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Clinically relevant prognostic and predictive markers for immune-checkpoint-inhibitor (ICI) therapy in non-small cell lung cancer (NSCLC)

Abstract

Background

Immune checkpoint inhibitors (ICI) either alone or in combination with chemotherapy have expanded our choice of agents for the palliative treatment of non-small cell lung cancer (NSCLC) patients. Unfortunately, not all patients will experience favorable response to treatment with ICI and may even suffer from severe side effects. Therefore, prognostic and predictive markers, beyond programmed death ligand 1 (PD-L1) expression status, are of utmost importance for decision making in the palliative treatment. This review focuses on clinical, laboratory and genetic markers, most of them easily to obtain in the daily clinical practice.

Results

Recently, a number of prognostic and predictive factors in association to palliative ICI therapy have been described in NSCLC. Besides biometric parameters and clinical characteristics of the tumor, there are useful markers from routine blood sampling as well as innovative soluble genetic markers which can be determined before and during ICI treatment. Additionally, the level of evidence is noted.

Conclusions

These factors can be helpful to predict patients’ outcome and tumor response to ICI. They should be implemented prospectively in ICI based clinical trials to develop reliable algorithms for palliative NSCLC treatment.

Peer Review reports

Key points

  • PD-L1 alone is the only approved prognostic biomarker for ICI therapy in NSCLC patients to date.

  • Patients in a poor PS status or those needing oral corticosteroids or antibiotics before treatment are probably no good candidates for an ICI based therapy unless these medications can be stopped. Additionally, patients with a malignant pleural effusion might not have a benefit from immunotherapy.

  • In addition to clinical features, laboratory parameters like CRP or the neutrophil-to-lymphocyte ration (NLR) or genetic factors like sPD-L1 or ctDNA in the blood measured before and during ICI treatment may become evident as prognostic or predictive markers in the near future. As there is a difference in predictive value between TMB, KRAS and STK11/KEAP1 between trial with ICI monotherapy and those with ICI-combinations those markers are not yet ready for being used in the clinical setting.

Background

In recent years the choice of standard therapeutic options for metastatic NSCLC patients has been expanded by immune-checkpoint-inhibitor (ICI) therapy either as monotherapy or in combination with chemotherapy, anti-angiogenic antibodies or with other forms of ICI. ICI, namely antibodies directed against PD-1 or PD-L1, were approved for the 2nd or 3rd line treatment of metastatic NSCLC in patients without treatable driver mutations in 2015 [1,2,3,4,5,6,7]. Since then, ICIs have been approved in the 1st line setting, either alone in tumors with PD-L1 ≥ 50% expression or in combination with chemotherapy independent of the receptor status [8,9,10,11,12,13]. Some patients treated with ICIs have exceptionally long-lasting responses and survival. For instance, up to 16% of NSCLC stage IV patients treated with the PD-1 inhibitor nivolumab in 2nd line and 31.9% of patients in 1st line with the PD-1 inhibitor pembrolizumab survived 5 years, respectively [11, 14, 15].

On the other hand, many mortality curves of the ICI therapy showed an increased risk of death during the initial phase of ICI treatment. Reasons for this phenomenon may be primary resistance with hyper progressive tumor growth as well as immune-related adverse events. Therefore, it is of utmost importance for the treating physician to reliably know whether the ICI will be beneficial for the individual NSCLC patient or not. This prediction of response can be based on prognostic and, if other treatment options are available, predictive factors. However, in most pivotal trials those factors are not analyzed comprehensively. Furthermore, due to strict exclusion criteria, those trials do not represent the broad spectrum of patients in the daily clinical practice presenting with e.g. low performance status and possible co-morbidities. Harvey et al. recently presented a revised and broadened list of clinical trial eligibility criteria to enable more patients to participate, make future trial populations more representative and results more generalizable [16]. They focused on including patients with other primary cancers, brain metastases irrespective of treatment status and clinical stability as well as a creatinine clearance > 30 mL/min, what enabled nearly twice as many NSCLC patients to consider trial participation in their real-world data analysis. It is of paramount importance that clinical trial sponsors adopt these criteria to increase the practical value of clinical studies.

PD-L1 expression on cancer cells determined by immunohistochemistry to date is the only fully in clinical practice implemented, FDA-approved biomarker for ICI response so far. The use of the ICI-agents pembrolizumab and atezolizumab as monotherapy in the palliative 1st line setting is therefore only approved for tumors with a PD-L1 Tumor Proportion Score (TPS-Score) of 50% or higher [10, 17, 18]. For second line in stage IV with pembrolizumab the tumor must have a PD-L1 expression of at least 1% [19,20,21].

Nevertheless, the predictive power of PD-L1 expression is limited:

  • the expression of PD-L1 seems to be lower in primary tumor samples than in metastases. This indicates that an advanced disease may be more likely to express PD-L1 than an earlier stage disease [22].

  • the extent of PD-L1 expression in metastases varies modestly by anatomic site. The proportion of cases with “PD-L1 high expression” was greatest in lymph nodes (30% PD-L1 high) and least in bone (16% PD-L1 high), so lymph node sampling may require an adjusted scale [22].

  • among patients with primary tumor tissue sampling, there was lower PD-L1 expression when resection specimens were used compared to biopsy samples [22]. These weaknesses of PD-L1 emphasize the demand of additional markers.

The biomarkers discussed in this review will be categorized into prognostic and predictive markers by the definition used by Clark et al. [23, 24]. They defined a prognostic marker as a predictor of the natural history of disease. These markers are associated with the clinical outcome in the absence of therapy or when a standard therapy is applied [24, 25]. Predictive markers on the other hand indicate a differential benefit from a particular therapy. Furthermore, we divided the potential markers into A) biometric data and clinical findings, B) blood based parameters, and C) tumor based immunohistochemical and DNA markers [24, 25]. Details of the following data and the studies included in this review are summarized in Table 1A, B and C.

Table 1 Prognostic and predictive factors in A) clinical findings, B) laboratory values and C) immunohistochemistry and genetics for ICI treatment in NSCLC discussed in this review in more detail. Level of evidence is given for every marker. Sequence of studies as appearance in the review. n.s., not significant; + statistically significant positive prognostic/predictive factor; − statistically significant negative prognostic/predictive factor

Due to the growing importance of ICI in the treatment of NSCLC, a number of recent reviews have been published that focus on prognostic factors. However, most of them focused on PD-L1 and TMB as biomarkers, went into detail on specifically selected pathways, e.g. the WNT signaling pathways, or summarized prognostic factors for both treatment success and for overcoming resistance to ICI [68,69,70]. Beside including the latest results for biomarkers from post hoc analyses of recently published Phase III trials, this review focuses on factors that are easily identified in daily clinical practice prior to initiating ICI based therapy. In addition, we have briefly presented the scientific basis explaining why each of these biomarkers may be prognostic for the effect of ICI therapy and provide the available evidence level for the predictive value of the biomarker. Finally, we provide a comprehensive chart overview of the various positive prognostic and predictive factors in the treatment of patients with ICI discussed in this review. (Fig. 1).

Fig. 1
figure1

Comprehensive overview about the different positive prognostic and predictive factors in treating patients with ICI discussed in the review

The electronic databases Pubmed and PMC as well as abstracts from the ESMO, ASCO, AACR and WCLC congresses were systematically searched to identify eligible studies evaluating predictive and prognostic biomarkers in NSCLC patients receiving ICI therapy. There was no limitation of language and study type. All resources in this article were classified with the Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence table and evaluated the endpoints PFS or OS [71, 72].

Main text

Clinical findings predicting the efficacy of ICI therapy

Gender

Gender is a relevant factor that modulates immune response and has effects on the expression and function of PD-L1 and PD-1 [26, 73, 74]. Many studies on various tumor types including NSCLC could show that not women but men have an increased efficacy of single agent ICI treatment [4, 26, 75, 76]. On the other hand, women apparently have a greater advantage from a combination of ICI and chemotherapy [8, 12, 21, 27, 60]. A possible hypothesis to explain this finding could be that the immune responses of women tend to be stronger, which means that a woman’s lung cancer requires more effective mechanisms to escape the immune system and is therefore less immunogenic than a comparable tumor in a male patient and therefore more resistant to immunotherapy [26, 77]. This is consistent with the observation that men have more partially exhausted cytotoxic T-lymphocytes (peCTLs), such as tumor-infiltrating CD8+ T-cells, which exhibit high amounts of CTLA-4 and PD-1 [78].. These peCTLs seem to be significantly correlated with a better response to anti–PD-1 monotherapy. The observation that additional chemotherapy increases mutational burden and leads to increased non-antigenic exposure of the immune system could explain the greater benefit of this combined treatment strategy in women [27, 79, 80].

Eastern Cooperative Oncology Group Performance Status (ECOG-PS)

Various studies on ICI therapy have demonstrated that patients with an excellent or good performance status (ECOG-PS ≤ 2) have a better PFS and OS [28, 29, 81, 82]. Naqash et al. explained this finding with the limited physiological reserves of patients with bad PS which may be a result of either the harmful character and advanced stage of their tumors or from their medical comorbidities [29] . These reduced physiological reserves in turn impair these patients’ tolerance of therapy and therapy-related side effects, causing therapy interruptions or discontinuations. Furthermore, a reduced PS could be an indicator of aggressive tumor biology with intrinsic resistance to ICI therapy. In a meta-analysis of real world NSCLC data including 19 studies it could be shown that PS ≥2 is a poor prognostic factor in terms of RR, PFS and OS for patients treated with ICI [30]. The authors stated that ECOG PS ≥2 patients constitute an inhomogeneous group of patients where the poor clinical condition might be linked with comorbidities, cancer or both. Therefore, patients with an ECOG PS of ≥2 are often excluded in Phase-II and -III trials, nevertheless these patients often show up in the daily clinical practice and need effective treatment options.

Body mass index and weight change

In contrast to cardiovascular risk which increases with body weight, it might come as a surprise that patients who presented with a BMI ≥ 25 showed a longer TTF, PFS and OS with ICI treatment than those with a lower BMI [31]. Shepshelovich et al. also reported a U-shaped association between BMI at diagnosis and OS with greater mortality in the extreme groups of underweight and morbidly obese patients, but with the best outcomes in those who are overweight or obese [32]. The results were consistent in all documented tumor entities, disease stages and treatment methods, supposing that this might be a prognostic factor. They did not only compare the BMI at the start of treatment, but also the BMI during youth of the patients, taking a time interval between 18 and 25 years. Relative to the BMI during early adulthood, a decrease in BMI at diagnosis was associated with worse OS when compared to patients who had similar or increased BMI at the time of diagnosis.

Another study analyzed the role of weight change between initial diagnosis and the start of second-line nivolumab immunotherapy in lung cancer patients [33]. They demonstrated that patients with weight loss of more than 5% had a worse OS (median OS of 2 months) than patients who lost less than 5% or even increased their weight (median OS of 10 months). In addition, their results suggest that CT-derived measurements of muscle and fat mass are less clinically relevant than simple, medical history-based assessment of weight loss.

