Cited by 441Cancer Immunotherapy and BiomarkersInflammatory Biomarkers in Disease PrognosisFerroptosis and cancer prognosis

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Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors

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Valero, Cristina, Lee, Mark, Hoen, Douglas, Weiss, Kate, Kelly, Daniel W., Adusumilli, Prasad S., Paik, Paul K., Plitas, George, Ladanyi, Marc, Postow, Michael A., Ariyan, Charlotte E., Shoushtari, Alexander N., Balachandran, Vinod P., Hakimi, A. Ari, Crago, Aimee M., Long Roche, Kara C., Smith, J. Joshua, Ganly, Ian, Wong, Richard J., Patel, Snehal G., Shah, Jatin P., Lee, Nancy Y., Riaz, Nadeem, Wang, Jingming, Zehir, Ahmet, Berger, Michael F., Chan, Timothy A., Seshan, Venkatraman E., Morris, Luc G. T.·2021·Nature Communications

DOI 10.1038/s41467-021-20935-9PMC PMC7851155

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Abstract

Treatment with immune checkpoint inhibitors (ICI) has demonstrated clinical benefit for a wide range of cancer types. Because only a subset of patients experience clinical benefit, there is a strong need for biomarkers that are easily accessible across diverse practice settings. Here, in a retrospective cohort study of 1714 patients with 16 different cancer types treated with ICI, we show that higher neutrophil-to-lymphocyte ratio (NLR) is significantly associated with poorer overall and progression-free survival, and lower rates of response and clinical benefit, after ICI therapy across multiple cancer types. Combining NLR with tumor mutational burden (TMB), the probability of benefit from ICI is significantly higher (OR = 3.22; 95% CI, 2.26-4.58; P < 0.001) in the NLR low/TMB high group compared to the NLR high/TMB low group. NLR is a suitable candidate for a cost-effective and widely accessible biomarker, and can be combined with TMB for additional predictive capacity.

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Fig. 1 Pan-cancer outcomes based on neutrophil-to-lymphocyte ratio (NLR). a Overall survival, b progression-free survival, c response, and d clinical benefit at a pan-cancer level based on high vs. low NLR. The cutoff was the top 20th percentile within each cancer type. P-values are according to log-rank test for a and b, and Pearson’s χ2-test for c and d.CC BY · PubMed Central

Fig. 2 Outcomes based on neutrophil-to-lymphocyte ratio (NLR) stratified by cancer type. Forest plots showing hazard ratios (HRs) with 95% CIs for a overall survival and b progression-free survival across cancer types, comparing top 20th NLR percentile within each cancer type to bottom 80th. HRs were calculated with Cox proportional hazard regression. Abreviations: OS, overall survival; Haz. Ratio, hazard ratio; CI, confidence interval; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer; PFS, progression-free survival.CC BY · PubMed Central

Fig. 3 Pan-cancer outcomes according to a combined neutrophil-to-lymphocyte ratio (NLR) and tumor mutational burden (TMB) categorization. a Overall survival, b progression-free survival, c response, and d clinical benefit at a pan-cancer level. Patients were classified into four categories based on NLR low/high and TMB low/high, using median within cancer type as cutoff for both variables. P-values are according to log-rank test for a and b, and Pearson’s χ2-test for c and d.CC BY · PubMed Central

Fig. 4 Outcomes across quartile-based categories of neutrophil-to-lymphocyte ratio (NLR) and tumor mutational burden (TMB). Heat maps of hazard ratios (HRs) for a overall survival and b progression-free survival, and heat maps of odds ratios (ORs) for c response and d clinical benefit based on 16 within cancer type quartile-based categories of NLR and TMB. HRs were calculated with Cox proportional hazard regression. ORs were calculated with logistic regression.CC BY · PubMed Central

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Authors: Valero, C., Lee, M., Hoen, D., Weiss, K., Kelly, D. W., Adusumilli, P. S., Paik, P. K., Plitas, G., Ladanyi, M., Postow, M. A., Ariyan, C. E., Shoushtari, A. N., Balachandran, V. P., Hakimi, A. A., Crago, A. M., Long Roche, K. C., Smith, J. J., Ganly, I., Wong, R. J., Patel, S. G., Shah, J. P., Lee, N. Y., Riaz, N., Wang, J., Zehir, A., Berger, M. F., Chan, T. A., Seshan, V. E., & Morris, L. G. T.
Affiliations: Memorial Sloan Kettering Cancer Center (US)
Corresponding author: Chan, Timothy A.
Seshan, Venkatraman E.
Morris, Luc G. T.
ORCID: Valero, Cristina
Lee, Mark
Weiss, Kate
Kelly, Daniel W.
Adusumilli, Prasad S.
Paik, Paul K.
Plitas, George
Ladanyi, Marc
Postow, Michael A.
Ariyan, Charlotte E.
Shoushtari, Alexander N.
Balachandran, Vinod P.
Hakimi, A. Ari
Crago, Aimee M.
Smith, J. Joshua
Ganly, Ian
Wong, Richard J.
Patel, Snehal G.
Shah, Jatin P.
Lee, Nancy Y.
Riaz, Nadeem
Wang, Jingming
Zehir, Ahmet
Berger, Michael F.
Chan, Timothy A.
Seshan, Venkatraman E.
Morris, Luc G. T.
Venue: Nature Communications
Publisher: Springer Science and Business Media LLC
Published: 2021-02-01
Volume: 12
Issue: 1
Pages: 729-729
ISSN: 2041-1723 (ISSN-L)
DOI: 10.1038/s41467-021-20935-9
PMC: PMC7851155
OpenAlex: W3127371144
Cited by: 441
References: 57
Citations by year: 2026: 53 · 2025: 74 · 2024: 116 · 2023: 92 · 2022: 80 · 2021: 26
FWCI: 28.60
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Nature Communications (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, other-oa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Fundación Alfonso Martín Escudero (N/A)
U.S. Department of Health & Human Services | NIH | National Cancer Institute (R01 CA205426, R35 CA232097)
Topics: Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Health Sciences › Medicine › Oncology › Inflammatory Biomarkers in Disease Prognosis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Ferroptosis and cancer prognosis
Keywords: immune checkpoint inhibitors, neutrophil-to-lymphocyte ratio, tumor mutational burden, predictive biomarkers, tumor response, overall survival, progression-free survival, cancer immunotherapy, prognostic factors
MeSH terms: Progression-Free Survival, Immune Checkpoint Inhibitors, Aged, Female, Follow-Up Studies, Humans, Lymphocytes, Male, Middle Aged, Neoplasms, Neutrophils, Predictive Value of Tests, Retrospective Studies, Lymphocyte Count, Drug Resistance, Neoplasm, Mutation Rate

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Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors

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