exposure has been shown in several studies, how the dosage and time of administration modulate this effect is yet to be clarified. A better understanding of these aspects would be of great value in optimizing steroid use in oncological patients. In this real-world data (RWD) study, we aimed to collect detailed information on the dose and timing of steroid administration in a multi-tumor cohort of patients treated with ICI and to evaluate their impact on clinical outcomes. 2. METHODS a. Study design and population This is a retrospective single-center study performed after approval from the Clinical Investigation Ethical Committee (CEIC) of our institution (Ramon y Cajal University Hospital, Madrid), including adult patients (>18 years old) with metastatic and/or unresectable solid tumors who received systemic treatment with ICI between April 2015 and October 2022. Patients who received ICI in the context of a clinical trial, as well as those treated with combinations of ICI with chemotherapy, tyrosine-kinase inhibitors, or any other anticancer agents, were excluded from the analysis. In accordance with the CEIC, verbal informed consent was obtained from all patients. Clinical data were extracted from pharmacy databases and electronic institutional medical files. For the selected patients, we obtained data regarding their demographics, previous medical history (including autoimmune disease or human immunodeficiency virus [HIV] infection), oncologic history (tumor histology, date of diagnosis, cancer stage according to the American Joint Committee on Cancer [AJCC] staging system, disease burden, previous treatments), performance status (PS) according to the Eastern Cooperative Oncology Group (ECOG) score 17 , treatment with ICI (including type of ICI, treatment duration, and immune-related toxicity) and laboratory data, including neutrophil-to-lymphocyte (N/L) ratio. We defined the date of progression as either that of radiological (according to RECIST criteria V.1.1) or clinical evidence of progressive disease. For each patient, data regarding steroid administration were collected from 30 days before the first cycle of ICI (C1) until disease progression or death, including the type of steroids, clinical indication, and daily dose in mg/kg of prednisone (using the following dose conversion: 1 mg of dexamethasone = 6.67 mg of prednisone; 1 mg of metilprednisolone = 1.25 mg of prednisone; 1 mg of hydrocortisone = 0.25 mg of prednisone; 1 mg of deflazacort = 0.67 mg of prednisone). Patients without detailed information regarding steroid dosage were excluded from the study. For each patient, we calculated the total steroid dose within different periods of time: 30 days before C1 (-30D), 30 days after C1 (D1-30), 90 days after C1 (D1-90), and after 6 months from C1 (>6m) in those without disease progression (irrespective of previous steroids exposure within the first 6 months). b. Statistical analysis Statistical analysis was performed using the STATA software. The distribution of qualitative variables was summarized using frequencies and percentages, and the associations between them were evaluated using Fisher’s exact or chi-square tests. The distribution of each continuous variable was summarized using mean and standard deviation. Analysis of variance (ANOVA) was used to determine the association between quantitative variables. Survival outcomes were estimated using Kaplan-Meier curves, and the log-rank test was used for multivariable logistic regression analysis. Statistical significance was set at P values <0.05. All statistical evaluations were two sided. 3. RESULTS a. General characteristics A total of 475 patients with advanced solid tumors were included in the analysis (66.7% male), with a median age of 67.5 years. The tumor subtype distribution was as follows: 161 patients with non-small cell lung cancer (NSCLC) (33.9%), 82 with urothelial cancer (17.3%), 65 with renal cancer (13.7%), 53 with melanoma (11.2%), 52 with head and neck squamous tumors (11.0%), 15 with gastrointestinal tumors (3.2%), 11 with gynecological tumors (2.3%), 6 with anal canal carcinoma (1.3%), 5 with malignant mesothelioma (1.1%), 5 with skin epidermoid tumors (1.1%), and 18 with other tumors (3.8%). Thirty-four patients (7.2%) with a previous diagnosis of autoimmune disease and thirteen patients (2.7%) with a controlled HIV infection were included. Regarding the type of ICI, 306 patients (64.4%) were treated with anti-PD-1 monotherapy (161 pembrolizumab, 136 nivolumab, 8 cemiplimab, and 1 spartalizumab), 97 patients (20.4%) received anti-PD-L1 monotherapy (81 atezolizumab, 14 avelumab, and 2 durvalumab), 63 patients (13.3%) were treated with combined anti-PD-1 + anti-CTLA-4 (nivolumab plus ipilimumab), and 9 patients (1.9%) received anti-CTLA-4 (ipilimumab) as monotherapy. A total of 200 patients (42.1%) received ICI as the 1 st line of treatment, 205 patients (43.2%) as 2 nd line, and 70 patients (14.7%) in 3 rd or further lines. The baseline ECOG PS was 0 in 147 patients (31.0%), 1 in 254 patients (53.6%), and ≥2 in 73 patients (15.4%). Immune-related adverse events (irAEs) were diagnosed in 157 patients (33.1%), with grade 3-4 events in 55 (11.6%) and one toxic death (0.2%) (pembrolizumab-related Stevens-Johnson syndrome). The most frequent irAEs were colitis (7.6%), hypothyroidism (7.0%), cutaneous toxicity (6.3%), nephritis (4.4%) and hepatitis (3.4%). The baseline neutrophil-to-lymphocyte (N/L) ratio was >4 in 197 patients (42.7%). Steroid use was documented in 229 patients (48.2%). 78 patients received steroids within 30 days prior to C1 of ICI (16.4%), mainly due to cancer-related symptoms (71.8%). Dexamethasone was the most frequently administered drug (73.1%). After ICI initiation, 105 patients (22.1%) received steroids for immune-related toxicity, and 159 patients (33.5%) received steroids for other reasons. In total, 155 patients received steroids within 90 days after C1 (32.6%). Among the 182 patients (38.3%) who remained with responsive or stable disease at 6 months, 79 (43.4%) received steroids after 6 months from C1. The baseline characteristics of the patients, divided into groups based on steroid use during different periods of time, are summarized in Table 1 . Table 1 . Baseline characteristics, patients were grouped according to steroid use over different periods of time. ST: steroids; ICI: immune checkpoint inhibitor; -30D: 30 days before the first cycle of ICI (C1); D1-90: 90 days after C1; >6m: after 6 months from C1 (in those without disease progression at 6 months). Clinical characteristics Total ( n: 475) No ST in -30D ( n: 393) ST in -30D ( n: 82) No ST in D1-90 ( n: 320) ST in D1-90 ( n: 155) No ST >6m ( n: 103) ST >6m ( n: 79) Sex Male 317 (66.7%) 262 (66.7%) 55 (67.1%) 218 (68.1%) 99 (63.9%) 73 (70.9%) 54 (68.3%) Female 158 (33.3%) 121 (33.3%) 27 (32.9%) 102 (31.9%) 56 (36.1%) 30 (29.1%) 25 (31.7%) Type of tumor NSCLC 161 (33.9%) 125 (31.8%) 36 (43.9%) 98 (30.6%) 63 (40.6%) 36 (35.0%) 33 (41.8%) Urothelial 82 (17.2%) 71 (18.1%) 11 (13.4%) 65 (20.3%) 17 (11.0%) 11 (10.7%) 10 (12.7%) Renal 65 (13.7%) 58 (14.8%) 7 (8.5%) 45 (14.1%) 20 (12.9%) 15 (14.5%) 15 (19.0%) Melanoma 53 (11.2%) 39 (9.9%) 14 (17.1%) 28 (8.8%) 25 (16.1%) 9 (8.8%) 6 (7.6%) Head/neck 52 (10.9%) 45 (11.5%) 7 (8.6%) 37 (11.6%) 15 (9.7%) 15 (14.6%) 9 (11.4%) GI 15 (3.2%) 13 (3.3%) 2 (2.4%) 10 (3.1%) 5 (3.2%) 5 (4.8%) 1 (1.2%) Others 47 (9.9%) 42 (10.6%) 5 (6.1%) 37 (11.5%) 10 (6.5%) 12 (11.6%) 5 (6.3%) Type of ICI Anti-PD-1 306 (64.4%) 259 (65.9%) 47 (57.3%) 213 (66.6%) 93 (60.0%) 73 (70.9%) 58 (73.4%) Anti-PD-L1 97 (20.4%) 82 (20.9%) 15 (18.3%) 71 (22.2%) 26 (16.8%) 13 (12.6%) 12 (15.2%) Anti-PD-1 + anti-CTLA-4 63 (13.3%) 44 (11.2%) 19 (23.2%) 30 (9.4%) 33 (21.3%) 16 (15.5%) 9 (11.4%) Anti-CTLA-4 9 (1.9%) 8 (2.0%) 1 (1.2%) 6 (1.8%) 3 (1.9%) 1 (1.0%) 0 (0%) Line of treatment 1 st line 200 (42.1%) 170 (43.3%) 30 (36.6%) 137 (42.8%) 63 (40.1%) 58 (56.3%) 37 (46.9%) 2 nd line 205 (43.2%) 166 (42.2%) 39 (47.6%) 139 (43.4%) 66 (42.6%) 33 (32.0%) 34 (43.0%) 3 rd line or further 70 (14.7%) 57 (14.5%) 13 (15.8%) 44 (13.8%) 26 (17.3%) 12 (11.7%) 8 (10.1%) ECOG PS prior to ICI 0 147 (31.0%) 131 (33.3%) 16 (19.8%) 112 (35.0%) 35 (22.7%) 47 (45.6%) 31 (39.2%) 1 254 (53.6%) 215 (54.7%) 39 (48.1%) 169 (52.8%) 85 (55.2%) 47 (45.6%) 45 (47.0%) 2 66 (13.9%) 43 (11.0%) 23 (28.4%) 36 (11.3%) 30 (19.5%) 8 (7.8%) 3 (3.8%) 3 or 4 7 (1.5%) 4 (1.0%) 3 (3.7%) 3 (0.9%) 4 (2.6%) 1 (1.0%) 0 (0%) Disease burden prior to ICI Metastases ≥3 organs 156 (33.5%) 111 (28.8%) 45 (55.6%) 90 (28.9%) 66 (42.6%) 26 (26.3%) 19 (24.7%) Brain metastases 74 (15.6%) 38 (9.7%) 36 (43.9%) 28 (8.8%) 46 (29.7%) 11 (10.8%) 14 (17.7%) Liver metastases 120 (25.3%) 98 (25.0%) 22 (26.8%) 78 (24.5%) 42 (27.1%) 19 (18.6%) 15 (19.0%) Lung metastases 216 (45.6%) 173 (44.1%) 43 (52.4%) 139 (43.6%) 77 (49.7%) 44 (43.1%) 32 (40.5%) Bone metastases 133 (28.1%) 101 (25.8%) 32 (39.0%) 78 (24.5%) 55 (35.5%) 23 (22.6%) 15 (19.0%) b. Impact of steroids on clinical outcomes In our cohort, exposure to steroids around ICI initiation was correlated with remarkably worse clinical outcomes. Compared to steroid-naïve patients, those exposed to steroids within 30 days before C1 had significantly lower ORR (20.3% vs. 36.7%; p <0.01) and DCR (29.1% vs. 52.1%; p <0.001) (Fig. 1A) . Similar results were observed for the use of steroids within 30 days after C1, with a significant decrease in ORR (21.1% vs. 37.78%; p <0.01) and DCR (27.5% vs. 54.6%; p <0.001) (Fig. 1B) , and 90 days after C1, with a significantly lower ORR (25.7% vs. 37.7%; p =0.011) and DCR (36.5% vs. 53.7%; p 6m) did not seem to have worse outcomes, and there was a non-significant trend for longer PFS when compared to patients without late steroid exposure (median PFS 23.2 vs. 17.6 months; hazard ratio [HR]: 0.68; p =0.065). c. Impact of steroids dosage When patients were divided into quartiles (Q) based on the total steroid dose received within 30 days before C1 (-30D), an inverse correlation between the cumulative dosage and clinical benefit was observed. The dose upper limit for each group was 2.36 mg/kg/30d (Q1), 4.05 mg/kg/30d (Q2), 7.94 mg/kg/30d (Q3) and 40.73 mg/kg/30d (Q4). The ORR for each group was: 36.7% (no steroids), 25% (Q1), 20% (Q2), 15.8% (Q3), and 20% (Q4) ( p =0.093); the DCR for each group was: 52.1% (no steroids), 45% (Q1), 30% (Q2), 15.8% (Q3) and 25% (Q4) ( p =0.001) (Fig. 2A) . The mean cumulative dose of steroids was significantly higher in non-responders than in responders to ICI (1.59 mg/kg/30d [95% CI 1.02-2.16] vs. 0.58 mg/kg/30d [95% CI 0.22-0.94]; p =0.0175). Similar results were obtained for the cumulative dose within 30 days after C1 (D1-30) ( Fig. 2B ). The dose upper limit for each group was 2.9 mg/kg/30d (Q1), 6.15 mg/kg/30d (Q2), 11.41 mg/kg/30d (Q3), and 48.08 mg/kg/30d (Q4). The ORR for each group was 37.8% (no steroids), 29.6% (Q1), 21.4% (Q2), 11.1% (Q3) and 22.2% (Q4) ( p =0.012). The DCR for each group was 54.6% (no steroids), 29.6% (Q1), 21.4% (Q2), 22.2% (Q3), and 37.0% (Q4) ( p <0.001) (Fig. 2B) . The mean cumulative dose was also higher in non-responders to ICI (2.48 mg/kg/30d [95% CI 1.81-3.14] vs. 1.23 mg/kg/30d [95% CI 0.44-2.01] in responders; p =0.024). No clear correlation was found between the cumulative steroid dose within 90 days after C1 (D1-90) and clinical outcomes (Fig. 2C) , and the difference between the mean cumulative doses for responders and non-responders was not statistically significant ( p =0.33). When the impact of steroid dosage after 6 months from C1 on survival outcomes was analyzed, the correlation seemed to reverse, with significantly higher PFS for those patients receiving higher doses: 17.6 months (no ST), 17.3 months (Q1), 37.1 months (Q2), 19.9 months (Q3), and 42.1 months (Q4) (HR: 0.86, 95% CI 0.75-0.99; p =0.039). d. Association between irAEs and clinical outcomes The presence of immune-related toxicity was correlated with a higher ORR (46.6% vs. 27.8%; p <0.001), longer PFS (11.6 vs. 3.6 months; HR: 0.59; p <0.001) and longer OS (20.7 vs 8.0 months; HR: 0.63; p <0.001). The association between irAEs and survival outcomes was confirmed by multivariable analysis including PS, tumor type, disease burden, and line of treatment ( p <0.001). e. Impact of the neutrophil-to-lymphocyte ratio In our cohort, the baseline neutrophil-to-lymphocyte (N/L) ratio was positively correlated with irAEs and negatively correlated with clinical outcomes. A N/L ratio >4 was led to a significantly lower DCR (39.6% vs. 54.6%; p =0.001) and incidence of irAEs (26.4% vs. 39.5%; p 4 was significantly higher among those exposed to steroids within 30 days before ICI initiation (67.1% vs. 37.7%; p <0.0001). 