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Lung Immune Therapy Evaluation (LITE) Risk, A novel prognostic model for patients with advanced non-small cell lung cancer treated with immune checkpoint blockade

  • Vishal Navani
    Correspondence
    Corresponding author: Dr. Vishal Navani Division of Medical Oncology Department of Oncology University of Calgary 1331 29th St. NW, Calgary, AB T2N 4N2, Telephone: +1 825 994 2463
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada

    Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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  • Author Footnotes
    # These authors contributed to this work equally
    Daniel E. Meyers
    Footnotes
    # These authors contributed to this work equally
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada
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  • Yibing Ruan
    Affiliations
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, AB, Canada

    Forzani & MacPhail Colon Cancer Screening Centre, University of Calgary, Calgary, Alberta, Canada
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  • Devon J. Boyne
    Affiliations
    Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, AB, Canada
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  • Dylan E O'Sullivan
    Affiliations
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, AB, Canada

    Forzani & MacPhail Colon Cancer Screening Centre, University of Calgary, Calgary, Alberta, Canada
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  • Samantha Dolter
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada
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  • Heidi AI Grosjean
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada
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  • Igor Stukalin
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada
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  • Daniel Y.C. Heng
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada

    Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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  • Don G. Morris
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada

    Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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  • Darren R. Brenner
    Affiliations
    Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, AB, Canada
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  • Randeep Sangha
    Affiliations
    Department of Medical Oncology, Cross Cancer Institute, Edmonton, Alberta, Canada”
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  • Winson Y. Cheung
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada

    Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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  • Aliyah Pabani
    Affiliations
    Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada

    Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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  • Author Footnotes
    # These authors contributed to this work equally
Open AccessPublished:January 20, 2023DOI:https://doi.org/10.1016/j.cllc.2022.12.014

      Highlights

      • Question: Can readily available clinical data be used to inform a prognostic scoring model in patients with advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICI)?
      • Findings: In this cohort study of 495 patients with NSCLC, a prognostic scoring system was derived and externally validated. Patients were parsed into 3 discrete risk groups. The final model consisted of the following baseline characteristics: Eastern Oncology Cooperative Group (ECOG) performance status, derived neutrophil to lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH).
      • Meaning: A simple prognostic scoring tool utilizing accessible clinical data can discriminate survival outcomes in patients with advanced NSCLC treated with single-agent ICI.

      Abstract

      Background

      Immune checkpoint inhibitors (ICI) have revolutionized non-small cell lung cancer (NSCLC). We aimed to identify baseline characteristics, that are prognostic factors for overall survival (OS) in patients with NSCLC treated with ICI monotherapy, in order to derive the Lung Immune Therapy Evaluation (LITE) risk, a prognostic model.

      Methods

      Multi-centre observational cohort study of patients with advanced NSCLC that received ≥1 dose of ICI monotherapy. The training set (n=342) consisted of patients with NSCLC who received 1st line ICI. The test set (n=153) used for external validation was a discrete cohort of patients who received 2nd line ICI. 20 candidate prognostic factors were examined. Penalized Cox regression was used for variable selection. Multiple imputation was used to address missingness.

      Results

      Three baseline characteristics populated the final model: ECOG (0, 1 or ≥2), lactate dehydrogenase>upper limit of normal, and derived neutrophil to lymphocyte ratio ≥3. Patients were parsed into 3 risk groups; favorable (n=146, risk score 0-1), intermediate (n=101, risk score 2) and poor (n=95, risk score ≥3). The c-statistic of the training cohort was 0.702 and 0.694 after bootstrapping. The test cohort c-statistic was 0.664.
      The median OS for favorable, intermediate and poor LITE risk were; 28.3 months, 9.1 months and 2.1 months respectively. Improving LITE risk group was associated with improved OS, intermediate vs favorable HR 2.08 (95%CI 1.46–2.97, p<0.001); poor vs favorable HR 5.21 (95%CI 3.69–7.34, p<0.001).

      Conclusions

      A simple prognostic model, utilizing accessible clinical data, can discriminate survival outcomes in patients with advanced NSCLC.
      In this cohort study of 495 patients with NSCLC, a prognostic scoring system was derived and externally validated. Patients were parsed into 3 discrete risk groups. The final model included baseline Eastern Oncology Cooperative Group performance status, derived neutrophil to lymphocyte ratio and lactate dehydrogenase. A simple prognostic scoring tool utilizing accessible clinical data can discriminate survival outcomes in patients with treated with single-agent ICI.

