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. 2024 Mar 3;38(2):864–872. doi: 10.21873/invivo.13512

Association of Nutritional Indices With Adverse Effects and Time-to-Treatment-Failure in Triple Therapy for Lung Cancer

YASUHISA HASHINO 1, TAKUMU MATUSHITA 1, TAE HATSUYAMA 2, AZUSA WAKAMOTO 2, KEISUKE GOTO 2, TAKANOBU HOSHI 1, KUNINORI IWAYAMA 1, KOICHI OHTAKI 1, TAKAKI TODA 1, HIDEKI SATO 1
PMCID: PMC10905434  PMID: 38418111

Abstract

Background/Aim

Recent lung cancer treatments include an immune checkpoint inhibitor (ICI) pembrolizumab, platinum-based agents, plus an additional cytotoxic anticancer agent. Nutritional indices, such as the geriatric nutritional risk index (GNRI) and the prognostic nutritional index (PNI), are known to correlate with the prognosis of cancer chemotherapy. Several previous studies have investigated the relationship between PNI and treatment response in non-small cell lung cancer patients, reporting significantly increased OS and PFS in the high PNI group before treatment. However, the relationship between the three-drug combination and GNRI/PNI is unclear. The current study aimed to investigate the association of nutritional indices with duration of treatment success and occurrence of side effects in triple therapy.

Patients and Methods

Seventy-two patients with non-small cell lung cancer, treated with combination of carboplatin, pemetrexed, and pembrolizumab from November 2019 to September 30, 2022, were classified into two groups (High and Low) for GNRI and PNI, and a retrospective study was performed.

Results

In terms of time-to-treatment-failure (TTF), univariate and multivariate Cox proportional hazards regression analysis showed the Low-PNI group to have significantly shorter TTF than the High-PNI group (p=0.006); multivariate analysis results also showed PNI as a factor affecting TTF (HR=2.791, 95%CI=1.362-5.721, p=0.005). On the other hand, GNRI was not shown to be a factor affecting TTF.

Conclusion

PNI at the start of treatment was an independent prognostic factor affecting treatment success time (TTF) in non-small cell lung cancer patients receiving triple therapy. However, PNI was not shown to be a prognostic predictor of irAE development.

Keywords: Pembrolizumab, non-small cell lung cancer, immunotherapy, prognostic factor, geriatric nutritional risk index, prognostic nutritional index

Pembrolizumab, an immune checkpoint inhibitor (ICI), is an antibody against human programmed cell death-1 (PD-1) and believed to inhibit tumor growth by inhibiting the binding of PD-1 to its ligand, thereby activating cytotoxic T cells (1).

In a global phase III study (KEYNOTE-189), the efficacy and safety of 200 mg pembrolizumab, pemetrexed sodium hydrate, and platinum at 3-week intervals, in combination therapy, with placebo, pemetrexed sodium hydrate, and platinum were studied in patients with unresectable advanced or recurrent non-squamous non-small cell lung cancer in a double-blind controlled study (2,3). Results showed a significant increase in overall survival (OS) and progression-free survival (PFS).

One of the prognostic predictors of ICI is the expression rate of PD-L1 as measured by PD-L1 tumor proportion score (TPS). However, it is considered inaccurate as a prognostic predictor, since the response rate is low despite a high PD-L1-expression rate (4).

Recently, nutritional status has been reported to be associated with prognosis in patients with cancer, and pretreatment nutritional status has been shown to be associated with resistance to treatment, incidence of adverse events, and even oncologic prognosis (5,6). Therefore, assessment of nutritional status prior to initiation of cancer chemotherapy may ensure the efficacy and safety of cancer chemotherapy and improve the quality of life and prognosis of patients with cancer. The geriatric nutritional risk index (GNRI) and the prognostic nutritional index (PNI) are used to assess the nutritional status of patients (7) and are considered predictors of patients with cancer (8,9). GNRI is calculated from serum albumin and the ratio of current weight to ideal weight, and is therefore a useful index for assessing the nutritional status of the elderly (10). The PNI, first reported by Onodera et al. in 1987, is a nutritional index based on albumin levels and peripheral lymphocyte counts that reflects the immune response and nutritional status of patients (11). Several previous studies had examined the relationship between PNI and treatment response in patients with non-small cell lung cancer, and reported a significant increase in OS and PFS in the high-PNI group before treatment (12,13). However, despite the reports examining the duration of treatment and occurrence of side effects, as indexed by GNRI and PNI, in cancer chemotherapy with ICI alone or in combination with cytotoxic agents in non-small cell lung cancer (14,15), recent treatment methods are increasingly combining ICI and platinum plus one more cytotoxic agent in a three-drug combination, and the relationship between the three-drug combination and GNRI/PNI is unknown.

