Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 17  |  Issue : 1  |  Page : 114-121

Translational relevance of baseline peripheral blood biomarkers to assess the efficacy of anti-programmed cell death 1 use in solid malignancies


1 Department of Translational Medicine and Therapeutics, Healthcare Global Enterprises Limited, Bengaluru, Karnataka, India
2 Department of Medical Oncology, Healthcare Global Enterprises Limited, Bengaluru, Karnataka, India
3 Department of Clinical Pharmacology, Healthcare Global Enterprises Limited, Bengaluru, Karnataka, India

Date of Submission03-Jul-2020
Date of Decision29-Aug-2020
Date of Acceptance30-Sep-2020
Date of Web Publication15-Mar-2021

Correspondence Address:
Hrishi Varayathu
Department of Translational Medicine and Therapeutics, Healthcare Global Enterprises Limited, Bengaluru - 560 027, Karnataka
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcrt.JCRT_910_20

Rights and Permissions
 > Abstract 


Background: This study is an overall clinical analysis of anti-programmed cell death 1 (PD1) antibodies used in a single institution, emphasizing the role of baseline peripheral blood markers as a prognostic or predictor biomarker of immunotherapy.
Methods: Sixty-one patients were retrospectively analyzed from hospital medical records. The endpoint of this study was death from any cause and the survival time was calculated from the date of start of immunotherapy to the date of death. Descriptive and survival statistics was performed using SPSS version 23. Cutoff values for baseline biomarkers (neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], neutrophil-to-eosinophil ratio [NER], and lymphocyte-to-monocyte ratio [LMR]) were obtained using cutp function of Evaluate Cutpoints software (R survMisc package). Pearson and Pearman correlation coefficients were used to examine the relationship of peripheral blood biomarkers.
Results: Nighty-eight percent of the study population had Stage IV disease and total median overall survival postanti-PD1 therapy was 10.7 months. Patients receiving more than 5 doses of anti-PD1 therapy (12.6 m vs. 4.4 m, P < 0.001) and used in front lines (18.9 m vs. 10.7 m vs. 10.1 m vs. 2.8 m in first line, second line, third line, and >3 lines, respectively, P = 0.049) were found to have an impact in overall survival. Pembrolizumab showed a better survival compared to nivolumab (17.4 m vs. 8.2 m, P = 0.049) in our study. Among baseline biomarkers assessed, NLR (cutoff − 2.81, P = 0.003) and LMR (cutoff – 5.76, P = 0.017) has shown a statistically significant relationship with immunotherapy response. NER (cutoff − 24.32, P = 0.051) and PLR (cutoff – 190.8, P = 0.072) were also found to exhibit a strong relationship with anti-PD1 therapy response. NLR exhibits a statistically significant positive correlation with PLR (r = 0.917 P < 0.001) and NER (r = 0.400 P = 0.014).
Conclusion: Real-life data analysis of anti-PD1 use for solid cancers highlights that baseline NLR, PLR, NER, and LMR have a significant role as immunotherapy biomarkers. However, larger studies are required to further prove the specificity and sensitivity.

Keywords: Anti-programmed cell death 1 therapy, lymphocyte-to-monocyte ratio, neutrophil-to-eosinophil ratio, neutrophil-to-lymphocyte ratio, peripheral blood biomarkers, platelet-to-lymphocyte ratio


How to cite this article:
Varayathu H, Sarathy V, Thomas BE, Mufti SS, Sangi L, Thungappa SC, Tripathi P, Naik R. Translational relevance of baseline peripheral blood biomarkers to assess the efficacy of anti-programmed cell death 1 use in solid malignancies. J Can Res Ther 2021;17:114-21

How to cite this URL:
Varayathu H, Sarathy V, Thomas BE, Mufti SS, Sangi L, Thungappa SC, Tripathi P, Naik R. Translational relevance of baseline peripheral blood biomarkers to assess the efficacy of anti-programmed cell death 1 use in solid malignancies. J Can Res Ther [serial online] 2021 [cited 2021 Apr 17];17:114-21. Available from: https://www.cancerjournal.net/text.asp?2021/17/1/114/311075