      Introduction

      Immune checkpoint inhibitors (ICIs) have drastically altered the treatment paradigm for non-small cell lung cancer (NSCLC) over the past decade
      • Reck M
      • Rodriguez-Abreu D
      • Robinson AG
      • et al.
      Five-Year Outcomes With Pembrolizumab Versus Chemotherapy for Metastatic Non-Small-Cell Lung Cancer With PD-L1 Tumor Proportion Score >/= 50.
      ,
      • Borghaei H
      • Gettinger S
      • Vokes EE
      • et al.
      Five-Year Outcomes From the Randomized, Phase III Trials CheckMate 017 and 057: Nivolumab Versus Docetaxel in Previously Treated Non-Small-Cell Lung Cancer.
      . However, studies of real-world populations suggests the existence of a concerning efficacy-effectiveness gap
      • Gan CL
      • Stukalin I
      • Meyers DE
      • et al.
      Outcomes of patients with solid tumour malignancies treated with first-line immuno-oncology agents who do not meet eligibility criteria for clinical trials.
      • Meyers DE
      • Stukalin I
      • Vallerand IA
      • et al.
      The Lung Immune Prognostic Index Discriminates Survival Outcomes in Patients with Solid Tumors Treated with Immune Checkpoint Inhibitors.
      • Grosjean HAI
      • Dolter S
      • Meyers DE
      • et al.
      Effectiveness and Safety of First-Line Pembrolizumab in Older Adults with PD-L1 Positive Non-Small Cell Lung Cancer: A Retrospective Cohort Study of the Alberta Immunotherapy Database.
      • Youn B
      • Trikalinos NA
      • Mor V
      • Wilson IB
      • Dahabreh IJ.
      Real-world use and survival outcomes of immune checkpoint inhibitors in older adults with non-small cell lung cancer.
      . As such, robust prognostic scoring tools are needed to aid with risk stratification in order to engage in data-driven shared decision making with patients. Given this evidence gap, we sought to derive and validate a simple and clinically accessible prognostic model, termed the lung immune therapy evaluation (LITE)-risk, to stratify patients with advanced NSCLC treated with ICI monotherapy.

      Methods

      Study Design

      The Alberta Immunotherapy Database (AID) is a multi-centre observational cohort study. Baseline demographic, clinical and tumour characteristics, alongside survival outcomes are identified using a standard template and collected retrospectively
      • Meyers DE
      • Stukalin I
      • Vallerand IA
      • et al.
      The Lung Immune Prognostic Index Discriminates Survival Outcomes in Patients with Solid Tumors Treated with Immune Checkpoint Inhibitors.
      ,
      • Grosjean HAI
      • Dolter S
      • Meyers DE
      • et al.
      Effectiveness and Safety of First-Line Pembrolizumab in Older Adults with PD-L1 Positive Non-Small Cell Lung Cancer: A Retrospective Cohort Study of the Alberta Immunotherapy Database.
      . This database captures outcomes from patients from the time of ICI monotherapy initiation. The training cohort consisted of treatment naïve patients with histologically confirmed advanced NSCLC that received first line (1L) pembrolizumab monotherapy, primarily with PD-L1 tumour proportion score (TPS) ≥ 50%. The test cohort included patients treated with nivolumab or atezolizumab in the second line (2L). We sought to identify baseline characteristics at ICI commencement, independently associated with inferior OS in order to derive a prognostic risk model, initially using the training cohort and then validated on the test cohort. 20 candidate variables were examined based on known clinically accessible, baseline characteristics, with reported prognostic or predictive associations with OS from the contemporary literature
      • Grosjean HAI
      • Dolter S
      • Meyers DE
      • et al.
      Effectiveness and Safety of First-Line Pembrolizumab in Older Adults with PD-L1 Positive Non-Small Cell Lung Cancer: A Retrospective Cohort Study of the Alberta Immunotherapy Database.
      • Youn B
      • Trikalinos NA
      • Mor V
      • Wilson IB
      • Dahabreh IJ.
      Real-world use and survival outcomes of immune checkpoint inhibitors in older adults with non-small cell lung cancer.
      • Popat S
      • Liu SV
      • Scheuer N
      • et al.
      Association Between Smoking History and Overall Survival in Patients Receiving Pembrolizumab for First-Line Treatment of Advanced Non-Small Cell Lung Cancer.
      • Mezquita L
      • Auclin E
      • Ferrara R
      • et al.
      Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer.
      • Banna GL
      • Cortellini A
      • Cortinovis DL
      • et al.
      The lung immuno-oncology prognostic score (LIPS-3): a prognostic classification of patients receiving first-line pembrolizumab for PD-L1 >/= 50% advanced non-small-cell lung cancer.
      • Alessi JV
      • Ricciuti B
      • Jimenez-Aguilar E
      • et al.
      Outcomes to first-line pembrolizumab in patients with PD-L1-high (>/=50%) non-small cell lung cancer and a poor performance status.
      .
      Patients initiated treatment between January 2010 and December 2019, with data analysis undertaken in February 2022. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were followed. This study was approved by the Health Research Ethics Board of Alberta – Cancer Committee (HREBA.CC-19-0380).