The explanation for this phenomenon might be found in the role of white adipose tissue, which is the effectively increased in obese individuals [31, 83]. This tissue is involved in the induction and coordination of host defenses, being a source of cytokines and chemokines [31, 84]. In fact, adipose tissue modulates the Th1/Th2 balance, decreases the activation of Treg cells through adiponectin and increases pro-inflammatory macrophages as well as the inflammatory status through the CD-40 pathway [31, 85,86,87]. Individuals with this type of inflammatory status may be more sensitive to the immune checkpoint blockade by PD-1/PD-L1 inhibitors.

Metastases and their localization

Malignant pleural effusion

Several studies have demonstrated, that not only their incidence, but also the localization of metastases is associated with the individual’s prognosis [28, 34]. In particular, the occurrence of malignant pleural effusion seems to be an independent negative predictor for PFS in patients treated with first-line pembrolizumab or second-line nivolumab, possibly because of its correlation with tumor induced immunosuppression [28, 35]. Kawachi et al. hypothesized that this is mediated by vascular endothelial growth factor (VEGF), which promotes malignant pleural effusion by increasing vascular and mesothelial permeability, as well as capillary fluid leakage. Elevated VEGF in patients with malignant pleural effusions is a strong predictor of unfavorable response to pembrolizumab monotherapy in NSCLC patients despite high PD-L1 expression [28, 88,89,90,91,92]. Therefore, the combination of chemotherapy, an ICI agent and bevacizumab, an anti-VEGF antibody, seems to be a promising treatment option in these cases [8, 9, 12, 13, 28, 92,93,94]. Another problem associated with malignant pleural effusions is the increased number of severe adverse events seen during PD-1 inhibitor based therapy in these patients [34]. This might be explained by higher PD-1 expression on lymphocytes found in the malignant effusions, that may induce excessive immune reactions, which might boost pleural effusion as well as increase the risk of severe advance effects of ICI [34, 95].

Liver metastases

Furthermore, the presence of liver metastases in NSCLC patients is associated with distinctly worse outcomes irrespective of PD-L1 status [35, 37]. Many authors tried to explain this finding as to why liver metastases represent such a challenge for clinicians. In melanoma patients it has been observed that the density of CD8-positive T-cells at the tumor margin of liver metastasis was markedly reduced and hypothesized that this might reduce the efficacy of ICI therapy [96].

In turn, Reck et al. hypothesized that the similarity of NSCLC liver metastasis to hepatocellular carcinoma (HCC) might explain why the combination therapy of atezolizumab + bevacizumab + carboplatin and paclitaxel (ABCP) showed superiority in terms of PFS and OS compared with atezolizumab and chemotherapy (ACP) despite lower PD-L1 expression in patients with liver metastases in the IMpower150 trial [10].

HCC is associated with hypoxic tumor conditions, high VEGF expression and increased angiogenesis, which can contribute to the induction of immunosuppressive cells like regulatory T cells and the promotion of immune tolerance in the tumor microenvironment (TME) [36, 97, 98]. The poor response of patients with liver metastases to ICI monotherapy might be due to this liver-tissue-specific immunoregulation and the addition of bevacizumab potentially reverses this phenomenon [10]. Data seem to be very robust for this ICI and anti-angiogenic combination as the presence of liver metastases was a stratification factor in this trial.

Brain metastases

Another organ frequently affected by metastases in NSCLC is the brain. The blood-brain barrier reduces the intracerebral availability of drugs and circulating antibodies and prevents the influx of lymphocytes [74]. However, the KEYNOTE-024 trail (first line pembrolizumab) and the KEYNOTE-189 trial (pembrolizumab + pemetrexed & platinum) showed superior OS in all subgroups, even in the group of patients with brain metastasis [8, 17]. This makes ICI therapy a therapeutic option with the need of future prospective trials in patients with active brain metastasis. In a recently published Phase II study in NSCLC patients with CNS metastases without cranial radiotherapy, pembrolizumab monotherapy resulted in an intracranial response in 29.7% of patients with greater than 1% PD-L1 expression. In contrast, no patient with PD-L1 status < 1% had any such response [99].

Baseline systemic corticosteroid use

Many NSCLC patients are active or former smokers and thus developed chronic obstructive pulmonary disease (COPD) as a comorbidity. Some of these patients need oral corticosteroids for acute exacerbations or other reasons. However, use of systemic corticosteroids may reduce the clinical beneficial effects of ICIs. In the study of Scott et al. the median OS of patients receiving corticosteroids (≥ 10 mg of prednisone equivalent) during the first 30 days of nivolumab therapy was significantly reduced [38]. This finding was supported by recent retrospective studies [35, 39, 40, 100] and was recently confirmed by retrospective analyses on melanoma, RCC and NSCLC patients treated with nivolumab and pembrolizumab [101]. Patients with a steroid dose of > 10 mg prednisone for more than 2 weeks during ICI treatment had worse outcomes. As a consequence, chemotherapy might be the preferred treatment option, if systemic corticosteroids cannot be avoided in individual patients.

Antibiotics given before and during ICI therapy

Patients with NSCLCs are prone to bronchopulmonary bacterial infections and antibiotic therapy may become necessary before or during ICI therapy. However, recent studies show that the antibiotic therapy may be associated with reduced therapeutic effects of ICI therapy. Routy et al. published data suggesting that antibiotic use within 60 days before treatment initiation is associated with a negative effect on OS in a mixed cohort of NSCLC, urothelial carcinoma and RCC patients [41, 102].

In a follow-up study with a larger population of NSCLC patients, it was observed that antibiotic use within 1 month before immunotherapy was associated with a significantly worse OS [42, 102]. These findings are consistent with other results demonstrating that broad spectrum antibiotic therapy within 2 weeks prior to or after ICI use is associated with shorter PFS and a lower response rate [43, 102].

Recently, a pooled post hoc analysis of the OAK and POPLAR trials was published, where atezolizumab was compared with docetaxel in NSCLC patients [44]. 22.3% of patients in the atezolizumab group receiving antibiotics had a significant shorter survival (8.5 months) compared to those ICI patients without anti-microbiologic agents (14.1 months) (HR 1.32, p = 0.001). In addition, there was no association between antibiotic use and PFS or OS within the docetaxel population, indicating that antibiotic use was a predictive factor in this analysis. The precise mechanism of these findings has not yet been established. Some experts speculate that antibiotic-related dysmicrobiosis might lead to reduced diversity of the intestinal microbiome and probably eradication of immunogenic bacteria required to support the immune system during ICI therapy [42]. In this context Hakozaki et al. performed a prospective fecal sampling study in 70 NSCLC patients treated with ICI monotherapy [45]. They could demonstrate that those who received antibiotic therapy prior to treatment (22.8%) tended to have a lower bacterial diversity with an underrepresentation especially of Ruminococcaceae UCG 13 and Agathobacter in their intestinal microbiome. These findings were associated with a worse outcome in terms of OS compared to antibiotic-free patients. This suggests that the intestinal microbiome could possibly be developed as a potential predictive marker in the future.

Interestingly, the role of the intestinal microbiome can not only be analyzed by elaborate microbiological examinations, but can also be visualized very elegantly using PET-CT. Cvetkovic et al. analyzed the physiologic colonic uptake of 18F-FDG on PET/CTs of 71 NSCLC prior to ICI therapy [103]. The cohort with a high colonic uptake had a higher risk to experience non-response to ICI and had lowered PFS as well as decreased baseline gut microbiome diversity. Patients were also analyzed according to their cecum uptake, which was also associated with decreased OS in those with a higher cecum uptake. The physiological colonic and cecum uptakes could serve as surrogate markers of gut microbiome diversity, predict clinical outcomes and support the previously mentioned dysbiosis-theory.

Blood based biomarkers

Blood parameters measured before and during ICI therapy are an important source of information and may help clinicians to decide which therapy to choose and whether ICI might be combined with chemotherapy or anti-angiogenetic agents. Laboratory parameters obtained from blood samples are an easily accessible, non-invasive alternative for parameters analyzed in tissue samples.

Baseline Neutrophil-to-Lymphocyte Ratio (NLR) and combination-indexes

Pretreatment elevated baseline NLR calculated from complete blood counts was found to be a prognostic biomarker for patients with NSCLC. A high NLR was a predictor of rapid progression, poor OS and short PFS not only in patients on chemotherapy and targeted therapy, but also in ICI treatment and chemo-ICI combinations [46,47,48, 51, 82, 104, 105]. Cao et al. analyzed 1225 patients in a meta-analysis and found 1.44-fold better PFS and a 1.75 times higher OS for patients with low pre-treatment NLR (< 5) [46]. In contrast low NLR and PLR (platelet-to lymphocyte ratio) at baseline were reported to be significantly associated with the development of immune related toxicities in NSCLC patients [82]. However, only PLR was an independent prognostic factor for toxicity in this retrospective analysis.

In immunotherapy it is hypothesized that, due to the systemic inflammation initiated by the tumor itself, the increased infiltration of neutrophils promotes cancer progression via secreting interleukin-10 (IL-10), tumor necrosis factor α (TNF-α), and VEGF [104, 106]. TNF-α and IL-10 both reduce the number of lymphocytes and inhibit with T-lymphocyte-mediated antitumor effects. Accordingly, the NLR has the potential to serve as a predictive marker for the outcome of ICI therapy [104]. NLR is also used as part of various indexes like Systemic-Immune-Inflammation Index (SII) [platelet count x NLR] [49, 107]. Here Liu et al. established the SII of 44 NSCLC patients before 2nd line nivolumab treatment initiation and reported an increased OS and PFS for patients with a low SII (< 603.5) [median OS: 19.8 months; median PFS: 6.9 months]. The Lung Immune Prognostic Index (LIPI) used by Ruiz-Banobre et al. combines NLR > 3 and LDH greater than upper limit of norm [108]. They classified their cohort of 153 NSCLC patients receiving 2nd line nivolumab in three groups according to LIPI and reported median OS for their poor, intermediate, and good LIPI subgroups to be 3.4 months, 7.3 months and 20.8 months respectively [108].

Lactate Dehydrogenase levels (LDH)

In a meta-analysis out of six studies with 1136 patients, Zhang et al. confirmed the hypothesis that low pretherapeutic LDH levels were correlated with significantly longer PFS and OS of NSCLC patients treated by ICI [53]. LDH levels seem to be related to intratumoral hypoxia and increased angiogenesis mediated by macrophages [109,110,111,112,113]. However, it remains uncertain which cut-off value of LDH is best to estimate the survival of patients with NSCLC. The values varied in the individual studies between 217 and 400 U/L [53]. Furthermore, since LDH is a dynamic marker, the optimal time and frequency of LDH measurements is also unclear [53]. It was hypothesized that only decreasing LDH levels from baseline to week 12 of CTLA4-antibody therapy were associated with longer PFS and OS in melanoma patients [114, 115]. However, these data need further confirmation.