4. DISCUSSION a. The relevance of timing Our results are consistent with those of previous studies on the negative clinical impact of early steroid exposure around ICI initiation. A meta-analysis of 15 retrospective studies, including patients with brain metastases ( n : 1102), showed significantly worse PFS (HR: 2.00, p <0.01) and OS (HR: 1.84, p <0.01) for those exposed to steroids 18 . In a retrospective study of 151 patients with metastatic NSCLC, the early use of steroids (within 28 days after ICI initiation) was correlated with worse DCR (odds ratio [OR]: 0.32; p =0.006), PFS (HR: 1.8; p =0.003) and OS (HR: 2.6; p <0.001) 19 . In our cohort, the negative impact of steroids also seemed to prevail around ICI initiation, with a significant decrease in the ORR and DCR among patients exposed between -30D and D90 from C1. However, this effect was not observed for steroid exposure after 6 months in long-term responders, with a tendency for longer PFS when compared to the steroid-naïve group. Since early exposure to steroids (-30D to D90) is mainly due to cancer-related conditions, some of which (as symptomatic brain metastases) inherently imply poor prognosis, and late exposure in responding patients (>6m) is mostly due to irAEs, which are in contrast correlated with better outcomes, there might be a potential indication bias in the observed time-dependent effect of steroid exposure. There is solid evidence regarding the correlation between irAEs and favorable outcomes in patients treated with ICI. In a meta-analysis of 51 studies, the development of irAEs was associated with better survival outcomes among patients with metastatic melanoma (OS HR: 0.46, p <0.0001; PFS HR: 0.51, p <0.00001) and advanced NSCLC (OS HR: 0.40, p <0.00001; PFS HR: 0.46, p <0.00001) 21 . Similar results were obtained by Zhou et al. 22 in a meta-analysis of 30 studies including 4971 patients, with a significant benefit in OS (HR: 0.54; p <0.001) and PFS (HR: 0.2; p <0.001) for those who developed irAEs, particularly low-grade endocrine and cutaneous reactions. In addition, some studies have suggested a negative impact of steroids only when indicated for cancer-related events, but not for the management of irAEs. A systematic review and meta-analysis of 16 studies ( n = 4045) showed an increased risk of death and progression among patients treated with ICI receiving steroids for supportive care (HR: 2.5; 95% CI 1.41–4.43) or brain metastases (HR: 1.51; 95% CI 1.22–1.87), but not among those with irAEs 23 . Similar results have been obtained in other studies that analyzed large cohorts of patients treated with ICI for advanced NSCLC 24 , 25 . However, Maslov et al. 26 evaluated the outcomes of 247 patients treated with ICI and concurrently exposed to steroids, analyzing the effect of steroid timing, and reported significantly longer PFS for those exposed to steroids within the first 2 months after ICI initiation (HR: 0.30, p <0.001), irrespective of the clinical indication. The median PFS was significantly longer when steroids were prescribed after 2 months from C1, both in the group with irAEs (HR 0.33; p <0.0001) and in the group treated with steroids for other reasons (HR 0.27; p <0.0001). Although a potential skew related to steroid indication cannot be completely dismissed, these results suggest that steroid biological effects might be intrinsically time-dependent, probably more relevant at impairing the achievement of a successful T cell antitumor response than at inducing loss of clinical benefit in responders, thus prevailing around ICI initiation, irrespective of indication. b. The relevance of dosage Although the impact of steroid use on ICI outcomes has been assessed in several studies, the influence of dosage has not been previously analyzed in detail. In our cohort, an inverse correlation was found between cumulative doses and clinical outcomes, with higher doses leading to lower ORR and DCR, both within 30 days before and 30 days after C1. In addition, significantly higher doses around ICI initiation were registered among non-responders, supporting the hypothesis of a dose-dependent effect. Interestingly, even low doses of steroids seemed to lead to worse DCR compared with no steroid exposure. Most ICI clinical trials have excluded patients with baseline daily doses ≥10 mg prednisone, usually considering doses below that threshold as physiological. However, in our cohort, patients from the first quartile of baseline steroid dose (<2.36 mg/kg/30d, nearly half the cumulative dose of a 70 kg patient receiving 10 mg prednisone per day) had significantly worse DCR than steroid-naïve patients, questioning the assumption that there is a ‘safe’ dose of steroids prior to initiation of immunotherapy. After the first month of treatment, this effect seemed to attenuate (no significant differences were observed when steroids in D1-90 were analyzed together) and eventually became inverted, since higher doses after 6 months from C1 were significantly associated with longer PFS. Again, the better outcomes of patients with late exposure to high-dose steroids could be explained by the higher incidence of irAEs in this subgroup of long-term responders, provided the established correlation between irAEs and clinical benefit. c. The relevance of immune cells profile The neutrophil-to-lymphocyte (N/L) ratio seems to predict clinical response and immune-related toxicity in patients treated with ICI. In a retrospective study of 1714 patients with 16 different types of solid tumors, Valero et al. 27 found a significant association between a higher N/L ratio and poorer PFS and response rates. Combining the N/L ratio and TMB, the benefit of ICI was significantly higher in the N/L-low/TMB-high group than in the N/L-high/TMB-low group (OR: 3.22; p <0.001). Xie et al. 28 published a meta-analysis of 14 studies incorporating 1751 participants, showing that elevated pre-treatment N/L ratio was associated with poorer OS (HR: 2.61; p <0.001) and PFS (HR: 1.74; p <0.0001). In a meta-analysis of 7 published articles on the utility of the baseline N/L ratio, Sacdalan et al. 29 found worse OS (HR: 1.92; p =0.001) and PFS (HR: 1.66; p <0.0001) among patients with higher N/L ratios across several tumor types. Similar results were obtained by Takenaka et al. 30 in a meta-analysis of 14 studies with 929 patients with head and neck tumors, as well as individual studies analyzing large cohorts of patients with advanced NSCLC 31 , 32 , 38 , renal cell carcinoma 33 , 34 , pancreatic cancer 35 and upper gastro-intestinal cancer 36 , 37 , among others. Exposure to steroids appears to modulate the N/L ratio during ICI treatment. Fucà et al. 19 found a correlation between steroids use and a higher median N/L ratio, both 4 and 6 weeks after C1, hypothesizing that steroids may hinder antitumor response by modulating peripheral blood immune cells. The effect of steroids may be particularly detrimental in patients with a lower N/L ratio. Lauko et al. 40 studied 171 patients with brain metastases from NSCLC, reporting decreased OS (10.5 vs. 17.9 months; p =0.03) and intracranial PFS (5.0 vs. 8.7 months; p =0.045) in those with upfront steroids; interestingly, OS differences were only significant in the subgroup of patients with a baseline N/L ratio <4, and there was a strong interaction between the N/L ratio and upfront steroids when modeled together ( p =0.0008). Our study is the first to confirm that steroid use not only modulates the N/L ratio throughout treatment, but also can remarkably increase the pre-treatment N/L ratio when administered before ICI initiation. A reasonable potential critique of this study is the reflection that the baseline steroid requirement could be driven by intrinsically tumor-related bad-prognosis scenarios, such as symptomatic brain metastases, with steroid use being the consequence and not the cause of worse clinical results. The demonstration that they modulate the profile of blood immune cells, whose correlation with ICI outcomes has been thoroughly proven, strongly suggests that there is indeed a biological basis supporting the steroid-induced impairment of ICI efficacy. 5. CONCLUSIONS Our study is consistent with the proven negative effect of early exposure to steroids on the outcomes of immune checkpoint inhibitors as a consequence of their diverse immunosuppressive properties, leading to a significant decrease in the response and disease control rates. Their detrimental effect seemed to prevail around ICI initiation (at least from − 30D to D90) and was not observed in our cohort after 6 months of treatment. This finding might be biased by the positive prognostic impact driven by immune-related toxicity, which mostly explains the late exposure to steroids in long-term responders, as opposed to cancer-related complications leading to their early use around ICI initiation, although previous clinical evidence has suggested an intrinsically time-dependent biological effect. Although the influence of steroid dose has not been previously analyzed, our study suggests an inverse correlation between DCR and cumulative dose around ICI initiation. The finding of significantly higher cumulative doses among non-responders supports the dose-dependent effect hypothesis. In addition, the remarkable drop in the DCR observed in the lowest-dose quartile of patients from our cohort suggests that even the usually considered as ‘physiological’ low doses of steroids might interfere with ICI efficacy. Interestingly, steroid use within 30 days before ICI initiation correlated with higher baseline neutrophil-to-lymphocyte ratios, which led to significantly poorer clinical outcomes. As an established biomarker for response to ICI, the demonstration that the N/L ratio is modulated by steroids challenges the idea of steroid exposure as a mere bystander in the natural history of worse-prognosis tumors, providing a clue to understanding the biological basis of their detrimental influence on ICI outcomes. Further research and prospective validation of these results would be of great value to better understand the