Immune-nutritional indexes for predicting the response to nivolumab immunotherapy in gastric cancer

doi:10.21203/rs.3.rs-3238431/v1

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Immune-nutritional indexes for predicting the response to nivolumab immunotherapy in gastric cancer

https://doi.org/10.21203/rs.3.rs-3238431/v1

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Background: Immune checkpoint inhibitors reportedly improve survival outcomes in patients with gastric cancer (GC). However, most patients develop disease progression, for which treatment options are limited. Although research has shown some benefits from nivolumab, accurate prediction of the response to nivolumab has not been achieved. We examined biomarkers for identification of patients with good response for promising treatment like immune checkpoint inhibitors.

Methods: We analyzed 40 patients with clinical unresectable or recurrent GC who received nivolumab immunotherapy and divided them into a responder group (n=7) and non-responder group (n=33) based on the RECIST criteria. We prioritized the following biomarkers: nutritional markers (prognostic nutritional index [PNI], lymphocyte-to-plasma C-reactive protein ratio [LCR], and neutrophil–lymphocyte ratio [NLR]), sarcopenia, and osteopenia.

Results: Our model predicted the response to nivolumab with excellent accuracy in immune-nutritional markers (LCR: area under the curve [AUC]=0.74, NLR: AUC=0.75, PNI: AUC=0.60), osteopenia (AUC=0.83), and sarcopenia (AUC=0.73). Moreover, the predictive accuracy of the risk-assessment model was superior to that of each parameter (AUC=0.93). The high-risk group based on the risk-assessment model was associated with poor overall survival (P<0.01) and progression-free survival (P=0.08). Moreover, univariate Cox proportional hazard regression analysis revealed that our model was an independent predictor of survival outcomes (hazard ratio=3.22, 95% confidence interval=1.62–6.42, P<0.01).

Conclusions: Measurement of nutritional markers, osteopenia, and sarcopenia allowed prediction of a response to nivolumab immunotherapy in patients with GC. Our novel risk-assessment model for predicting the response to nivolumab has potential for clinical translation.

NLR

LCR

PNI

osteopenia

sarcopenia

model

Gastric cancer (GC) is one of the most common cancer and associated with cancer-related death worldwide [1]. Most of all patients are tended to be diagnosed with unresectable status, resulting in poor prognosis with median overall survival (OS) of several months without any treatment [2]. Although a systemic chemotherapy is a gold standard treatment for advanced or unresectable GC, most of all patients experience disease progression due to the limited treatment options [2]. Therefore, there is an emerging need for both novel treatments and effective biomarkers to identify the patients of good response to promising treatments.

Immune checkpoint inhibitors (ICIs) enhance anti-tumor T-cell activity of ICIs like programmed death-1 (PD-1) and PD ligand 1 (PD-L1) [3]. The host immune escape mechanisms corelated to the immune checkpoints in several cancers [3]. ICIs like nivolumab and pembrolizumab are reported to target PD-1 and improve survival outcomes with durable responses in several solid tumors [4–7]. Accordingly, some phase I and II clinical trials have revealed anti-tumor effective of ICIs against advanced GC [8–10].

Nivolumab extended OS in patients who received two or more lines of therapy, resulting in the approval in Asian countries in the phase III ATTRACTION-2 trial [11]. Also, the patients with PD-L1 + pretreated advanced GC benefited from pembrolizumab in the KEYNOTE-059 trial, leading to the approval in the United States [10]. In the CheckMate 649 trial, first-line therapy with nivolumab and chemotherapy showed better survival outcome compared with chemotherapy alone in patients with PD-L1 expression of ≥ 5% [12]. However, in ATTRACTION 04 trials and KEYNOTE 062, ICIs plus chemotherapy did not lead to an OS benefit compared with chemotherapy alone [13]. These data indicate that the novel biomarkers are urgently needed to screen patients who will benefit from ICIs.

To solve this issue, some researchers have utilized patients’ baseline clinical characteristics and showed serum sodium and neutrophil–lymphocyte ratio (NLR) for the benefit from nivolumab [14]. The combinations including age, serum sodium, and peritoneal metastasis was associated with the benefit from nivolumab [14]. Also, a high level of NLR was reported poor prognosis in patients treated with chemotherapy [15] and nivolumab [16]. In addition to these nutritional markers, osteopenia and sarcopenia have also recently received attention with respect to several cancers and have been reported to predict survival outcomes and responses to ICIs [17–23]. Although accumulating reports show some benefit from nivolumab, their findings are less accurate with respect to predicting the response to ICIs.