      Outcomes of Interest

      The primary endpoint was overall survival (OS), defined as the time from commencement of ICI to death from any cause or date of last follow-up.

      Statistical Analysis

      Baseline laboratory markers were dichotomized based on their relationship to normal ranges and continuous variables based on conventional cut points. All variables were binary in nature except Eastern Cooperative Oncology Group (ECOG) performance status, where patients were stratified as ECOG = 0, ECOG = 1, or ECOG ≥ 2. For this study, we used least absolute shrinkage and selection operator (LASSO) Cox regression for variable selection. LASSO is a statistical method that performs parsimonious variable selection and helps to improve the generalizability of the model to external datasets by reducing the degree of overfitting
      • Ranstam J
      • Cook JA.
      LASSO regression.
      . To deal with missing data, we used multiple imputations by chained equations (MICE). This approach is commonly used and is less biased compared to a complete case analysis that excludes all patients with missing data
      • Azur MJ
      • Stuart EA
      • Frangakis C
      • Leaf PJ.
      Multiple imputation by chained equations: what is it and how does it work?.
      . LASSO Cox regression was fit on the original dataset through 10-fold cross-validation to identify the shrinkage parameter λs that yielded a 3-, 4-, or 5-variable model. Cross-fold validation is a resampling method that uses different portions of the dataset to for testing and training in order to predict how a model will perform on an independent dataset that was not used to derive it. In this way an attempt is made to avoid overfitting or selection bias.
      The shrinkage parameter controls the degree to which the model coefficients are shrunk towards zero and number of covariates selected. Model performance was assessed using Harrell's C-statistic. The C-statistic gives the probability a randomly selected patient who experienced an event (death) had a higher risk score than a patient who had not experienced the event. A value of 0.5 means that the model is no better than predicting the outcome than random chance, where values of 0.7 or higher indicate a good model.
      The prognostic risk model generated was built with independent prognostic factors using the training cohort and then validated using the test cohort. It has been established that prognostic models such as this perform better on data on that informed model construction compared to new data
      • Bleeker SE
      • Moll HA
      • Steyerberg EW
      • et al.
      External validation is necessary in prediction research: a clinical example.
      . Because of this, internal and external validation are required. Internal validation was carried out internally using 500 iterations of bootstrapped samples in the training cohort. Bootstrapping is a statistical procedure that resamples a single dataset with replacement in order to create many simulated samples
      • Stine R.
      An Introduction to Bootstrap Methods:Examples and Ideas.
      . The same variable selection process through LASSO Cox regression was carried out in each bootstrap sample to develop a prognostic model. C-statistics of this model on the bootstrap sample (bootstrap performance) and in the original sample (test performance) were calculated. Optimism, the difference between training error and test error, was estimated as the mean difference between the bootstrapped performance and the test performance after 500 iterations. External validation was conducted using an independent test set comprised of individuals who initiated 2L systemic therapy with an ICI following a 1L therapy other than pembrolizumab. No patients in the training set were included in the test set. OS curves were estimated using the Kaplan-Meier method.
      All statistical analyses were performed in R v.4.0.2 (R, Vienna, Austria). All statistical tests were 2 sided with a significance level of ≤0.05.