effects of steroids and optimize their use in patients undergoing treatment with immunotherapy. Authors contribution : VA (Albarrán): study conception and design, data collection, analysis and interpretation of results, and manuscript preparation; PG, CGQ, CG, JC, DIR, JM, JCC, PPA, VA (Alía), PS, AMB, MSR, PAB: data collection; JJS: analysis and interpretation of results; AS, MEO, CS, AC, AG, YL, AR, MRF, FL, PG (Garrido): treating oncologists, review and editing of the manuscript; PG (Gajate): study conception, analysis and interpretation of results, review of the manuscript and supervision of the work. Abbreviations ICI immune checkpoint inhibitors irAEs immune-related adverse events ORR objective response rate DCR disease control rate PFS progression-free survival OS overall survival C1 first cycle N/L ratio neutrophil-to-lymphocyte ratio NK natural killer TAA tumor-associated antigens APC antigen presenting cells TCR T cell receptor MHC major histocompatibility complex CTLA-4 cytotoxic T lymphocyte associated protein 4 LAG-3 lymphocyte activation gene 3 TIM-3, T cell immunoglobulin domain and mucin domain 3 TILs tumor-infiltrating lymphocytes PD-1 programmed cell death protein 1 PD-L1 programmed cell death protein ligand 1 TME tumor microenvironment TMB tumor mutational burden MDSCs myeloid-derived suppressor cells TAMs tumor-associated macrophages T-reg regulatory T cells GR glucocorticoid receptor TGF-β tumor growth factorβ RWD real-world data CEIC Clinical Investigation Ethical Committee HIV human immunodeficiency virus ECOG Eastern Cooperative Oncology Group PS performance status -30D 30 days before C1 D1-30 30 days after C1 D1-90 90 days after C1 >6m after 6 months from C1 NSCLC non-small cell lung cancer ST steroids HR hazard ratio CI confidence interval Q quartile CD cumulative doses. Declarations Authors contribution : VA (Albarrán): study conception and design, data collection, analysis and interpretation of results, and manuscript preparation; PG, CGQ, CG, JC, DIR, JM, JCC, PPA, VA (Alía), PS, AMB, MSR, PAB: data collection; JJS: analysis and interpretation of results; AS, MEO, CS, AC, AG, YL, AR, MRF, FL, PG (Garrido): treating oncologists, review and editing of the manuscript; PG (Gajate): study conception, analysis and interpretation of results, review of the manuscript and supervision of the work. Funding : this research received no external funding. Competing interests : CS declares speakers’ fees from Novartis and AstraZeneca. AC declares speakers’ and advisory fees from GlaxoSmithKline, AstraZeneca, PharmaMar, Daiichi Sankyo, MSD, Eisai and Accord Healthcare. PG (Garrido) declares speakers’ fees from Janssen, MSD, Novartis, Medscape, Takeda, TouchTime and Medscape and advisory fees from Abbvie, Amgen, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Daiichi Sankyo, GlaxoSmithKline, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi and Takeda. PG (Gajate) declares advisory fees from BMS, Roche, Pfizer, Ipsen, MSD, Merck, Janssen, Astellas, Eisai and Novartis. The other authors declare no relevant conflicts of interest. Data availability statement : the original data that support the findings of this study are available upon request. References Chen DS, Mellman I (2013) Oncology Meets Immunology: The Cancer-Immunity Cycle. Immunity 39(1):1–10 Dustin ML (2014) The immunological synapse. Cancer Immunol Res 2(11):1023–1033 Chen L, Flies DB (2013) Molecular mechanisms of T cell co-stimulation and co-inhibition. 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Anticancer Res 42(6):2977–2987 Guo J-C, Lin C-C, Lin C-Y, Hsieh M-S, Kuo H-Y, Lien M-Y et al (2019) Neutrophil–to–lymphocyte Ratio and Use of Antibiotics Associated With Prognosis in Esophageal Squamous Cell Carcinoma Patients Receiving Immune Checkpoint Inhibitors. Anticancer Res 39(10):5675–5682 Pavan A, Calvetti L, Dal Maso A, Attili I, Del Bianco P, Pasello G et al (2019) Peripheral Blood Markers Identify Risk of Immune-Related Toxicity in Advanced Non-Small Cell Lung Cancer Treated with Immune-Checkpoint Inhibitors. Oncologist 24(8):1128–1136 Ruste V, Goldschmidt V, Laparra A, Messayke S, Danlos F-X, Romano-Martin P et al (2021) The determinants of very severe immune-related adverse events associated with immune checkpoint inhibitors: A prospective study of the French REISAMIC registry. Eur J Cancer 158:217–224 Lauko A, Thapa B, Sharma M, Muhsen B, Barnett A, Rauf Y et al (2021) Neutrophil-to-lymphocyte ratio influences impact of steroids on efficacy of immune checkpoint inhibitors in lung cancer brain metastases. Sci Rep 11(1):7490 Additional Declarations Competing interest reported. CS declares speakers’ fees from Novartis and AstraZeneca. AC declares speakers’ and advisory fees from GlaxoSmithKline, AstraZeneca, PharmaMar, Daiichi Sankyo, MSD, Eisai and Accord Healthcare. PG (Garrido) declares speakers’ fees from Janssen, MSD, Novartis, Medscape, Takeda, TouchTime and Medscape and advisory fees from Abbvie, Amgen, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Daiichi Sankyo, GlaxoSmithKline, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi and Takeda. PG (Gajate) declares advisory fees from BMS, Roche, Pfizer, Ipsen, MSD, Merck, Janssen, Astellas, Eisai and Novartis. The other authors declare no relevant conflicts of interest. Cite Share Download PDF Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Revision requested 06 May, 2024 Reviews received at journal 20 Apr, 2024 Reviewers agreed at journal 10 Apr, 2024 Reviewers agreed at journal 05 Apr, 2024 Reviewers invited by journal 30 Mar, 2024 Submission checks completed at journal 26 Mar, 2024 Editor assigned by journal 26 Mar, 2024 First submitted to journal 24 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4159119","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283900264,"identity":"70d4d895-892b-4c09-992c-f448a3121acf","order_by":0,"name":"Víctor 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A significant decrease in the objective response rate (ORR) and disease control rate (DCR) was observed in patients exposed steroids within the 30 days before (A), 30 days after (B) and 90 days after ICI initiation (C). For each value, horizontal bars represent the upper and lower limits of 95% confidence intervals (CI).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4159119/v1/bcc2b014acaeb3923ec642bc.png"},{"id":53756629,"identity":"692813a9-5094-4419-8ebc-ffb03d7d24c2","added_by":"auto","created_at":"2024-03-29 19:04:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95811,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eImpact of steroid cumulative doses (CD) on clinical outcomes. A negative correlation between DCR and CD was found both within 30 days before (A) and 30 days after C1 (B), but no statistical differences were observed when considering CD within 90 days after C1 (C).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4159119/v1/5f2a16552ffae570b61af467.png"},{"id":61793410,"identity":"0fc62c0a-0487-45f6-a769-0dee71213691","added_by":"auto","created_at":"2024-08-05 16:12:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":931713,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4159119/v1/158e8c15-e907-483f-adcc-88d2c6ac5bc3.pdf"}],"financialInterests":"Competing interest reported. CS declares speakers’ fees from Novartis and AstraZeneca. AC declares speakers’ and advisory fees from GlaxoSmithKline, AstraZeneca, PharmaMar, Daiichi Sankyo, MSD, Eisai and Accord Healthcare. PG (Garrido) declares speakers’ fees from Janssen, MSD, Novartis, Medscape, Takeda, TouchTime and Medscape and advisory fees from Abbvie, Amgen, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Daiichi Sankyo, GlaxoSmithKline, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi and Takeda. PG (Gajate) declares advisory fees from BMS, Roche, Pfizer, Ipsen, MSD, Merck, Janssen, Astellas, Eisai and Novartis. The other authors declare no relevant conflicts of interest.","formattedTitle":"Negative impact of steroids on the efficacy of immunotherapy in a multi- tumor cohort: time and dose-dependent","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eThis study confirms the detrimental impact of steroids on the efficacy of immunotherapy in a large multi-tumor cohort.\u003c/li\u003e\n \u003cli\u003eOur data suggest a time-dependent effect, with a negative impact on response rates, PFS and OS around ICI initiation.\u003c/li\u003e\n \u003cli\u003eThis is the first study to show that steroids impact on immunotherapy outcomes may be dose-dependent.\u003c/li\u003e\n \u003cli\u003eOur results suggest a modulation of neutrophil-to-lymphocyte ratio by steroid exposure as a potential underlying mechanism.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe activation and migration of T lymphocytes plays a key role in the immune response against solid tumors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Necrosis of malignant cells following the attack of macrophages and natural killer (NK) cells, main components of our innate immunity, leads to the release of tumor-associated antigens (TAA), which are phagocytosed by dendritic and other antigen-presenting cells (APC). In lymph nodes, these antigens are presented to na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T cells through the interaction between T cell receptors (TCR) and major histocompatibility complex class I (MHC-I) molecules on the APC surface, unleashing adaptive immunity. This process requires co-stimulatory signals, such as the coupling between co-receptor B7 (CD80) and CD28, and is downregulated by competitive co-receptors on the T cell membrane (CTLA-4, LAG-3, TIM-3), which induce negative \u003cem\u003efeedback\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Following T cell activation, a shift in membrane protein expression promotes their trafficking to the tumor site, giving rise to a population of tumor-infiltrating lymphocytes (TILs) capable of recognizing TAA on the surface of tumor cells, thus inducing a cytotoxic cascade. As in the priming phase, this process is downregulated by inhibitory T-cell co-receptors such as programmed cell death receptor 1 (PD-1), which is activated by PD-1 ligand PD-L1 and other immunosuppressive molecules released by tumor cells and cells from the tumor microenvironment (TME). Tumor immune escape through pathological upregulation of these checkpoints provides a rationale for the use of immune checkpoint inhibitors (ICI)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eICI have changed the therapeutic landscape of many solid tumors. However, huge variability in immunogenicity and responsiveness to treatment has been observed among different patients. This is a highly complex phenomenon influenced by multiple biomarkers, including tumor mutational burden (TMB)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, PD-L1 expression\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, quantity and characteristics of infiltrating T lymphocytes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, B cells phenotype\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and the interaction between the immune system and the TME\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, among others. The presence of immunosuppressive cells in the TME, such as myeloid-derived suppressor cells (MDSCs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e), tumor-associated macrophages (TAMs\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e) and regulatory T cells (T-reg\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e), strongly interferes with the efficacy of the anti-tumor response, leading to immune-resistant \u0026lsquo;cold\u0026rsquo; tumors and promoting tumor progression.