Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations (PTPN11, BRAF) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes as well as with differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor’s clonal evolution during treatment.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Inflammation and Regeneration Open Access 01 October 2022
Pancancer landscape analysis of the thymosin family identified TMSB10 as a potential prognostic biomarker and immunotherapy target in glioma
Cancer Cell International Open Access 26 September 2022
BMC Cancer Open Access 18 August 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
All the custom code will be made available upon request.
All the sequencing data have been deposited in SRA PRJNA482620. Processed data and basic association analyses will be made available upon request.
Gilbert, M. R. et al. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 699–708 (2014).
Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013).
Garon, E. B. et al. Pembrolizumab for the treatment of non–small-cell lung cancer. N. Engl. J. Med. 372, 2018–2028 (2015).
Ansell, S. M. et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin’s lymphoma. N. Engl. J. Med. 372, 311–319 (2015).
Filley, A. C., Henriquez, M. & Dey, M. Recurrent glioma clinical trial, CheckMate-143: the game is not over yet. Oncotarget 8, 91779 (2017).
Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science 348, 124–128 (2015).
Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568 (2014).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415 (2013).
Wen, P. Y. et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28, 1963–1972 (2010).
Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nat. Genet. 48, 768 (2016).
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).
Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).
Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).
Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).
Peng, W. et al. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. 6, 202–216 (2016).
Parsa, A. T. et al. Loss of tumor suppressor PTEN function increases B7-H1 expression and immunoresistance in glioma. Nat. Med. 13, 84 (2007).
Dong, C., Davis, R. J. & Flavell, R. A. MAP kinases in the immune response. Annu. Rev. Immunol. 20, 55–72 (2002).
Pan, D. et al. A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing. Science 359, 770–775 (2018).
Arrieta, V. A. et al. The possibility of cancer immune editing in gliomas. A critical review. Oncoimmunology 7, e1445458 (2018).
Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32, 42–56. e46 (2017).
Yao, X. et al. Levels of peripheral CD4+FoxP3+ regulatory T cells are negatively associated with clinical response to adoptive immunotherapy of human cancer. Blood 119, 5688–5696 (2012).
Lin, K.-Y. et al. Ectopic expression of vascular cell adhesion molecule-1 as a new mechanism for tumor immune evasion. Cancer Res. 67, 1832–1841 (2007).
Yuan, J. et al. Single-cell transcriptome analysis of lineage diversity and microenvironment in high-grade glioma. Genome Med. 10, 57 (2018).
Rizvi, A. H. et al. Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nat. Biotechnol. 35, 551–560 (2017).
Senbabaoglu, Y. et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 17, 231 (2016).
Zhang, C. et al. Tumor purity as an underlying key factor in glioma. Clin. Cancer Res. 23, 6279–6291 (2017).
Hussain, S. F. et al. The role of human glioma-infiltrating microglia/macrophages in mediating antitumor immune responses. Neuro-oncology 8, 261–279 (2006).
Gartrell, R. D. et al. Quantitative analysis of immune infiltrates in primary melanoma. Cancer Immunol. Res. 6, 481–493 (2018).
Stack, E. C., Wang, C., Roman, K. A. & Hoyt, C. C. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70, 46–58 (2014).
Mooney, K. L. et al. The role of CD44 in glioblastoma multiforme. J. Clin. Neurosci. 34, 1–5 (2016).
George, S. et al. Loss of PTEN is associated with resistance to anti-PD-1 checkpoint blockade therapy in metastatic uterine leiomyosarcoma. Immunity 46, 197–204 (2017).
Lastwika, K. J. et al. Control of PD-L1 expression by oncogenic activation of the AKT–mTOR pathway in non-small cell lung cancer. Cancer Res. 76, 227–238 (2016).
Noh, K. H. et al. Activation of Akt as a mechanism for tumor immune evasion. Mol. Ther. 17, 439–447 (2009).
Bedognetti, D., Roelands, J., Decock, J., Wang, E. & Hendrickx, W. The MAPK hypothesis: immune-regulatory effects of MAPK-pathway genetic dysregulations and implications for breast cancer immunotherapy. Emerg. Top. Life Sci. 1, 429–445 (2017).
Ebert, P. J. et al. MAP kinase inhibition promotes T cell and anti-tumor activity in combination with PD-L1 checkpoint blockade. Immunity 44, 609–621 (2016).
Deken, M. A. et al. Targeting the MAPK and PI3K pathways in combination with PD1 blockade in melanoma. Oncoimmunology 5, e1238557 (2016).
Toso, A. et al. Enhancing chemotherapy efficacy in Pten-deficient prostate tumors by activating the senescence-associated antitumor immunity. Cell Rep. 9, 75–89 (2014).
Tran, E. et al. Immunogenicity of somatic mutations in human gastrointestinal cancers. Science 350, 1387–1390 (2015).
Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).
Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996 (2005).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Trifonov, V., Pasqualucci, L., Tiacci, E., Falini, B. & Rabadan, R. SAVI: a statistical algorithm for variant frequency identification. BMC Syst. Biol. 7, S2 (2013).
Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016).
Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413 (2012).
Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).
Iyer, M. K., Chinnaiyan, A. M. & Maher, C. A. ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics 27, 2903–2904 (2011).
Abate, F. et al. Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer. BMC Syst. Biol. 8, 97 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2013).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152 (2015).
