To our knowledge, our study is the first to assess whether NLR estimated years before diagnosis is associated with mortality among individuals who go on to develop lung cancer. In this study of heavy smokers from CARET, we observed that pre-diagnosis mdNLR was associated with increased mortality for SCLC cases, but not for adenocarcinoma cases or squamous cell carcinoma cases.
Approximately 15% of lung cancer diagnoses are SCLC . SCLC is the most aggressive lung cancer histotype with distinctive tumor behavior characterized by rapid growth, early and widespread metastases, genomic instability, and acquired chemoresistance . Median survival in SCLC patients is just seven months ; we observed a median survival of 8.4 months in our 81 SCLC patient subset from the CARET study. SCLC is not amenable to early detection by screening due to its short preclinical phase , so smoking cessation and improved treatments are the main targets for reducing mortality from this highly lethal and primarily smoking-related cancer [48, 50]. There are currently over 200 ongoing and recruiting clinical trials for SCLC , yet biomarkers for targeted therapy selection and immunotherapy in SCLC remain scarce .
NLR is an index of systemic inflammation that estimates the balance between the innate and adaptive immune systems . Immune homeostasis is a complex and dynamic process that includes maintaining relatively constant component leukocyte proportions within physiologic ranges [43, 53]. Therefore, elevated NLR may indicate immune dysregulation that is evident from abnormal CBC components, such as high neutrophil or low lymphocyte counts, or the ratio measure may indicate low-grade immune dysregulation despite within-range CBCs. When measured prior to treatment at lung cancer diagnosis, higher NLR is thought to reflect the disease state and likelihood of progression since higher neutrophil counts have been shown to promote metastasis [54–56], and lower lymphocyte counts have been observed to be associated with loss of tumor suppressor activities .
We previously reported that higher pre-diagnosis mdNLR was not associated with an increased risk of developing SCLC (Odds Ratio = 1.06, 95% CI: 0.77, 1.47) in 68 matched case-control pairs of high-risk heavy smokers from CARET . Here, we observed that among 81 SCLC cases (including the 68 SCLC patients from the aforementioned case-control study), higher pre-diagnosis mdNLR was associated with increased mortality. Individuals in the highest quartile of mdNLR had 2.5-fold increased mortality compared to those in the lowest quartile. Higher mdNLR was most strongly associated with increased SCLC-specific and all-cause mortality in current smokers, those assigned to the placebo arm, and males compared to their counterpart stratum. The systemic inflammatory profile indicated by higher NLR could indicate a lesser ability to mount a robust immune response to a developing cancer and/or a favorable environment for the pathogenesis of more aggressive SCLC molecular histotypes [58, 59]. Given the short preclinical period of SCLC and the lack of association between mdNLR and SCLC risk in our previous work, we hypothesize that higher NLR measured years before a clinical SCLC diagnosis may reflect a systemic low-grade inflammatory profile that enables poorer post-diagnosis survival rather than occult carcinogenesis. Our sensitivity analysis excluding 23% SCLC cases who were diagnosed within two years of blood draw supports this hypothesis since results were similar, and even stronger for comparisons of the top to the bottom mdNLR quartile (2.5-fold increased SCLC mortality among all SCLC and 3.5-fold increased SCLC mortality in individuals diagnosed more than two years after their blood draw).
In the extensive literature on NLR and mortality in lung cancer patients, pre-treatment NLR is typically measured at diagnosis or up to 30 days prior to treatment [37, 60], and it has been reported to be associated with mortality in meta-analyses of both NSCLC and SCLC [31–37]. However, since blood was drawn on average 4.9 years (median 4.7 years) prior to lung cancer diagnosis in our study, these studies are not directly comparable to ours. One other study currently available in preprint is similar to our work in that respect—a study of 205 lung cancer cases from the “Give Us a Clue to Cancer and Heart Disease” cohorts (CLUE I/II) , with mdNLR measured a median of 14 years prior to diagnosis. They found that each standard deviation increase in pre-diagnosis mdNLR was associated with increased lung cancer-specific mortality (HR = 1.27, 95% CI: 1.08, 1.50). This association was strongest in adenocarcinoma cases (N = 67, HR = 2.12, 95% CI: 1.41, 3.19), but no results were presented for SCLC due to limited sample size (N = 29). In contrast to the CLUE I/II study which included never smokers , our study only includes heavy smokers, and our participants were older and had shorter times from blood draw to diagnosis. In addition, their mdNLR mean was lower, and standard deviations smaller, than those observed in the present study for all lung cancer cases (CLUE I/II mean 1.48 and SD 0.82, CARET mdNLR mean 2.18 and SD 1.46). Meta-analyses of pre-treatment NLR and mortality in lung cancer patients report NLR cut-offs for mortality associations between 2.2 and 5.9 [31–33], with a median NLR cut-off of 3.7 identified across 20 SCLC studies . In our study, blood was collected on average 4.5 years prior to diagnosis for SCLC cases, so mdNLR was more consistent with adult population-level estimates of NLR (from populations with respective mean ages 52 and 48 years) [62, 63]. We did not examine associations using the pre-treatment NLR literature-based cut-offs, as just 3.7% of the SCLC cases in our study had pre-diagnosis mdNLR > 5, and 4.9% had mdNLR > 3.7.
We estimated methylation-based blood cell type proportions from archival samples based on CpGs that were recently identified using deconvolution algorithms applied to EPIC 850K CpG array data (i.e. EPIC-optimized) by Salas et al , in contrast to our prior work in which we used CpGs that were identified using the 450K CpG array data [46, 47]. Cell type estimates, and therefore mdNLR, obtained from the two arrays are highly correlated in our study (mdNLR Spearman r = 0.99, P = 7.0E-301) and in the literature . We opted to use the now available EPIC-optimized method for cell type estimation in this publication since 69% of the EPIC-optimized CpGs are unique to the EPIC array .
Like most NLR studies, our study is limited by a single timepoint of estimated mdNLR. Given that NLR is dynamic in the presence of acute physiologic stress such as infections and disease development, any regression dilution bias in our prospective assessment would be expected to attenuate mortality associations . This may have impacted our ability to observe associations between mdNLR and mortality in adenocarcinoma and squamous cell histotypes. Though we were able to examine mortality within each histotype, histotype data was missing for 7.5% of cases and stage data were missing for 21% of cases. Our study is observational in design and included a limited number of SCLC cases, so our results must be replicated to ensure that they did not arise due to chance or due to confounding by missing or inadequately captured covariates. Since CARET was a phase III chemoprevention trial, a major strength of our study is detailed participant and outcome data. Trial eligibility required that all participants have heavy smoking histories, so all cases had similar exposure to high-risk smoking behavior, making our study robust to confounding of the mdNLR and mortality associations by smoking.
Our results suggest that higher pre-diagnosis mdNLR, which may indicate a low-grade systemic inflammatory profile, is associated with poorer post-diagnosis survival following the most aggressive form of lung cancer, SCLC. Our study provides preliminary evidence suggesting that pre-diagnosis CBCs in heavy smokers at high risk of lung cancer could possibly be leveraged to provide patient-level prognostic information that ultimately may have applications in risk stratification as well as aiding clinical treatment choice and monitoring [41, 55].