\u003c/p\u003e \u003cp\u003eSteroids have anti-inflammatory and immunosuppressive effects, which imply both innate and adaptive immunity. Glucocorticoid receptor (GR) activation alters the transcription of numerous genes encoding pro-inflammatory cytokines (NF-kB, AP-1) and genes related to T-cell survival, trafficking, and cytotoxic activity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Steroids have been proven to reduce immune cell infiltration\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, facilitate MDSCs expansion\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, promote T-reg proliferation, and enhance the production of T-reg-activating cytokines (such as TGF-β)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Increased GR signaling has been correlated with CD8\u0026thinsp;+\u0026thinsp;TILs dysfunction and upregulation of several immune checkpoints\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSteroids are frequently prescribed for patients with advanced solid tumors owing to a wide range of clinical scenarios, including symptomatic brain metastasis, chemotherapy-induced emesis, cancer-related complications, and additional chronic comorbidities. In addition, except for endocrine toxicity, steroids are the mainstay of management for grade\u0026thinsp;\u0026ge;\u0026thinsp;2 immune-related adverse events (irAEs), usually starting at 0.5-2 mg/kg/day of prednisone until clinical improvement, and subsequent slow tapering over 4\u0026ndash;6 weeks\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Given their immunosuppressive properties, there is growing concern regarding their detrimental effects on the clinical outcomes of immunotherapy.\u003c/p\u003e \u003cp\u003eAlthough the negative impact of steroid exposure has been shown in several studies, how the dosage and time of administration modulate this effect is yet to be clarified. A better understanding of these aspects would be of great value in optimizing steroid use in oncological patients. In this real-world data (RWD) study, we aimed to collect detailed information on the dose and timing of steroid administration in a multi-tumor cohort of patients treated with ICI and to evaluate their impact on clinical outcomes.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003e\u003cstrong\u003ea. \u0026nbsp; Study design and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a retrospective single-center study performed after approval from the Clinical Investigation Ethical Committee (CEIC) of our institution (Ramon y Cajal University Hospital, Madrid), including adult patients (\u0026gt;18 years old) with metastatic and/or unresectable solid tumors who received systemic treatment with ICI between April 2015 and October 2022. Patients who received ICI in the context of a clinical trial, as well as those treated with combinations of ICI with chemotherapy, tyrosine-kinase inhibitors, or any other anticancer agents, were excluded from the analysis. In accordance with the CEIC, verbal informed consent was obtained from all patients. Clinical data were extracted from pharmacy databases and electronic institutional medical files.\u003c/p\u003e\n\u003cp\u003eFor the selected patients, we obtained data regarding their demographics, previous medical history (including autoimmune disease or human immunodeficiency virus [HIV] infection), oncologic history (tumor histology, date of diagnosis, cancer stage according to the American Joint Committee on Cancer [AJCC] staging system, disease burden, previous treatments), performance status (PS) according to the Eastern Cooperative Oncology Group (ECOG) score\u003csup\u003e17\u003c/sup\u003e, treatment with ICI (including type of ICI, treatment duration, and immune-related toxicity) and laboratory data, including neutrophil-to-lymphocyte (N/L) ratio. We defined the date of progression as either that of radiological (according to RECIST criteria V.1.1) or clinical evidence of progressive disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each patient, data regarding steroid administration were collected from 30 days before the first cycle of ICI (C1) until disease progression or death, including the type of steroids, clinical indication, and daily dose in mg/kg of prednisone (using the following dose conversion: 1 mg of dexamethasone = 6.67 mg of prednisone; 1 mg of metilprednisolone = 1.25 mg of prednisone; 1 mg of hydrocortisone = 0.25 mg of prednisone; 1 mg of deflazacort = 0.67 mg of prednisone). Patients without detailed information regarding steroid dosage were excluded from the study. For each patient, we calculated the total steroid dose within different periods of time: 30 days before C1 (-30D), 30 days after C1 (D1-30), 90 days after C1 (D1-90), and after 6 months from C1 (\u0026gt;6m) in those without disease progression (irrespective of previous steroids exposure within the first 6 months).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. \u0026nbsp; Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using the STATA software. The distribution of qualitative variables was summarized using frequencies and percentages, and the associations between them were evaluated using Fisher\u0026rsquo;s exact or chi-square tests. The distribution of each continuous variable was summarized using mean and standard deviation. Analysis of variance (ANOVA) was used to determine the association between quantitative variables. Survival outcomes were estimated using Kaplan-Meier curves, and the log-rank test was used for multivariable logistic regression analysis. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e values \u0026lt;0.05. All statistical evaluations were two sided.\u0026nbsp;\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003ea. \u0026nbsp; General characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 475 patients with advanced solid tumors were included in the analysis (66.7% male), with a median age of 67.5 years. The tumor subtype distribution was as follows: 161 patients with non-small cell lung cancer (NSCLC) (33.9%), 82 with urothelial cancer (17.3%), 65 with renal cancer (13.7%), 53 with melanoma (11.2%), 52 with head and neck squamous tumors (11.0%), 15 with gastrointestinal tumors (3.2%), 11 with gynecological tumors (2.3%), 6 with anal canal carcinoma (1.3%), 5 with malignant mesothelioma (1.1%), 5 with skin epidermoid tumors (1.1%), and 18 with other tumors (3.8%). Thirty-four patients (7.2%) with a previous diagnosis of autoimmune disease and thirteen patients (2.7%) with a controlled HIV infection were included.\u003c/p\u003e\n\u003cp\u003eRegarding the type of ICI, 306 patients (64.4%) were treated with anti-PD-1 monotherapy (161 pembrolizumab, 136 nivolumab, 8 cemiplimab, and 1 spartalizumab), 97 patients (20.4%) received anti-PD-L1 monotherapy (81 atezolizumab, 14 avelumab, and 2 durvalumab), 63 patients (13.3%) were treated with combined anti-PD-1 + anti-CTLA-4 (nivolumab plus ipilimumab), and 9 patients (1.9%) received anti-CTLA-4 (ipilimumab) as monotherapy. A total of 200 patients (42.1%) received ICI as the 1\u003csup\u003est\u003c/sup\u003e line of treatment, 205 patients (43.2%) as 2\u003csup\u003end\u003c/sup\u003e line, and 70 patients (14.7%) in 3\u003csup\u003erd\u003c/sup\u003e or further lines. The baseline ECOG PS was 0 in 147 patients (31.0%), 1 in 254 patients (53.6%), and \u0026ge;2 in 73 patients (15.4%). Immune-related adverse events (irAEs) were diagnosed in 157 patients (33.1%), with grade 3-4 events in 55 (11.6%) and one toxic death (0.2%) (pembrolizumab-related Stevens-Johnson syndrome). The most frequent irAEs were colitis (7.6%), hypothyroidism (7.0%), cutaneous toxicity (6.3%), nephritis (4.4%) and hepatitis (3.4%). The baseline neutrophil-to-lymphocyte (N/L) ratio was \u0026gt;4 in 197 patients (42.7%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSteroid use was documented in 229 patients (48.2%). 78 patients received steroids within 30 days prior to C1 of ICI (16.4%), mainly due to cancer-related symptoms (71.8%). Dexamethasone was the most frequently administered drug (73.1%). After ICI initiation, 105 patients (22.1%) received steroids for immune-related toxicity, and 159 patients (33.5%) received steroids for other reasons. In total, 155 patients received steroids within 90 days after C1 (32.6%). Among the 182 patients (38.3%) who remained with responsive or stable disease at 6 months, 79 (43.4%) received steroids after 6 months from C1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of the patients, divided into groups based on steroid use during different periods of time, are summarized in \u003cem\u003eTable 1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e. Baseline characteristics, patients were grouped according to steroid use over different periods of time. ST: steroids; ICI: immune checkpoint inhibitor; -30D: 30 days before the first cycle of ICI (C1); D1-90: 90 days after C1; \u0026gt;6m: after 6 months from C1 (in those without disease progression at 6 months).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97222222222222%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003echaracteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en:\u0026nbsp;\u003c/em\u003e475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.194444444444445%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo ST in -30D\u0026nbsp;\u003c/strong\u003e(\u003cem\u003en:\u0026nbsp;\u003c/em\u003e393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.555555555555555%\"\u003e\n \u003cp\u003e\u003cstrong\u003eST in -30D\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en:\u0026nbsp;\u003c/em\u003e82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo ST in D1-90\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en:\u0026nbsp;\u003c/em\u003e320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.555555555555555%\"\u003e\n \u003cp\u003e\u003cstrong\u003eST in D1-90\u0026nbsp;\u003c/strong\u003e(\u003cem\u003en:\u0026nbsp;\u003c/em\u003e155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.555555555555555%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo ST \u0026gt;6m\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en:\u0026nbsp;\u003c/em\u003e103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.555555555555555%\"\u003e\n \u003cp\u003e\u003cstrong\u003eST \u0026gt;6m\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en:\u0026nbsp;\u003c/em\u003e79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.540915395284328%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e317 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.176144244105409%\"\u003e\n \u003cp\u003e262 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e55 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e218 (68.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e99 (63.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e73 (70.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e54 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e158 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e121 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e27 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e102 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e56 (36.