In the present study, we analyzed immune-nutritional indexes, osteopenia, and sarcopenia (which can be easily used in clinical decision-making for patients with GC) to establish an accurate model with which to predict the probability of response to nivolumab.

Patients

We have enrolled patients with recurrent or unresectable GC who received nivolumab (240 mg/kg every 2 weeks) at Tokushima University hospital from December 2017 to December 2020 in this retrospective study. The protocol of this study was approved by the Ethics Committee on Human Research of Tokushima University hospital. The inclusion criteria was a diagnosis of histologically GC and clinical stage IV as a performance of an imaging evaluation such as computed tomography (CT) or positron emission tomography-CT. Treatment response was assessed using the Response Evaluation Criteria in Solid Tumors version 1.1 [24]. The non-responder group comprised patients who developed progressive disease. The responder group comprised patients who experienced stable disease, partial response, or complete response as the best response.

Nutritional indexes

The patients’ medical records were reviewed at Tokushima University, and their demographic characteristics, histological features, previous treatments, and nutritional indexes were collected. The nutritional indexes were calculated as follows: The prognostic nutritional index (PNI) was calculated as albumin concentration (g/dL) × 10 + total lymphocyte count × 0.005. The NLR and lymphocyte-to-plasma C-reactive protein ratio (LCR) was calculated as the ratio of each parameter.

Sarcopenia

Using the CT images, the skeletal muscle was measured at the level of the third lumbar vertebra [25]. Based on the previous report, the skeletal muscle area was normalized by the patient’s height (skeletal muscle index, cm²/m²). The sarcopenia was classified by the cutoff level which was defined in Japanese Society of Hepatology guidelines with 38 cm²/m² for women and 42 cm²/m² for men.

Osteopenia

Using the CT images, bone mineral density (BMD) of the trabecular bone was measured by calculating the average pixel density in the center of circle at 11th thoracic vertebral level. The cutoff value was 160 Hounsfield units (HU); patients with BMD of < 160 HU: osteopenia, and BMD of ≥ 160 HU: no osteopenia [26, 27].

Statistical analysis

The clinicopathological patient characteristics were compared between the responder and non-responder groups using the Mann–Whitney U test or chi-square test. Statistical analyses were performed using JMP Pro v13.0 statistical software (SAS Institute Japan, Tokyo, Japan) and MedCalc statistical software V.16.2.0 (MedCalc Software bvba, Ostend, Belgium). A P value of < 0.05 was considered statistically significant.

Patient characteristics

We enrolled 40 patients with clinical stage IV GC who received nivolumab immunotherapy as third-line therapy. The treatment duration ranged from 1 to 33 months and included 1 to 71 courses of therapy. We divided the patients into responder and non-responder groups based on the RECIST criteria, with 7 patients as responders and 33 patients as non-responders to nivolumab immunotherapy. The patients’ detailed clinicopathological characteristics are provided in Table 1.

Clinical immune-nutritional potential for predicting treatment response to nivolumab

To evaluate the potential of a response to nivolumab immunotherapy, we performed logistic regression analysis to compare the immune-nutritional indexes (LCR, NLR, and PNI) between the responder and non-responder groups. We confirmed the impact of LCR for predicting a response, with an area under the receiver operating characteristic curve (AUC) of 0.74 (95% confidence interval [CI]=0.67–0.92) (Fig. 1a). NLR showed an AUC of 0.75 (95% CI=0.59–0.87) (Fig. 1b), and PNI showed an AUC of 0.60 (95% CI=0.43–0.75) (Fig. 1c). We next investigated the ability of osteopenia and sarcopenia to predict a response to nivolumab; osteopenia showed an AUC of 0.83 (95% CI=0.67–0.93) (Fig. 2a), and sarcopenia showed an AUC of 0.73 (95% CI=0.71–0.92) (Fig. 2b). Taken together, the nutritional markers, osteopenia, and sarcopenia allowed prediction of a response to nivolumab immunotherapy in patients with GC.