C-Reactive Protein (CRP)

C-reactive protein (CRP) is an acute phase protein of hepatic origin that reflects tissue injury and systemic inflammation. Since inflammation forms an essential aspect of the neoplastic process, it came as no surprise that elevated baseline CRP levels seems to be a predictive marker for better PFS and OS after ICI initiation in NSCLC and melanoma patients [35, 48, 54, 116, 117]. We recently could show that a doubling of baseline CRP level was associated with a 1.4-fold higher relative risk of disease progression or death and that a 10 mg/mL/month faster increase in CRP levels over time predicted for 13-fold higher risk of experiencing a PFS event. In addition, a decreasing CRP during the first 8 weeks after ICI initiation was strongly associated with favorable PFS experience (10% reduction in CRP predicted for a 0.9-fold reduction in PFS) [118].

A locally inflamed TME promotes cancer progression by stimulating cell proliferation, angiogenesis and cancer cell migration. Furthermore, cancer cells also promote systemic inflammation, which is induced by diverse immune cells, cytokines and acute phase proteins [119, 120]. Systemic inflammation often indicates cancer progression and is considered to cause cancer-related complications such as cachexia, pyrexia and fatigue [119, 121,122,123,124,125,126,127]. Moreover, systemic inflammation as reflected by serum CRP has been suggested to correlate with low levels of CD4+ T-cells, which play a key role in ICI mediated antitumor immune response [128].

Genetic markers from tumor tissue and/or blood

Soluble PD-L1 in plasma

Beside the immunohistochemical PD-L1 staining on tumor cells, which is currently a standard procedure in the initial histological diagnosis of NSCLC, plasma markers are also of particular importance. Two forms of PD-L1 are evident: a membrane-bound one (mPD-L1) and a soluble form (sPD-L1) that may be present in plasma of patients with solid tumors [129]. The key advantage of using sPD-L1 as a clinical marker is its easy accessibility without the need to obtain additional tumor tissue thus giving the opportunity of easy repetitive measurements during ICI therapy. Okuma et al. analyzed the plasma of stage IV NSCLC patients treated with nivolumab in 2nd -line setting and measured baseline sPD-L1 with ELISA. Patients with high sPD-L1 levels (cutoff value: 3357 ng/mL) demonstrated significantly shorter OS and a reduced time to treatment failure (TTF). Conversely, low levels of sPD-L1 were associated with high rates of tumor control [55, 129]. In another study sPD-L1 levels were measured at initial diagnosis, at nivolumab initiation and at first post-therapeutic tumor evaluation in NSCLC patients [56]. It was observed that increasing sPD-L1 levels between the initiation of ICI therapy and first tumor evaluation were associated with worse ORR, shorter PFS and reduced OS. Higher sPD-L1 levels were found in non-responders and low levels were associated with clinical benefits [56]. A major limitation for the clinical implementation of sPD-L1 as a prognostic marker during routine care is the lack of standardization of sPD-L1 measurements, with a number of different ELISA kits and varying thresholds [129].

VEGF in serum

Vascular endothelial growth factor (VEGF) plays an essential role in tumor angiogenesis, which is crucial for cancer proliferation, migration and metastasis [57]. In addition, inhibition of dendritic cell maturation, reduction of T-cell tumor infiltration, and promotion of inhibitory cells in the tumor microenvironment (TME) appear to be important in VEGF-mediated immunosuppression [130, 131]. Antiangiogenic agents could not only antagonize these VEGF-driven effects, but could also affect tumor blood vessels by reducing their size and length in the tumor and promoting vessel maturation, resulting in improved tissue penetration of chemotherapy and ICI substances [132,133,134].. We have recently demonstrated the effectiveness of this in NSCLC patients [135]. NSCLC patients receiving docetaxel and ramucirumab, a VEGFR antibody, in the 3rd line after 2nd line treatment with an ICI not only had a particular encouraging mOS of nearly 1 year, but also showed an increased response rate of this combination compared to the previously given immunotherapy.

VEGF expression can be easily measured by ELISA and seems to be negatively associated with survival in NSCLC patients when measured in the tumor [136,137,138]. Furthermore, high pretreatment serum levels of VEGF have been shown to have a prognostically negative value in patients undergoing palliative chemotherapy with gemcitabine and vinorelbine [139]. Surprisingly, there is little data on VEGF in pretreatment tissue from NSCLC patients receiving ICI. In a small retrospective cohort of NSCLC patients receiving PD-1 treatment, high levels of pretreatment serum VEGF were a significant predictor of shorter progression free survival, especially in elderly (> 75 years) and ECOG PS 2 patients [57]. Further analyses of this promising marker are necessary to assess its further prognostic and/or predictive potential.

Tumor Mutational Burden (TMB)

A common hypothesis among experts is that tumors with a high mutational burden express also many neoantigens, which are captured and presented by dendritic cells and thus leading to a high diversity of primed T-cells [140, 141]. The more diverse T-cells are, the higher is their chance of localizing and destroying tumor cells. One particular impressive example are patients presenting with a DNA mismatch repair mutation. In these patients many different proteins and epitopes are generated through microsatellite instability. Excellent results with ICI treatment in those patients led to approval across a whole variety of tumor types [142,143,144]. However, there is no consensus among experts as to which mutations should be used for the calculation of tissue Tumor Mutational Burden (tTMB). Some authors report all mutations, others use only non-synonymous mutations, and yet others consider only missense mutations in exons as relevant [145,146,147,148,149,150,151].

At last year’s ESMO congress Herbst et al. presented exploratory data from the KEYNOTE-010 and KEYNOTE-042 trials, showing that a high tTMB ≥175 mutations per exome might be of positive predictive value [152]. Patients in these studies treated with pembrolizumab monotherapy with a high tTMB level had a significant better outcome in terms of ORR, PFS and OS in contrast to patients treated with chemotherapy. Conversely, tTMB was not associated with outcomes in the chemotherapy-arm in either study. In contrast, no association between tTMB levels and efficacy in neither RR, PFS nor OS of pembrolizumab and/or chemotherapy were found in the KEYNOTE-021, − 189 or 407 studies [60]. Therefore, tTMB is not established as a marker for ICI efficacy in NSCLC so far.

Less invasive methods have been sought, such as measuring TMB on circulating tumor DNA (ctDNA) in patient plasma termed blood TMB (bTMB). Wang et al. evaluated bTMB with the cancer gene panel NCC-GP150, which was designed and virtually validated using the Cancer Genome Atlas database [129, 153]. The correlation between ICI-treated patients’ bTMB estimated by NCC-GP150 and tissue TMB (tTMB) measured by whole exome sequencing (WES) was evaluated in matched blood and tissue samples from 48 patients with advanced NSCLC and a bTMB ≥6 was associated with an increased PFS, clinical tumor shrinkage and superior ORR. This way of measuring needed a smaller panel size (only 150 genes) and showed superior cost effectiveness compared to tTMB evaluation. However, this approach has to be tested in prospective trials before being used in the clinical setting.

Taking the KEYNOTE-189 trial again, 235 participants’ bTMBs have been analyzed post hoc and the results were presented recently [154]. Garassino et al. observed that a bTMB > 15 mut/mB was associated with worse PFS in the pembrolizumab + chemotherapy-arm, but not with reduced OS and that tTMB was not significantly associated with efficacy in both groups, what confirmed the previously mentioned results. Nevertheless, Rizvi et al. recently observed improvements in OS for patients treated with durvalumab (D) + tremelimumab (T) with a bTMB ≥20 mut/mB in their exploratory phase-III trial (MYSTIC) where first-line D + T was compared with chemotherapy (21.9 months vs 10.0 months) [59]. This study did not meet its primary endpoints but is still highlighting the need for further investigation and prospective validation of blood tumor mutational burden as a predictive biomarker for immunotherapy agents.

Driver mutations and translocations

The identification of somatic mutations and translocations in adenocarcinoma of the lung is common practice and allows targeted therapy with tyrosine kinase inhibitors (TKI), especially for patients with stage IV NSCLC. Interestingly enough, a retrospective study showed that most patients with a targetable driver mutation/translocation hardly have any benefit from ICI therapy [155, 156].

In most phase III trials patients with known driver mutations/transloctions, especially EGFR and ALK were excluded. The only trial were those patients were treated was the ImPower 150 study [21]. It could be shown, that those patients though not stratified had a significant benefit in terms of OS from a chemo-antiangiogenesis-ICI combination and that especially the ICI is relevant for the outcome. Further trials should include patients with driver mutations at least after progression to a TKI.

In western countries, the most frequent driver mutation in adenocarcinoma is found in the Kirsten rat sarcoma viral oncogene homolog (KRAS) [157,158,159]. RAS proteins act as a cellular switch that is turned on by extracellular stimuli activating different signaling pathways that regulate fundamental cell processes [158, 160]. Activating KRAS mutations in adenocarcinoma are usually found in smokers and unlike other oncogene-driven lung cancers, they frequently appear with other major genetic co-mutations [158, 159]. In two recently reported exploratory analyses of trials where pembrolizumab was used either as monotherapy (KN042) or in combination with chemotherapy (KN189), ICI was superior to chemotherapy alone irrespective of the KRAS status [61, 62]. However, while there was no difference among patients in the ICI-chemotherapy arm as a consequence of the KRAS status, an improved ORR and a longer PFS time were seen in the ICI monotherapy arm when the tumors exhibited a KRAS mutation. Furthermore, in the pembrolizumab mono therapy arm, there was a benefit in OS of those patients exhibiting a KRAS mutation of 28 vs 15 months for those with the wild-type.

STK11/KEAP1

Many previous published studies examined the role of co-mutations in ICI treated driver mutation-positive adenocarcinoma. Tumor-cell-intrinsic oncogenic pathways including STK11/LKB1 and KEAP1 are associated with a “cold”, non-T cell-inflamed TME, thus significantly impairing clinical responses to ICI treatment [22, 63, 64, 161, 162]. The inactivation of STK 11 increases production of G-CSF, CXCL7 and IL-6, which recruit tumor-associated neutrophils that in turn increase T-cell dysfunction via arginase A and IL-10 release [22, 162]. Along with that, this genomic alteration has been identified as a major driver of de-novo resistance to PD-1 axis resistance in KRAS-positive lung adenocarcinoma [63].

Linked with the inactivation of STK11 is the loss of KEAP1, an adaptor protein that mediates ubiquitination and proteasomal degradation of NRF2, which in turn is a key transcription factor in cellular antioxidant, metabolic, cytoprotective and anti-inflammatory pathway [162, 163].