      Results

      342 systemic therapy naïve patients receiving pembrolizumab were examined in the training cohort. The external validation cohort (n=153) consisted of patients treated in the 2L with nivolumab (95.4%) or atezolizumab (4.6%) monotherapy As per Table 1, notable significant differences between the test cohort and training cohorts included a lower proportion of patients with adenocarcinoma (62.1% vs 72.8% p=0.05) and PD-L1 TPS ≥ 50% (2.6% vs 95% p<0.0001). More patients in the validation cohort had metastatic disease at ICI initiation (92.2% vs 84% p=0.04) and a lactate dehydrogenase (LDH) > upper limit of normal (ULN) (41% vs 29.3% p=0.029).
      Table 1Baseline Characteristics of Training & Test Cohorts
      Training Cohort n=342Test Cohort n=153P Value
      Mean Age (Range)69.2 (33.2 - 88.9)66.5 (33.1 - 85.2)0.24
        Missing00
      Gender (%)
        Female177 (51.6)81 (52.9)0.78
        Male166 (48.4)72 (47.1)
        Missing00
      Mean BMI (Range)26.2 (14.5 - 42.4)26.0 (15.6 - 44.0)0.97
        Missing102
      Histology
        Squamous74 (21.6)43 (28.1)
        Adenocarcinoma249 (72.8)95 (62.1)
        Other19 (5.6)15 (9.8)0.05
        Missing00
      Smoker
        Yes305 (89.2)135 (91.2)0.57
        Missing145
      ECOG
        051 (14.9)16 (10.5)
        1201 (58.6)84 (54.9)
        275 (21.9)43 (28.1)
        315 (4.3)6 (3.9)0.056
        Missing04
      Stage
        III54 (15.7)12 (7.8)
        IV288 (84)141 (92.2)0.04
        Missing00
      PD-L1
        <1%062 (40.5)
        1-49%16 (4.7)22 (14.4)
        >=50%326 (95.0)4 (2.6)<0.0001
        Missing065
      LDH
        >ULN66 (29.3)48 (41)
        Normal159 (70.7)69 (59)0.029
        Missing11736
      Albumin
        <LLN42 (15.6)21 (16.9)
        Normal227 (84.4)103 (83.1)0.74
        Missing7429
      Haemoglobin
        <LLN58 (19.3)45 (32.9)
        Normal243 (80.7)92 (67.1)0.002
        Missing4216
      Platelets
        >ULN76 (25.3)32 (23.5)
        Normal224 (74.7)104 (76.4)0.69
        Missing4217
      Leukocytes
        <LLN68 (23)39 (28.9)
        Normal228 (77)96 (71.1)0.18
        Missing4618
      Neutrophil Lymphocyte Ratio
        >3123 (41.4)58 (42.7)
        <3174 (58.6)78 (57.3)0.81
        Missing4517
      Calcium
        Normal168 (69.7)83 (70.9)
        >ULN73 (30.3)34 (29.1)0.81
        Missing10136
      Liver Metastases
        Present58 (16.9)37 (24.2)0.13
        Missing10
      Brain Metastases
        Present45 (13.2)21 (13.77)0.63
        Missing20
      Bone Metastases
        Present90 (26.4)53 (34)0.17
        Missing10
      ICI
        Pembrolizumab343 (100)0
        Nivolumab0146 (95.4)
        Atezolizumab07 (4.6)
        Missing00
      Mean No. Cycles ICI (Range)10.5 (1 - 82)8.9 (1 - 68)0.87