 > Introduction Top


Cancer immunotherapy is an effective and promising option to eliminate tumor cells and has changed the treatment paradigm for advanced cancers across many tumor types. Cancer immunotherapy was overlooked for more than 50 years since William Bradley Coley first tried to use a mixture of bacterial toxins for the treatment of bone cancer in 1891.[1] Later, James Allison and Tasuku Honjo discovered CTLA4 and programmed cell death ligand-1 (PDL-1), respectively, as therapeutic targets which opened doors to the field of cancer immunotherapy we know today. At present, six immune checkpoint inhibitors (ICIs) have been approved by the US Food and Drug Administration (FDA), of which five have also received market authorization by the European Medicines Agency. Regulatory approval of the expanding list of anti-programmed cell death 1 (PD1)/PDL-1 antibody in various solid cancers and hematological malignancies was led by the notable antitumor activity of PD-L1/PD-1 inhibition in non-small cell lung cancer (NSCLC), renal cell carcinoma, and melanoma.[2],[3] While meaningful durable antitumor responses were achieved in some patients, the majority of patients did not respond to these agents. Poor sensitivity of available biomarkers was a limiting factor in appropriate selection of patients eligible for immunotherapy. PD-L1 protein expression on tumor or immune cells emerged as the first promising predictive biomarker for immune checkpoint blockade. PD-L1 protein expression determined through immunohistochemistry has many limitations such as heterogeneous expression which makes a single biopsy under representative of the entire tumor. There is also heterogeneity in PD-L1 expression between primary and metastatic tumors and between primary and recurrent tumors further decreasing the biomarker specificity of PD-L1. PD-L1 expression is recommended and feasible only prior to initiation of immunotherapy and thus cannot be used during therapy to monitor response.[4] An analysis of all US FDA approvals of ICIs showed that PD-L1 was predictive only in 28.9% of cases.[5] Therefore, better predictive biomarkers are needed to determine which patients will ideally benefit from immunotherapy and also help to monitor response during treatment.

As inflammation has shown to play a crucial role in the pathogenesis and progression of cancer, inflammatory biomarkers have gained more attention as potential predictive and prognostic parameters in immunotherapy in recent years.[6],[7],[8] Neutrophils play an important role in inflammation as effectors of both innate immunity and cell signaling in the adaptive immune response. They also inhibit the activity of cytotoxic T-lymphocytes in vitro. Monocytes contribute to inflammatory process through their patterns of differentiation into macrophages or dendritic cells in the tissue microenvironment. Platelets also have an active role in inflammation by releasing vascular endothelial growth factor, which mediates migration and extravasation of leukocytes, and platelet-derived growth factor, a chemokine that recruits neutrophils and monocytes. Hence, it would be worthy to check the significance of these markers as predictive biomarkers for immunotherapy efficacy.

Our study included patients who received anti-PD-1 immunotherapy and analyzed the prognostic significance of baseline blood biomarkers in various solid tumors.


 > Methods Top


We retrospectively screened 61 patients who received more than 3 doses of immunotherapy for solid tumors from 2017. We used data from a study by Salpeter et al. to determine the expected median survival for comparison of overall survival in our patients.[9] In our study, we defined overall survival as the time from initiation of immunotherapy till death of a patient due to any cause. Peripheral blood biomarkers were recorded before the initiation of anti-PD-1 therapy. These values were used to calculate neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), neutrophil-to-eosinophil ratio (NER), and lymphocyte-to-monocyte ratio (LMR) [Supplementary Table 1], which was then correlated with response to anti-PD-1 therapy. NER is used to analyze the possibility of nonlymphocyte mediated antitumor immunity.



Statistical analysis

Descriptive and survival statistics was performed using IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp. The endpoint of this study was death from any cause and the survival time was calculated from the date of start of immunotherapy to the date of death. Overall survival and survival post immunotherapy were estimated using the Kaplan–Meier method. The log-rank test was used to test for an association of individual biomarkers with survival and 2-sided P < 0.05 was considered statistical significance.

The peripheral blood biomarker (NLR, PLR, NER, and LMR) cutoff scores were explored using cutp function of the Evaluate Cutpoints software (R survMisc package). The score with maximum split in survival post immunotherapy (based on log-rank statistics and lowest P value) was chosen as the cutoff. Pearson correlation coefficients were used to examine the relationship inbetween NLR, PLR, NER, and LMR. Spearman correlation was performed to assess the relationship of NLR, PLR, NER, and LMR with overall survival.