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e30 (29.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e25 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.540915395284328%\" rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of tumor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e161 (33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.176144244105409%\"\u003e\n \u003cp\u003e125 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e36 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e98 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e63 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e36 (35.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e33 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eUrothelial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e82 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e71 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e11 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e65 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e17 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e11 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e10 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eRenal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e65 (13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e58 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e7 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e45 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e20 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e15 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e15 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eMelanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e53 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e39 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e14 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e28 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e25 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e9 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e6 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eHead/neck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e52 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e45 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e7 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e37 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e15 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e15 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e9 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e15 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e13 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e2 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e10 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e5 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e5 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e47 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e42 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e5 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e37 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e10 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e12 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e5 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.540915395284328%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of ICI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003eAnti-PD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e306 (64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.176144244105409%\"\u003e\n \u003cp\u003e259 (65.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e47 (57.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e213 (66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e93 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e73 (70.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e58 (73.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eAnti-PD-L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e97 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e82 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e15 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e71 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e26 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e13 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e12 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eAnti-PD-1 + anti-CTLA-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e63 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e44 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e19 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e30 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e33 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e16 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e9 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eAnti-CTLA-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e9 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e8 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e6 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e3 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e1 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.540915395284328%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eLine of treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e200 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.176144244105409%\"\u003e\n \u003cp\u003e170 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e30 (36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e137 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e63 (40.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e58 (56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e37 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e205 (43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e166 (42.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e39 (47.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e139 (43.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e66 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e33 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e34 (43.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e line or further\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e70 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e57 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e13 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e44 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e26 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e12 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e8 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.540915395284328%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG PS prior to ICI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e147 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.176144244105409%\"\u003e\n \u003cp\u003e131 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e16 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e112 (35.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e35 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e47 (45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e31 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e254 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e215 (54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e39 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e169 (52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e85 (55.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e47 (45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e45 (47.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e66 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e43 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e23 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e36 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e30 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e8 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e3 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e3 or 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e7 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e4 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e3 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e3 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e4 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e1 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.540915395284328%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease burden prior to ICI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003eMetastases \u0026ge;3 organs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e156 (33.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.176144244105409%\"\u003e\n \u003cp\u003e111 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e45 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.789181692094314%\"\u003e\n \u003cp\u003e90 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e66 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e26 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.540915395284328%\"\u003e\n \u003cp\u003e19 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eBrain metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e74 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e38 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e36 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e28 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e46 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e11 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e14 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eLiver metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e120 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e98 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e22 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e78 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e42 (27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e19 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e15 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eLung metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e216 (45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e173 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e43 (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e139 (43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e77 (49.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e44 (43.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e32 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003eBone metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e133 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\n \u003cp\u003e101 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e32 (39.