Establishment of model for prediction of response

To confirm the diagnosis of the nutritional indexes, osteopenia, and sarcopenia, we evaluated the diagnostic accuracy of our model to predict a response to nivolumab. Using logistic regression analysis, we trained a model which identified a response to nivolumab in patients with advanced GC (AUC=0.93, 95% CI=0.80–0.99) (Fig. 2a, b). We developed this risk-stratification model based on the following coefficients from individual markers by using the parameters: Logit (P) = (0.87056*NLR) + (−0.032370*PNI) + (0.000014350*LCR) + (−0.086853*Osteopenia) + (−0.21471*Sarcopenia). Our findings allowed us to establish a novel model for prediction of a response to nivolumab in patients with advanced GC. We determined a specific diagnosis for our model. The sensitivity, specificity, positive predictive value, and negative predictive value were 93.6%, 83.3%, 96.7%, and 71.4%, respectively (Table 2). This highlights the superiority of our developed risk-stratification model to identify a response to nivolumab in patients with GC.

Correlation of response prediction with survival outcomes

Because cancer relapse is often associated with poor survival in patients with advanced GC, we next evaluated the prognostic potential of our model. In this regard, we analyzed the survival outcomes (OS and progression-free survival [PFS]), and the median follow-up time was 131 days. Moreover, we observed that our model derived from five parameters with high-risk group demonstrated a significant worse prognosis (PFS: P=0.08, OS: P<0.01_ (Fig. 3a, b). Using univariate Cox proportional hazard regression analysis, we revealed that our model was an independent predictor of survival outcomes (hazard ratio=3.22, 95% CI=1.62–6.42, P<0.01) (Table 3). Accordingly, this highlights the potential clinical significance of our model in the identification of a response to nivolumab in patients with advanced GC.

Nivolumab has been reported to be effective in patients with unresectable or recurrent GC, both as initial therapy and third-line or later therapy [11, 12, 28, 29]. However, there are concerns about the economics of nivolumab because of its high cost, and how to identify patients who would benefit from nivolumab remains unclear [30, 31]. Although the economic benefit of nivolumab for patients with GC is high because of the large number of cases, there are no reports in which the response to nivolumab was predicted with a high degree of accuracy. In the present study, we examined the characteristics of responders to nivolumab among patients with advanced GC. To our knowledge, this is the first study to combine and provide detailed information on clinicopathologic characteristics associated with a response to nivolumab in patients with advanced GC.

PD-L1 is one of the reasonable biomarkers for predicting the response to ICIs like anti-PD-L1 and anti-PD-1 treatments. The patients with PD-L1 positive (combined positive score [CPS] ≥ 10%) had higher objective response rate than the patients with PD-L1 negative (CPS < 10%) (15.5% vs. 6.4%, respectively, when treated with pembrolizumab as the third-line therapy) in the KEYNOTE-059 phase II study [10]. Also, the patients with pembrolizumab significantly improved OS compared with chemotherapy alone as second-line therapy in patients with PD-L1 positive, whereas OS and PFS between the pembrolizumab and chemotherapy alone crossed in patients with PD-L1 positive in the KEYNOTE-061 phase III study [32].

In addition, the serum sodium and NLR level may be predictive biomarkers of nivolumab in patients previously treated for advanced GC. NLR low level correlated to a higher objective response rate and disease control rate and was noted as an important novel factor for improved OS and PFS in the multivariate analysis. Although there are some reports on the prognostic factors of NLR level in patients treated with chemotherapy alone or ICIs [15, 16], most studies were designed as single arm and evaluated prognosis only in patients with anti-cancer therapy. These are the limitations of fully assessing its prognosis compared with other therapies. A high sodium concentration enhances inflammatory M1 macrophages and decreases M2 macrophages [33–35]. The effects of sodium on immune cell function may subsequently influence the development and progression of diseases, including cancer. Several studies have shown that the effects of sodium on immune cell function are not limited to the immune system.