 > Results Top


Baseline characters

Sixty-one patients who received minimum 3 doses of anti-PD1 therapy were included in the final analysis. The baseline characteristics are listed in [Table 1]. The median age was 58 years and 69% of patients were male. About 75% were of Asian ethnicity and 25% were African. The data included 15 patients with NSCLC, 13 patients with gastrointestinal malignancies, 6 patients with renal cell cancer, 4 patients with dual malignancy, 1 patient with breast cancer, 10 patients with melanoma, 10 patients with head and neck cancer, and 1 each with neuroendocrine and primitive neuroectodermal tumor 98% (60) of patients were Stage IV and only 1 patient had Stage IIIb disease. Nivolumab (62%) was used more frequently than pembrolizumab (38%) as the ICI of choice. Forty-four percent of the study population received ICIs as the second line, 35% as the third line, 18% as the first line, and 3% received ICI as the fourth line and beyond. Median baseline values of NLR, PLR, NER, and LMR were recorded (3.2, 256.7, 36.73, and 4.6 respectively). Majority of patients were not tested for MSI (80%) and PD-L1 (75%). About 74% of patients had an estimated median overall survival ≤6 months as determined in a study by Salpeter et al. prior to initiation of immunotherapy.
Table 1: Baseline parameters

Click here to view


Response to therapy

Females were found to have a better median survival of 18 months (10.2–20) as compared to males who had an OS of 8.2 months (8.7–14.6), which was not statistically significant (P = 0.246). The median overall survival in African and Asian patients was 17.4 months and 8.7 months, respectively (P = 0.176). Patients were classified into two age groups (<59 years and >59 years) and the median survival (10.7 months vs. 10.8 months) was almost the same in both the groups (P = 0.838). Post immunotherapy, the median overall survival was 10.7 months (P = 0.001) [Table 2]. Among all malignancies, melanoma had the highest median survival with 18 months. Line of therapy and treatment response was statistically significant (P = 0.049) with a median survival of 18.9 months when ICIs were used in the first line, 10.7 months in the second line, 10.1 months in the third line, and only 2.8 months when ICIs were used beyond the third line. Patients in the pembrolizumab group had better median survival as compared to those treated with nivolumab (17.4 months vs. 8.2 months) (P = 0.049). Patients receiving more than 5 doses of ICI responded better with a median survival of 12.6 months versus 4.4 months in patients receiving <5 doses [Figure 1].
Table 2: Survival analysis of overall study population

Click here to view
Figure 1: Kaplan–Meier plot of various parameters assessed. (a) Median survival in males (green line) versus females (blue line). (b) Median survival in Asian (green line) versus African (blue line). (c) Median survival in >59 (green line) versus <59 (blue line). (d) Median survival in >5 doses (green line) versus <5 doses (blue line). (e) Median survival in pembrolizumab (green line) versus nivolumab (blue line). (f) Median survival in ≤6 months (green line) versus >6 months (blue line)

Click here to view


Peripheral blood biomarkers

Cutoff values for baseline blood biomarkers such as NLR, PLR, NER, and LMR were obtained and are depicted in [Figure 2]. Cutoff values obtained for NLR (2.81) and LMR (5.76) showed statistical significance (P = 0.00429 and P = 0.0235, respectively), whereas NER (24.32) and PLR (190.8) were close to significance (P = 0.051 and P = 0.0718). Median survival of patients with baseline NLR <2.81 and >2.81 was 18 months and 7 months, respectively, which was statistically significant (P = 0.003) [Figure 3]. Patients who had a baseline NER <24.32 showed a median survival of 14.4 months compared to only 7.7 months in those with NER >24.32 (P = 0.051). Patients with PLR <190.8 achieved a median survival of 12.6 months versus 11.3 months (P = 0.072) in PLR >190.8. A statistically significant difference in median survival was obtained in patients with baseline LMR >5.76 compared with to those with LMR <5.76 (26.2 months vs. 7.7 months respectively), which was statistically significant with P = 0.017.
Figure 2: Biomarker cut-points evaluated using cutp function of Evaluate Cutpoints software