This study aimed to investigate the association of GNRI and PNI with the duration of treatment success and occurrence of side-effects in patients with non-small cell lung cancer treated with the combination of carboplatin, pemetrexed, and pembrolizumab.

Patients and Methods

Eligible patients. Seventy-two patients with non-small cell lung cancer, treated with the combination of carboplatin, pemetrexed, and pembrolizumab at the Sapporo Minami Sanjo Hospital from November 2019 to September 30, 2022, were included in the study. The electronic medical records of physicians, nurses, and pharmacists were used to retrospectively investigate the patient background and occurrence of side effects in the surveyed patients.

The fourth course of this therapy consisted of three drugs as induction therapy, and the fifth and subsequent courses consisted of two drugs as maintenance therapy. The regimen of triple therapy with carboplatin, pemetrexed, and pembrolizumab, administered up to the fourth course, and the regimen of double-drug maintenance therapy with pemetrexed and pembrolizumab, administered after the fifth course, are shown in Figure 1.

Figure 1. Regimen table. Three drugs were used as induction therapy until the fourth course of this treatment, and two drugs were used as maintenance therapy after the fifth course.

Figure 1

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Endpoints. Time-to-treatment-failure (TTF) was defined as the period from the start of triple therapy with carboplatin, pemetrexed, and pembrolizumab to the date of treatment discontinuation or the last day of the study period (September 30, 2022).

Adverse drug reactions were evaluated based on the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Severity of each case was determined by the highest grade of the entire course.

Nutritional indices were evaluated based on GNRI and PNI, and were classified into High-GNRI group (GNRI>93) and Low-GNRI group (GNRI<93), and High-PNI group (PNI >37.1) and Low-PNI group (PNI <37.1), according to cut-off values calculated by receiver operating characteristic (ROC) curve. GNRI and PNI were calculated using the following formula:

GNRI=(14.89×Alb (g/dl))+41.7×

(current weight (kg)/ideal weight (kg))

PNI=10×Alb (g/dl)+0.005×total lymphocyte count (/mm3)

Survey items. Patient background included age, sex, Eastern Cooperative Oncology Group-Performance Status (ECOG-PS), number of treatment regimens, staging, body mass index (BMI), neutrophil/lymphocyte ratio (NLR), and key laboratory values on the day of administration (serum creatinine level, creatinine clearance, and C-reactive protein (CRP)).

The adverse effects investigated were skin disorders, oral disorders, interstitial pneumonia, myositis, peripheral neuropathy, hypoadrenocorticism, hypopituitarism, hyper/hypothyroidism, infusion reaction (IF), general fatigue, nausea/vomiting, abdominal pain, diarrhea, constipation, arthralgia/myalgia, abnormal taste, edema, fever, headache, anorexia, and blood tests (hypoalbuminemia, increased AST, ALT, LDH, GTP, CK, Cre, decreased eGFR, hyperuricemia, hyperglycemia, leukopenia, decreased Hb, decreased neutrophils, Lymphopenia, and decreased platelets).

The occurrence of adverse events was evaluated based on the CTCAE version 5.0. The severity of each symptom was calculated using the highest grade among all courses.

Statistical analysis. The t-test or Fisher’s direct probability test was used for patient background and adverse effects, and the Kaplan-Meier method was used for TTF comparisons, with p<0.05 being considered statistically significant. Pearson’s correlation matrix was used to evaluate the correlation between GNRI and PNI. For univariate and multivariate analyses, the Cox proportional hazards model was used to evaluate the impact of individual covariates on TTF. Explanatory variables used in the multivariate analysis included factors with p<0.1 in the univariate analysis. JMP® Pro ver. 16 (SAS Institute Inc., Cary, NC, USA) was used for statistical analysis.

Ethical considerations. This study was conducted in compliance with the ethical guidelines for medical research involving human subjects and was approved by the Ethics Committee of Sapporo Minami-Sanjo Hospital (Approval No: R4-4). It was conducted in full consideration of the protection of personal information, and data were anonymized before handling. All the study participants were informed about the study and assured of the opportunity to opt-out. All the patients provided written informed consent to participate in the study prior to its commencement. We ensured that confidential patient information was protected. Data were anonymized prior to processing.

