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The Association of Improved Overall Survival with NSAIDs in Non-Small Cell Lung Cancer Patients Receiving Immune Checkpoint Inhibitors

Open AccessPublished:January 20, 2023DOI:https://doi.org/10.1016/j.cllc.2022.12.013

      Highlights

      • Patients on immune checkpoint inhibitors had improved survival when taking NSAIDs
      • Diclofenac has strongest association with overall survival
      • Concomitant NSAIDs may improve ICI efficacy, but this requires further study

      Abstract

      Background

      Immune checkpoint inhibitors (ICI) are commonly used in the management of patients with advanced non-small cell lung cancer (NSCLC), but response is suboptimal. Preclinical data suggest ICI efficacy may be enhanced with concomitant nonsteroidal anti-inflammatory (NSAID) medications.

      Patients and Methods

      In this retrospective study, the VA Corporate Data Warehouse was queried for patients diagnosed with NSCLC and treated with ICI from 2010-2018. Concomitant NSAID use was defined as NSAID dispensation by a VA pharmacy within 90 days of the any ICI infusion. To mitigate immortal time bias, patients who started NSAIDs 60 or more days after ICI initiation were excluded from analysis. Survival was measured from start of ICI.

      Results

      We identified 3,634 patients with NSCLC receiving ICI; 2,336 (64.3%) were exposed to concomitant NSAIDs. On multivariable analysis, NSAIDs were associated with better overall survival (HR = 0.90; 95% CI 0.83 – 0.98; p= 0.010). When stratifying by NSAID type, diclofenac was the only NSAID with significant association with overall survival (HR = 0.75; 95% CI 0.68 – 0.83; p<0.001). Propensity score matching of the original cohort yielded 1,251 patients per cohort balanced in characteristics. NSAIDs remained associated with improved overall survival (HR = 0.85; 95% CI 0.78 – 0.92; p<0.001).

      Conclusion

      This study of Veterans with NSCLC treated with ICI demonstrated that concomitant NSAIDs are associated with longer OS. This may indicate that NSAIDs can enhance ICI-induced antitumor immunity and should prospectively validated.
      MicroAbstract: Response rates of immune checkpoint inhibitors (ICI) in advanced non-small cell lung cancer are suboptimal. In this retrospective cohort study of over 3,600 Veterans with advanced or metastatic non-small cell lung cancer, concomitant use of NSAIDs was associated with improved survival when given with ICI. This may indicate possibility of enhancing ICI efficacy using concomitant NSAIDs, although requires validation.

      Keywords

      Clinical Practice Points
      Immune checkpoint inhibitor (ICI) response is suboptimal in advanced non-small cell lung cancer (NSCLC) and strategies are needed to improve outcomes. Preclinical studies suggest that cyclooxygenase-dependent pathways modulate antitumor immunity. In our study, we found that patients who received immune checkpoint inhibitors (ICI) for advanced non-small cell lung cancer while taking nonsteroidal anti-inflammatory drugs (NSAIDs) had better overall survival than patients who were not taking NSAIDs. This may indicate that concurrent administration of NSAIDs may enhance ICI efficacy, although this requires further study.

      Introduction

      Lung cancer remains the leading cause of cancer mortality in the United States and worldwide.
      • Siegel R.L.
      • et al.
      Cancer Statistics, 2021.
      Immunotherapy, predominantly in the form of immune checkpoint inhibitors (ICIs), has brought substantial improvements in overall survival in patients with metastatic and locally advanced non-small cell lung cancer (NSCLC)
      • Dafni U.
      • et al.
      Immune checkpoint inhibitors, alone or in combination with chemotherapy, as first-line treatment for advanced non-small cell lung cancer. A systematic review and network meta-analysis.
      ,
      • Antonia S.J.
      • et al.
      Durvalumab after Chemoradiotherapy in Stage III Non–Small-Cell Lung Cancer.
      . However, clinical response rates with ICIs are suboptimal and the majority of patients eventually have disease progression, possibly due to development of tumor resistance to further immune blockade.
      • Wang F.
      • Wang S.
      • Zhou Q.
      The Resistance Mechanisms of Lung Cancer Immunotherapy.
      Identification of clinical or pharmacologic factors that modify immunotherapy's efficacy may be useful in tailoring patient selection for treatment, predicting response, or augmenting response to therapy. There is preclinical data suggesting non-steroidal anti-inflammatory drugs (NSAIDs) improve the efficacy of anti-PD-1 therapies by abating COX-dependent tumor progression.
      • Zelenay S.
      • et al.
      Cyclooxygenase-Dependent Tumor Growth through Evasion of Immunity.
      ,
      • Botti G.
      • et al.
      COX-2 expression positively correlates with PD-L1 expression in human melanoma cells.
      Given the potential immune-modulatory effects of NSAIDs, we sought to study the association of concomitant NSAIDs with overall survival in a real-world cohort of Veterans receiving ICI for advanced NSCLC.