      Identifying Prognostic Variables

      The median follow-up time in the training cohort was 21 months (95% CI 19.6 – 22.9).
      One clinical (ECOG) and two laboratory (derived neutrophil to lymphocyte ratio [dNLR] ≥ 3 and lactate dehydrogenase [LDH] ≥ ULN) variables were included in the final multivariable model, based on LASSO Cox regression and independent adverse association with OS, Table 2. The 20 candidate baseline candidate clinical and laboratory variables examined are outlined in Table 3.
      Table 2Multivariable Cox regression analyses of final clinical variables derived from the training cohort (n=342)
      Prognostic VariableTraining Cohort (N = 342)
      HR (95% CI)p-value
       ECOG =12.83 (1.59-5.03)<0.001
       ECOG ≥25.55 (3.04 – 10.13)<0.001
       dNLR ≥ 32.13 (1.60-2.83)<0.001
       LDH > ULN1.68 (1.26-2.24)<0.001
      Table 3Multivariable Cox regression analyses of all examined candidate variables from the training cohort (n=342)
      Prognostic VariableTraining Cohort (N = 342)
      HR (95% CI)p-value
       ECOG =12.42 (1.33, 4.40)0.004
       ECOG ≥24.50 (2.38, 8.48)<.001
       dNLR ≥ 32.00 (1.46, 2.73)<.001
       LDH > ULN1.46 (1.03, 2.06)0.036
       Male sex1.23 (0.90, 1.69)0.19
       Autoimmune Condition at baseline1.25 (0.88, 1.79)0.21
       Age at ICI ≥ 701.06 (0.78, 1.43)0.72
       Lung metastasis1.29 (0.94, 1.78)0.11
       Liver metastasis1.18 (0.82, 1.71)0.37
       Bone metastasis1.16 (0.84, 1.62)0.37
       Brain metastasis1.15 (0.74, 1.79)0.52
       Adrenal metastasis1.76 (1.17, 2.66)0.007
       Other sites of metastases1.67 (1.21, 2.31)0.002
       BMI >=301.47 (0.99, 2.18)0.054
       Hemoglobulin < LLN1.38 (0.98, 1.93)0.06
       Thrombocytosis0.80 (0.55, 1.16)0.24
       Corrected calcium > 2.60.97 (0.65, 1.45)0.89
       Albumin < LLN1.23 (0.87, 1.75)0.24
       Creatinine > 120 umol/L1.50 (0.91, 2.47)0.12
       Smoking history0.88 (0.51, 1.54)0.66
      ICI = Immune Checkpoint Inhibitor, LLN = lower limit of normal