Results

Patient background and correlation between GNRI and PNI. The total number of patients included in the study was 72, 34 in the Low-GNRI group, 38 in the High-GNRI group, 35 in the Low-PNI group, and 37 in the High-PNI group.

Among the two GNRI groups, the Low-GNRI group had significantly lower BMI (p<0.001) and significantly higher NLR, staging, and CRP (p<0.001) at the start of treatment (Table I). Further, between the two PNI groups, NLR, staging, and CRP were significantly higher (p<0.001) in the Low-PNI group at the start of treatment (Table I). However, there was no significant difference in age, sex, PS, serum creatinine level, or creatinine clearance in both GNRI and PNI groups.

Table I. Patient background.

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PS: Eastern Cooperative Oncology Group Performance Status; BMI: Body mass index; NLR: neutrophil-lymphocyte ratio; CRP: C-reactive protein. Statistically significant p<0.05 values are shown in bold.

A positive correlation was observed between GNRI and PNI (r=0.689) (Figure 2).

Figure 2. Correlation between GNRI and PNI. A positive correlation was observed between GNRI and PNI (r=0.689). GNRI: Geriatric nutritional risk index; PNI: prognostic nutritional index.

Figure 2

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Kaplan-Meier curve comparing treatment success time (TTF) between the High and Low groups. The TTF for the High-PNI group was 12.0 months (95%CI=8.3-15.7) while the TTF for the Low-PNI group it was 4.9 months (95%CI=3.2-6.8); the Low-PNI group had a significantly shorter TTF (p=0.006) (Figure 3a). On the other hand, the TTF for the High-GNRI group was 9.6 months (95%CI=6.8-12.4) while the TTF for the Low-GNRI group was 6.9 months (95%CI=3.7-10.2); the Low-GNRI group had a shorter but not significantly different TTF (p=0.091) (Figure 3b).

Figure 3. (a) Kaplan Meier curves of TTF for pembrolizumab triple treated patients with PNI <37.1 (dotted line) or PNI ≥37.1 (solid line). High PNI group had significantly longer TTF (12.0 months vs. 4.9 months, p=0.006). (b) Kaplan Meier curves of TTF for pembrolizumab triple treated patients with GNRI <93 (dotted line) or GNRI ≥93 (solid line). No significant difference was seen in TTF between High and Low groups (9.6 months vs. 6.9 months, p=0.091). GNRI: Geriatric nutritional risk index; PNI: prognostic nutritional index; TTF: treatment success period.

Figure 3

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Univariate and multivariate Cox proportional hazards regression analysis of TTF risk factors. Covariates that might affect the efficacy of ICI were age, staging (≥IV), GNRI (<93), creatinine clearance (<92.4 ml/min), PNl (<37.1), BMI (<21.9), and NLR (≥3.1). Univariate analysis showed significant differences in PNI and BMI (Table II).

Table II. Univariate analysis and multivariate Cox proportional hazards regression analysis for time to treatment failure (TTF).

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GNRI: Geriatric nutritional risk index; LDH: lactate dehydrogenase; ChE: choline esterase; PNI: prognostic nutritional index; BMI: body mass index; NLR: neutrophil-lymphocyte. Statistically significant p<0.05 values are shown in bold.

Multivariate analysis was performed based on the four factors of age, GNRI, BMI, and PNI, which were p<0.1 in the univariate analysis, resulting in a significant difference in PNI (HR=2.791, 95%CI=1.362-5.721, p=0.005) and BMI (HR=2.548, 95%CI=1.255-5.175, p=0.009) (Table II).

Incidence of adverse reactions. Adverse drug reactions, including immune-related Adverse Events (irAE), were significantly higher in the Low-PNI group than in the High-PNI group for interstitial pneumonia, anorexia, fatigue, and fever (p<0.05) (Table III).

Table III. PNI group-adverse drug reactions.

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AST: Aspartate aminotransferase; ALT: alanine aminotransferase; LDH: lactate dehydrogenase; GTP: γ-Glutamyl transferase; CK: creatine kinase; Cre: creatinine; eGFR: estimated glomerular filtration rate; Hb: hemoglobin. Statistically significant p<0.05 values are shown in bold.