      Patients and Methods

      The Veterans Health Administration (VHA) is the largest integrated health care system in the United States and serves nine million Veterans annually, approximately 50,000 with cancer. Clinical and administrative data are routinely collected on each VHA enrollee from over 150 VA hospitals and thousands of clinics into the Corporate Data Warehouse. We queried this resource for NSCLC diagnoses occurring from 2010, the first year ICIs were approved in the United States, through 2018, the most recent year for which cases would have 12 months of follow-up.
      We conducted a nested cohort study of NSCLC patients receiving ICI. Utilization of ICI, date of ICI initiation, and duration of ICI therapy were ascertained from the VA Corporate Data Warehouse using a secure VA Informatics and Computing Infrastructure (VINCI) workspace, focusing on the four agents approved for use in NSCLC through 2018 (nivolumab, pembrolizumab, durvalumab, atezolizumab). Similarly, NSAID exposure for the purpose of this analysis was defined as the Veteran having picked up a prescription for a systemic (oral or intravenous) NSAID within 90 days before or after any ICI administration. To mitigate the impact of immortal time bias, patients who initiated NSAID more than 60 days after start of ICI were excluded from analysis entirely. Patients were categorized a priori into groups for sociodemographic factors (age, race, gender, population density, employment status, marital status), clinical characteristics (Elixhauser comorbidity index), cancer-specific features (histology, stage at diagnosis, year of diagnosis), and treatment-related variables (time from diagnosis to ICI initiation, sequence of ICI with respect to chemotherapy). Pearson's χ2 tests were used to assess the associations between variables and NSAID usage. We separately assessed the impact of any NSAID and of each NSAID agent together. To isolate the impact of the most promising agents, pairwise comparisons were then performed between patients exposed to these specific agents and patients without any NSAID exposure.
      The primary outcome evaluated was overall survival, measured from date of first ICI administration to date of last follow-up or death. Vital status was ascertained from Department of Defense data. Kaplan-Meier analysis without adjustment for covariates was initially used to compare survival between groups. Univariable Cox proportional hazards regression was then performed, with survival associations expressed as hazard ratios (HR) and corresponding 95% confidence intervals (95%CI), and HR > 1 indicating greater risk of mortality. Using backward elimination with an alpha level of removal of 0.05, multivariable Cox proportional hazards regression was then performed.
      A generalized propensity score was estimated for each analysis by multinomial logistic regression treating the comparator groups (i.e. NSAID exposure) as the outcome and covariates as predictors. A generalized propensity score matching (PSM) algorithm was applied to create a pseudo-sample where all covariates of interest were balanced among the comparison groups. The covariate balance was checked before and after PSM by the standardized difference, with values <0.2 considered an acceptable imbalance. Associations with survival were then examined in the matched samples using the same tests as above. Statistical analyses were performed using SAS Enterprise Guide 7.1 (SAS Institute Inc., Cary, NC). Tests were two-sided with a level of significance of p=0.05.

