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Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma

Nature Medicine volume 25, pages 462–469 (2019)Cite this article

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An Author Correction to this article was published on 17 April 2019

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Abstract

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.

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Fig. 1: Analysis pipeline and clinical characteristics of the cohort.
Fig. 2: Mutational landscape, genomic correlates of response, and tumor evolution under anti-PD-1 therapy.
Fig. 3: Transcriptomic signatures related to response to anti-PD-1 therapy.
Fig. 4: Tumor microenvironment profiling through qmIF.

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Code availability

All the custom code will be made available upon request.

Data availability

All the sequencing data have been deposited in SRA PRJNA482620. Processed data and basic association analyses will be made available upon request.

Change history

  • 17 April 2019

    In the version of this article originally published, the graph in Extended Data Fig. 2c was a duplication of Extended Data Fig. 2b. The correct version of Extended Data Fig. 2c is now available online.

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Acknowledgements

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.

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Author notes

  1. These authors contributed equally: Junfei Zhao, Andrew X. Chen.

Authors and Affiliations

  1. Department of Systems Biology, Columbia University, New York, NY, USA

    Junfei Zhao, Andrew X. Chen, Luis Aparicio, Tim Chu, Ioan Filip, Rose Orenbuch & Raul Rabadan

  2. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Junfei Zhao, Luis Aparicio, Tim Chu, Jinzhou Yuan, Peter Sims & Raul Rabadan

  3. Department of Pediatrics, Pediatric Hematology/Oncology/SCT, Columbia University Irving Medical Center, New York, NY, USA

    Robyn D. Gartrell, Andrew M. Silverman, Darius Bordbar & David Shan

  4. Department of Neurosurgery, Columbia University, New York, NY, USA

    Jorge Samanamud, Aayushi Mahajan & Jeffrey N. Bruce

  5. Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Morgan Goetz

  6. Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Jonathan T. Yamaguchi, Michael Cloney, Craig Horbinski & Adam M. Sonabend

  7. Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Craig Horbinski

  8. Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Rimas V. Lukas & Jeffrey Raizer

  9. Department of Neurological Surgery, Oregon Health & Sciences University, Portland, OR, USA

    Ali I. Rae

  10. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

    Peter Canoll

  11. Department of Medicine, Hematology/Oncology, Columbia University Irving Medical Center, New York, NY, USA

    Yvonne M. Saenger

  12. Department of Neurology, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

    Fabio M. Iwamoto

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  1. Junfei Zhao
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Contributions

J.Z. performed the majority of experiments and analyses. R.R., A.M. Sonabend, J.Z., and A.X.C wrote the manuscript. A.I.R., J.T.Y., M.C., R.V.L., and J.R. compiled the clinical data for analysis. J.N.B., J.S., A.M., P.C., and C.H. procured and reviewed the tumor specimens for sequencing. J.Y. and P.S. provided the single-cell transcriptomic data. R.D.G., A.M. Silverman, D.B., D.S., and Y.M.S. provided the single-cell immunofluorescence. L.A. performed single-cell transcriptomic data analysis. I.F. and R.O. performed HLA genotyping. M.G. performed survival analysis. T.C. conducted figure design. R.R., A.M. Sonabend, and F.M.I. designed and supervised the entire project.

Corresponding authors

Correspondence to Fabio M. Iwamoto, Adam M. Sonabend or Raul Rabadan.

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Extended data

Extended Data Fig. 1 Additional clinical characteristics of the cohort.

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).

Extended Data Fig. 4 Clonal diversity of lymphocytes before and after immunotherapy.

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.

Extended Data Figure 6 Tumor subtype.

Expression subtyping of tumors from nine patients (pre- and post-treatment) into proneural, mesenchymal, and classical subtypes.

Extended Data Figure 7 GSEA analysis.

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.

Extended Data Figure 8 Enrichment of Treg cell signature.

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.

Extended Data Figure 9 Single-cell RNA-seq data analysis.

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).

Extended Data Figure 10 Tumor purity analysis.

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.

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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

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