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\"\u003e\n \u003cp\u003e78 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e55 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e23 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.782945736434108%\"\u003e\n \u003cp\u003e15 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. \u0026nbsp; Impact of steroids on clinical outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our cohort, exposure to steroids around ICI initiation was correlated with remarkably worse clinical outcomes. Compared to steroid-na\u0026iuml;ve patients, those exposed to steroids within 30 days before C1 had significantly lower ORR (20.3% vs. 36.7%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01) and DCR (29.1% vs. 52.1%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) \u003cem\u003e(Fig. 1A)\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar results were observed for the use of steroids within 30 days after C1, with a significant decrease in ORR (21.1% vs. 37.78%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01) and DCR (27.5% vs. 54.6%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) \u003cem\u003e(Fig. 1B)\u003c/em\u003e, and 90 days after C1, with a significantly lower ORR (25.7% vs. 37.7%; \u003cem\u003ep\u003c/em\u003e=0.011) and DCR (36.5% vs. 53.7%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01) \u003cem\u003e(Fig. 1C)\u003c/em\u003e. Among patients without evidence of progression at 6 months, those exposed to steroids (\u0026gt;6m) did not seem to have worse outcomes, and there was a non-significant trend for longer PFS when compared to patients without late steroid exposure (median PFS 23.2 vs. 17.6 months; hazard ratio [HR]: 0.68; \u003cem\u003ep\u003c/em\u003e=0.065).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. \u0026nbsp; Impact of steroids dosage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen patients were divided into quartiles (Q) based on the total steroid dose received within 30 days before C1 (-30D), an inverse correlation between the cumulative dosage and clinical benefit was observed. The dose upper limit for each group was 2.36 mg/kg/30d (Q1), 4.05 mg/kg/30d (Q2), 7.94 mg/kg/30d (Q3) and 40.73 mg/kg/30d (Q4). The ORR for each group was: 36.7% (no steroids), 25% (Q1), 20% (Q2), 15.8% (Q3), and 20% (Q4) (\u003cem\u003ep\u003c/em\u003e=0.093); the DCR for each group was: 52.1% (no steroids), 45% (Q1), 30% (Q2), 15.8% (Q3) and 25% (Q4) (\u003cem\u003ep\u003c/em\u003e=0.001) \u003cem\u003e(Fig. 2A)\u003c/em\u003e. The mean cumulative dose of steroids was significantly higher in non-responders than in responders to ICI (1.59 mg/kg/30d [95% CI 1.02-2.16] vs. 0.58 mg/kg/30d [95% CI 0.22-0.94]; \u003cem\u003ep\u003c/em\u003e=0.0175).\u003c/p\u003e\n\u003cp\u003eSimilar results were obtained for the cumulative dose within 30 days after C1 (D1-30) (\u003cem\u003eFig. \u0026nbsp;\u003c/em\u003e\u003cem\u003e2B\u003c/em\u003e). The dose upper limit for each group was 2.9 mg/kg/30d (Q1), 6.15 mg/kg/30d (Q2), 11.41 mg/kg/30d (Q3), and 48.08 mg/kg/30d (Q4). The ORR for each group was 37.8% (no steroids), 29.6% (Q1), 21.4% (Q2), 11.1% (Q3) and 22.2% (Q4) (\u003cem\u003ep\u003c/em\u003e=0.012). The DCR for each group was 54.6% (no steroids), 29.6% (Q1), 21.4% (Q2), 22.2% (Q3), and 37.0% (Q4) (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) \u003cem\u003e(Fig. 2B)\u003c/em\u003e. The mean cumulative dose was also higher in non-responders to ICI (2.48 mg/kg/30d [95% CI 1.81-3.14] vs. 1.23 mg/kg/30d [95% CI 0.44-2.01] in responders; \u003cem\u003ep\u003c/em\u003e=0.024).\u003c/p\u003e\n\u003cp\u003eNo clear correlation was found between the cumulative steroid dose within 90 days after C1 (D1-90) and clinical outcomes \u003cem\u003e(Fig. 2C)\u003c/em\u003e, and the difference between the mean cumulative doses for responders and non-responders was not statistically significant (\u003cem\u003ep\u003c/em\u003e=0.33). When the impact of steroid dosage after 6 months from C1 on survival outcomes was analyzed, the correlation seemed to reverse, with significantly higher PFS for those patients receiving higher doses: 17.6 months (no ST), 17.3 months (Q1), 37.1 months (Q2), 19.9 months (Q3), and 42.1 months (Q4) (HR: 0.86, 95% CI 0.75-0.99; \u003cem\u003ep\u003c/em\u003e=0.039).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. \u0026nbsp; Association between irAEs and clinical outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe presence of immune-related toxicity was correlated with a higher ORR (46.6% vs. 27.8%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), longer PFS (11.6 vs. 3.6 months; HR: 0.59; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and longer OS (20.7 vs 8.0 months; HR: 0.63; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). The association between irAEs and survival outcomes was confirmed by multivariable analysis including PS, tumor type, disease burden, and line of treatment (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. \u0026nbsp; Impact of the neutrophil-to-lymphocyte ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our cohort, the baseline neutrophil-to-lymphocyte (N/L) ratio was positively correlated with irAEs and negatively correlated with clinical outcomes. A N/L ratio \u0026gt;4 was led to a significantly lower DCR (39.6% vs. 54.6%; \u003cem\u003ep\u003c/em\u003e=0.001) and incidence of irAEs (26.4% vs. 39.5%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01). Interestingly, the proportion of patients with a baseline N/L ratio \u0026gt;4 was significantly higher among those exposed to steroids within 30 days before ICI initiation (67.1% vs. 37.7%; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003ea. \u0026nbsp; The relevance of timing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results are consistent with those of previous studies on the negative clinical impact of early steroid exposure around ICI initiation. A meta-analysis of 15 retrospective studies, including patients with brain metastases (\u003cem\u003en\u003c/em\u003e: 1102), showed significantly worse PFS (HR: 2.00, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01) and OS (HR: 1.84, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01) for those exposed to steroids\u003csup\u003e18\u003c/sup\u003e. In a retrospective study of 151 patients with metastatic NSCLC, the early use of steroids (within 28 days after ICI initiation) was correlated with worse DCR (odds ratio [OR]: 0.32; \u003cem\u003ep\u003c/em\u003e=0.006), PFS (HR: 1.8; \u003cem\u003ep\u003c/em\u003e=0.003) and OS (HR: 2.6; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001)\u003csup\u003e19\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our cohort, the negative impact of steroids also seemed to prevail around ICI initiation, with a significant decrease in the ORR and DCR among patients exposed between -30D and D90 from C1. However, this effect was not observed for steroid exposure after 6 months in long-term responders, with a tendency for longer PFS when compared to the steroid-na\u0026iuml;ve group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince early exposure to steroids (-30D to D90) is mainly due to cancer-related conditions, some of which (as symptomatic brain metastases) inherently imply poor prognosis, and late exposure in responding patients (\u0026gt;6m) is mostly due to irAEs, which are in contrast correlated with better outcomes, there might be a potential indication bias in the observed time-dependent effect of steroid exposure.\u003c/p\u003e\n\u003cp\u003eThere is solid evidence regarding the correlation between irAEs and favorable outcomes in patients treated with ICI. In a meta-analysis of 51 studies, the development of irAEs was associated with better survival outcomes among patients with metastatic melanoma (OS HR: 0.46, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001; PFS HR: 0.51, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.00001) and advanced NSCLC (OS HR: 0.40, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.00001; PFS HR: 0.46, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.00001)\u003csup\u003e21\u003c/sup\u003e. Similar results were obtained by Zhou et al.\u003csup\u003e22\u003c/sup\u003e in a meta-analysis of 30 studies including 4971 patients, with a significant benefit in OS (HR: 0.54; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and PFS (HR: 0.2; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) for those who developed irAEs, particularly low-grade endocrine and cutaneous reactions.\u003c/p\u003e\n\u003cp\u003eIn addition, some studies have suggested a negative impact of steroids only when indicated for cancer-related events, but not for the management of irAEs. A systematic review and meta-analysis of 16 studies (\u003cem\u003en\u003c/em\u003e = 4045) showed an increased risk of death and progression among patients treated with ICI receiving steroids for supportive care (HR: 2.5; 95% CI 1.41\u0026ndash;4.43) or brain metastases (HR: 1.51; 95% CI 1.22\u0026ndash;1.87), but not among those with irAEs\u003csup\u003e23\u003c/sup\u003e. Similar results have been obtained in other studies that analyzed large cohorts of patients treated with ICI for advanced NSCLC\u003csup\u003e24\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, Maslov et al.\u003csup\u003e26\u003c/sup\u003e evaluated the outcomes of 247 patients treated with ICI and concurrently exposed to steroids, analyzing the effect of steroid timing, and reported significantly longer PFS for those exposed to steroids within the first 2 months after ICI initiation (HR: 0.30, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), irrespective of the clinical indication. The median PFS was significantly longer when steroids were prescribed after 2 months from C1, both in the group with irAEs (HR 0.33; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001) and in the group treated with steroids for other reasons (HR 0.27; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003eAlthough a potential skew related to steroid indication cannot be completely dismissed, these results suggest that steroid biological effects might be intrinsically time-dependent, probably more relevant at impairing the achievement of a successful T cell antitumor response than at inducing loss of clinical benefit in responders, thus prevailing around ICI initiation, irrespective of indication. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. \u0026nbsp; The relevance of dosage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the impact of steroid use on ICI outcomes has been assessed in several studies, the influence of dosage has not been previously analyzed in detail. In our cohort, an inverse correlation was found between cumulative doses and clinical outcomes, with higher doses leading to lower ORR and DCR, both within 30 days before and 30 days after C1. In addition, significantly higher doses around ICI initiation were registered among non-responders, supporting the hypothesis of a dose-dependent effect.\u003c/p\u003e\n\u003cp\u003eInterestingly, even low doses of steroids seemed to lead to worse DCR compared with no steroid exposure. Most ICI clinical trials have excluded patients with baseline daily doses \u0026ge;10 mg prednisone, usually considering doses below that threshold as physiological. However, in our cohort, patients from the first quartile of baseline steroid dose (\u0026lt;2.36 mg/kg/30d, nearly half the cumulative dose of a 70 kg patient receiving 10 mg prednisone per day) had significantly worse DCR than steroid-na\u0026iuml;ve patients, questioning the assumption that there is a \u0026lsquo;safe\u0026rsquo; dose of steroids prior to initiation of immunotherapy.