Click here to view
Figure 3: represents Kaplan–Meier plot of peripheral blood biomarkers. (a) NLR: NLR >2.81 (green line) versus NLR <2.81 (blue line). (b) PLR: PLR >190.8 versus PLR <190.8. (c) NER: NER >24.32 (green line) versus NER <24.32 (blue line). (d) LMR: LMR >5.76 (green line) versus LMR <5.76 (blue line). NLR = Neutrophil-to-lymphocyte ratio, PLR = Platelet-to-lymphocyte ratio, NER = Neutrophil-to-eosinophil ratio, LMR = Lymphocyte-to-monocyte ratio

Click here to view


Co-relation of peripheral blood biomarkers

NLR positively correlated with PLR (r = 0.917 P < 0.001) and NER (r = 0.400 P = 0.014) and had a negative correlation with LMR (r = −0.186 P = 0.270). Spearman correlation analysis was performed to assess the correlation of each peripheral blood biomarkers with overall survival. NLR (rho = −0.511, P = 0.001), NER (rho = −0.295, P = 0.077), and PLR (rho = −0.182, P = 0.024) was found to exhibit negative correlation with survival outcome. LMR (rho = 0.369, P = 0.024) has shown a positive correlation with survival outcome. Both the correlations are depicted in [Supplementary Figure 1].



Adverse events reported

Eleven (18%) patients reported adverse events during immune checkpoint therapy. Grade 3 reactions were found only in 2 patients, of which 1 had pancreatitis and the other had skin reactions. Immune-mediated hypothyroidism was reported in 4 patients where 3 had Grade 1 and only 1 patient had Grade 2 hypothyroidism. Grade 2 hepatitis was found in 2 patients and hypoglycemia, dry mouth, and diplopia were reported in one patient each, respectively. The number of patients with treatment-related toxicities was comparable with 6 patients in the pembrolizumab arm and 5 in nivolumab [Table 3].
Table 3: Adverse events reported

Click here to view



 > Discussion Top


Our study was intended to analyze the safety and efficacy of anti-PD1 therapy in solid malignancies correlating them with clinical response and baseline serum biomarkers. We found that response to anti-PD-1 therapy depends on the cancer type, immunotherapy used, line of treatment, number of doses administered, and baseline serum biomarkers such as NLR, NER, and LMR. PLR also showed a trend towards statistical significance (P = 0.072) in predicting response to immunotherapy. Studies have reported the significance of NLR, PLR, and LMR as a biomarker for ICIs.[10],[11],[12],[13] However, this is the first study to our knowledge reporting the significance of NER as a predictive biomarker in solid malignancies receiving ICIs. The role of neutrophils and eosinophils in antitumor immune response of ICIs has been demonstrated in several studies. Sonja et al. reported that clinical response of pembrolizumab and nivolumab/ipilimumab combination in melanoma correlated with tumor-infiltrating eosinophils as well as circulating eosinophils. Martín-Ruiz et al. observed tumor regression in a patient-derived xenograft model of early-stage NSCLC devoid of host lymphoid cells treated with single-agent anti-PD-1 and in combination with chemotherapy. They found that anti-PD-L1 treatment enhanced myeloid cell mobilization and produced acute inflammatory reactions mediated by neutrophils. This study suggests potential cytotoxic action of neutrophils secondary to PD-1 inhibition of effector cells.[14],[15] Therefore, the correlation of NER with a response to ICI demonstrated in our study further opens doors to research on nonlymphocyte-mediated antitumor immunity.

Among biomarkers assessed, NLR was found to have statistically significant positive correlation with PLR (r = 0.917 P < 0.001) and NER (r = 0.400 P = 0.014) with respect to survival outcome. NLR (rho = −0.51, P = 0.001) and LMR (rho = 0.369, P = 0.024) followed by NER (rho = −0.295, P = 0.077) were found to have better correlation with overall survival in our study. A negative correlation of NLR, NER, and PLR indicates that an increase in their baseline value can worsen the prognosis. Since our study is a pilot study, these values should be analyzed further in larger population studies to assess the exact relationship. However, IPY Prabawa et al. reported a strong positive correlation between the staging of cervical cancer with NLR (r = 0.638) and PLR (r = 0.668). The mutual correlation among biomarkers and their individual correlation with survival outcome indicate their potential role as a biomarker.[16]