This study had three main limitations. First, the retrospective study design might have resulted in selection bias. Thus, a prospective clinical trial is required to further confirm the accuracy of our risk-assessment model. Second, our model was based on a cohort of patients from a single institution. Thus, additional studies involving a group of independent external institutions are required to answer clinical questions regarding which patients need another treatment regimen and which can be managed with nivolumab immunotherapy after tumor control. Third, we selected the candidate parameters from clinically available features. However, we could not evaluate the molecular features. The predictive value of the tumor mutation burden has been well established in several types of cancer [36–39]. Previous researchers have used molecular features to develop a model with excellent performance for individualized, noninvasive prediction of a response to nivolumab, and this model has potential in predicting a non-response to neoadjuvant therapy in patients with GC. Therefore, we plan to adopt molecular features in the context of dynamic changes in circulating tumor DNA and imaging modalities in future prospective studies. Likewise, we did not analyze PD-L1 expression, DNA mismatch repair, the Epstein–Barr virus status, or cancer genome alterations in the patients enrolled in this study, which warrants further evaluations in a larger cohort.

In conclusion, we have identified a novel risk-assessment model with a combination of immune-nutritional indexes, osteopenia, and sarcopenia for prediction of a response to nivolumab by using clinically available parameters. Our findings highlight the potential clinical impact of our model for improved selection and management of patients with GC.

AUC: area under the receiver operating characteristic curve

BMD: bone mineral density

CI: confidence interval

CPS: combined positive score

CT: computed tomography

GC: gastric cancer

HU: Hounsfield units

ICI: immune checkpoint inhibitor

NLR: neutrophil–lymphocyte ratio

LCR: lymphocyte-to-plasma C-reactive protein ratio

OS: overall survival

PD-1: programmed death-1

PD-L1: programmed death ligand-1

PNI: prognostic nutritional index

PFS: progression-free survival

Conflict of interest

The authors declare no conflicts of interest for this article.

Funding

None.

Authors’ contributions

YW, MN, and MS designed the study. YW, MN, KY, CT, TT, and TN performed the data analyses. YW, MN, MS, HK, and TY drafted the manuscript. SY, YI, and MS revised the manuscript. All authors read and approved the final manuscript.

Acknowledgment

We thank Angela Morben, DVM, ELS, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki. A written informed consent was obtained from all patients, and the study was approved by the institutional review boards of all participating institutions.

Data availability

The datasets used or analysed during the current study available from the corresponding author on reasonable request.