The Low-PNI group was significantly higher than the High-PNI group in Grade 3 or higher GTP elevations (p<0.033). On the other hand, there was no significant difference in any of the hematologic toxicities of all grades other than GTP elevation (Table III). There was no significant difference in skin symptoms between the Low- and High-PNI groups (Table III).

For the GNRI groups, the Low-GNRI group was significantly higher than the High-GNRI group in Grade 3 hemoglobin reduction (p=0.030). No other side effect was significantly different (data not shown).

Discussion

In this study, we investigated the relationship of GNRI and PNI with TTF and adverse drug reactions in patients with non-small cell lung cancer treated with the immune checkpoint inhibitor pembrolizumab in combination with the cell-killing anticancer agents carboplatin and pemetrexed. GNRI and PNI were defined as 93 and 37.1, respectively, based on the results of ROC analysis. The values were similar to those in other reports (12,14).

With respect to patient background (Table I), the Low-GNRI and -PNI groups had significantly higher staging (p<0.001), NLR (p<0.001), and CRP (p<0.002) than the High-GNRI and -PNI groups. In univariate and multivariate Cox regression proportional hazards analysis (Table II), PNI was found to be a factor influencing TTF; GNRI, NLR, and CRP had been previously reported to be as useful as PNI as a factor influencing survival (16-18). Therefore, we considered the combination of nutritional indices and inflammatory markers other than PNI to enable more accurate assessment of nutritional status. Sambataro et al. reported that CRP is an inflammatory parameter that indicates the risk of malnutrition (19). If a negative correlation between PNI and CRP is observed, the cause of the elevated CRP in the Low PNI group may be due to systemic inflammation caused by cancer, which is also supported by the elevated NLR (Table I). A possible reason for the shortened TTF in the Low PNI group is the activation of regulatory T cells (Tregs), which play an important role in “immune tolerance” to evade immune responses against self, but also in “immune evasion” of cancer cells to suppress antitumor immune responses. It has been reported that Tregs activate immunosuppressive responses under oxidative stress, thereby nullifying the anti-tumor activity of PD-1 receptor inhibition (20). Systemic inflammation produces reactive oxygen species (ROS) and oxidative stress (21), which may lead to immunosuppressive responses associated with Treg activation. In cases of low nutritional status, evaluation of inflammatory markers such as C-reactive protein (CRP) may also improve patient prognosis.

BMI is also a factor influencing TTF, and had previously been reported to be a predictor of prognosis in patients with non-small cell lung cancer (22), similar to that in this study. Cancer-related malnutrition is associated with increased morbidity and mortality (17). Obesity has been suggested to be associated with a lower incidence of lung cancer and lower mortality in patients with higher BMI (23,24). This is possibly because obesity induces T cell dysfunction and increases the number of PD-1-positive T cells through leptin production (25,26). Therefore, BMI causes metabolic abnormalities and systemic inflammation as cancer progresses, resulting in reduced immune function (22,27). It is, therefore, possible that the low BMI status reduced the number of PD-1-positive T cells, thereby decreasing the efficacy of immune checkpoint inhibitors and leading to a shortening of TTF.

TTF was significantly shorter in the Low-PNI group than in the High-PNI group (p=0.006), according to the Kaplan-Meier curve (Figure 3a). Univariate and multivariate Cox proportional hazards regression analysis (Table II) showed PNI as a factor influencing TTF. In a previous report, PNI was reported to be useful not only before the start of cancer chemotherapy, but also as a prognostic factor for surgical patients (28,29). Therefore, PNI may be useful as a predictor in cancer treatment in general. On the other hand, GNRI was not found to be a factor influencing TTF in both univariate and multivariate analyses, since there was no significant difference in TTF between the Low and High groups (Figure 3b).

Since GNRI was positively correlated with PNI (Figure 2), we speculated that GNRI may be useful in predicting treatment outcome, as nutritional status improves. However, GNRI was not an independent prognostic factor in a multivariate analysis, although a previous study had reported PFS to be significantly longer in the High-GNRI group than in the Low-GNRI group. The reason that GNRI was not a prognostic factor was that PS strongly influences PFS, and significantly more patients in the Low-GNRI group had poor PS (9). In the present study, since there was no significant difference in TTF between the High and Low groups, and no significant difference in PS either (Table I), GNRI was not considered a prognostic factor for TTF. Although GNRI has been reported to be useful as a prognostic factor for conventional cytotoxic anticancer agents or ICI alone (14,15), prognostic factors for GNRI in cancer chemotherapy with multiple agents have not been reported yet. In the present study, GNRI was not found to be a prognostic predictor, and hence, further investigation is warranted in that regard.