At this year’s ASCO congress, Pavan et al. presented their results about the impact of STK11, KRAS and TP53 mutations on the clinical outcome of prospectively screened NSCLC patients treated with ICI therapy in the VISION and MAGICAL trail. They detected that STK11 mutated patients have a trend for worse OS compared with wild-type counterpart in patients undergoing immunotherapy. This phenomenon was not seen in the control group receiving no ICI medication, making STK11 a possible negative predictive factor for ICI therapy. In contrast, Cho et al. made a post hoc analysis of the KEYNOTE-042 study and observed that pembrolizumab monotherapy had similar efficacy in patients regardless of STK11 or KEAP1 status, but an increased efficacy compared to the platinum-based chemotherapy arm [65]. Since they also detected a lower PD-L1 expression in the STK11 mutated group, there is a need for more prospective studies to evaluate this approach.

TP53

Since TP53 tended to be associated with an Inflamed or “hot” tumor environment, some experts stated that patients with this mutation should have a high rate of clinical response to PD-1 axis immunotherapy, increased PD-1 expression and an increased PFS and OS in a NSCLC trial [162]. They assumed that the biological background might be the activation of NF-ΚB through the loss of TP53, which increases production of inflammatory cytokines [22, 161]. However, Pavan’s et al. analysis identified TP53 as a negative prognostic marker which was associated with decreased OS in both, the ICI cohort as well as the control group (95% CI: 1,2-5,2; p = 0,014). As the results regarding the impact of TP53 mutations have currently such a large variation, there is an urgent need for larger, systematic studies addressing this question.

Circulating tumor DNA (ctDNA)

Circulating tumor DNA (cTDNA) and RNA (cTRNA), shed by tumor cells into the blood can be used as a liquid biopsy, providing noninvasive access to cancer-specific mutations [164]. In particular, the detection of EGFR sensitizing mutations before starting therapy and the detection of acquired resistance mutations during therapy with EGFR tyrosine kinase inhibitors (TKIs) has become a standard clinical method in recent years [165]. In addition, in patients receiving 1st-line TKI treatment, ctDNA follow-up measurements of activating EGFR mutations can be used to monitor the effectiveness of treatment. Patients treated with osimertinib in the Phase III FLAURA study, in whom the mutation is no longer detectable 3 or 6 weeks after the start of the treatment, had significantly higher PFS (HR 0.57 and 0.51) compared to patients in whom the mutation was retained [166].

Longitudinal ctDNA analyses are also in the focus of translational research during ICI therapy. In the recently published INSPIRA trial, a prospective phase II study, 94 patients with solid tumors treated with pembrolizumab where included and serial plasma samples were obtained before and during therapy [66]. Baseline ctDNA concentrations below the mean were correlated with an increased response rate and better outcome in terms of PFS and OS. Furthermore, a reduction or degradation of ctDNA during ICI therapy indicated a favorable risk group. In contrast, a dynamic increase in ctDNA over time was associated with an early progress of the disease. In line with these results are the data presented by Bonanno et al. at the recent 2020 ESMO congress on longitudinal ctDNA analyses of plasma samples from NSCLC patients treated by ICI [167]. An increase of the mean fold change in the variant allele fraction between start and after 3–4 weeks of treatment was observed frequently in patients with an early progress or even a hyperprogression. The same research group followed 34 NSCLC patients under ICI treatment with longitudinal ctDNA analysis and detection of KRAS mutations in the blood [67]. The detection of a KRAS mutation before treatment had no clear prognostic value. However, a new KRAS mutation becoming apparent after 3 to 4 weeks of therapy was associated with a significant shorter PFS and a trend towards a shorter OS.

In summary, ctDNA monitoring is not yet ready for clinical practice because the cohorts studied so far are still small and different molecular methods make them difficult to compare. So far, however, all published results go in the same direction and show that a decrease in ctDNA during treatment is associated with a favorable outcome, while an increase is often associated with an early progression or an even hyperprogressive disease. Close ctDNA monitoring could become a valuable tool for therapy guidance in the near future.

Conclusion

Immunotherapy is a new and exciting treatment approach in NSCLC patients. The pivotal challenge will be to guide the choice of individual therapeutic concepts and how to target the TME and tumor itself appropriately at the right time. There is a great number of clinical-, laboratory- and genetic data, that might help to predict the individual response to ICI therapy, but so far, these data are not incorporated comprehensively into clinical studies and most of these assumed predictive factors are not ready for daily routine yet. While the understanding of the mode of action of ICI therapy and their interaction with the immune response as well as TME grows further, we probably will discover more and more relevant genetic parameters which might be helpful for clinical decision making. The performance of predictive biomarkers for ICIs might also be improved by combining different markers to improve their specificity. Many clinical studies and a significant amount of translational research on easily accessible, cost-effective, standardized biomarkers and measurement techniques is still needed to develop reliable algorithms for treatment choice for patients with NSCLC.

In summary Fig. 1 gives a comprehensive overview about the clinically relevant positive prognostic and/or predictive factors.

Availability of data and materials

All data have been either published either as full text in PubMed or were presented at one of the recent international oncology symposia. Where full presentations of studies or congress data cannot be obtained, the datasets used are available from the corresponding author on request. Figure 1 was designed using free vector icons from Graphic Surf (https://graphicsurf.com).

Abbreviations

BMI:

Body mass index

bTMB:

Blood tumor mutational burden

CI:

Confidence interval

COPD:

Chronic obstructive pulmonary disease

CR:

Complete response

CRP:

C-reactive protein

ctDNA:

Circulating tumor DNA

ctRNA:

Circulating tumor RNA

CTC:

Common toxicity criteria

CT:

Computer tomography

DCR:

Disease control rate

DOR:

Duration of response

ECOG PS:

Eastern Cooperative Oncology Group performance status

EGFR:

Epidermal growth factor receptor

FDA:

Food and Drug Administration (USA)

HCC:

Hepatocellular carcinoma

HR:

Hazard ratio

ICI:

Immune-checkpoint inhibitor

KRAS:

Kirsten rat sarcoma viral oncogene homolog

LDH:

Lactate dehydrogenase

LIPI:

Lung Immune Prognostic Index

NGS:

Next generation sequencing

NSCLC:

Non-small cell lung cancer

PD:

Progressive disease

RECIST:

Response Evaluation Criteria in Solid Tumors version 1.1.8

PD-1:

Programmed cell death protein 1

PD-L1:

Programmed cell death-ligand 1

peCTL:

Partially exhausted cytotoxic T-lymphocytes

ORR:

Objective response rate

OS:

Overall survival

PFS:

Progression-free survival

PR:

Partial response

RCC:

Renal cell carcinoma

SD:

Stable disease

SII:

Systemic-Immune-Inflammation Index

sPD-L1:

Soluble programmed cell death-ligand 1

TKI:

Tyrosine kinase inhibitor

TME:

Tumor microenvironment

TNF-α:

Tumor necrosis factor α

TPS score:

Tumor proportion score

Treg:

Regulatory T cell

TTF:

Time to treatment failure

tTMB:

Tissue tumor mutational burden

UICC:

Union for International Cancer Control

VEGF:

Vascular endothelial growth factor

VEGFR:

VEGF receptor

References

  1. 1.

    Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus Docetaxel in advanced nonsquamous non-small-cell lung Cancer. N Engl J Med. 2015;373(17):1627–39.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Corral J, Majem M, Rodríguez-Abreu D, Carcereny E, Cortes ÁA, Llorente M, et al. Efficacy of nintedanib and docetaxel in patients with advanced lung adenocarcinoma treated with first-line chemotherapy and second-line immunotherapy in the nintedanib NPU program. Clin Transl Oncol. 2019;21(9):1270–9.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet. 2016;387(10030):1837–46.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Herbst RS, Baas P, Kim DW, Felip E, Perez-Gracia JL, Han JY, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet. 2016;387(10027):1540–50.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Horn L, Spigel DR, Vokes EE, Holgado E, Ready N, Steins M, et al. Nivolumab versus Docetaxel in previously treated patients with advanced non-small-cell lung Cancer: two-year outcomes from two randomized, open-label, phase III trials (CheckMate 017 and CheckMate 057). J Clin Oncol. 2017;35(35):3924–33.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Reck M, Kaiser R, Mellemgaard A, Douillard JY, Orlov S, Krzakowski M, et al. Docetaxel plus nintedanib versus docetaxel plus placebo in patients with previously treated non-small-cell lung cancer (LUME-lung 1): a phase 3, double-blind, randomised controlled trial. Lancet Oncol. 2014;15(2):143–55.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Rittmeyer A, Barlesi F, Waterkamp D, Park K, Ciardiello F, von Pawel J, 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. Lancet. 2017;389(10066):255–65.

    Article  PubMed  Google Scholar 

  8. 8.

    Gandhi L, Rodríguez-Abreu D, Gadgeel S, Esteban E, Felip E, De Angelis F, et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung Cancer. N Engl J Med. 2018;378(22):2078–92.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Paz-Ares L, Luft A, Vicente D, Tafreshi A, Gümüş M, Mazières J, et al. Pembrolizumab plus chemotherapy for squamous non-small-cell lung Cancer. N Engl J Med. 2018;379(21):2040–51.

    CAS  Article  Google Scholar 

  10. 10.

    Reck M, Mok TSK, Nishio M, Jotte RM, Cappuzzo F, Orlandi F, et al. Atezolizumab plus bevacizumab and chemotherapy in non-small-cell lung cancer (IMpower150): key subgroup analyses of patients with EGFR mutations or baseline liver metastases in a randomised, open-label phase 3 trial. Lancet Respir Med. 2019;7(5):387–401.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Reck M, Rodríguez-Abreu D, Robinson AG, Hui R, Csőszi T, Fülöp A, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung Cancer. N Engl J Med. 2016;375(19):1823–33.

    CAS  Article  Google Scholar 

  12. 12.

    Socinski MA, Jotte RM, Cappuzzo F, Orlandi F, Stroyakovskiy D, Nogami N, et al. Atezolizumab for first-line treatment of metastatic nonsquamous NSCLC. N Engl J Med. 2018;378(24):2288–301.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    West H, McCleod M, Hussein M, Morabito A, Rittmeyer A, Conter HJ, et al. Atezolizumab in combination with carboplatin plus nab-paclitaxel chemotherapy compared with chemotherapy alone as first-line treatment for metastatic non-squamous non-small-cell lung cancer (IMpower130): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol. 2019;20(7):924–37.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Gettinger S, Horn L, Jackman D, Spigel D, Antonia S, Hellmann M, et al. Five-year follow-up of Nivolumab in previously treated advanced non-small-cell lung Cancer: results from the CA209-003 study. J Clin Oncol. 2018;36(17):1675–84.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Shiono A, Kaira K, Mouri A, Yamaguchi O, Hashimoto K, Uchida T, et al. Improved efficacy of ramucirumab plus docetaxel after nivolumab failure in previously treated non-small cell lung cancer patients. Thorac Cancer. 2019;10(4):775–81.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Harvey RD, Rubinstein WS, Ison G, Khozin S, Chen L, Miller RS, Jun M, Stepanski E, Hyde B, Thomas S. Editor Impact of broadening clinical trial eligibility criteria for advanced non-small cell lung cancer patients: Real-world analysis. J Clin Oncol. 2019;37:18_suppl, LBA108-LBA108.