Siegel RL, Miller KD, Fuchs HE, Jemal A: Cancer statistics, 2022. CA Cancer J Clin 2022, 72(1):7-33.
Wagner AD, Syn NL, Moehler M, Grothe W, Yong WP, Tai BC, Ho J, Unverzagt S: Chemotherapy for advanced gastric cancer. Cochrane Database Syst Rev 2017, 8(8):Cd004064.
Comprehensive molecular characterization of gastric adenocarcinoma. Nature 2014, 513(7517):202-209.
Ferris RL, Blumenschein G, Jr., Fayette J, Guigay J, Colevas AD, Licitra L, Harrington K, Kasper S, Vokes EE, Even C et al: Nivolumab for Recurrent Squamous-Cell Carcinoma of the Head and Neck. N Engl J Med 2016, 375(19):1856-1867.
Motzer RJ, Escudier B, McDermott DF, George S, Hammers HJ, Srinivas S, Tykodi SS, Sosman JA, Procopio G, Plimack ER et al: Nivolumab versus Everolimus in Advanced Renal-Cell Carcinoma. N Engl J Med 2015, 373(19):1803-1813.
Bellmunt J, de Wit R, Vaughn DJ, Fradet Y, Lee JL, Fong L, Vogelzang NJ, Climent MA, Petrylak DP, Choueiri TK et al: Pembrolizumab as Second-Line Therapy for Advanced Urothelial Carcinoma. N Engl J Med 2017, 376(11):1015-1026.
Herbst RS, Baas P, Kim DW, Felip E, Pérez-Gracia JL, Han JY, Molina J, Kim JH, Arvis CD, Ahn MJ 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):1540-1550.
Janjigian YY, Bendell J, Calvo E, Kim JW, Ascierto PA, Sharma P, Ott PA, Peltola K, Jaeger D, Evans J et al: CheckMate-032 Study: Efficacy and Safety of Nivolumab and Nivolumab Plus Ipilimumab in Patients With Metastatic Esophagogastric Cancer. J Clin Oncol 2018, 36(28):2836-2844.
Muro K, Chung HC, Shankaran V, Geva R, Catenacci D, Gupta S, Eder JP, Golan T, Le DT, Burtness B et al: Pembrolizumab for patients with PD-L1-positive advanced gastric cancer (KEYNOTE-012): a multicentre, open-label, phase 1b trial. Lancet Oncol 2016, 17(6):717-726.
Fuchs CS, Doi T, Jang RW, Muro K, Satoh T, Machado M, Sun W, Jalal SI, Shah MA, Metges JP et al: Safety and Efficacy of Pembrolizumab Monotherapy in Patients With Previously Treated Advanced Gastric and Gastroesophageal Junction Cancer: Phase 2 Clinical KEYNOTE-059 Trial. JAMA Oncol 2018, 4(5):e180013.
Kang YK, Boku N, Satoh T, Ryu MH, Chao Y, Kato K, Chung HC, Chen JS, Muro K, Kang WK et al: Nivolumab in patients with advanced gastric or gastro-oesophageal junction cancer refractory to, or intolerant of, at least two previous chemotherapy regimens (ONO-4538-12, ATTRACTION-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 2017, 390(10111):2461-2471.
Kang YK, Chen LT, Ryu MH, Oh DY, Oh SC, Chung HC, Lee KW, Omori T, Shitara K, Sakuramoto S et al: Nivolumab plus chemotherapy versus placebo plus chemotherapy in patients with HER2-negative, untreated, unresectable advanced or recurrent gastric or gastro-oesophageal junction cancer (ATTRACTION-4): a randomised, multicentre, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol 2022, 23(2):234-247.
Shitara K, Van Cutsem E, Bang YJ, Fuchs C, Wyrwicz L, Lee KW, Kudaba I, Garrido M, Chung HC, Lee J et al: Efficacy and Safety of Pembrolizumab or Pembrolizumab Plus Chemotherapy vs Chemotherapy Alone for Patients With First-line, Advanced Gastric Cancer: The KEYNOTE-062 Phase 3 Randomized Clinical Trial. JAMA Oncol 2020, 6(10):1571-1580.
Kang YK, Morita S, Satoh T, Ryu MH, Chao Y, Kato K, Chung HC, Chen JS, Muro K, Kang WK et al: Exploration of predictors of benefit from nivolumab monotherapy for patients with pretreated advanced gastric and gastroesophageal junction cancer: post hoc subanalysis from the ATTRACTION-2 study. Gastric Cancer 2022, 25(1):207-217.
Templeton AJ, McNamara MG, Šeruga B, Vera-Badillo FE, Aneja P, Ocaña A, Leibowitz-Amit R, Sonpavde G, Knox JJ, Tran B et al: Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst 2014, 106(6):dju124.
Sacdalan DB, Lucero JA, Sacdalan DL: Prognostic utility of baseline neutrophil-to-lymphocyte ratio in patients receiving immune checkpoint inhibitors: a review and meta-analysis. Onco Targets Ther 2018, 11:955-965.
Kano M, Hihara J, Tokumoto N, Kohashi T, Hara T, Shimbara K, Takahashi S: Association between skeletal muscle loss and the response to nivolumab immunotherapy in advanced gastric cancer patients. Int J Clin Oncol 2021, 26(3):523-531.
Nakao T, Shimada M, Yoshikawa K, Tokunaga T, Nishi M, Kashihara H, Takasu C, Wada Y, Yoshimoto T, Yamashita S: Impact of osteopenia and neutropenia in patients with colorectal cancer treated with FOLFOXIRI: a retrospective cohort study. Int J Clin Oncol 2022, 27(9):1439-1449.