TTF should additionally consider the “Beyond PD” effect, in which treatment is continued even after the diagnosis of PD for the purpose of improving prognosis; however, in this study, there was no “Beyond PD” patient, so it was unlikely that TTF contributed to treatment prolongation.

In terms of occurrence of side-effects, the latter occurred in both the Low-PNI and High-PNI groups, with a significant difference in interstitial pneumonia. In this study, the incidence of interstitial pneumonia and adverse reactions of fever, anorexia, and fatigue was significantly higher in the Low-PNI group (Table III). For Grade 3 or higher adverse reactions, γ-GTP was significantly elevated in the Low-PNI group, which may reflect worsening of liver function.

Immune-related adverse events (irAEs) are side effects that occur when the immune system is not regulated properly by the effects of an ICI, and develop in the form of autoimmune disease, which is relatively common in the skin, gastrointestinal tract, liver, lungs, and endocrine organs (30). Among the side effects that developed, interstitial pneumonia and liver dysfunction were the most diagnosed side effects derived from irAE. As for fever, anorexia, and fatigue, whether these side effects were derived from irAE or not still remains known.

In the present study, however, all the side effects that showed significantly higher incidence were in the Low-PNI group and not in the High-PNI group, where prolonged TTF was observed. Zhong et al. reported that irAEs of the skin, endocrine organs, and gastrointestinal tract were highly correlated with efficacy, whereas irAEs of pneumonia, liver, and biliary system showed no correlation with efficacy (31). Thus, interstitial pneumonia and elevated γ-GTP in liver dysfunction did not correlate with efficacy. Several studies had previously reported that skin symptoms that appear when ICIs are used strongly correlate with efficacy (31). Skin irAEs have been postulated to possibly cause both therapeutic efficacy and toxicity as T cells attack antigens present in both tumor and normal tissues (32). However, in this study, no significant difference in the incidence of skin symptoms was identified in both the Low-PNI and High-PNI groups. The incidence was more than 50% in both groups, suggesting that there may have been a reasonable effect in the High-PNI group. This may have masked the cutaneous symptoms caused by irAE, since pemetrexed and ICI together cause a high frequency of cutaneous symptoms (33).

Study limitations. Limitations of the study include the fact that it was a single-center study and that the number of cases was small. Increasing the number of cases would allow more data to be accumulated and more accurate data to be extracted. Furthermore, prognostic factors of various nutritional indices, such as CRP and NLR, in multidrug therapy including ICI have not been studied. Further studies are needed to elucidate the optimal nutritional indices.

Conclusion

PNI and BMI at treatment initiation were independent prognostic factors of TTF in patients with non-small cell lung cancer, receiving triple therapy with pembrolizumab, an ICI cytotoxic anticancer agent. Nutritional status at the start of treatment may influence the occurrence of side effects and the duration of treatment success (TTF). However, PNI at the start of treatment was not shown to be a prognostic predictor of development of irAE.

Conflicts of Interest

There are no conflicts of interest to declare.

Authors’ Contributions

HY conceived the study and drafted the manuscript; MT, HT, WA, and GK made major contributions to data collection; SH and HT made major contributions to the design of the study and interpretation of the data; SH supervised the conduct of the study; OK, IK, and TK contributed majorly in the revision of the manuscript. All Authors approved the submitted manuscript and agreed to accept responsibility for any part of the study.

Acknowledgements

We greatly thank the patients, their families, and all investigators involved in this study.