Hundal, J. et al. pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).
Karosiene, E., Lundegaard, C., Lund, O. & Nielsen, M. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics 64, 177–186 (2012).
Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380 (2015).
Baddeley, A, Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC Press, Boca Raton, FL, USA, 2015).
This work has been funded by NIH grants R01 CA185486 (R.R.), R01 CA179044 (R.R.), U54 CA193313, U54 209997 (R.R.), R01 NS103473 (P.C., J.N.B., P.S.), NSF/SU2C/V-Foundation Ideas Lab Multidisciplinary Team (PHY-1545805) (R.R.), 2018 Stand Up To Cancer Phillip A. Sharp Innovation in Collaboration Awards (R.R.) and Keep Punching Foundation (F.M.I.). Funding support from Northwestern 5DP5OD021356-04 (A.M. Sonabend), P50CA221747 SPORE for Translational Approaches to Brain Cancer (A.M.Sonabend, R.V.L., C.H.). Developmental funds from The Robert H Lurie NCI Cancer Center Support Grant no. P30CA060553 (A.M. Sonabend). A.X.C. is funded by the Medical Scientist Training Program (T32GM007367). R.D.G. is funded by CUIMC CTSA as TL1 Precision Medicine Fellow (1TL1TR001875-01) and Swim Across America.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Venn diagram of the data modalities available across the 66-patient cohort. b,c, Kaplan–Meier curve comparing post-treatment survival (b) and overall survival from diagnosis (c) of patients who responded to anti-PD-1 therapy (n = 13) with those that did not respond (n = 29; P =4.5 × 10–5 (b), P = 0.0045 (c), two-sided log-rank test), assessed across the entire cohort. d, Univariate survival analysis reveals that response to anti-PD-1 therapy is still most correlated with post-treatment survival of the patients when assessed across the entire cohort (n = 42, 13 responders, 29 non-responders; P value, two-sided log-rank test).
Extended Data Fig. 2 Additional analysis of genomic correlates of response to anti-PD-1 immunotherapy.
a, Mutation burden by response group (n = 17 patients). b, Tumor purity, as estimated by ABSOLUTE, by response group. c, Ratio of subclonal to clonal mutations, as estimated by ABSOLUTE, by response group. d, Aneuploidy score analysis of non-responders versus responders. Boxplots show the median, interquartile range, and whiskers (1.5 times interquartile range).
Extended Data Fig. 3 Additional analysis of transcriptomic correlates of response to anti-PD-1 immunotherapy.
a, GSEA enrichment score of gene-set KIM_PTEN_TARGETS_UP for non-responders versus responders (n = 12 patients). The boxplot shows the median, interquartile range, and whiskers (1.5 times interquartile range). b, Boxplot of CD274 (encoding PD-L1) messenger RNA expression in responders versus non-responders (n = 12 patients). The boxplot shows the median, interquartile range, and whiskers (1.5 times interquartile range).
Within seven patients with longitudinal information on TCR and immunoglobulin (Ig) RNA expression, MiXCR was used to group reads into T cell (a) and B cell clones (b). Each color on a bar represents the fractional presence of a different clone, with the total clonal read count, n, listed above.
Extended Data Figure 5 Non-responders demonstrate a greater increase in clonal diversity of B cells following immunotherapy.
B cell clonal diversity before and after immunotherapy was assessed by identifying immunoglobulin RNA sequences within the tumor. Non-responders had a greater increase in Shannon entropy among B cells compared with responders (P = 0.048, two-sided exact Mann–Whitney U test; n = 16 independent timepoints from seven patients). The boxplot shows the median, interquartile range, and whiskers (1.5 times interquartile range); the violin plot represents sample distributions via kernel density estimation.
Expression subtyping of tumors from nine patients (pre- and post-treatment) into proneural, mesenchymal, and classical subtypes.
GSEA enrichment plots (n = 12 patients; six responders versus six non-responders) of two Treg-cell-related gene sets; P = 0.004 (left), P = 0.013 (right), two-sided Kolmogorov–Smirnov test.
a, Cells associated with the Treg cell signature were enriched in a PTEN-mutated tumor. b, Tumors associated with the Treg cell signature were enriched in PTEN-mutated samples.
Topological data analysis of single-cell RNA-seq data (n = 4,000 cells) from a PTEN-mutated tumor, demonstrating clusters of cells with high expression of CD44 (A, in red) and of microglial signatures (B, in red).
PTEN-mutated GBM tumors have significantly lower tumor purity compared with PTEN wild-type tumors (n = 172, two-sided Wilcoxon rank-sum test). The boxplot shows the median, interquartile range, and whiskers (1.5 times interquartile range); the violin plot represents sample distributions via kernel density estimation.
About this article
Cite this article
Zhao, J., Chen, A.X., Gartrell, R.D. et al. Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma. Nat Med 25, 462–469 (2019). https://doi.org/10.1038/s41591-019-0349-y
BMC Cancer (2022)
Acta Neuropathologica Communications (2022)
BMC Medicine (2022)
Tumor antigens and immune subtypes of glioblastoma: the fundamentals of mRNA vaccine and individualized immunotherapy development
Journal of Big Data (2022)
Molecular landscape of IDH-mutant astrocytoma and oligodendroglioma grade 2 indicate tumor purity as an underlying genomic factor
Molecular Medicine (2022)