      Results

      We identified a total of 3,634 Veterans with NSCLC treated with ICI, 2,336 (64.3%) of whom were treated with NSAIDs (Table 1). Patients treated with NSAIDs were characterized by larger proportion of Black patients, higher comorbidity index, adenocarcinoma histology, more recent year of diagnosis, shorter duration between diagnosis and initiation of ICI, and more patients who received chemotherapy during or after ICI. Patients treated with NSAIDs were most commonly treated with two or more NSAIDs (40.2% of NSAID patients), aspirin (32.0%), ketorolac (10.3%), ibuprofen (6.3%), or diclofenac (4.3%).
      Table 1Descriptive statistics of the overall cohort.
      NSAID
      VariableCategoriesNoYes
      N=1298N=2336
      N%N%p
      Age≤6534326.4608260.29
      66-7038229.475232.2
      71-7532024.656424.1
      >7525319.541217.6
      Racewhite96974.7168372.10.018
      black23618.251021.8
      other151.21081.5
      unknown786124.6
      Gendermale126697.5225996.70.16
      female322.5773.3
      Geographyurban83964.6156066.80.19
      rural45935.477633.2
      Employmentemployed27621.3466200.54
      not employed53040.899542.6
      retired4543579534
      unknown382.9803.4
      Marital Statusmarried62848.4107846.20.34
      not married66951.5125453.7
      unknown10.140.2
      Elixhauser Comorbidity Index0-444334.158925.2<0.001
      6-May30923.849821.3
      9-Jul32925.462026.5
      >921716.762926.9
      Histologysquamous cell carcinoma55742.976832.9<0.001
      adenocarcinoma53241120451.5
      other20916.136415.6
      Stage at Diagnosis040.3000.069
      I15712.129612.7
      II876.71747.5
      III37228.761326.2
      IV52040.196741.4
      unknown15812.228612.2
      Year of Diagnosis2010-201561847.692039.4<0.001
      2016-201868052.4141660.6
      Months from Diagnosis to ICI0-426220.261026.1<0.001
      10-May39030.164227.5
      19-Nov2992348720.9
      ≥ 2034726.759725.6
      Chemotherapynone17813.730212.9<0.001
      before ICI75758.3119851.3
      during ICI33926.176932.9
      after ICI241.9672.9
      Abbreviations: ICI, immune checkpoint inhibitor
      On univariable analysis, receipt of NSAIDs was associated with better overall survival (HR = 0.87; 95% CI 0.81 – 0.94; p < 0.001), with median overall survival of 10 months vs 8 months (Figure 1). Other factors associated with improved overall survival included chemotherapy during or after ICI, older age, black race, lower comorbidity burden, adenocarcinoma histology, earlier disease stage, earlier year of diagnosis, and longer time from diagnosis to ICI. On multivariable analysis, receipt of NSAIDs remained associated with better overall survival (HR = 0.83; 95% CI 0.76 – 0.89; p < 0.001). Improved overall survival was also associated with chemotherapy during or after ICI, older age, black race, female sex, employed employment status, adenocarcinoma histology, earlier stage, earlier year of diagnosis, and longer duration from diagnosis to initiation of ICI (Table 2). On multivariable analysis stratifying by NSAID type, only diclofenac (HR = 0.60; 95% CI 0.47 – 0.77; p < 0.001) and two or more NSAIDs (HR = 0.75; 95% CI 0.68 – 0.83; p < 0.001) had a statistically significant association with overall survival (Supplementary Table 1).
      Table 2Cox Regression for Overall Survival in Entire ICI Cohort
      VariableCategoriesUVAMVA
      NDeathsHR95%CIppHR95%CIpp
      NSAIDno1,2981,048---<0.001---0.042
      yes2,3361,7370.830.77-0.90<0.0010.830.76-0.89<0.001
      Age≤659517421.211.08-1.350.001<0.0011.431.26-1.62<0.001<0.001
      66-701,1349071.221.09-1.35<0.0011.341.20-1.50<0.001
      71-758846250.880.79-0.990.0380.920.82-1.040.195
      >75665511------
      Racewhite2,6522,053---0.009---0.012
      black7465450.860.79-0.950.0030.860.78-0.950.003
      other50371.020.73-1.410.9250.960.69-1.340.818
      unknown1861501.060.89-1.250.5191.090.93-1.290.294
      Gendermale3,5252,712---0.144---0.016
      female109730.850.67-1.070.1590.750.59-0.950.016
      Geographyurban2,3991,821---0.261
      rural1,2359641.040.97-1.130.275
      Employmentemployed742544---0.149---0.002
      not employed1,5251,1651.080.97-1.200.1411.060.96-1.180.268
      retired1,2499881.121.01-1.240.0361.231.10-1.37<0.001
      unknown118881.170.93-1.460.1761.120.89-1.410.317
      Marital Statusmarried1,7061,301---0.831
      not married1,9231,4790.990.92-1.060.755
      unknown551.240.52-2.990.629
      Elixhauser Comorbidity Index0-41,032754---<0.001---<0.001
      6-May8076171.121.00-1.240.0421.151.03-1.280.012
      9-Jul9497411.341.21-1.48<0.0011.331.20-1.48<0.001
      >98466731.241.12-1.37<0.0011.251.13-1.39<0.001