References

  • 1.Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, Patnaik A, Aggarwal C, Gubens M, Horn L, Carcereny E, Ahn MJ, Felip E, Lee JS, Hellmann MD, Hamid O, Goldman JW, Soria JC, Dolled-Filhart M, Rutledge RZ, Zhang J, Lunceford JK, Rangwala R, Lubiniecki GM, Roach C, Emancipator K, Gandhi L, KEYNOTE-001 Investigators Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015;372(21):2018–2028. doi: 10.1056/NEJMoa1501824. [DOI] [PubMed] [Google Scholar]
  • 2.Gandhi L, Rodríguez-Abreu D, Gadgeel S, Esteban E, Felip E, De Angelis F, Domine M, Clingan P, Hochmair MJ, Powell SF, Cheng SY, Bischoff HG, Peled N, Grossi F, Jennens RR, Reck M, Hui R, Garon EB, Boyer M, Rubio-Viqueira B, Novello S, Kurata T, Gray JE, Vida J, Wei Z, Yang J, Raftopoulos H, Pietanza MC, Garassino MC, KEYNOTE-189 Investigators Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N Engl J Med. 2018;378(22):2078–2092. doi: 10.1056/NEJMoa1801005. [DOI] [PubMed] [Google Scholar]
  • 3.Rodríguez-Abreu D, Powell SF, Hochmair MJ, Gadgeel S, Esteban E, Felip E, Speranza G, De Angelis F, Dómine M, Cheng SY, Bischoff HG, Peled N, Reck M, Hui R, Garon EB, Boyer M, Kurata T, Yang J, Pietanza MC, Souza F, Garassino MC. Pemetrexed plus platinum with or without pembrolizumab in patients with previously untreated metastatic nonsquamous NSCLC: protocol-specified final analysis from KEYNOTE-189. Ann Oncol. 2021;32(7):881–895. doi: 10.1016/j.annonc.2021.04.008. [DOI] [PubMed] [Google Scholar]
  • 4.Mazieres J, Rittmeyer A, Gadgeel S, Hida T, Gandara DR, Cortinovis DL, Barlesi F, Yu W, Matheny C, Ballinger M, Park K. Atezolizumab versus docetaxel in pretreated patients with NSCLC: final results from the randomized phase 2 POPLAR and phase 3 OAK clinical trials. J Thorac Oncol. 2021;16(1):140–150. doi: 10.1016/j.jtho.2020.09.022. [DOI] [PubMed] [Google Scholar]
  • 5.Castillo-Martínez L, Castro-Eguiluz D, Copca-Mendoza ET, Pérez-Camargo DA, Reyes-Torres CA, Ávila EA, López-Córdova G, Fuentes-Hernández MR, Cetina-Pérez L, Milke-García MDP. Nutritional assessment tools for the identification of malnutrition and nutritional risk associated with cancer treatment. Rev Invest Clin. 2018;70(3):121–125. doi: 10.24875/RIC.18002524. [DOI] [PubMed] [Google Scholar]
  • 6.Aprile G, Basile D, Giaretta R, Schiavo G, La Verde N, Corradi E, Monge T, Agustoni F, Stragliotto S. The clinical value of nutritional care before and during active cancer treatment. Nutrients. 2021;13(4):1196. doi: 10.3390/nu13041196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Bouillanne O, Morineau G, Dupont C, Coulombel I, Vincent JP, Nicolis I, Benazeth S, Cynober L, Aussel C. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82(4):777–783. doi: 10.1093/ajcn/82.4.777. [DOI] [PubMed] [Google Scholar]
  • 8.Lv GY, An L, Sun DW. Geriatric Nutritional Risk Index predicts adverse outcomes in human malignancy: a meta-analysis. Dis Markers. 2019;2019:4796598. doi: 10.1155/2019/4796598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Sonehara K, Tateishi K, Araki T, Komatsu M, Yamamoto H, Hanaoka M. Prognostic value of the geriatric nutritional risk index among patients with previously treated advanced non-small cell lung cancer who subsequently underwent immunotherapy. Thorac Cancer. 2021;12(9):1366–1372. doi: 10.1111/1759-7714.13909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Shoji F, Matsubara T, Kozuma Y, Haratake N, Akamine T, Takamori S, Katsura M, Toyokawa G, Okamoto T, Maehara Y. Preoperative Geriatric Nutritional Risk Index: A predictive and prognostic factor in patients with pathological stage I non-small cell lung cancer. Surg Oncol. 2017;26(4):483–488. doi: 10.1016/j.suronc.2017.09.006. [DOI] [PubMed] [Google Scholar]
  • 11.Onodera T, Goseki N, Kosaki G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi. 1984;85(9):1001–1005. [PubMed] [Google Scholar]