\u003c/p\u003e\n\u003cp\u003eAfter the first month of treatment, this effect seemed to attenuate (no significant differences were observed when steroids in D1-90 were analyzed together) and eventually became inverted, since higher doses after 6 months from C1 were significantly associated with longer PFS. Again, the better outcomes of patients with late exposure to high-dose steroids could be explained by the higher incidence of irAEs in this subgroup of long-term responders, provided the established correlation between irAEs and clinical benefit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. \u0026nbsp; The relevance of immune cells profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe neutrophil-to-lymphocyte (N/L) ratio seems to predict clinical response and immune-related toxicity in patients treated with ICI. In a retrospective study of 1714 patients with 16 different types of solid tumors, Valero et al.\u003csup\u003e27\u003c/sup\u003e found a significant association between a higher N/L ratio and poorer PFS and response rates. Combining the N/L ratio and TMB, the benefit of ICI was significantly higher in the N/L-low/TMB-high group than in the N/L-high/TMB-low group (OR: 3.22; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Xie et al.\u003csup\u003e28\u003c/sup\u003e published a meta-analysis of 14 studies incorporating 1751 participants, showing that elevated pre-treatment N/L ratio was associated with poorer OS (HR: 2.61; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and PFS (HR: 1.74; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001). In a meta-analysis of 7 published articles on the utility of the baseline N/L ratio, Sacdalan et al.\u003csup\u003e29\u003c/sup\u003e found worse OS (HR: 1.92; \u003cem\u003ep\u003c/em\u003e=0.001) and PFS (HR: 1.66; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001) among patients with higher N/L ratios across several tumor types. Similar results were obtained by Takenaka et al.\u003csup\u003e30\u003c/sup\u003e in a meta-analysis of 14 studies with 929 patients with head and neck tumors, as well as individual studies analyzing large cohorts of patients with advanced NSCLC\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e, renal cell carcinoma\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e, pancreatic cancer\u003csup\u003e35\u003c/sup\u003e and upper gastro-intestinal cancer\u003csup\u003e36\u003c/sup\u003e\u003cem\u003e\u003csup\u003e,\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e37\u003c/sup\u003e, among others.\u003c/p\u003e\n\u003cp\u003eExposure to steroids appears to modulate the N/L ratio during ICI treatment.\u0026nbsp;Fuc\u0026agrave; et al.\u003csup\u003e19\u003c/sup\u003e found a correlation between steroids use and a higher median N/L ratio, both 4 and 6 weeks after C1, hypothesizing that steroids may hinder antitumor response by modulating peripheral blood immune cells.\u0026nbsp;The effect of steroids may be particularly detrimental in patients with a lower N/L ratio. Lauko et al.\u003csup\u003e40\u003c/sup\u003e studied 171 patients with brain metastases from NSCLC, reporting decreased OS (10.5 vs. 17.9 months; \u003cem\u003ep\u003c/em\u003e=0.03) and intracranial PFS (5.0 vs. 8.7 months; \u003cem\u003ep\u003c/em\u003e=0.045) in those with upfront steroids; interestingly, OS differences were only significant in the subgroup of patients with a baseline N/L ratio \u0026lt;4, and there was a strong interaction between the N/L ratio and upfront steroids when modeled together (\u003cem\u003ep\u003c/em\u003e=0.0008). Our study is the first to confirm that steroid use not only modulates the N/L ratio throughout treatment, but also can remarkably increase the pre-treatment N/L ratio when administered before ICI initiation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA reasonable potential critique of this study is the reflection that the baseline steroid requirement could be driven by intrinsically tumor-related bad-prognosis scenarios, such as symptomatic brain metastases, with steroid use being the consequence and not the cause of worse clinical results. The demonstration that they modulate the profile of blood immune cells, whose correlation with ICI outcomes has been thoroughly proven, strongly suggests that there is indeed a biological basis supporting the steroid-induced impairment of ICI efficacy.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eOur study is consistent with the proven negative effect of early exposure to steroids on the outcomes of immune checkpoint inhibitors as a consequence of their diverse immunosuppressive properties, leading to a significant decrease in the response and disease control rates. Their detrimental effect seemed to prevail around ICI initiation (at least from \u0026minus;\u0026thinsp;30D to D90) and was not observed in our cohort after 6 months of treatment. This finding might be biased by the positive prognostic impact driven by immune-related toxicity, which mostly explains the late exposure to steroids in long-term responders, as opposed to cancer-related complications leading to their early use around ICI initiation, although previous clinical evidence has suggested an intrinsically time-dependent biological effect.\u003c/p\u003e \u003cp\u003eAlthough the influence of steroid dose has not been previously analyzed, our study suggests an inverse correlation between DCR and cumulative dose around ICI initiation. The finding of significantly higher cumulative doses among non-responders supports the dose-dependent effect hypothesis. In addition, the remarkable drop in the DCR observed in the lowest-dose quartile of patients from our cohort suggests that even the usually considered as \u0026lsquo;physiological\u0026rsquo; low doses of steroids might interfere with ICI efficacy.\u003c/p\u003e \u003cp\u003eInterestingly, steroid use within 30 days before ICI initiation correlated with higher baseline neutrophil-to-lymphocyte ratios, which led to significantly poorer clinical outcomes. As an established biomarker for response to ICI, the demonstration that the N/L ratio is modulated by steroids challenges the idea of steroid exposure as a mere bystander in the natural history of worse-prognosis tumors, providing a clue to understanding the biological basis of their detrimental influence on ICI outcomes. Further research and prospective validation of these results would be of great value to better understand the effects of steroids and optimize their use in patients undergoing treatment with immunotherapy.\u003c/p\u003e \u003cp\u003e\u003cb\u003eAuthors contribution\u003c/b\u003e: VA (Albarr\u0026aacute;n): study conception and design, data collection, analysis and interpretation of results, and manuscript preparation; PG, CGQ, CG, JC, DIR, JM, JCC, PPA, VA (Al\u0026iacute;a), PS, AMB, MSR, PAB: data collection; JJS: analysis and interpretation of results; AS, MEO, CS, AC, AG, YL, AR, MRF, FL, PG (Garrido): treating oncologists, review and editing of the manuscript; PG (Gajate): study conception, analysis and interpretation of results, review of the manuscript and supervision of the work.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune checkpoint inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eirAEs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune-related adverse events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eobjective response rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edisease control rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efirst cycle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eN/L ratio\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enatural killer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor-associated antigens\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantigen presenting cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT cell receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emajor histocompatibility complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTLA-4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecytotoxic T lymphocyte associated protein 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAG-3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymphocyte activation gene 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIM-3, T cell immunoglobulin domain and mucin domain 3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTILs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor-infiltrating lymphocytes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogrammed cell death protein 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD-L1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogrammed cell death protein ligand 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor mutational burden\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDSCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emyeloid-derived suppressor cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor-associated macrophages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT-reg\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eregulatory T cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglucocorticoid receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTGF-β\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor growth factorβ\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRWD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereal-world data\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Investigation Ethical Committee\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman immunodeficiency virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECOG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEastern Cooperative Oncology Group\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperformance status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e-30D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e30 days before C1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eD1-30\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e30 days after C1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eD1-90\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e90 days after C1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026gt;6m\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eafter 6 months from C1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esteroids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equartile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecumulative doses.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e: VA (Albarr\u0026aacute;n): study conception and design, data collection, analysis and interpretation of results, and manuscript preparation; PG, CGQ, CG, JC, DIR, JM, JCC, PPA, VA (Al\u0026iacute;a), PS, AMB, MSR, PAB: data collection; JJS: analysis and interpretation of results; AS, MEO, CS, AC, AG, YL, AR, MRF, FL, PG (Garrido): treating oncologists, review and editing of the manuscript; PG (Gajate): study conception, analysis and interpretation of results, review of the manuscript and supervision of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: this research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: CS declares speakers\u0026rsquo; fees from Novartis and AstraZeneca. AC declares speakers\u0026rsquo; and advisory fees from GlaxoSmithKline, AstraZeneca, PharmaMar, Daiichi Sankyo, MSD, Eisai and Accord Healthcare. PG (Garrido) declares speakers\u0026rsquo; fees from Janssen, MSD, Novartis, Medscape, Takeda, TouchTime and Medscape and advisory fees from Abbvie, Amgen, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Daiichi Sankyo, GlaxoSmithKline, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi and Takeda. PG (Gajate) declares advisory fees from BMS, Roche, Pfizer, Ipsen, MSD, Merck, Janssen, Astellas, Eisai and Novartis. The other authors declare no relevant conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: the original data that support the findings of this study are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen DS, Mellman I (2013) Oncology Meets Immunology: The Cancer-Immunity Cycle. 