      Lung Immune Therapy Evaluation (LITE)- Risk

      Figure 1A outlines the negative prognostic relationship seen with an increasing risk score in the training cohort.
      Figure 1
      Figure 1(A). Kaplan Meier Overall Survival curve for patients in the training cohort, parsed by individual risk score. (B). Kaplan Meier Overall Survival curve for patients in the training cohort, parsed by a 3 risk group model (favorable, intermediate and poor risk). (C). Kaplan Meier Overall Survival curve for patients in the test cohort, parsed by a 3 risk group model (favorable, intermediate and poor risk)
      Model validation indicated that 3-, 4-, and 5-factor models had similar optimism-corrected performance, while the 3-factor model displayed the best performance in the training cohort (Tables 5 & 6). Therefore, we developed a risk score-based prognostic model based on these 3 baseline variables. The presence of each variable received a score of 1, except for ECOG, in which patients with ECOG ≥ 2 received a score of 2. After comparing several prognostic groupings, a 3 group approach, termed LITE-risk gave the optimum combination of a high C-statistic, 0.702 (SE 0.017) and meaningful discrimination of patient outcomes (Figure 1B, Table 4, Table 7) with LITE favorable risk (n=146, risk score 0-1) median OS (mOS) 28.3 months (95% CI 20.8 – NE), LITE intermediate risk (n=101, risk score of 2) mOS 9.1 months (95% CI 7.1 – 15.3) and LITE poor risk (n=95, risk score ≥ 3) mOS 2.1 months (95% CI 1.7 – 3.7).
      Table 4Overall survival outcomes and performance of the final prognostic model in both the training and test cohorts
      LITE-Risk GroupHR (95% CI)mOS (95% CI)% 1-year OS (95% CI)% 2-year OS (95% CI)C-statistic (SE)
      Training Cohort (1L)0.702 (0.017)
        Favorable (n=146)ref28.3 (20.8 - NE)72.4 (65.3 - 80.2)53.6 (44.7 - 64.3)
      Intermediate (n=101)2.08 (1.46 - 2.97)9.1 (7.1 - 15.3)43.0 (34.3 - 54.0)33.4 (24.3 - 46.0)
        Poor (n=95)5.21 (3.69 - 7.34)2.1 (1.7 - 3.7)15.5 (9.6 - 25.2)12.8 (7.4 - 22.2)
      Test Cohort (2L)0.664 (0.024)
        Favorable (n=56)ref15.6 (10.9 - 23.6)55.6 (43.7 - 70.7)23.0 (12.3 - 43.2)
      Intermediate (n=45)1.82 (1.16 - 2.86)6.9 (4.9 - 8.6)26.5 (16.1 - 43.8)13.2 (5.9 - 29.9)
        Poor (n=52)3.40 (2.18 - 5.29)3.1 (2.5 - 4.5)13.0 (6.0 - 28.2)5.2 (1.4 - 19.6)
      Table 5Selected candidate variables with LASSO Cox regression and C-statistic alongside coefficients on a log scale.
      Training Cohort (n=342)
      No. of predictors3 Predictor4 Predictor5 Predictor6 Predictor
        C-statistic0.7490.7400.7390.736
        ECOG0.460.480.480.49
      N/L ratio≥ 3.00.400.420.420.43
      LDH > ULN0.120.150.160.17
      Hemoglobulin < LLN0.020.030.04
      Adrenal metastasis0.010.04
        Albumin < LLN0.01
      Table 6LASSO derived C-indices of the original model and the bootstrap validation
      C-statistic, estimate (SE or 95%CI)Imputed set
      No. of predictors3p4p5p
      Apparent performance
        Original sample (1st line)0.749 (0.018)0.740 (0.017)0.739 (0.017)
        Test dataset (2nd line)0.706 (0.027)0.700 (0.025)0.695 (0.024)
      Bootstrap validation
        Bootstrap sample0.749 (0.019)0.745 (0.018)0.747 (0.016)
        Test set (original sample)0.741 (0.007)0.735 (0.006)0.733 (0.006)
        Test dataset (2nd line)0.696 (0.015)0.694 (0.015)0.691 (0.016)
        Optimism0.008 (0.019)0.010 (0.018)0.014 (0.016)
      Corrected performance0.741 (0.695, 0.787)0.730 (0.687, 0.774)0.725 (0.685, 0.766)
      Table 7Performance and optimism of evaluated 3 factor risk score models
      Potential ModelRisk ScoreN patientsN eventsC-statistic (original dataset), estimate (SE)Optimism, Mean (SE)Corrected C-statistic, estimate (95% CI)
      Binary 10–1146580.651 (0.016)0.006 (0.014)0.646 (0.609, 0.682)
      2–4196145
      Binary 20–22471220.653 (0.016)0.006 (0.016)0.646 (0.607, 0.686)
      3–49581
      Ternary 10–1146580.673 (0.016)0.008 (0.015)0.666 (0.628, 0.703)
      2–3169119
      42726
      Ternary 20–1146580.702 (0.017)0.008 (0.017)0.694 (0.653, 0.735)
      210164
      3–49581
      Quaternary0-1146580.704 (0.016)0.008 (0.016)0.696 (0.655, 0.736)
      210164
      36855
      42726
      Individual035100.711 (0.016)0.008 (0.016)0.703 (0.664, 0.743)
      111148
      210164
      36855
      42726
      Compared to the favorable risk group, both intermediate risk (HR 2.08; 95% CI 1.46 – 2.97) and poor risk (HR 5.21; 95% CI 3.69 – 7.34) had a significantly increased risk of death (p < 0.001 for both comparisons).

      Model Validation

      We undertook internal validation on the training set with 500 random bootstrapped resampled datasets to generate an optimism corrected C-statistic of 0.694 (95% CI 0.653 – 0.735), implying high discriminatory ability of the model.
      The 3-factor model was then externally validated in the 2L test set, Table 4 and Figure 1C outline that the model consistently discriminated survival outcomes of patients. Compared to the favorable risk group, both intermediate risk (HR 1.82; 95% CI 1.16 – 2.86) and poor risk patients (HR 3.40; 95% CI 2.18 – 5.29) had a significantly increased risk of death (p < 0.001 for both comparisons), C-statistic 0.664 (SE 0.024).