      Histologysquamous cell carcinoma1,3141,081---<0.001---<0.001
      adenocarcinoma1,7121,2680.830.77-0.90<0.0010.830.77-0.91<0.001
      other5674360.960.86-1.070.4370.920.82-1.030.16
      Stage at Diagnosis0420.430.11-1.700.227<0.0010.440.11-1.790.254<0.001
      I4533450.890.79-1.000.0540.990.87-1.130.868
      II2611970.880.76-1.030.1050.960.81-1.120.573
      III9857240.830.76-0.91<0.0010.830.75-0.91<0.001
      IV1,4871,181------
      unknown4443361.090.96-1.230.1831.141.00-1.280.036
      Year of Diagnosis2010-20151,5381,3000.810.75-0.88<0.001<0.0010.860.78-0.950.0020.002
      2016-20182,0961,485------
      Months from Diagnosis to ICI0-48726361.381.24-1.54<0.001<0.0011.261.09-1.460.0020.021
      10-May1,0327961.291.17-1.43<0.0011.131.00-1.280.057
      19-Nov7866241.161.05-1.300.0051.090.97-1.220.147
      >19944729------
      Chemotherapynone4803641.060.95-1.190.296<0.0010.950.83-1.070.395<0.001
      before ICI1,9551,509------
      during ICI1,1088460.730.67-0.79<0.0010.710.65-0.78<0.001
      after ICI91660.60.48-0.79<0.0010.580.45-0.74<0.001
      Abbreviations: HR, hazard ratio; ICI, immune checkpoint inhibitor; MVA, multivariable analysis; UVA, univariable analysis; 95%CI, 95% confidence interval
      Note: Backward selection with an α of 0.05 was used. The following variables were removed from the model: geography and marital status
      After propensity score matching, there were 1,251 patients in each cohort who were balanced in baseline characteristics (Supplementary Table 2). Receipt of NSAIDs remained associated with improved overall survival (HR = 0.85; 95% CI 0.78 – 0.92; p < 0.001) (Figure 2).
      Given the association of diclofenac, specifically, with overall survival, a subset analysis was performed to specifically evaluate patients who received diclofenac versus those who did not receive NSAIDs. Compared to patients who did not receive any NSAIDs, patients who received diclofenac had statistically significant improved overall survival on univariate (HR = 0.65; 95% CI 0.51 – 0.83; p < 0.001) and multivariable analysis (HR = 0.62; 95% CI 0.48 – 0.79; p < 0.001) (Table 3). Additionally, improved overall survival was associated with older age, lower comorbidity index, earlier stage, earlier year of diagnosis, and chemotherapy during or after ICI. Propensity score matching yielded 98 patients in each cohort, well balanced in baseline characteristics (Supplementary Table 3). Receipt of diclofenac remained associated with improved overall survival when compared to no NSAIDs (HR = 0.57; 95% CI 0.41 – 0.79; p < 0.001) (Figure 3).
      Table 3Cox Regression for Overall Survival in subset analysis of patients who received diclofenac vs no NSAID
      VariableCategoriesMVA
      NDeathsHR95%CIpp
      Diclofenacno1,2981,048---<0.001
      yes101690.620.48-0.79<0.001
      Age≤653683031.261.05-1.500.011<0.001
      66-704103371.331.23-1.590.001
      71-753452620.950.79-1.150.615
      >75276215---
      Elixhauser Comorbidity Index0-4469362---0.005
      6-May3392791.171.00-1.370.054
      9-Jul3542851.331.14-1.56<0.001
      >92371911.10.92-1.320.284
      Stage at Diagnosis0420.410.10-1.650.210.021
      I1671270.870.71-1.060.162
      II93730.950.74-1.220.676
      III4083210.840.72-0.970.016
      IV560463---
      unknown1671311.160.95-1.410.148
      Year of Diagnosis2010-20156575710.830.73-0.940.0040.004
      2016-2018742546---
      Chemotherapynone1981530.990.82-1.190.924<0.001
      before ICI810655---
      during ICI3672910.670.59-0.77<0.001
      after ICI24180.60.38-0.970.036
      Abbreviations: HR, hazard ratio; ICI, immune checkpoint inhibitor; MVA, multivariable analysis; 95%CI, 95% confidence interval
      Note: Backward selection with an α of 0.05 was used. The following variables were removed from the model: Emoloyment status, gender, geography, histology, marital status, month from diagnosis to initiation of ICI, and race
      Given a proportion of patients had missing stage data, we conducted a sensitivity analysis excluding these patients. Receipt of NSAIDs was again associated with improved overall survival on multivariable analysis (HR = 0.84; 95% CI 0.77 – 0.91; p < 0.001) and after propensity score matching (n = 1118 per cohort; HR = 0.85; 0.78 – 0.93; p < 0.001). When NSAIDs were stratified by specific drug, diclofenac was again the only individual NSAID drug to be associated with overall survival (HR = 0.62; 95% CI 0.48 – 0.81; p < 0.001). When comparing patients who received diclofenac vs no NSAIDs (excluding patients who received other NSAIDs), diclofenac was associated with improved overall survival (HR = 0.64; 95% CI 0.50 – 0.83; p < 0.001) on multivariable analysis.