In our study, we observed a superior response with pembrolizumab than nivolumab. Even though both drugs have a similar mechanism of action and exhibit similar pharmacokinetic profile (terminal half-life, steady-state concentration, and clearance), their treatment approvals are overlapping in some but discordant in many other tumor types. Fessas et al. compared molecular, preclinical, and early clinical characteristics of nivolumab and pembrolizumab and reported that the discordance in response is mostly due to drug independent factors.[17] However, the binding affinity of both drugs are different (pembrolizumab Kd = 28 pM, nivolumab Kd = 3nM) and this has not been accounted for in previous studies. Since the affinity of nivolumab is far lesser than pembrolizumab; more doses of nivolumab may be required to elicit a response when compared to pembrolizumab. According to our data, patients receiving ≥5 doses had a better clinical response when compared to those receiving <5. Interestingly, 37%[14] of patients in the nivolumab group received <5 doses compared to 30% in the pembrolizumab group.[7] This could have also contributed to the diminished response in the nivolumab group.[18]

A significant improvement in survival was found when ICI was used in front lines. Median overall survival was significantly (P = 0.049) better in first-line therapy (18.9 months) followed by second (10.7 months), third line (10.1 months), and fourth line (2.8 months). The high burden of disease due to disease progression, multiple lines of therapy, and other patient-related factors such as comorbidities can impair the patient's performance status. This could have been the reason behind the decline in overall survival when ICI used as a last resort.

Despite the fact that clinical response was significantly higher in patients with a life expectancy of >6 months (median survival: 26.2 months), we also observed meaningful response in patients who had a life expectancy of ≤6 months (median survival: 7.3 months) as per a study by Salpeter et al. About 42%[19] of patients who had life expectancy of ≤6 months had received <5 doses due to multiple reasons such as poor performance status and affordability issues.[9]

Patient demographics such as age, gender, and ethnicity did not have any statistical significance with overall survival. Females had better median overall survival (18 m) than males (8.2 m).[19] Several reports stated that females including both children and adults have higher CD4+ T-cell counts and higher CD4/CD8 ratios than age-matched males.[20],[21],[22],[23] Number of females in our study was lesser than males which could have contributed to the difference in survival between the two genders. Median overall survival was almost the same in the age group ≥60 (10.8 m) and <60 (10.7 m) and this is in concordance with a meta-analysis analyzing 34 studies which reported that ICI had comparable efficacy in cancer patients aged <65 years and ≥65 years.[24] While data on ethnic differences in ICI response are less reported, we found better response in African patients (median survival: 17.4 months) as compared to Asians (median survival: 8.7 months). However, the data were failed to show a statistical significance.

Majority of patients had not undergone a test for PD-L1 (75%), MSI (80%), and EGFR (75%) status. Statistics for MSI by tumor subsite was not available because MSI-high patients[4] benefitted from ICI irrespective of the cancer site or their data were censored. EGFR expression was checked for one patient with esophageal cancer, a patient with head-and-neck cancer and all lung cancer patients[13] except in 2 patients due to insufficient tissue. Although many studies have reported decreased efficacy of ICIs in EGFR-mutated patients even if they have PD-L1 expression ≥50%, we found meaningful response in both EGFR mutated[5] and nonmutated[10] groups, while the EGFR wild-type group had a better survival (18.9 m) with ICI compared to the EGFR-mutated patients (10.8 m).[25],[26] Studies suggest a lack of PD-L1 expression and an un-inflamed tumor microenvironment as the hypothesis for reduced ICI response in EGFR mutations.[27] However, subgroup analyses of phase 3 IM power150 study found that median survival was not estimable in EGFR-mutated patients treated with the combination of atezolizumab + bevacizumab + paclitaxel + carboplatin compared with 18.7 months in bevacizumab + paclitaxel + carboplatin.[28] Most patients (75%) were not tested for PDL-1 expression in our study. Of the patients tested for PD-L1 expression, none had PD-L1 ≥50% and 10% and 15% had <1% and 1%–49% expression, respectively. Despite the uneven distribution of patients, we found that median survival (12.6 months) was slightly higher in PD-L 11%–49% group (12.6 months), followed by <1% (10.8 months) and not-tested groups (8.2 months). A Davis et al. evaluated PD-L1 as a predictive biomarker across 15 tumor types approved for ICI therapy and reported that PD-L1 was predictive only in 28.9% of approvals and was either not predictive (53.3%) or not tested in the reminder (17.8%).[29]

We observed a median survival of 10.7 months post immunotherapy across all cancer types. As most studies on ICIs report survival advantage specific to tumor type, this is the first study reporting median survival across various cancer subtypes, as per our knowledge.