  17. 17.

    Reck M, Rodríguez-Abreu D, Robinson AG, Hui R, Csőszi T, Fülöp A, et al. Updated analysis of KEYNOTE-024: Pembrolizumab versus platinum-based chemotherapy for advanced non-small-cell lung Cancer with PD-L1 tumor proportion score of 50% or greater. J Clin Oncol. 2019;37(7):537–46.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Jassem J, de Marinis F, Spigel DR, Lam S, Mocci S, Sandler A, et al. IMpower110: Phase III trial of 1L atezolizumab in PD-L1–selected chemotherapy-naive NSCLC. Ann Oncol. 2017;28:ii50.

    Article  Google Scholar 

  19. 19.

    Hanna N, Johnson D, Temin S, Baker S Jr, Brahmer J, Ellis PM, et al. Systemic therapy for stage IV non-small-cell lung Cancer: American Society of Clinical Oncology clinical practice guideline update. J Clin Oncol. 2017;35(30):3484–515.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Jeanson A, Barlesi F. MEDI 4736 (durvalumab) in non-small cell lung cancer. Expert Opin Biol Ther. 2017;17(10):1317–23.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Antonia SJ, Villegas A, Daniel D, Vicente D, Murakami S, Hui R, et al. Overall survival with Durvalumab after Chemoradiotherapy in stage III NSCLC. N Engl J Med. 2018;379(24):2342–50.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Schoenfeld AJ, Rizvi H, Bandlamudi C, Sauter JL, Travis WD, Rekhtman N, et al. Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas. Ann Oncol. 2020.

  23. 23.

    Clark GM, Zborowski DM, Culbertson JL, Whitehead M, Savoie M, Seymour L, et al. Clinical utility of epidermal growth factor receptor expression for selecting patients with advanced non-small cell lung cancer for treatment with erlotinib. J Thorac Oncol. 2006;1(8):837–46.

    Article  PubMed  Google Scholar 

  24. 24.

    Clark GM. Prognostic factors versus predictive factors: examples from a clinical trial of erlotinib. Mol Oncol. 2008;1(4):406–12.

    Article  PubMed  Google Scholar 

  25. 25.

    Shepherd FA, Rodrigues Pereira J, Ciuleanu T, Tan EH, Hirsh V, Thongprasert S, et al. Erlotinib in previously treated non-small-cell lung cancer. N Engl J Med. 2005;353(2):123–32.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Conforti F, Pala L, Bagnardi V, De Pas T, Martinetti M, Viale G, et al. Cancer immunotherapy efficacy and patients' sex: a systematic review and meta-analysis. Lancet Oncol. 2018;19(6):737–46.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Conforti F, Pala L, Bagnardi V, Viale G, De Pas T, Pagan E, et al. Sex-based heterogeneity in response to lung Cancer immunotherapy: a systematic review and meta-analysis. J Natl Cancer Inst. 2019;111(8):772–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Kawachi H, Tamiya M, Tamiya A, Ishii S, Hirano K, Matsumoto H, et al. Association between metastatic sites and first-line pembrolizumab treatment outcome for advanced non-small cell lung cancer with high PD-L1 expression: a retrospective multicenter cohort study. Investig New Drugs. 2020;38(1):211–8.

    CAS  Article  Google Scholar 

  29. 29.

    Naqash AR, Ricciuti B, Owen DH, Florou V, Toi Y, Cherry C, et al. Outcomes associated with immune-related adverse events in metastatic non-small cell lung cancer treated with nivolumab: a pooled exploratory analysis from a global cohort. Cancer Immunol Immunother. 2020;69(7):1177–87. https://doi.org/10.1007/s00262-020-02536-5. Epub 2020 Mar 5.

  30. 30.

    Dall'Olio FG, Maggio I, Massucci M, Mollica V, Fragomeno B, Ardizzoni A. ECOG performance status ≥2 as a prognostic factor in patients with advanced non small cell lung cancer treated with immune checkpoint inhibitors-a systematic review and meta-analysis of real world data. Lung Cancer. 2020;145:95–104.

    Article  PubMed  Google Scholar 

  31. 31.

    Cortellini A, Bersanelli M, Santini D, Buti S, Tiseo M, Cannita K, et al. Another side of the association between body mass index (BMI) and clinical outcomes of cancer patients receiving programmed cell death protein-1 (PD-1)/ programmed cell death-ligand 1 (PD-L1) checkpoint inhibitors: a multicentre analysis of immune-related adverse events. Eur J Cancer. 2020;128:17–26.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Shepshelovich D, Xu W, Lu L, Fares A, Yang P, Christiani D, et al. Body mass index (BMI), BMI change, and overall survival in patients with SCLC and NSCLC: a pooled analysis of the international lung Cancer consortium. J Thorac Oncol. 2019;14(9):1594–607.

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Magri V, Gottfried T, Di Segni M, Urban D, Peled M, Daher S, et al. Correlation of body composition by computerized tomography and metabolic parameters with survival of nivolumab-treated lung cancer patients. Cancer Manag Res. 2019;11:8201–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Kang DH, Chung C, Kim JO, Jung SS, Park HS, Park DI, et al. Pleural or pericardial metastasis: a significant factor affecting efficacy and adverse events in lung cancer patients treated with PD-1/PD-L1 inhibitors. Thorac Cancer. 2018;9(11):1500–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Adachi Y, Tamiya A, Taniguchi Y, Enomoto T, Azuma K, Kouno S, et al. Predictive factors for progression-free survival in non-small cell lung cancer patients receiving nivolumab based on performance status. Cancer Med. 2020;9(4):1383–91.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Reck M, Mok TSK, Nishio M, Jotte RM, Cappuzzo F, Orlandi F, et al. Atezolizumab plus bevacizumab and chemotherapy in non-small-cell lung cancer (IMpower150): key subgroup analyses of patients with <em>EGFR</em> mutations or baseline liver metastases in a randomised, open-label phase 3 trial. Lancet Respir Med. 2019;7(5):387–401.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Sridhar S, Paz-Ares L, Liu H, Shen K, Morehouse C, Rizvi N, et al. Prognostic significance of liver metastasis in Durvalumab-treated lung Cancer patients. Clin Lung Cancer. 2019;20(6):e601–e8.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Scott SC, Pennell NA. Early use of systemic corticosteroids in patients with advanced NSCLC treated with Nivolumab. J Thorac Oncol. 2018;13(11):1771–5.

    Article  PubMed  Google Scholar 

  39. 39.

    Fuca G, Galli G, Poggi M, Lo Russo G, Proto C, Imbimbo 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. ESMO Open. 2019;4(1):e000457.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Arbour KC, Mezquita L, Long N, Rizvi H, Auclin E, Ni A, et al. Impact of baseline steroids on efficacy of programmed cell Death-1 and programmed death-ligand 1 blockade in patients with non-small-cell lung Cancer. J Clin Oncol. 2018;36(28):2872–8.

    CAS  Article  Google Scholar 

  41. 41.

    Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daillère R, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018;359(6371):91–7.

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Derosa L, Hellmann MD, Spaziano M, Halpenny D, Fidelle M, Rizvi H, et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann Oncol. 2018;29(6):1437–44.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Ahmed J, Kumar A, Parikh K, Anwar A, Knoll BM, Puccio C, et al. Use of broad-spectrum antibiotics impacts outcome in patients treated with immune checkpoint inhibitors. Oncoimmunology. 2018;7(11):e1507670.

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Chalabi M, Cardona A, Nagarkar DR, Dhawahir Scala A, Gandara DR, Rittmeyer A, et al. Efficacy of chemotherapy and atezolizumab in patients with non-small-cell lung cancer receiving antibiotics and proton pump inhibitors: pooled post hoc analyses of the OAK and POPLAR trials. Ann Oncol. 2020;31(4):525–31.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Hakozaki T, Richard C, Elkrief A, Hosomi Y, Benlaïfaoui M, Mimpen I, et al. The gut microbiome associates with immune checkpoint inhibition outcomes in patients with advanced non-small cell lung Cancer. Cancer Immunol Res. 2020;8(10):1243–50.

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Cao D, Xu H, Xu X, Guo T, Ge W. A reliable and feasible way to predict the benefits of Nivolumab in patients with non-small cell lung cancer: a pooled analysis of 14 retrospective studies. Oncoimmunology. 2018;7(11):e1507262.

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Zhang L, Bai L, Liu X, Liu Y, Li S, Liu J, et al. Factors related to rapid progression of non-small cell lung cancer in Chinese patients treated using single-agent immune checkpoint inhibitor treatment. Thorac Cancer. 2020;11(5):1170–9. https://doi.org/10.1111/1759-7714.13370. Epub 2020 Mar 5.

  48. 48.

    Iivanainen S, Ahvonen J, Knuuttila A, Tiainen S, Koivunen JP. Elevated CRP levels indicate poor progression-free and overall survival on cancer patients treated with PD-1 inhibitors. ESMO Open. 2019;4(4):e000531.

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Liu J, Li S, Zhang S, Liu Y, Ma L, Zhu J, et al. Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio can predict clinical outcomes in patients with metastatic non-small-cell lung cancer treated with nivolumab. J Clin Lab Anal. 2019;33(8):e22964.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung Immune Prognostic Index with Immune Checkpoint Inhibitor Outcomes in patients with advanced non-small cell lung Cancer. JAMA Oncol. 2018;4(3):351–7.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Passaro A, Mancuso P, Gandini S, Spitaleri G, Labanca V, Guerini-Rocco E, et al. Gr-MDSC-linked asset as a potential immune biomarker in pretreated NSCLC receiving nivolumab as second-line therapy. Clin Transl Oncol. 2020;22(4):603–11.

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Tanizaki J, Haratani K, Hayashi H, Chiba Y, Nakamura Y, Yonesaka K, et al. Peripheral blood biomarkers associated with clinical outcome in non-small cell lung Cancer patients treated with Nivolumab. J Thorac Oncol. 2018;13(1):97–105.

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Zhang Z, Li Y, Yan X, Song Q, Wang G, Hu Y, et al. Pretreatment lactate dehydrogenase may predict outcome of advanced non small-cell lung cancer patients treated with immune checkpoint inhibitors: a meta-analysis. Cancer Med. 2019;8(4):1467–73.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Naqash AR, Stroud CRG, Butt MU, Dy GK, Hegde A, Muzaffar M, et al. Co-relation of overall survival with peripheral blood-based inflammatory biomarkers in advanced stage non-small cell lung cancer treated with anti-programmed cell death-1 therapy: results from a single institutional database. Acta Oncol. 2018;57(6):867–72.

    Article  PubMed  Google Scholar 

  55. 55.