      Discussion

      In this study of Veterans with NSCLC receiving ICIs, we identified an association of NSAIDs with improved overall survival. To our knowledge, this is the first study of a real world cohort that provides clinical evidence of an association between NSAIDs and concomitant immunotherapy with improved overall survival in NSCLC patients.
      Preclinical studies suggest that cyclooxygenase-dependent pathways mediate tumor growth and evasion of immunity, blockade of which inhibits tumor growth in vivo.
      • Zelenay S.
      • et al.
      Cyclooxygenase-Dependent Tumor Growth through Evasion of Immunity.
      ,
      • Kumar D.
      • et al.
      Aspirin Suppresses PGE2 and Activates AMP Kinase to Inhibit Melanoma Cell Motility, Pigmentation, and Selective Tumor Growth In Vivo.
      This is potentially because Prostaglandin E2, a product of COX, is a key mediator of inflammation in the tumor microenvironment and attenuates antitumor immunity through a variety of mechanisms, including upregulation of T helper 2 response, inhibition of cytotoxic T cells, modulation of dendritic cell response, and induction of myeloid-derived suppressor cells.
      • Wang D.
      • Dubois R.N.
      Eicosanoids and cancer.
      COX-2, specifically, has been found to modulate PD-L1 expression, and its inhibition has resulted in suppression of tumor metastases in vivo in melanoma and breast cancer models.
      • Zhou P.
      • et al.
      Combination therapy of PKCζ and COX-2 inhibitors synergistically suppress melanoma metastasis.
      • Kim K.M.
      • et al.
      Timosaponin AIII inhibits melanoma cell migration by suppressing COX -2 and in vivo tumor metastasis.
      • Sadhu S.S.
      • et al.
      In-vitro and in-vivo inhibition of melanoma growth and metastasis by the drug combination of celecoxib and dacarbazine.
      • Li Y.
      • et al.
      Hydrogel dual delivered celecoxib and anti-PD-1 synergistically improve antitumor immunity.
      • Prima V.
      • et al.
      COX2/mPGES1/PGE2 pathway regulates PD-L1 expression in tumor-associated macrophages and myeloid-derived suppressor cells.
      The preferential expression of the COX-2 isoform in states of inflammation or malignancy makes it a potentially valuable target in cancer-directed therapies.
      • Ferrer M.D.
      • et al.
      Cyclooxygenase-2 Inhibitors as a Therapeutic Target in Inflammatory Diseases.
      There is limited data regarding the potential effect of NSAIDs on ICI therapy. In a previous study evaluating various concomitant medications with ICI in advanced cancers, there was worse overall survival in NSCLC patients treated with ICI with NSAIDs.
      • Spakowicz D.
      • et al.
      Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications.
      Given this was a subset analysis in a group of patients with several types of advanced cancer, it is difficult to compare that study's findings to our current study of a large cohort of patients exclusively with NSCLC treated with ICI. In another study, NSAIDs were associated with higher likelihood of progression and death in a cohort of metastatic renal cell carcinoma.
      • Zhang Y.
      • et al.
      Adding Cyclooxygenase Inhibitors to Immune Checkpoint Inhibitors Did Not Improve Outcomes in Metastatic Renal Cell Carcinoma.
      Compared to the aforementioned two studies, it is possible the larger patient numbers in our study and ability to account for more confounders better mitigated selection bias associated with patient's taking NSAIDs for comorbid conditions. A separate meta-analysis evaluating the effect of concomitant analgesics on prognosis in patients with multiple types of cancer treated with ICIs found overall no significant difference in progression-free survival or overall survival in patients receiving NSAIDs; it did, however, find an association with better PFS in a subgroup of patients who received anti-PD-1 therapy.
      • Mao Z.
      • et al.
      Effect of Concomitant Use of Analgesics on Prognosis in Patients Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis.
      A prior study of 330 patients with advanced melanoma did not identify a significant association between NSAID use and improvement in overall survival or progression-free survival in patients who received anti-PD-1 therapy.
      • Wang D.Y.
      • et al.
      The Impact of Nonsteroidal Anti-Inflammatory Drugs, Beta Blockers, and Metformin on the Efficacy of Anti-PD-1 Therapy in Advanced Melanoma.
      The study contained a minority of patients who received NSAIDs, in comparison to our much larger study which perhaps affords greater power to detect statistically significant differences in overall survival. Additionally, it is unclear how many patients in that study received diclofenac, which our study specifically identified has the strongest association to improved outcomes. Another study did not show a benefit of aspirin with checkpoint inhibitors in a single-institution cohort. The study included heterogeneous cancer types and only a small number of patients who received aspirin. Also, its analysis included CTLA-4 inhibitors, which were not evaluated in our study and do not have the same extent of association to COX modulation as PD-L1.