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Immunity 53(3):658\u0026ndash;671e6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET et al (1982) Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 5(6):649\u0026ndash;655\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJessurun CAC, Hulsbergen AFC, de Wit AE, Tewarie IA, Snijders TJ, Verhoeff JJC et al (2021) The combined use of steroids and immune checkpoint inhibitors in brain metastasis patients: a systematic review and meta-analysis. Neuro-Oncol 23(8):1261\u0026ndash;1272\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuc\u0026agrave; G, Galli G, Poggi M, Lo Russo G, Proto C, Imbimbo M et al (2019) Modulation of peripheral blood immune cells by early use of steroids and its association with clinical outcomes in patients with metastatic non-small cell lung cancer treated with immune checkpoint inhibitors. ESMO Open 4(1):e000457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos-Casals M, Brahmer JR, Callahan MK, Flores-Ch\u0026aacute;vez A, Keegan N, Khamashta MA et al (2020) Immune-related adverse events of checkpoint inhibitors. Nat Rev Dis Primer 6(1):38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussaini S, Chehade R, Boldt RG, Raphael J, Blanchette P, Maleki Vareki S et al (2021) Association between immune-related side effects and efficacy and benefit of immune checkpoint inhibitors \u0026ndash; A systematic review and meta-analysis. Cancer Treat Rev 92:102134\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, Yao Z, Yang H, Liang N, Zhang X, Zhang F (2020) Are immune-related adverse events associated with the efficacy of immune checkpoint inhibitors in patients with cancer? A systematic review and meta-anals. BMC Med 18(1):87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrelli F, Signorelli D, Ghidini M, Ghidini A, Pizzutilo EG, Ruggieri L et al (2020) Association of Steroids Use with Survival in Patients Treated with Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis. Cancers 12(3):546\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Giglio A, Mezquita L, Auclin E, Blanc-Durand F, Riudavets M, Caramella C et al (2020) Impact of Intercurrent Introduction of Steroids on Clinical Outcomes in Advanced Non-Small-Cell Lung Cancer (NSCLC) Patients under Immune-Checkpoint Inhibitors (ICI). Cancers 12(10):2827\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRicciuti B, Dahlberg SE, Adeni A, Sholl LM, Nishino M, Awad MM (2019) Immune Checkpoint Inhibitor Outcomes for Patients With Non\u0026ndash;Small-Cell Lung Cancer Receiving Baseline Corticosteroids for Palliative Versus Nonpalliative Indications. J Clin Oncol 37(22):1927\u0026ndash;1934\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaslov DV, Tawagi K, Kc M, Simenson V, Yuan H, Parent C et al (2021) Timing of steroid initiation and response rates to immune checkpoint inhibitors in metastatic cancer. J Immunother Cancer 9(7):e002261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValero C, Lee M, Hoen D, Weiss K, Kelly DW, Adusumilli PS et al (2021) Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors. Nat Commun 12(1):729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie X, Liu J, Yang H, Chen H, Zhou S, Lin H et al (2019) Prognostic Value of Baseline Neutrophil-to-Lymphocyte Ratio in Outcome of Immune Checkpoint Inhibitors. Cancer Invest 37(6):265\u0026ndash;274\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacdalan DB, Lucero JA, Sacdalan DL (2018) Prognostic utility of baseline neutrophil-to-lymphocyte ratio in patients receiving immune checkpoint inhibitors: a review and meta-analysis. OncoTargets Ther 11:955\u0026ndash;965\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakenaka Y, Oya R, Takemoto N, Inohara H (2022) Neutrophil-to‐lymphocyte ratio as a prognostic marker for head and neck squamous cell carcinoma treated with immune checkpoint inhibitors: Meta‐analysis. Head Neck 44(5):1237\u0026ndash;1245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang N, Jiang J, Tang S, Sun G (2020) Predictive value of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in non-small cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis. Int Immunopharmacol 85:106677\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen F, Zhao T, Liu B, Pan L (2019) Neutrophil-lymphocyte ratio (NLR) predicted prognosis for advanced non-small-cell lung cancer (NSCLC) patients who received immune checkpoint blockade (ICB). OncoTargets Ther 12:4235\u0026ndash;4244\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Meng F, Jiang R (2021) Neutrophil-to-Lymphocyte Ratio as a Prognostic Biomarker for Patients With Metastatic Renal Cell Carcinoma Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis. Front Oncol 11:746976\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLalani A-KA, Xie W, Martini DJ, Steinharter JA, Norton CK, Krajewski KM et al (2018) Change in Neutrophil-to-lymphocyte ratio (NLR) in response to immune checkpoint blockade for metastatic renal cell carcinoma. J Immunother Cancer 6(1):5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang J, Han X, Zha H, Tao H, Li X, Yuan F et al (2021) Systemic Immune-Inflammation Index and Changes of Neutrophil-Lymphocyte Ratio as Prognostic Biomarkers for Patients With Pancreatic Cancer Treated With Immune Checkpoint Blockade. Front Oncol 11:585271\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBooka E, Kikuchi H, Haneda R, Soneda W, Kawata S, Murakami T et al (2022) Neutrophil-to-Lymphocyte Ratio to Predict the Efficacy of Immune Checkpoint Inhibitor in Upper Gastrointestinal Cancer. Anticancer Res 42(6):2977\u0026ndash;2987\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo J-C, Lin C-C, Lin C-Y, Hsieh M-S, Kuo H-Y, Lien M-Y et al (2019) Neutrophil\u0026ndash;to\u0026ndash;lymphocyte Ratio and Use of Antibiotics Associated With Prognosis in Esophageal Squamous Cell Carcinoma Patients Receiving Immune Checkpoint Inhibitors. Anticancer Res 39(10):5675\u0026ndash;5682\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePavan A, Calvetti L, Dal Maso A, Attili I, Del Bianco P, Pasello G et al (2019) Peripheral Blood Markers Identify Risk of Immune-Related Toxicity in Advanced Non-Small Cell Lung Cancer Treated with Immune-Checkpoint Inhibitors. Oncologist 24(8):1128\u0026ndash;1136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuste V, Goldschmidt V, Laparra A, Messayke S, Danlos F-X, Romano-Martin P et al (2021) The determinants of very severe immune-related adverse events associated with immune checkpoint inhibitors: A prospective study of the French REISAMIC registry. Eur J Cancer 158:217\u0026ndash;224\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLauko A, Thapa B, Sharma M, Muhsen B, Barnett A, Rauf Y et al (2021) Neutrophil-to-lymphocyte ratio influences impact of steroids on efficacy of immune checkpoint inhibitors in lung cancer brain metastases. Sci Rep 11(1):7490\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"steroids, immunotherapy, immune checkpoint inhibitors, dose, neutrophil-to-lymphocyte ratio, solid tumors","lastPublishedDoi":"10.21203/rs.3.rs-4159119/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4159119/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious studies have suggested a negative impact of steroids on the efficacy of immune checkpoint inhibitors (ICI), but how this effect is modulated by the dosage and time of administration is yet to be clarified. We have performed a retrospective analysis of 475 patients with advanced solid tumors treated with ICI as monotherapy from 2015 to 2022. Data regarding immune-related adverse events (irAEs) and clinical outcomes were collected. For each patient, the daily steroid dose (in mg/kg of prednisone) was registered until disease progression or death. The impact of cumulative doses on response rates and survival outcomes was analyzed within different periods. The objective response rate (ORR) was significantly lower among patients exposed to steroids within 30 days before the first cycle of ICI (C1) (20.3% vs. 36.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and within the first 90 days of treatment (25.7% vs. 37.7%, p\u0026thinsp;=\u0026thinsp;0.01). This negative effect was confirmed by multivariable analysis. Higher mean steroid doses were observed among non-responders, and cumulative doses were inversely correlated with the disease control rate (DCR) around ICI initiation. Remarkably, poorer outcomes were observed even in patients belonging to the lowest dose quartile compared to the steroid-na\u0026iuml;ve population. The detrimental impact of steroids was not observed in long-term responders exposed to steroids after 6 months from C1. Our results suggest that the negative impact of steroids on ICI efficacy seems to be time-dependent, prevailing around ICI initiation, and may also be dose-dependent, with modulation of neutrophil-to-lymphocyte ratio as a potential underlying mechanism.\u003c/p\u003e","manuscriptTitle":"Negative impact of steroids on the efficacy of immunotherapy in a multi- tumor cohort: time and dose-dependent","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 19:04:18","doi":"10.21203/rs.3.rs-4159119/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-06T08:33:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-20T10:19:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6e365a43-19e2-45ad-8b65-b7cf721d05f0","date":"2024-04-10T15:09:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"acd9547c-d8cc-4339-a077-bc17068a6c8d","date":"2024-04-05T11:57:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-31T02:42:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-26T05:34:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-26T05:34:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2024-03-24T18:09:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"892419fc-c3ba-47f1-80e2-7f794a4ce9bd","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:01:37+00:00","versionOfRecord":{"articleIdentity":"rs-4159119","link":"https://doi.org/10.1007/s00262-024-03772-9","journal":{"identity":"cancer-immunology-immunotherapy","isVorOnly":false,"title":"Cancer Immunology, Immunotherapy"},"publishedOn":"2024-08-02 15:57:17","publishedOnDateReadable":"August 2nd, 2024"},"versionCreatedAt":"2024-03-29 19:04:18","video":"","vorDoi":"10.1007/s00262-024-03772-9","vorDoiUrl":"https://doi.org/10.1007/s00262-024-03772-9","workflowStages":[]},"version":"v1","identity":"rs-4159119","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4159119","identity":"rs-4159119","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}