    Okuma Y, Wakui H, Utsumi H, Sagawa Y, Hosomi Y, Kuwano K, et al. Soluble Programmed Cell Death Ligand 1 as a Novel Biomarker for Nivolumab Therapy for Non-Small-cell Lung Cancer. Clin Lung Cancer. 2018;19(5):410–7 e1.

    CAS  Article  PubMed  Google Scholar 

  56. 56.

    Costantini A, Julie C, Dumenil C, Hélias-Rodzewicz Z, Tisserand J, Dumoulin J, et al. Predictive role of plasmatic biomarkers in advanced non-small cell lung cancer treated by nivolumab. Oncoimmunology. 2018;7(8):e1452581.

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Shibaki R, Murakami S, Shinno Y, Matsumoto Y, Yoshida T, Goto Y, et al. Predictive value of serum VEGF levels for elderly patients or for patients with poor performance status receiving anti-PD-1 antibody therapy for advanced non-small-cell lung cancer. Cancer Immunol Immunother. 2020;69(7):1229–36.

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Wang Y, Ma R, Liu F, Lee SA, Zhang L. Modulation of gut microbiota: a novel paradigm of enhancing the efficacy of programmed Death-1 and programmed death Ligand-1 blockade therapy. Front Immunol. 2018;9:374.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Rizvi NA, Cho BC, Reinmuth N, Lee KH, Luft A, Ahn MJ, et al. Durvalumab with or without Tremelimumab vs standard chemotherapy in first-line treatment of metastatic non-small cell lung Cancer: the MYSTIC phase 3 randomized clinical trial. JAMA Oncol. 2020;6(5):661–74.

    Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Paz-Ares L, Vicente D, Tafreshi A, Robinson A, Soto Parra H, Mazières J, et al. Pembrolizumab (pembro) + chemotherapy (chemo) in metastatic squamous NSCLC: final analysis and progression after the next line of therapy (PFS2) in KEYNOTE-407. Ann Oncol. 2019;30:v918–v9.

    Article  Google Scholar 

  61. 61.

    Herbst RS, Lopes G, Kowalski DM, Kasahara K, Wu YL, De Castro G Jr, et al. LBA4 Association of KRAS mutational status with response to pembrolizumab monotherapy given as first-line therapy for PD-L1-positive advanced non-squamous NSCLC in Keynote-042. Ann Oncol. 2019;30:xi63–xi4.

    Article  Google Scholar 

  62. 62.

    Gadgeel S, Rodriguez-Abreu D, Felip E, Esteban E, Speranza G, Reck M, et al. KRAS mutational status and efficacy in KEYNOTE-189: Pembrolizumab (pembro) plus chemotherapy (chemo) vs placebo plus chemo as first-line therapy for metastatic non-squamous NSCLC. Ann Oncol. 2019;30:xi64–xi5.

    Article  Google Scholar 

  63. 63.

    Skoulidis F, Goldberg ME, Greenawalt DM, Hellmann MD, Awad MM, Gainor JF, et al. STK11/LKB1 mutations and PD-1 inhibitor resistance in KRAS-mutant lung adenocarcinoma. Cancer Discov. 2018;8(7):822–35.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Arbour KC, Jordan E, Kim HR, Dienstag J, Yu HA, Sanchez-Vega F, et al. Effects of co-occurring genomic alterations on outcomes in patients with KRAS-mutant non-small cell lung Cancer. Clin Cancer Res. 2018;24(2):334–40.

    CAS  Article  PubMed  Google Scholar 

  65. 65.

    Cho BC, editor CT084 - Relationship between STK11 and KEAP1 mutational status and efficacy in KEYNOTE-042: pembrolizumab monotherapy versus platinum-based chemotherapy as first-line therapy for PD-L1-positive advanced NSCLC. Am Assoc Cancer Res Ann Meet; 2020 27.04; virtual meeting.

  66. 66.

    Bratman SV, Yang SYC, Iafolla MAJ, Liu Z, Hansen AR, Bedard PL, et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat Can. 2020;1(9):873–81.

    Article  Google Scholar 

  67. 67.

    Zulato E, Attili I, Pavan A, Nardo G, Del Bianco P, Boscolo Bragadin A, et al. Early assessment of KRAS mutation in cfDNA correlates with risk of progression and death in advanced non-small-cell lung cancer. Br J Cancer. 2020;123(1):81–91.

    CAS  Article  PubMed  Google Scholar 

  68. 68.

    Pu X, Wu L, Su D, Mao W, Fang B. Immunotherapy for non-small cell lung cancers: biomarkers for predicting responses and strategies to overcome resistance. BMC Cancer. 2018;18(1):1082.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Shien K, Papadimitrakopoulou VA, Wistuba II. Predictive biomarkers of response to PD-1/PD-L1 immune checkpoint inhibitors in non-small cell lung cancer. Lung Cancer. 2016;99:79–87.

    Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Blons H, Garinet S, Laurent-Puig P, Oudart JB. Molecular markers and prediction of response to immunotherapy in non-small cell lung cancer, an update. J Thorac Dis. 2019;11(Suppl 1):S25–s36.

    Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    OCEBM Levels of Evidence Working Group = Jeremy Howick ICJLL, Paul Glasziou, Trish Greenhalgh, Carl Heneghan, Alessandro Liberati, Ivan Moschetti, Bob Phillips, Hazel Thornton, Olive Goddard and Mary Hodgkinson. The Oxford Levels of Evidence 2. https://www.cebm.net/index.aspx?o=5653: Oxford Centre for Evidence-Based Medicine; 2011.

  72. 72.

    Burns PB, Rohrich RJ, Chung KC. The levels of evidence and their role in evidence-based medicine. Plast Reconstr Surg. 2011;128(1):305–10.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Klein SL, Flanagan KL. Sex differences in immune responses. Nat Rev Immunol. 2016;16(10):626–38.

    CAS  Article  PubMed  Google Scholar 

  74. 74.

    Passaro A, Attili I, Morganti S, Del Signore E, Gianoncelli L, Spitaleri G, et al. Clinical features affecting survival in metastatic NSCLC treated with immunotherapy: a critical review of published data. Cancer Treat Rev. 2020;89.

  75. 75.

    Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L, Steins M, et al. First-line Nivolumab in stage IV or recurrent non-small-cell lung Cancer. N Engl J Med. 2017;376(25):2415–26.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Brahmer J, Reckamp KL, Baas P, Crinò L, Eberhardt WEE, Poddubskaya E, et al. Nivolumab versus Docetaxel in advanced squamous-cell non–small-cell lung Cancer. N Engl J Med. 2015;373(2):123–35.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Schreiber RD, Old LJ, Smyth MJ. Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science. 2011;331(6024):1565–70.

    CAS  Article  PubMed  Google Scholar 

  78. 78.

    Loo K, Tsai KK, Mahuron K, Liu J, Pauli ML, Sandoval PM, et al. Partially exhausted tumor-infiltrating lymphocytes predict response to combination immunotherapy. JCI Insight. 2017;2(14):e93433. https://doi.org/10.1172/jci.insight.93433. eCollection 2017 Jul 20.

  79. 79.

    Salem ME, Xiu J, Lenz H-J, Atkins MB, Philip PA, Hwang JJ, et al. Characterization of tumor mutation load (TML) in solid tumors. J Clin Oncol. 2017;35(15_suppl):11517.

    Article  Google Scholar 

  80. 80.

    Xiao D, Pan H, Li F, Wu K, Zhang X, He J. Analysis of ultra-deep targeted sequencing reveals mutation burden is associated with gender and clinical outcome in lung adenocarcinoma. Oncotarget. 2016;7(16):22857–64.

    Article  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Passaro A, Spitaleri G, Gyawali B. Marinis Fd. Immunotherapy in non–small-cell lung Cancer patients with performance status 2: clinical decision making with scant evidence. J Clin Oncol. 2019;37(22):1863–7.

    CAS  Article  PubMed  Google Scholar 

  82. 82.

    Pavan A, Calvetti L, Dal Maso A, Attili I, Del Bianco P, Pasello G, et al. Peripheral blood markers identify risk of immune-related toxicity in advanced non-small cell lung Cancer treated with immune-checkpoint inhibitors. Oncologist. 2019;24(8):1128–36.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Sutherland LN, Capozzi LC, Turchinsky NJ, Bell RC, Wright DC. Time course of high-fat diet-induced reductions in adipose tissue mitochondrial proteins: potential mechanisms and the relationship to glucose intolerance. Am J Physiol Endocrinol Metab. 2008;295(5):E1076–83.

    CAS  Article  PubMed  Google Scholar 

  84. 84.

    Ouchi N, Parker JL, Lugus JJ, Walsh K. Adipokines in inflammation and metabolic disease. Nat Rev Immunol. 2011;11(2):85–97.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Seijkens T, Kusters P, Chatzigeorgiou A, Chavakis T, Lutgens E. Immune cell crosstalk in obesity: a key role for costimulation? Diabetes. 2014;63(12):3982–91.

    CAS  Article  PubMed  Google Scholar 

  86. 86.

    Magrone T, Jirillo E. Childhood obesity: immune response and nutritional approaches. Front Immunol. 2015;6:76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Jordan BF, Gourgue F, Cani PD. Adipose tissue metabolism and Cancer progression: novel insights from gut microbiota? Curr Pathobiol Rep. 2017;5(4):315–22.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Grove CS, Lee YC. Vascular endothelial growth factor: the key mediator in pleural effusion formation. Curr Opin Pulm Med. 2002;8(4):294–301.

    Article  PubMed  Google Scholar 

  89. 89.

    Ellis LM, Hicklin DJ. VEGF-targeted therapy: mechanisms of anti-tumour activity. Nat Rev Cancer. 2008;8(8):579–91.

    CAS  Article  PubMed  Google Scholar 

  90. 90.

    Chouaib S, Messai Y, Couve S, Escudier B, Hasmim M, Noman MZ. Hypoxia promotes tumor growth in linking angiogenesis to immune escape. Front Immunol. 2012;3:21.

    Article  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Hegde PS, Wallin JJ, Mancao C. Predictive markers of anti-VEGF and emerging role of angiogenesis inhibitors as immunotherapeutics. Semin Cancer Biol. 2018;52(Pt 2):117–24.

    CAS  Article  PubMed  Google Scholar 

  92. 92.

    Khan KA, Kerbel RS. Improving immunotherapy outcomes with anti-angiogenic treatments and vice versa. Nat Rev Clin Oncol. 2018;15(5):310–24.

    CAS  Article  PubMed  Google Scholar 

  93. 93.

    Tamiya M, Tamiya A, Yamadori T, Nakao K, Asami K, Yasue T, et al. Phase2 study of bevacizumab with carboplatin-paclitaxel for non-small cell lung cancer with malignant pleural effusion. Med Oncol. 2013;30(3):676.

    Article  CAS  PubMed  Google Scholar 

  94. 94.