  • 12.Oku Y, Toyokawa G, Wakasu S, Kinoshita F, Takamori S, Watanabe K, Haratake N, Nagano T, Kosai K, Takada K, Fujimoto A, Higashijima K, Shiraishi Y, Tanaka K, Takeoka H, Okamoto M, Yamashita T, Shimokawa M, Shoji F, Yamazaki K, Okamoto T, Seto T, Ueda H, Takeo S, Nakashima N, Okamoto I, Takenaka T, Yoshizumi T. Impact of the pretreatment prognostic nutritional index on the survival after first-line immunotherapy in non-small-cell lung cancer patients. Cancer Med. 2023;12(13):14327–14336. doi: 10.1002/cam4.6110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Shao Y, Cao W, Gao X, Tang M, Zhu D, Liu W. Pretreatment “prognostic nutritional index” as an indicator of outcome in lung cancer patients receiving ICI-based treatment: Systematic review and meta-analysis. Medicine (Baltimore) 2022;101(43):e31113. doi: 10.1097/MD.0000000000031113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Karayama M, Inoue Y, Yasui H, Hozumi H, Suzuki Y, Furuhashi K, Fujisawa T, Enomoto N, Nakamura Y, Inui N, Suda T. Association of the Geriatric Nutritional Risk Index with the survival of patients with non-small-cell lung cancer after platinum-based chemotherapy. BMC Pulm Med. 2021;21(1):409. doi: 10.1186/s12890-021-01782-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Karayama M, Inoue Y, Yoshimura K, Hozumi H, Suzuki Y, Furuhashi K, Fujisawa T, Enomoto N, Nakamura Y, Inui N, Suda T. Association of the Geriatric Nutritional Risk Index with the survival of patients with non-small cell lung cancer after nivolumab therapy. J Immunother. 2022;45(2):125–131. doi: 10.1097/CJI.0000000000000396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Mitsuyoshi T, Matsuo Y, Itou H, Shintani T, Iizuka Y, Kim YH, Mizowaki T. Evaluation of a prognostic scoring system based on the systemic inflammatory and nutritional status of patients with locally advanced non-small-cell lung cancer treated with chemoradiotherapy. J Radiat Res. 2018;59(1):50–57. doi: 10.1093/jrr/rrx060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Park S, Park S, Lee SH, Suh B, Keam B, Kim TM, Kim DW, Kim YW, Heo DS. Nutritional status in the era of target therapy: poor nutrition is a prognostic factor in non-small cell lung cancer with activating epidermal growth factor receptor mutations. Korean J Intern Med. 2016;31(6):1140–1149. doi: 10.3904/kjim.2015.062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Shimizu K, Okita R, Saisho S, Maeda A, Nojima Y, Nakata M. Preoperative neutrophil/lymphocyte ratio and prognostic nutritional index predict survival in patients with non-small cell lung cancer. World J Surg Oncol. 2015;13:291. doi: 10.1186/s12957-015-0710-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Sambataro D, Politi MR, Messina A, Scarpello L, Messina S, Guggino R, Carnaghi C, Caccialanza R, Gebbia V. Relationship of inflammatory parameters and nutritional status in cancer patients. Anticancer Res. 2023;43(6):2821–2829. doi: 10.21873/anticanres.16451. [DOI] [PubMed] [Google Scholar]
  • 20.Maj T, Wang W, Crespo J, Zhang H, Wang W, Wei S, Zhao L, Vatan L, Shao I, Szeliga W, Lyssiotis C, Liu JR, Kryczek I, Zou W. Oxidative stress controls regulatory T cell apoptosis and suppressor activity and PD-L1-blockade resistance in tumor. Nat Immunol. 2017;18(12):1332–1341. doi: 10.1038/ni.3868. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Takahashi A, Hanson MG, Norell HR, Havelka AM, Kono K, Malmberg KJ, Kiessling RV. Preferential cell death of CD8+ effector memory (CCR7−CD45RA−) T cells by hydrogen peroxide-induced oxidative stress. J Immunol. 2005;174(10):6080–6087. doi: 10.4049/jimmunol.174.10.6080. [DOI] [PubMed] [Google Scholar]