      Discussion

      The findings of this study suggest that a simple and clinically accessible prognostic model can parse patients with advanced NSCLC treated with single-agent ICI into three discrete LITE-risk groups with clinically impactful differing OS, LITE intermediate vs favorable risk (HR 2.08; 95% CI 1.46 – 2.97) and poor vs favorable risk (HR 5.21; 95% CI 3.69 – 7.34), c-statistic 0.702. Consistent model performance was seen in the internally validated training and externally validated test cohorts.
      The final 3-variable model included baseline patient ECOG performance status, dNLR and LDH; all of which have previously been described as being independent prognosticators of poor OS in advanced NSCLC
      • Grosjean HAI
      • Dolter S
      • Meyers DE
      • et al.
      Effectiveness and Safety of First-Line Pembrolizumab in Older Adults with PD-L1 Positive Non-Small Cell Lung Cancer: A Retrospective Cohort Study of the Alberta Immunotherapy Database.
      ,
      • Mezquita L
      • Auclin E
      • Ferrara R
      • et al.
      Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer.
      • Banna GL
      • Cortellini A
      • Cortinovis DL
      • et al.
      The lung immuno-oncology prognostic score (LIPS-3): a prognostic classification of patients receiving first-line pembrolizumab for PD-L1 >/= 50% advanced non-small-cell lung cancer.
      • Alessi JV
      • Ricciuti B
      • Jimenez-Aguilar E
      • et al.
      Outcomes to first-line pembrolizumab in patients with PD-L1-high (>/=50%) non-small cell lung cancer and a poor performance status.
      ,
      • Cortellini A
      • Ricciuti B
      • Borghaei H
      • et al.
      Differential prognostic effect of systemic inflammation in patients with non-small cell lung cancer treated with immunotherapy or chemotherapy: A post hoc analysis of the phase 3 OAK trial.
      . Combining patient focused ECOG with known established markers of systemic inflammation such as LDH
      • Mezquita L
      • Auclin E
      • Ferrara R
      • et al.
      Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer.
      and an inflammatory tumour microenvironment via dNLR
      • Fuca G
      • Galli G
      • Poggi M
      • et al.
      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.
      is attractive mechanistically. Although other groups have derived prognostic models in a similar treatment context
      • Mezquita L
      • Auclin E
      • Ferrara R
      • et al.
      Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer.
      ,
      • Banna GL
      • Cortellini A
      • Cortinovis DL
      • et al.
      The lung immuno-oncology prognostic score (LIPS-3): a prognostic classification of patients receiving first-line pembrolizumab for PD-L1 >/= 50% advanced non-small-cell lung cancer.
      , our work adds value by providing external validation in a patient cohort treated in the second line setting. Prognostic factors should be intrinsic and not affected by systemic treatment. The clinically meaningful performance of this model in the 1L and 2L settings, which have established differences in baseline characteristics that impact ICI activity, such as level of PD-L1 expression and histology
      • Reck M
      • Rodriguez-Abreu D
      • Robinson AG
      • et al.
      Five-Year Outcomes With Pembrolizumab Versus Chemotherapy for Metastatic Non-Small-Cell Lung Cancer With PD-L1 Tumor Proportion Score >/= 50.
      give us confidence in the clinical relevance of LITE-risk groups. Our optimism adjusted c-statistic values in the training and test sets are also comparable to other prognostic models in this context
      • Banna GL
      • Cortellini A
      • Cortinovis DL
      • et al.
      The lung immuno-oncology prognostic score (LIPS-3): a prognostic classification of patients receiving first-line pembrolizumab for PD-L1 >/= 50% advanced non-small-cell lung cancer.
      . It is important to note that our statistical approach increased internal validity via bootstrap analysis, which was not undertaken in other similar studies
      • Mezquita L
      • Auclin E
      • Ferrara R
      • et al.
      Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer.
      ,
      • Banna GL
      • Cortellini A
      • Cortinovis DL
      • et al.
      The lung immuno-oncology prognostic score (LIPS-3): a prognostic classification of patients receiving first-line pembrolizumab for PD-L1 >/= 50% advanced non-small-cell lung cancer.
      . LITE-risk groups are able to provide clinicians new benchmarks for real world OS.
      Previous prognostic models in this context such as LIPS-39 utilized the same datasets for training and validation, and this single split of datasets have been established to give erroneous estimation of model performance
      • Harrington PB.
      Multiple Versus Single Set Validation of Multivariate Models to Avoid Mistakes.
      . Our use of bootstrapping for internal validation, LASSO Cox regression to reduce overfitting and reporting of an optimism-corrected C-statistic improves the estimation of the generalization performance of LITE-risk when compared to LIPS-3
      • Xu Y
      • Goodacre R.
      On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning.
      . We were not able to formally compare our model with LIPS-3 to determine statistical robustness due to the lack of data regarding pre-treatment steroids, a LIPS-3 predictor, in our dataset.
      The inferior survival outcomes seen in the LITE-poor risk groups in both the training and validation cohorts, mOS of 2.1 and 3.1 months, respectively are striking. These data are helpful to allow informed conversations about expectations of therapy, and provides further justification for the early involvement of palliative care
      • Sullivan DR
      • Chan B
      • Lapidus JA
      • et al.
      Association of Early Palliative Care Use With Survival and Place of Death Among Patients With Advanced Lung Cancer Receiving Care in the Veterans Health Administration.
      . It is unclear whether these patients would benefit from treatment intensification with chemo-immunotherapy.