    Usui K, Sugawara S, Nishitsuji M, Fujita Y, Inoue A, Mouri A, et al. A phase II study of bevacizumab with carboplatin-pemetrexed in non-squamous non-small cell lung carcinoma patients with malignant pleural effusions: north East Japan study group trial NEJ013A. Lung Cancer. 2016;99:131–6.

    Article  PubMed  Google Scholar 

  95. 95.

    Li L, Yang L, Wang L, Wang F, Zhang Z, Li J, et al. Impaired T cell function in malignant pleural effusion is caused by TGF-beta derived predominantly from macrophages. Int J Cancer. 2016;139(10):2261–9.

    CAS  Article  PubMed  Google Scholar 

  96. 96.

    Tumeh PC, Hellmann MD, Hamid O, Tsai KK, Loo KL, Gubens MA, et al. Liver metastasis and treatment outcome with anti-PD-1 monoclonal antibody in patients with melanoma and NSCLC. Cancer Immunol Res. 2017;5(5):417–24.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Facciabene A, Peng X, Hagemann IS, Balint K, Barchetti A, Wang LP, et al. Tumour hypoxia promotes tolerance and angiogenesis via CCL28 and T(reg) cells. Nature. 2011;475(7355):226–30.

    CAS  Article  PubMed  Google Scholar 

  98. 98.

    Ren L, Yu Y, Wang L, Zhu Z, Lu R, Yao Z. Hypoxia-induced CCL28 promotes recruitment of regulatory T cells and tumor growth in liver cancer. Oncotarget. 2016;7(46):75763–73.

    Article  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Goldberg SB, Schalper KA, Gettinger SN, Mahajan A, Herbst RS, Chiang AC, et al. Pembrolizumab for management of patients with NSCLC and brain metastases: long-term results and biomarker analysis from a non-randomised, open-label, phase 2 trial. Lancet Oncol. 2020;21(5):655–63.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Jove M, Vilarino N, Nadal E. Impact of baseline steroids on efficacy of programmed cell death-1 (PD-1) and programmed death-ligand 1 (PD-L1) blockade in patients with advanced non-small cell lung cancer. Transl Lung Cancer Res. 2019;8(Suppl 4):S364–s8.

    Article  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Pan EY, Merl MY, Lin K. The impact of corticosteroid use during anti-PD1 treatment. J Oncol Pharm Pract. 2020;26(4):814–22.

    CAS  Article  PubMed  Google Scholar 

  102. 102.

    Shaikh FY, Gills JJ, Sears CL. Impact of the microbiome on checkpoint inhibitor treatment in patients with non-small cell lung cancer and melanoma. EBioMedicine. 2019;48:642–7.

    Article  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Cvetkovic L, editor Physiologic colonic uptake of 18F-FDG on PET/CT predicts immunotherapy response and gut microbiome diversity in patients with advanced non-small cell lung cancer (NSCLC). ASCO Virtual Scientific Program; 2020.

    Google Scholar 

  104. 104.

    Ren F, Zhao T, Liu B, Pan L. Neutrophil-lymphocyte ratio (NLR) predicted prognosis for advanced non-small-cell lung cancer (NSCLC) patients who received immune checkpoint blockade (ICB). Onco Targets Ther. 2019;12:4235–44.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Wang Z, Zhan P, Lv Y, Shen K, Wei Y, Liu H, et al. Prognostic role of pretreatment neutrophil-to-lymphocyte ratio in non-small cell lung cancer patients treated with systemic therapy: a meta-analysis. Transl Lung Cancer Res. 2019;8(3):214–26.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357(9255):539–45.

    CAS  Article  PubMed  Google Scholar 

  107. 107.

    Putzu C, Cortinovis DL, Colonese F, Canova S, Carru C, Zinellu A, et al. Blood cell count indexes as predictors of outcomes in advanced non-small-cell lung cancer patients treated with Nivolumab. Cancer Immunol Immunother. 2018;67(9):1349–53.

    CAS  Article  PubMed  Google Scholar 

  108. 108.

    Ruiz-Bañobre J, Areses-Manrique MC, Mosquera-Martínez J, Cortegoso A, Afonso-Afonso FJ, de Dios-Álvarez N, et al. Evaluation of the lung immune prognostic index in advanced non-small cell lung cancer patients under nivolumab monotherapy. Transl Lung Cancer Res. 2019;8(6):1078–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Ulas A, Turkoz FP, Silay K, Tokluoglu S, Avci N, Oksuzoglu B, et al. A laboratory prognostic index model for patients with advanced non-small cell lung cancer. PLoS One. 2014;9(12):e114471.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. 110.

    Goldman RD, Kaplan NO, Hall TC. Lactic dehydrogenase in human neoplastic tissues. Cancer Res. 1964;24:389–99.

    CAS  PubMed  Google Scholar 

  111. 111.

    Wigren T. Confirmation of a prognostic index for patients with inoperable non-small cell lung cancer. Radiother Oncol. 1997;44(1):9–15.

    CAS  Article  PubMed  Google Scholar 

  112. 112.

    Mandrekar SJ, Schild SE, Hillman SL, Allen KL, Marks RS, Mailliard JA, et al. A prognostic model for advanced stage nonsmall cell lung cancer. Pooled analysis of north central Cancer treatment group trials. Cancer. 2006;107(4):781–92.

    Article  PubMed  Google Scholar 

  113. 113.

    Albain KS, Crowley JJ, LeBlanc M, Livingston RB. Survival determinants in extensive-stage non-small-cell lung cancer: the southwest oncology group experience. J Clin Oncol. 1991;9(9):1618–26.

    CAS  Article  PubMed  Google Scholar 

  114. 114.

    Buder-Bakhaya K, Hassel JC. Biomarkers for clinical benefit of immune checkpoint inhibitor treatment-a review from the melanoma perspective and beyond. Front Immunol. 2018;9:1474.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Simeone E, Gentilcore G, Giannarelli D, Grimaldi AM, Caracò C, Curvietto M, et al. Immunological and biological changes during ipilimumab treatment and their potential correlation with clinical response and survival in patients with advanced melanoma. Cancer Immunol Immunother. 2014;63(7):675–83.

    CAS  Article  PubMed  Google Scholar 

  116. 116.

    Philip M, Rowley DA, Schreiber H. Inflammation as a tumor promoter in cancer induction. Semin Cancer Biol. 2004;14(6):433–9.

    CAS  Article  PubMed  Google Scholar 

  117. 117.

    Shalapour S, Karin M. Immunity, inflammation, and cancer: an eternal fight between good and evil. J Clin Invest. 2015;125(9):3347–55.

    Article  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Jakob M. Riedl DAB, Wolfgang M, Brueckl, Gloria, Zeitler VF, Stefanie Mollnar, Michael Stotz, Christopher H., Rossmann AT, Marija Balic, Tobias Niedrist, Thomas Bertsch, , Herbert Stoeger MP, Horst Olschewski, Gudrun Absenger, Joachim, H, Ficker AG, Florian Posch. C-Reactive Protein (Crp) Levels In Immune Checkpoint Inhibitor. Response And Progression In Advanced Non-Small Cell Lung Cancer: A Bi-Center. Study Cancers (Basel). 2020; 12(8):2319. doi: https://doi.org/10.3390/cancers12082319.

    Article  Google Scholar 

  119. 119.

    Diakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15(11):e493–503.

    Article  PubMed  Google Scholar 

  120. 120.

    Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454(7203):436–44.

    CAS  Article  PubMed  Google Scholar 

  121. 121.

    Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. Cancer-associated cachexia. Nat Rev Dis Prim. 2018;4(1):17105.

    Article  PubMed  Google Scholar 

  122. 122.

    Bye A, Wesseltoft-Rao N, Iversen PO, Skjegstad G, Holven KB, Ulven S, et al. Alterations in inflammatory biomarkers and energy intake in cancer cachexia: a prospective study in patients with inoperable pancreatic cancer. Med Oncol. 2016;33(6):54.

    Article  CAS  PubMed  Google Scholar 

  123. 123.

    Fearon KC, Glass DJ, Guttridge DC. Cancer cachexia: mediators, signaling, and metabolic pathways. Cell Metab. 2012;16(2):153–66.

    CAS  Article  PubMed  Google Scholar 

  124. 124.

    Kurzrock R. The role of cytokines in cancer-related fatigue. Cancer. 2001;92(6 Suppl):1684–8.

    CAS  Article  PubMed  Google Scholar 

  125. 125.

    Mahmoud FA, Rivera NI. The role of C-reactive protein as a prognostic indicator in advanced cancer. Curr Oncol Rep. 2002;4(3):250–5.

    Article  PubMed  Google Scholar 

  126. 126.

    Proctor MJ, Morrison DS, Talwar D, Balmer SM, O'Reilly DS, Foulis AK, et al. An inflammation-based prognostic score (mGPS) predicts cancer survival independent of tumour site: a Glasgow inflammation outcome study. Br J Cancer. 2011;104(4):726–34.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  127. 127.

    Walsh D, Mahmoud F, Barna B. Assessment of nutritional status and prognosis in advanced cancer: interleukin-6, C-reactive protein, and the prognostic and inflammatory nutritional index. Support Care Cancer. 2003;11(1):60–2.

    Article  PubMed  Google Scholar 

  128. 128.

    Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515(7528):568–71.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Costantini A, Takam Kamga P, Dumenil C, Chinet T, Emile JF, Giroux LE. Plasma Biomarkers and Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer: New Tools for Better Patient Selection? Cancers (Basel). 2019;11(9):1269. https://doi.org/10.3390/cancers11091269.

  130. 130.

    Harada D, Takata K, Mori S, Kozuki T, Takechi Y, Moriki S, et al. Previous immune checkpoint inhibitor treatment to increase the efficacy of Docetaxel and Ramucirumab combination chemotherapy. Anticancer Res. 2019;39(9):4987–93.

    CAS  Article  PubMed  Google Scholar 

  131. 131.

    Gabrilovich D, Ishida T, Oyama T, Ran S, Kravtsov V, Nadaf S, et al. Vascular endothelial growth factor inhibits the development of dendritic cells and dramatically affects the differentiation of multiple hematopoietic lineages in vivo. Blood. 1998;92(11):4150–66.

    CAS  Article  PubMed  Google Scholar 

  132. 132.

    Fukumura D, Jain RK. Tumor microvasculature and microenvironment: targets for anti-angiogenesis and normalization. Microvasc Res. 2007;74(2–3):72–84.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Liang H, Wang M. Prospect of immunotherapy combined with anti-angiogenic agents in patients with advanced non-small cell lung cancer. Cancer Manag Res. 2019;11:7707–19.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Tong RT, Boucher Y, Kozin SV, Winkler F, Hicklin DJ, Jain RK. Vascular normalization by vascular endothelial growth factor receptor 2 blockade induces a pressure gradient across the vasculature and improves drug penetration in tumors. Cancer Res. 2004;64(11):3731–6.