      • Gandhi S.
      • et al.
      Impact of concomitant medication use and immune-related adverse events on response to immune checkpoint inhibitors.
      The strength of the association of diclofenac with improved overall survival raises the possibility that diclofenac may modulate a unique mechanistic pathway independent of COX. Diclofenac, unlike other NSAIDs, has been shown to decrease lactate secretion of tumor cells through inhibition of lactate transporters monocarboxylate transporter 1 and 4.
      • Renner K.
      • et al.
      Restricting Glycolysis Preserves T Cell Effector Functions and Augments Checkpoint Therapy.
      Although lactate has complex immunomodulatory effects on different subtypes of immune cells, it generally is generally immunosuppressive.
      • Caslin H.L.
      • et al.
      Lactate Is a Metabolic Mediator That Shapes Immune Cell Fate and Function.
      Studies suggest that the metabolic effect of diclofenac causes increase in intratumoral lactate and corresponding decrease in the tumor microenvironment, resulting in enhanced local antitumor immune response.
      • Chirasani S.R.
      • et al.
      Diclofenac inhibits lactate formation and efficiently counteracts local immune suppression in a murine glioma model.
      Preclinical data suggests administration of diclofenac enhances the effect of checkpoint inhibitors through pH-dependent upregulation of IFNγ, IL-2, and PD-1 expression; this effect is unique to diclofenac and not seen with aspirin administration.
      • Renner K.
      • et al.
      Restricting Glycolysis Preserves T Cell Effector Functions and Augments Checkpoint Therapy.
      Diclofenac has also been shown to have direct antitumor effects as well,
      • Pantziarka P.
      • et al.
      Repurposing Drugs in Oncology (ReDO)-diclofenac as an anti-cancer agent.
      such as through downregulation of VEFG via increased arginase activity in tumor stroma,
      • Mayorek N.
      • Naftali-Shani N.
      • Grunewald M.
      Diclofenac Inhibits Tumor Growth in a Murine Model of Pancreatic Cancer by Modulation of VEGF Levels and Arginase Activity.
      downregulation of c-Myc expression,
      • Yang L.
      • et al.
      Diclofenac impairs the proliferation and glucose metabolism of triplenegative breast cancer cells by targeting the cMyc pathway.
      and induction of apoptotic pathways.
      • Marinov L.
      • et al.
      Cytotoxic and antiproliferative effects of the nonsteroidal anti-inflammatory drug diclofenac in human tumour cell lines.
      It is possible that the effects of diclofenac are unique among NSAIDs in enhancing antitumor response when combined with ICIs.
      The data presented here should be interpreted cautiously given our study's retrospective design, which entails several limitations that are worth noting. Given that overall survival was the only available endpoint for analysis, we were not able to analyze the potential impact of NSAIDs on progression, patterns of failure, or lung cancer-specific survival. It is possible that the overall survival benefit may in part be due to selection bias, despite the use of propensity score matching to mitigate the effect of identifiable confounding variables. The analyzed cohort consisted of a heterogeneous group with regard to several key variables including disease stage, year of diagnosis, and timing of chemotherapy. Although this was accounted for in regression and propensity score matching, it is difficult to account for all of the potential treatments these patients received (e.g. surgery, radiation, chemotherapy type and duration) and the impact of these treatments on survival. Additionally, given that NSAID use was defined by the presence of an active prescription, it is difficult to account for variability in use (e.g. oral versus parenteral, as-needed versus scheduled regimens, inconsistent adherence, variable dosing, differences in duration). Along these lines, we are unable to account for patients who may have taken NSAIDs over the counter. As a proportion of patients in the cohort without NSAIDs may have received such over the counter medications, it is possible that exposure misclassification may be lowering the measured magnitude of effect of NSAIDs (both as a group and on an individual basis) on overall survival. Finally, although we noted a unique statistically significant association of survival with diclofenac, it should be emphasized that our study lacks the statistical power to evaluate individual NSAIDs, some of which comprised a very small proportion of NSAIDs.
      In summary, we identified an association of improved overall survival with ICI and NSAID use. This may indicate that NSAIDs can enhance ICI-induced antitumor immunity, potentially through COX inhibition or distinct antitumor pathways. These findings merit prospective study.