Kirk B, Zanker J, Duque G: Osteosarcopenia: epidemiology, diagnosis, and treatment-facts and numbers. J Cachexia Sarcopenia Muscle 2020, 11(3):609-618.
Isaacson J, Brotto M: Physiology of Mechanotransduction: How Do Muscle and Bone "Talk" to One Another? Clin Rev Bone Miner Metab 2014, 12(2):77-85.
Sjöblom S, Suuronen J, Rikkonen T, Honkanen R, Kröger H, Sirola J: Relationship between postmenopausal osteoporosis and the components of clinical sarcopenia. Maturitas 2013, 75(2):175-180.
Basile D, Corvaja C, Caccialanza R, Aprile G: Sarcopenia: looking to muscle mass to better manage pancreatic cancer patients. Curr Opin Support Palliat Care 2019, 13(4):279-285.
Tsukagoshi M, Yokobori T, Yajima T, Maeno T, Shimizu K, Mogi A, Araki K, Harimoto N, Shirabe K, Kaira K: Skeletal muscle mass predicts the outcome of nivolumab treatment for non-small cell lung cancer. Medicine (Baltimore) 2020, 99(7):e19059.
Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M et al: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009, 45(2):228-247.
Messina C, Maffi G, Vitale JA, Ulivieri FM, Guglielmi G, Sconfienza LM: Diagnostic imaging of osteoporosis and sarcopenia: a narrative review. Quant Imaging Med Surg 2018, 8(1):86-99.
Sharma P, Parikh ND, Yu J, Barman P, Derstine BA, Sonnenday CJ, Wang SC, Su GL: Bone mineral density predicts posttransplant survival among hepatocellular carcinoma liver transplant recipients. Liver Transpl 2016, 22(8):1092-1098.
Toshima T, Yoshizumi T, Kosai-Fujimoto Y, Inokuchi S, Yoshiya S, Takeishi K, Itoh S, Harada N, Ikegami T, Soejima Y et al: Prognostic Impact of Osteopenia in Patients Who Underwent Living Donor Liver Transplantation for Hepatocellular Carcinoma. World J Surg 2020, 44(1):258-267.
Janjigian YY, Shitara K, Moehler M, Garrido M, Salman P, Shen L, Wyrwicz L, Yamaguchi K, Skoczylas T, Campos Bragagnoli A et al: First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial. Lancet 2021, 398(10294):27-40.
Boku N, Ryu MH, Kato K, Chung HC, Minashi K, Lee KW, Cho H, Kang WK, Komatsu Y, Tsuda M et al: Safety and efficacy of nivolumab in combination with S-1/capecitabine plus oxaliplatin in patients with previously untreated, unresectable, advanced, or recurrent gastric/gastroesophageal junction cancer: interim results of a randomized, phase II trial (ATTRACTION-4). Ann Oncol 2019, 30(2):250-258.
Gong J, Chehrazi-Raffle A, Reddi S, Salgia R: Development of PD-1 and PD-L1 inhibitors as a form of cancer immunotherapy: a comprehensive review of registration trials and future considerations. J Immunother Cancer 2018, 6(1):8.
Verma V, Sprave T, Haque W, Simone CB, 2nd, Chang JY, Welsh JW, Thomas CR, Jr.: A systematic review of the cost and cost-effectiveness studies of immune checkpoint inhibitors. J Immunother Cancer 2018, 6(1):128.
Shitara K, Özgüroğlu M, Bang YJ, Di Bartolomeo M, Mandalà M, Ryu MH, Fornaro L, Olesiński T, Caglevic C, Chung HC et al: Pembrolizumab versus paclitaxel for previously treated, advanced gastric or gastro-oesophageal junction cancer (KEYNOTE-061): a randomised, open-label, controlled, phase 3 trial. Lancet 2018, 392(10142):123-133.
Willebrand R, Kleinewietfeld M: The role of salt for immune cell function and disease. Immunology 2018, 154(3):346-353.
Kleinewietfeld M, Manzel A, Titze J, Kvakan H, Yosef N, Linker RA, Muller DN, Hafler DA: Sodium chloride drives autoimmune disease by the induction of pathogenic TH17 cells. Nature 2013, 496(7446):518-522.
Hernandez AL, Kitz A, Wu C, Lowther DE, Rodriguez DM, Vudattu N, Deng S, Herold KC, Kuchroo VK, Kleinewietfeld M et al: Sodium chloride inhibits the suppressive function of FOXP3+ regulatory T cells. J Clin Invest 2015, 125(11):4212-4222.
Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, Schrock A, Campbell B, Shlien A, Chmielecki J et al: Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 2017, 9(1):34.
Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, Lee W, Yuan J, Wong P, Ho TS et al: Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015, 348(6230):124-128.
Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, Sucker A, Hillen U, Foppen MHG, Goldinger SM et al: Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 2015, 350(6257):207-211.
Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, Børresen-Dale AL et al: Signatures of mutational processes in human cancer. Nature 2013, 500(7463):415-421.