Limitations

Our study is an analysis of two anti-PD1 ICIs across various tumor types. Hence, the parameters assessed are not specific to an individual cancer type. The role of peripheral blood biomarkers obtained in the study is considered as an outcome of a pilot study because of small sample size.


 > Conclusion Top


Our study addresses the various factors associated with survival benefit when treated with nivolumab and pembrolizumab across various cancer types. Current markers approved for ICI indications include MSI and PDL1 which have shown poor predictability across cancer types. Hence, we report the feasibility of using simple and easily accessible blood parameters (NLR, NER, PLR, and LMR) as predictive and prognostic markers in patients receiving nivolumab and pembrolizumab. Further, large-scale studies will be required to further confirm the findings. We also highlight the importance of real-world data on ICI usage and practice to analyze the trends of ICI benefit across institutions. This would enable us to determine the factors affecting ICI response and facilitate a better selection of patients for immune checkpoint inhibitor use for solid cancers.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
 > References Top

1.
Dobosz P, Dzieciątkowski T. The Intriguing History of Cancer Immunotherapy. In: Frontiers in Immunology. Vol. 10. Publisher location is Poland: Frontiers Media S.A.; 2019.  Back to cited text no. 1
    
2.
Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med 2012;366:2443-54.  Back to cited text no. 2
    
3.
Brahmer JR, Tykodi SS, Chow LQ, Hwu WJ, Topalian SL, Hwu P, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med 2012;366:2455-65.  Back to cited text no. 3
    
4.
Ancevski Hunter K, Socinski MA, Villaruz LC. PD-L1 testing in guiding patient selection for PD-1/PD-L1 inhibitor therapy in lung cancer. Mol Diagn Ther 2018;22:1-0.  Back to cited text no. 4
    
5.
Davis AA, Patel VG. The role of PD-L1 expression as a predictive biomarker: An analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J Immunother Cancer 2019;7:278.  Back to cited text no. 5
    
6.
Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature 2008;454:436-44. doi.org/10.1038/nature07205.  Back to cited text no. 6
    
7.
Coussens, L., Werb, Z. Inflammation and cancer. Nature 2002;420: 860–7. https://doi.org/10.1038/nature01322.  Back to cited text no. 7
    
8.
Shi L, Qin X, Wang H, Xia Y, Li Y, Chen X, et al. Elevated neutrophil-to-lymphocyte ratio and monocyte-tolymphocyte ratio and decreased platelet-to-lymphocyte ratio are associated with poor prognosis in multiple myeloma. Oncotarget 2017;8:18792-801.  Back to cited text no. 8
    
9.
Salpeter SR, Malter DS, Luo EJ, Lin AY, Stuart B. Systematic review of cancer presentations with a median survival of six months or less. J Palliat Med 2012;15:175-85. Available from: http://www.liebertpub.com/doi/100.1089/jpm. 2011.0192. [Last accessed on 2020 May 25].  Back to cited text no. 9
    
10.
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;11:955-65.  Back to cited text no. 10
    
11.
Bilen MA, Martini DJ, Liu Y, Lewis C, Collins HH, Shabto JM, et al. The prognostic and predictive impact of inflammatory biomarkers in patients who have advanced-stage cancer treated with immunotherapy. Cancer 2019;125:127-34.  Back to cited text no. 11
    
12.
Petrova MP, Eneva MI, Arabadjiev JI, Conev NV, Dimitrova EG, Koynov KD, et al. Neutrophil to lymphocyte ratio as a potential predictive marker for treatment with pembrolizumab as a second line treatment in patients with non-small cell lung cancer. Biosci Trends 2020;14:48-55.  Back to cited text no. 12
    
13.
Katayama Y, Shimamoto T, Yamada T, Takeda T, Yamada T, Shiotsu S, et al. Retrospective efficacy analysis of immune checkpoint inhibitor rechallenge in patients with non-small cell lung cancer. J Clin Med 2019;9:102.  Back to cited text no. 13
    
14.
Martín-Ruiz A, Fiuza-Luces C, Martínez-Martínez E, Arias CF, Gutiérrez L, Ramírez M, et al. Effects of anti-PD-1 immunotherapy on tumor regression: Insights from a patient-derived xenograft model. Sci Rep 2020;10:7078.  Back to cited text no. 14
    