  • 22.Kichenadasse G, Miners JO, Mangoni AA, Rowland A, Hopkins AM, Sorich MJ. Association between body mass index and overall survival with immune checkpoint inhibitor therapy for advanced non-small cell lung cancer. JAMA Oncol. 2020;6(4):512–518. doi: 10.1001/jamaoncol.2019.5241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Morel H, Raynard B, d’Arlhac M, Hauss PA, Lecuyer E, Oliviero G, Marty C, Gury JP, Asselain B, Grivaux M, Debieuvre D. Prediagnosis weight loss, a stronger factor than BMI, to predict survival in patients with lung cancer. Lung Cancer. 2018;126:55–63. doi: 10.1016/j.lungcan.2018.07.005. [DOI] [PubMed] [Google Scholar]
  • 24.Yang Y, Dong J, Sun K, Zhao L, Zhao F, Wang L, Jiao Y. Obesity and incidence of lung cancer: A meta-analysis. Int J Cancer. 2013;132(5):1162–1169. doi: 10.1002/ijc.27719. [DOI] [PubMed] [Google Scholar]
  • 25.Wang Z, Aguilar EG, Luna JI, Dunai C, Khuat LT, Le CT, Mirsoian A, Minnar CM, Stoffel KM, Sturgill IR, Grossenbacher SK, Withers SS, Rebhun RB, Hartigan-O’Connor DJ, Méndez-Lagares G, Tarantal AF, Isseroff RR, Griffith TS, Schalper KA, Merleev A, Saha A, Maverakis E, Kelly K, Aljumaily R, Ibrahimi S, Mukherjee S, Machiorlatti M, Vesely SK, Longo DL, Blazar BR, Canter RJ, Murphy WJ, Monjazeb AM. Paradoxical effects of obesity on T cell function during tumor progression and PD-1 checkpoint blockade. Nat Med. 2019;25(1):141–151. doi: 10.1038/s41591-018-0221-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Murphy KA, James BR, Sjaastad FV, Kucaba TA, Kim H, Brincks EL, Chua SC Jr, Wilber A, Griffith TS. Cutting edge: Elevated leptin during diet-induced obesity reduces the efficacy of tumor immunotherapy. J Immunol. 2018;201(7):1837–1841. doi: 10.4049/jimmunol.1701738. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Kobayashi S, Karube Y, Inoue T, Araki O, Maeda S, Matsumura Y, Chida M. Advanced lung cancer inflammation index predicts outcomes of patients with pathological stage IA lung adenocarcinoma following surgical resection. Ann Thorac Cardiovasc Surg. 2019;25(2):87–94. doi: 10.5761/atcs.oa.18-00158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Otsuka R, Hayashi H, Uesato M, Hayano K, Murakami K, Toyozumi T, Matsumoto Y, Kurata Y, Nakano A, Takahashi Y, Arasawa T, Matsubara H. Inflammatory and nutritional indices as prognostic markers in elderly patients with gastric cancer. Anticancer Res. 2023;43(11):5261–5267. doi: 10.21873/anticanres.16728. [DOI] [PubMed] [Google Scholar]
  • 29.Miyahara N, Hiratsuka M, Okamoto Y, Teishikata T, Kamohara K. Is the preoperative Prognostic Nutritional Index a useful marker for the decision to perform limited resection in high-risk patients with stage I non-small cell lung cancer. Anticancer Res. 2023;43(8):3659–3664. doi: 10.21873/anticanres.16547. [DOI] [PubMed] [Google Scholar]
  • 30.Postow MA. Managing immune checkpoint-blocking antibody side effects. Am Soc Clin Oncol Educ Book. 2015;(35):76–83. doi: 10.14694/EdBook_AM.2015.35.76. [DOI] [PubMed] [Google Scholar]
  • 31.Zhong L, Wu Q, Chen F, Liu J, Xie X. Immune-related adverse events: promising predictors for efficacy of immune checkpoint inhibitors. Cancer Immunol Immunother. 2021;70(9):2559–2576. doi: 10.1007/s00262-020-02803-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Berner F, Bomze D, Diem S, Ali OH, Fässler M, Ring S, Niederer R, Ackermann CJ, Baumgaertner P, Pikor N, Cruz CG, van de Veen W, Akdis M, Nikolaev S, Läubli H, Zippelius A, Hartmann F, Cheng HW, Hönger G, Recher M, Goldman J, Cozzio A, Früh M, Neefjes J, Driessen C, Ludewig B, Hegazy AN, Jochum W, Speiser DE, Flatz L. Association of checkpoint inhibitor-induced toxic effects with shared cancer and tissue antigens in non-small cell lung cancer. JAMA Oncol. 2019;5(7):1043–1047. doi: 10.1001/jamaoncol.2019.0402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Rusthoven JJ, Eisenhauer E, Butts C, Gregg R, Dancey J, Fisher B, Iglesias J. Multitargeted antifolate LY231514 as first-line chemotherapy for patients with advanced non-small-cell lung cancer: a phase II study. J Clin Oncol. 1999;17(4):1194–1194. doi: 10.1200/JCO.1999.17.4.1194. [DOI] [PubMed] [Google Scholar]

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