Immune Cell Profiling of the Cerebrospinal Fluid Enables the Characterization of the Brain Metastasis Microenvironment

Overview

Journal Nat Commun

Specialty Biology

Date 2021 Mar 9

PMID 33686071

Citations 38

Authors

Carlota Rubio-Perez

Ester Planas-Rigol

Juan L Trincado

Ester Bonfill-Teixidor

Alexandra Arias

Domenica Marchese

Catia Moutinho

Garazi Serna

Leire Pedrosa

Raffaella Iurlaro

Francisco Martinez-Ricarte

Laura Escudero

Esteban Cordero

Marta Cicuendez

Sara Ruiz

Genis Parra

Paolo Nuciforo

Josep Gonzalez

Estela Pineda

Juan Sahuquillo

Josep Tabernero

Holger Heyn

Joan Seoane

Affiliations

Soon will be listed here.

Abstract

Brain metastases are the most common tumor of the brain with a dismal prognosis. A fraction of patients with brain metastasis benefit from treatment with immune checkpoint inhibitors (ICI) and the degree and phenotype of the immune cell infiltration has been used to predict response to ICI. However, the anatomical location of brain lesions limits access to tumor material to characterize the immune phenotype. Here, we characterize immune cells present in brain lesions and matched cerebrospinal fluid (CSF) using single-cell RNA sequencing combined with T cell receptor genotyping. Tumor immune infiltration and specifically CD8 T cell infiltration can be discerned through the analysis of the CSF. Consistently, identical T cell receptor clonotypes are detected in brain lesions and CSF, confirming cell exchange between these compartments. The analysis of immune cells of the CSF can provide a non-invasive alternative to predict the response to ICI, as well as identify the T cell receptor clonotypes present in brain metastasis.

Citing Articles

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Jacome M, Wu Q, Chen J, Mohamed Z, Mokhtari S, Pina Y Int J Mol Sci. 2025; 26(5).

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References

Sharma P, Hu-Lieskovan S, Wargo J, Ribas A . Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell. 2017; 168(4):707-723. PMC: 5391692. DOI: 10.1016/j.cell.2017.01.017. View

Brastianos P, Carter S, Santagata S, Cahill D, Taylor-Weiner A, Jones R . Genomic Characterization of Brain Metastases Reveals Branched Evolution and Potential Therapeutic Targets. Cancer Discov. 2015; 5(11):1164-1177. PMC: 4916970. DOI: 10.1158/2159-8290.CD-15-0369. View

Seoane J, De Mattos-Arruda L . Brain metastasis: new opportunities to tackle therapeutic resistance. Mol Oncol. 2014; 8(6):1120-31. PMC: 5528619. DOI: 10.1016/j.molonc.2014.05.009. View

Cibulskis K, Lawrence M, Carter S, Sivachenko A, Jaffe D, Sougnez C . Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013; 31(3):213-9. PMC: 3833702. DOI: 10.1038/nbt.2514. View

Hugo W, Zaretsky J, Sun L, Song C, Moreno B, Hu-Lieskovan S . Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell. 2016; 165(1):35-44. PMC: 4808437. DOI: 10.1016/j.cell.2016.02.065. View

Virtanen P, Gommers R, Oliphant T, Haberland M, Reddy T, Cournapeau D . SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020; 17(3):261-272. PMC: 7056644. DOI: 10.1038/s41592-019-0686-2. View

Herbst R, Soria J, Kowanetz M, Fine G, Hamid O, Gordon M . Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014; 515(7528):563-7. PMC: 4836193. DOI: 10.1038/nature14011. View

Klemm F, Maas R, Bowman R, Kornete M, Soukup K, Nassiri S . Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells. Cell. 2020; 181(7):1643-1660.e17. PMC: 8558904. DOI: 10.1016/j.cell.2020.05.007. View

Li H . A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011; 27(21):2987-93. PMC: 3198575. DOI: 10.1093/bioinformatics/btr509. View

10.

Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert B, Gillette M . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102(43):15545-50. PMC: 1239896. DOI: 10.1073/pnas.0506580102. View

11.

Cesano A . nCounter(®) PanCancer Immune Profiling Panel (NanoString Technologies, Inc., Seattle, WA). J Immunother Cancer. 2015; 3:42. PMC: 4678588. DOI: 10.1186/s40425-015-0088-7. View

12.

Miller C, McMichael J, Dang H, Maher C, Ding L, Ley T . Visualizing tumor evolution with the fishplot package for R. BMC Genomics. 2016; 17(1):880. PMC: 5100182. DOI: 10.1186/s12864-016-3195-z. View

13.

Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder M, Vivancos A, Rovira A . Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 2018; 10(1):25. PMC: 5875005. DOI: 10.1186/s13073-018-0531-8. View

14.

Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M . The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32(4):381-386. PMC: 4122333. DOI: 10.1038/nbt.2859. View

15.

Bowman R, Klemm F, Akkari L, Pyonteck S, Sevenich L, Quail D . Macrophage Ontogeny Underlies Differences in Tumor-Specific Education in Brain Malignancies. Cell Rep. 2016; 17(9):2445-2459. PMC: 5450644. DOI: 10.1016/j.celrep.2016.10.052. View

16.

Saunders C, Wong W, Swamy S, Becq J, Murray L, Cheetham R . Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics. 2012; 28(14):1811-7. DOI: 10.1093/bioinformatics/bts271. View

17.

Friebel E, Kapolou K, Unger S, Gonzalo Nunez N, Utz S, Rushing E . Single-Cell Mapping of Human Brain Cancer Reveals Tumor-Specific Instruction of Tissue-Invading Leukocytes. Cell. 2020; 181(7):1626-1642.e20. DOI: 10.1016/j.cell.2020.04.055. View

18.

Zilionis R, Engblom C, Pfirschke C, Savova V, Zemmour D, Saatcioglu H . Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species. Immunity. 2019; 50(5):1317-1334.e10. PMC: 6620049. DOI: 10.1016/j.immuni.2019.03.009. View

19.

Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman D . IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017; 127(8):2930-2940. PMC: 5531419. DOI: 10.1172/JCI91190. View

20.

De Mattos-Arruda L, Mayor R, Ng C, Weigelt B, Martinez-Ricarte F, Torrejon D . Cerebrospinal fluid-derived circulating tumour DNA better represents the genomic alterations of brain tumours than plasma. Nat Commun. 2015; 6:8839. PMC: 5426516. DOI: 10.1038/ncomms9839. View