      CRediT authorship contribution statement

      Nikhil T. Sebastian: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. William A. Stokes: Conceptualization, Methodology, Investigation, Writing – review & editing. Madhusmita Behera: Conceptualization, Methodology, Writing – review & editing. Renjian Jiang: Data curation, Methodology, Formal analysis, Data curation, Visualization. David A. Gutman: Data curation, Software, Formal analysis, Supervision. Zhonglu Huang: Methodology, Formal analysis, Data curation, Visualization. Abigail Burns: Project administration, Resources. Vidula Sukhatme: Conceptualization, Funding acquisition. Michael C. Lowe: Conceptualization, Methodology, Investigation. Suresh S. Ramalingam: Conceptualization, Methodology, Investigation, Writing – review & editing. Vikas P. Sukhatme: Conceptualization, Funding acquisition, Methodology, Writing – review & editing. Drew Moghanaki: Conceptualization, Funding acquisition, Methodology, Investigation, Writing – original draft, Writing – review & editing.

      Declaration of Competing Interest

      NTS has no disclosures. WAS has no disclosures. MB has no disclosures. RJ has no disclosures. DAG has no disclosures. ZH has no disclosures. AB has no disclosures. VS has no disclosures. MCL has no disclosures. SSR has received grant funding and/or other support (for consultancy) from Amgen, AstraZeneca, Bristol-Myers Squibb, Merck, Takeda, Tesaro, Advaxis, AbbVie, and Genentech/Roche. VPS is on the SAB of BERG and HiFiBio Therapeutics, and an equity holder in Aggamin Pharmaceuticals and Victa Biotherapeutics. DM has received travel support and speaking honoraria from Varian Medical Systems.

      Acknowledgements

      This work was supported by the Morningside Center for Innovative and Affordable Medicine and by the Veterans Administration.

      Appendix. Supplementary materials

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