      Limitations

      The retrospective nature of the work, and accrual of all patients from one province may limit the generalizability of our results. The lack of a control group, receiving combined chemo-immunotherapy or chemotherapy alone prevents us from assessing any predictive capabilities of LITE-risk. Our focus on overall survival as the primary outcome, and data capture only from time of ICI initiation, means that delineating the benefit of any subsequent lines of chemotherapy (for the 1L training cohort) from the survival gain provided by ICI is difficult.
      Due to the limited patient population studied, it is not clear if our prognostic model applies in patients treated with other systemic therapies alone such cytotoxic chemotherapy or in other, earlier disease states amenable to curative intent ICI
      • Antonia SJ
      • Villegas A
      • Daniel D
      • et al.
      Overall Survival with Durvalumab after Chemoradiotherapy in Stage III NSCLC.
      after definitive chemoradiation. Our use of a 2L cohort as an external validation population may be controversial, due to the known differences in baseline characteristics and survival outcomes compared to an ICI naïve setting and thus prospective validation from larger, international datasets are required. PD-L1 expression is an established predictive biomarker in the 1L
      • Reck M
      • Rodriguez-Abreu D
      • Robinson AG
      • et al.
      Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer.
      and 2L ICI
      • Borghaei H
      • Paz-Ares L
      • Horn L
      • et al.
      Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung Cancer.
      ,
      • Rittmeyer A
      • Barlesi F
      • Waterkamp D
      • et al.
      Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial.
      contexts. However, in our dataset the rate of PD-L1 expression “missingness” in the test cohort (2L) was 42.4% (65/153), as opposed to 0% in the training cohort (1L), as it is not a public reimbursement payor requirement prior to receipt of 2L therapy, as opposed to 1L therapy. Statistical approaches would be unreliable in this context as the data are not missing at random, making MICE an inappropriate tool, therefore we elected not to evaluate this as a candidate variable. The assessment of LITE-risk in association with PD-L1 expression should be evaluated prospectively to outline the prognostic/and or predictive value of this model.
      A prognostic variable used, LDH was missing data in 34% of patients. However, when examining the impact of this missing data on variable selection, it is likely that LDH is missing at random, and so MICE is an appropriate tool
      • Madley-Dowd P
      • Hughes R
      • Tilling K
      • Heron J.
      The proportion of missing data should not be used to guide decisions on multiple imputation.
      . Other modelling approaches within oncology may achieve a higher discriminatory ability, e.g. nomograms
      • Pires da Silva I
      • Ahmed T
      • McQuade JL
      • et al.
      Clinical Models to Define Response and Survival With Anti-PD-1 Antibodies Alone or Combined With Ipilimumab in Metastatic Melanoma.
      , however we intentionally focused on a LITE-risk score that was easy to derive, utilising commonly used a priori cut-offs. This provides parsimony and the ease of use should not be understated were this to be incorporated into practice. Statistical information and power is commonly lost with categorisation in this way, but is common in medicine.

      Conclusions

      This study describes the derivation and external validation of a simple prognostic LITE-risk model utilizing readily accessible clinical data points for patients with advanced NSCLC treated with ICI monotherapy.

      Credit Author Statements

      Dr. Vishal Navani had full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. All information and material in the manuscript are original.
      Dr Heng reported receiving grants from Pfizer, Novartis, Bristol-Myers Squibb, Ipsen, Merck, and Eisai outside the submitted work.
      Dr Navani reports personal consulting fees from Kwoya Kirin, Novotech PTY, Pfizer, Astra Zeneca, EMD Serono and IPSOS
      The other authors report no disclosures

      Acknowledgements

      A provinical statewide Alberta ethics (Health Research Ethics Board of Alberta) has provided overseeing ethics approval for this study (HREBA.CC-19-0380).
      Data analysis and statistical code utilised can be made available on reasonable request
      No funding was provided for this study

      Appendix. Supplementary materials

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