    CAS  Article  PubMed  Google Scholar 

  135. 135.

    Brueckl WM, Reck M, Rittmeyer A, Kollmeier J, Wesseler C, Wiest GH, et al. Efficacy of Docetaxel plus Ramucirumab as palliative third-line therapy following second-line immune-checkpoint-inhibitor treatment in patients with non-small-cell lung Cancer stage IV. Clin Med Insights Oncol. 2020;14:1179554920951358. https://doi.org/10.1177/1179554920951358.

    Article  PubMed  PubMed Central  Google Scholar 

  136. 136.

    Lin Q, Guo L, Lin G, Chen Z, Chen T, Lin J, et al. Clinical and prognostic significance of OPN and VEGF expression in patients with non-small-cell lung cancer. Cancer Epidemiol. 2015;39(4):539–44.

    Article  PubMed  Google Scholar 

  137. 137.

    Naikoo NA, Rasool R, Shah S, Ahangar AG, Siddiqi MA, Shah ZA. Upregulation of vascular endothelial growth factor (VEGF), its role in progression and prognosis of non-small cell lung carcinoma. Cancer Gene Ther. 2017;216–217:67–73.

    Article  CAS  Google Scholar 

  138. 138.

    Farhat FS, Tfayli A, Fakhruddin N, Mahfouz R, Otrock ZK, Alameddine RS, et al. Expression, prognostic and predictive impact of VEGF and bFGF in non-small cell lung cancer. Crit Rev Oncol Hematol. 2012;84(2):149–60.

    Article  PubMed  Google Scholar 

  139. 139.

    Laack E, Scheffler A, Burkholder I, Boeters I, Andritzky B, Schuch G, et al. Pretreatment vascular endothelial growth factor (VEGF) and matrix metalloproteinase-9 (MMP-9) serum levels in patients with metastatic non-small cell lung cancer (NSCLC). Lung Cancer. 2005;50(1):51–8.

    Article  PubMed  Google Scholar 

  140. 140.

    Endris V, Buchhalter I, Allgäuer M, Rempel E, Lier A, Volckmar AL, et al. Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real-life analysis of three larger gene panels. Int J Cancer. 2019;144(9):2303–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Chen H, Chong W, Teng C, Yao Y, Wang X, Li X. The immune response-related mutational signatures and driver genes in non-small-cell lung cancer. Cancer Sci. 2019;110(8):2348–56.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Chang L, Chang M, Chang HM, Chang F. Microsatellite instability: a predictive biomarker for Cancer immunotherapy. Appl Immunohistochem Mol Morphol. 2018;26(2):e15–21. https://doi.org/10.1097/PAI.0000000000000575.

    CAS  Article  PubMed  Google Scholar 

  143. 143.

    Kubecek O, Trojanova P, Molnarova V, Kopecky J. Microsatellite instability as a predictive factor for immunotherapy in malignant melanoma. Med Hypotheses. 2016;93:74–6.

    CAS  Article  PubMed  Google Scholar 

  144. 144.

    Baretti M, Le DT. DNA mismatch repair in cancer. Pharmacol Ther. 2018;189:45–62.

    CAS  Article  PubMed  Google Scholar 

  145. 145.

    Friedlaender A, Nouspikel T, Christinat Y, Ho L, McKee T, Addeo A. Tissue-plasma TMB comparison and plasma TMB monitoring in patients with metastatic non-small cell lung Cancer receiving immune checkpoint inhibitors. Front Oncol. 2020;10:142.

    Article  PubMed  PubMed Central  Google Scholar 

  146. 146.

    Schrock AB, Ouyang C, Sandhu J, Sokol E, Jin D, Ross JS, et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer. Ann Oncol. 2019;30(7):1096–103.

    CAS  Article  PubMed  Google Scholar 

  147. 147.

    Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51(2):202–6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  148. 148.

    Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703–13.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  149. 149.

    Hellmann MD, Ciuleanu TE, Pluzanski A, Lee JS, Otterson GA, Audigier-Valette C, et al. Nivolumab plus Ipilimumab in lung Cancer with a high tumor mutational burden. N Engl J Med. 2018;378(22):2093–104.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Hellmann MD, Paz-Ares L, Bernabe Caro R, Zurawski B, Kim SW, Carcereny Costa E, et al. Nivolumab plus Ipilimumab in advanced non-small-cell lung Cancer. N Engl J Med. 2019;381(21):2020–31.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Koeppel F, Blanchard S, Jovelet C, Genin B, Marcaillou C, Martin E, et al. Whole exome sequencing for determination of tumor mutation load in liquid biopsy from advanced cancer patients. PLoS One. 2017;12(11):e0188174.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Herbst RSL, Kowalski G, Nishio M, TSK M. Association between tissue TMB (tTMB) and clinical outcomes with pembrolizumab monotherapy (pembro) in PD-L1 positive advanced NSCLC in the KEYNOTE-010 and -042 trials. Ann Oncol. 2019;30(suppl_5):v851–v934. https://doi.org/10.1093/annonc/mdz394.

  153. 153.

    Wang Z, Duan J, Cai S, Han M, Dong H, Zhao J, et al. Assessment of blood tumor mutational burden as a potential biomarker for immunotherapy in patients with non-small cell lung Cancer with use of a next-generation sequencing Cancer gene panel. JAMA Oncol. 2019;5(5):696–702.

    Article  PubMed  PubMed Central  Google Scholar 

  154. 154.

    Garassino MC, Gadgeel SM, Rodriguez-Abreu D, Felip E, Esteban E, Speranza G, Hochmair M, Powell SF, Garon EB, Hui R, Nogami N, Cristescu R, Morrissey M, Andrey Loboda, Kobie J, Ayers M, Piperdi B, Pietanza MC, Snyder A, Reck M. Evaluation of blood TMB (bTMB) in KEYNOTE-189: Pembrolizumab (pembro) plus chemotherapy (chemo) with pemetrexed and platinum versus placebo plus chemo as first-line therapy for metastatic nonsquamous NSCLC. J Clin Oncol. 2020;38:15_suppl, 9521-9521.

  155. 155.

    Proto C, Ferrara R, Signorelli D, Lo Russo G, Galli G, Imbimbo M, et al. Choosing wisely first line immunotherapy in non-small cell lung cancer (NSCLC): what to add and what to leave out. Cancer Treat Rev. 2019;75:39–51.

    CAS  Article  PubMed  Google Scholar 

  156. 156.

    Mazieres J, Drilon A, Lusque A, Mhanna L, Cortot AB, Mezquita L, et al. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the IMMUNOTARGET registry. Ann Oncol. 2019;30(8):1321–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  157. 157.

    Shepherd FA, Domerg C, Hainaut P, Janne PA, Pignon JP, Graziano S, et al. Pooled analysis of the prognostic and predictive effects of KRAS mutation status and KRAS mutation subtype in early-stage resected non-small-cell lung cancer in four trials of adjuvant chemotherapy. J Clin Oncol. 2013;31(17):2173–81.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  158. 158.

    Ferrer I, Zugazagoitia J, Herbertz S, John W, Paz-Ares L, Schmid-Bindert G. KRAS-mutant non-small cell lung cancer: from biology to therapy. Lung Cancer. 2018;124:53–64.

    Article  PubMed  Google Scholar 

  159. 159.

    Dogan S, Shen R, Ang DC, Johnson ML, D'Angelo SP, Paik PK, et al. Molecular epidemiology of EGFR and KRAS mutations in 3,026 lung adenocarcinomas: higher susceptibility of women to smoking-related KRAS-mutant cancers. Clin Cancer Res. 2012;18(22):6169–77.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  160. 160.

    Barbacid M. ras genes. Annu Rev Biochem. 1987;56:779–827.

    CAS  Article  PubMed  Google Scholar 

  161. 161.

    Remon J, Passiglia F, Ahn MJ, Barlesi F, Forde PM, Garon EB, et al. Immune checkpoint inhibitors in thoracic malignancies: review of the existing evidence by an IASLC expert panel and recommendations. J Thorac Oncol. 2020.

  162. 162.

    Skoulidis F, Heymach JV. Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy. Nat Rev Cancer. 2019;19(9):495–509.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Jaramillo MC, Zhang DD. The emerging role of the Nrf2-Keap1 signaling pathway in cancer. Genes Dev. 2013;27(20):2179–91.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  164. 164.

    Cabel L, Proudhon C, Romano E, Girard N, Lantz O, Stern MH, et al. Clinical potential of circulating tumour DNA in patients receiving anticancer immunotherapy. Nat Rev Clin Oncol. 2018;15(10):639–50.

    CAS  Article  PubMed  Google Scholar 

  165. 165.

    Rolfo C, Mack PC, Scagliotti GV, Baas P, Barlesi F, Bivona TG, et al. Liquid biopsy for advanced non-small cell lung Cancer (NSCLC): a statement paper from the IASLC. J Thorac Oncol. 2018;13(9):1248–68.

    Article  PubMed  Google Scholar 

  166. 166.

    Zhou C, Imamura F, Cheng Y, Okamoto I, Cho BC, Lin MC, et al. Early clearance of plasma EGFR mutations as a predictor of response to osimertinib and comparator EGFR-TKIs in the FLAURA trial. J Clin Oncol. 2019;37(15_suppl):9020.

    Article  Google Scholar 

  167. 167.

    Bonanno L, Zulato E, Attili I, Nardo G, Pavan A, Paola DB, et al. 1939P potential role of longitudinal plasma next generation sequencing (NGS) in advanced non-small cell lung cancer (aNSCLC) patients (pts) experiencing hyperprogression (HPD) and early death (ED) during treatment with immune checkpoint inhibitors (ICIs). Ann Oncol. 2020;31:S1096.

    Article  Google Scholar 

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Acknowledgements

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Funding

This work was funded by an unrestricted grant from the W.-Lutz Stiftung, Nuremberg, Germany to WMB and was used to pay for a medical research assistant (GZ). There was no role of the funding body in the design of the review, data collection, analysis and interpretation of data or in writing the manuscript.

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GZ conducted literature research and discussed the results with WB. Together they wrote this review and JF did the final proofreading and helped generating this manuscript. All authors have read and approved the manuscript.

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Correspondence to Wolfgang M. Brueckl.

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WMB and JHF have received honoraria for consulting from Astra Zeneca, BMS, Boehringer Ingelheim, Celgene, Lilly, MSD, Pfizer and Roche Pharma. GZ reported no conflict of interest.

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Brueckl, W.M., Ficker, J.H. & Zeitler, G. Clinically relevant prognostic and predictive markers for immune-checkpoint-inhibitor (ICI) therapy in non-small cell lung cancer (NSCLC). BMC Cancer 20, 1185 (2020). https://doi.org/10.1186/s12885-020-07690-8

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Keywords

  • Immunotherapy
  • Immune checkpoint inhibitor (ICI)
  • PD-L1 inhibitor
  • NSCLC
  • Prognostic
  • Predictive
  • Biomarker