15.
Simon SC, Hu X, Panten J, Grees M, Renders S, Thomas D, et al. Eosinophil accumulation predicts response to melanoma treatment with immune checkpoint inhibitors. Oncoimmunology 2020;9:1727116. doi:10.1080/2162402X.2020.1727116.  Back to cited text no. 15
    
16.
Prabawa IP, Bhargah A, Liwang F, Tandio DA, Tandio AL, Lestari AA, et al. Pretreatment Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) as a Predictive Value of Hematological Markers in Cervical Cancer Asian Pac J Cancer Prev 2019;20:863-8.  Back to cited text no. 16
    
17.
Fessas P, Lee H, Ikemizu S, Janowitz T. Molecular and preclinical comparison of the PD-1-targeted T-cell checkpoint inhibitors nivolumab and pembrolizumab. Semin Oncol 2017;44:136-40.  Back to cited text no. 17
    
18.
Gridelli C, Ardizzoni A, Barberis M, Cappuzzo F, Casaluce F, Danesi R, et al. Predictive biomarkers of immunotherapy for non-small cell lung cancer: results from an Experts Panel Meeting of the Italian Association of Thoracic Oncology. Transl Lung Cancer Res. 2017;6(3):373-386. doi:10.21037/tlcr.2017.05.09.  Back to cited text no. 18
    
19.
Klein S, Flanagan K. Sex differences in immune responses. Nat Rev Immunol 2016;16:626-38. https://doi.org/10.1038/nri.2016.90  Back to cited text no. 19
    
20.
Abdullah M, Chai PS, Chong MY, Tohit ER, Ramasamy R, Pei CP, et al. Gender effect on in vitro lymphocyte subset levels of healthy individuals. Cell Immunol 2012;272:214-9.  Back to cited text no. 20
    
21.
Lee BW, Yap HK, Chew FT, Quah TC, Prabhakaran K, Chan GS, et al. Age- and sex-related changes in lymphocyte subpopulations of healthy Asian subjects: From birth to adulthood. Cytometry 1996;26:8-15.  Back to cited text no. 21
    
22.
Lisse IM, Aaby P, Whittle H, Jensen H, Engelmann M, Christensen LB. T-lymphocyte subsets in West African children: Impact of age, sex, and season. J Pediatr 1997;130:77-85.  Back to cited text no. 22
    
23.
Uppal SS, Verma S, Dhot PS. Normal values of CD4 and CD8 lymphocyte subsets in healthy indian adults and the effects of sex, age, ethnicity, and smoking. Cytometry B Clin Cytom 2003;52:32-6.  Back to cited text no. 23
    
24.
Huang XZ, Gao P, Song YX, Sun JX, Chen XW, Zhao JH, et al. Efficacy of immune checkpoint inhibitors and age in cancer patients. Immunotherapy 2020;12:587-603.  Back to cited text no. 24
    
25.
Lisberg A, Cummings A, Goldman JW, Bornazyan K, Reese N, Wang T, et al. A phase II study of pembrolizumab in EGFR-mutant, PD-L1þ, tyrosine kinase inhibitor naïve patients with advanced NSCLC. J Thorac Oncol 2018;13:1138-45.  Back to cited text no. 25
    
26.
Efficacy of Pembrolizumab in Key Subgroups of Patients with Advanced NSCLC. MINI ORAL SESSIONS. J Thorac Oncol 2015;10(9):S261–406.  Back to cited text no. 26
    
27.
Dong ZY, Zhang JT, Liu SY, Su J, Zhang C, Xie Z, et al. EGFR mutation correlates with uninflamed phenotype and weak immunogenicity, causing impaired response to PD-1 blockade in non-small cell lung cancer. Oncoimmunology 2017;6:e1356145.  Back to cited text no. 27
    
28.
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;7:387-401.  Back to cited text no. 28
    
29.
Davis AA, Patel VG. The role of PD-L1 expression as a predictive biomarker: An analysis of all US food and drug administration (FDA) approvals of immune checkpoint inhibitors. J Immunother Cancer 2019;7:278.  Back to cited text no. 29
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  >Abstract>Introduction>Methods>Results>Discussion>Conclusion>Article Figures>Article Tables
  In this article
>References

 Article Access Statistics
    Viewed332    
    Printed2    
    Emailed0    
    PDF Downloaded15    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]