Table 1. Clinicopathological characteristics according to responder and non-responder groups
Factors	Responder (n=7)	Non-responder (n=33)	p
Age
	72.4±2.36	72.2±0.80	0.76
Gender
Male	6	26
Female	1	7	0.68

CEA level	2.3±0.58	2.2±0.34	0.69

CA19-9 level	17.7±4.45	36.3±16.1	0.93

LCR	14956±5428	8077±1716	0.07

NLR	1.84±0.17	3.88±0.69	0.04

PNI	33.7±7.2	32.5±2.4	0.42

Osteopenia	168.3±6.2	146.4±2.8	<0.01

Sarcopenia	41.1±2.2	36.9±0.6	0.06

Adverse effect	2	3	0.40
Adrenal disorder	1	1
Tyroid disorder	0	1
Myasthenia gravis	0	1
Interstitial pneumonia	1	0

1^st Chemotherapy			0.79
DCS	2	15
SOX	3	11
Nab/PTX	2	3
Others	0	4

2^nd Chemotherapy			0.11
Abx/RAM	0	11
Nab/PTX	4	11
RAM/PTX	1	5
Others	2	6

CEA, carcinoembryonic antigen; NLR, neutrophil–lymphocyte ratio

LCR, lymphocyte-to-plasma C-reactive protein ratio; PNI, prognostic nutritional index

Table 2: Performance of the model in estimating the risk of response

Variable

Value (95% CI)

LCR

NLR

PNI

Osteopenia

Sarcopenia

Signature

Sensitivity, %

51.6

(33.1-69.8)

66.7

(48.2-82.0)

84.9

(43.5-76.9)

84.9

(43.5-76.9)

36.4

(46.5-80.3)

93.6

(56.4-91.0)

Specificity, %

100.0

(54.1-100.0)

100.0

(59.0-100.0)

42.9

(72.6-97.8)

71.4

(72.6-97.8)

100.0

(74.9-99.1)

83.3

(44.9-92.2)

AUC, %

73.7

(67.2-92.4)

74.9

(58.7-87.2)

59.7

(43.1-74.9)

82.5

(67.2-92.6)

72.7

(71.4-91.7)

93.0

(79.6-98.8)

PPV, %

100.0

87.5

(78.4-93.1)

93.3

(81.1-97.9)

100.0

96.7

(82.9-99.4)

NPV, %

28.6

(21.8-36.5)

38.9

(28.2-50.8)

37.5

(15.6-66.0)

50.0

(28.2-71.8)

25.0

(20.5-30.1)

71.4

(38.4-90.9)

AUC, area under the curve; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value

Table 3. Cox proportional hazard regression analyses
	Univariate analysis
Factors	HR	95% CI	p
Age
(≥70 vs. <70 years)	1.72	0.74-3.97	0.21
Gender
(Male vs. Female)	1.26	0.52-3.07	0.61
CEA level
(≥5 vs. <5)	1.95	0.67-5.66	0.22
CA19-9 level
(≥37 vs. <37)	0.94	0.28-3.14	0.91
1^st regimen
(DCS vs. SOX)	1.13	0.28-4.41	0.88
2^nd regimen
(Abx/RAM vs. Nab/PTX)	1.11	0.43-2.89	0.83
LCR
(Low vs. High)	1.38	0.63-3.03	0.42
NLR
(High vs. Low)	1.55	0.69-3.50	0.29
PNI
(Low vs. High)	1.55	0.75-3.22	0.24
Osteopenia
(High vs. Low)	1.72	0.76-3.88	0.19
Sarcopenia
(High vs. Low)	1.98	0.98-3.99	0.06
Signature
(High vs. Low)	3.15	1.42-6.99	<0.01

CEA, carcinoembryonic antigen; NLR, neutrophil–lymphocyte ratio

LCR, lymphocyte-to-plasma C-reactive protein ratio; PNI, prognostic nutritional index

No competing interests reported.

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Immune-nutritional indexes for predicting the response to nivolumab immunotherapy in gastric cancer

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