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Functional landscapes of POLE and POLD1 mutations in checkpoint blockade-dependent antitumor immunity

Nature Genetics volume 54, pages 996–1012 (2022)Cite this article

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Abstract

Defects in pathways governing genomic fidelity have been linked to improved response to immune checkpoint blockade therapy (ICB). Pathogenic POLE/POLD1 mutations can cause hypermutation, yet how diverse mutations in POLE/POLD1 influence antitumor immunity following ICB is unclear. Here, we comprehensively determined the effect of POLE/POLD1 mutations in ICB and elucidated the mechanistic impact of these mutations on tumor immunity. Murine syngeneic tumors harboring Pole/Pold1 functional mutations displayed enhanced antitumor immunity and were sensitive to ICB. Patients with POLE/POLD1 mutated tumors harboring telltale mutational signatures respond better to ICB than patients harboring wild-type or signature-negative tumors. A mutant POLE/D1 function-associated signature-based model outperformed several traditional approaches for identifying POLE/POLD1 mutated patients that benefit from ICB. Strikingly, the spectrum of mutational signatures correlates with the biochemical features of neoantigens. Alterations that cause POLE/POLD1 function-associated signatures generate T cell receptor (TCR)-contact residues with increased hydrophobicity, potentially facilitating T cell recognition. Altogether, the functional landscapes of POLE/POLD1 mutations shape immunotherapy efficacy.

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Fig. 1: Mouse tumors harboring Pole/d1 functional mutations are sensitive to immunotherapy.
Fig. 2: Immune microenvironment of tumors harboring PoleP286R functional mutations.
Fig. 3: Dissecting the de novo mutational signatures in the B16F10 PoleP286R model.
Fig. 4: Statistical models based on mutational signatures can accurately identify tumors harboring POLE/D1 functional mutations from WES data and target panel sequencing data.
Fig. 5: POLE/D1 functional mutation/signature-positive tumors are more immune active and share similar immune features with the baseline mouse PoleP286R tumors.
Fig. 6: Patients with POLE/D1 functional mutations/signatures have better response and survival after anti-PD-1/PD-L1 immunotherapy.
Fig. 7: POLE/D1 function-associated signatures positive status is an independent predictor that can enrich patients who benefit from immunotherapy in the patient population with POLE/D1 mutation.
Fig. 8: Trinucleotide context spectrum of SBS mutational signatures and immunogenicity of neoantigens.

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Data availability

Mutation data from the TCGA pancancer study (mc3.v0.2.8.PUBLIC.maf.gz) were downloaded from NCI genomic data commons (https://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc). Mutation data of ICGC and CCLE were downloaded from the ICGC data portal (https://dcc.icgc.org/api/v1/download?fn=/current/Summary/simple_somatic_mutation.aggregated.vcf.gz) and the Broad Institute website (https://ndownloader.figshare.com/files/34008434), respectively. To generate the ICGC/CCLE test set, TCGA samples in the ICGC cohort were removed manually. BED files of MSK-IMPACT sequencing regions are acquired from AACR-Genie project database (https://www.synapse.org/#!Synapse:syn7222066/wiki/405659). COSMIC SBS exome signature v.3 and COSMIC SBS exome TSB signature v.3 were downloaded from the Synapse database (https://www.synapse.org/#!Synapse:syn11967914). The mouse GRCm38 genome was download from UCSC genome browser (https://hgdownload.soe.ucsc.edu/goldenPath/mm10/bigZips/mm10.fa.gz). A baseline list of known POLE/D1 functional mutations was generated from previous publications and the OncoKB database (https://www.oncokb.org/)12,35. Additional lists of clinical associated POLE/D1 mutations and POLE/D1 mutator alleles in other species were curated manually from the literature and are available as Supplementary Tables 2 and 3. Gene expression profiles and xCell-based cell type deconvolutions of TCGA-endometrial cohort were downloaded from the TIMER database (http://timer.cistrome.org/infiltration_estimation_for_tcga.csv.gz)85,86. TCRb repertoire data were downloaded from NCI genomic data commons (TCGA_mitcr_cdr3_result_161008.tsv, from https://gdc.cancer.gov/about-data/publications/panimmune)87.TCGA predicted neoantigen information was acquired from TSNAdb (http://biopharm.zju.edu.cn/tsnadb/download/)88. Mouse WES and RNA-seq data were deposited to SRA (PRJNA701709). All the other data were deposited with Synapse (as Synapse project syn29404148, https://www.synapse.org/#!Synapse:syn29404148/wiki/617361) with public access. All samples underwent evaluation for model building (Synapse ID: syn29477489.1), the mutational signature matrix for training and test the WES (Synapse ID: syn29478035, Synapse ID: syn29478033) and MSK-IMPACT models (Synapse ID: syn29478110, Synapse ID: syn29478151), the genomic, response and survival data of the MSK-IMPACT immunotherapy cohorts (Synapse ID: syn29478036).

Code availability

All customized code including code for generating the models (Synapse ID: syn29479495)89, analysis of the MSK-IMPACT cohort (Synapse ID: syn29479497)90 and other associated code (Synapse ID: syn30137113)91 have been deposited with Synapse (Synapse project ID: syn29404148, https://www.synapse.org/#!Synapse:syn29404148/wiki/617361) with public access. Code for processing WES and RNA-seq was from published tools and is available from the authors of the tools as described in Methods.

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Acknowledgements

We thank colleagues at the Cleveland Clinic flow core, Cleveland Clinic genomics core, Center for Immunotherapy and Precision-Immuno-Oncology Shared Platforms, MSK core facilities (including Integrated Genomics Operation (IGO), Flow Cytometry Core Facility (FCCF) and Molecular Cytology Core Facility (MCCF)) for processing our samples and providing important suggestions. We thank colleagues at the Molecular Diagnostics Service in the Department of Pathology, and the Marie-Josee and Henry R. Kravis Center for Molecular Oncology Marie-Josée of MSK for establishing and generating the MSK-IMPACT data. We thank all the members and alumni of the Chan laboratory at MSK and CCF, as well as the LRI of CCF and HOPP of MSK for their generous help and support of this study. The results presented here are based in part on data generated or collected by the TCGA Research Network, Broad CCLE, ICGC. We acknowledge funding sources including National Institutes of Health (NIH) R01 CA205426 (T.A.C.), NIH R35 CA232097 (T.A.C.), the STARR Cancer Consortium (T.A.C.), NIH DP5 OD028171 (R.M.S), the Burroughs Wellcome Fund (R.M.S.) and the NIH/National Cancer Institute Cancer Center Support Grant P30 CA008748 (MSKCC).

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Authors and Affiliations

  1. Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic Foundation, Cleveland, OH, USA

    Xiaoxiao Ma, Vladimir Makarov, Douglas Hoen, Tyler J. Alban, Ivan Juric, Prerana Bangalore Parthasarathy, Yu Zhao, Richa Verma, Raghvendra M. Srivastava, Xiao Zhang, Lawrence K. Chu, Stephen L. Wang & Timothy A. Chan

  2. Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Xiaoxiao Ma, Vladimir Makarov, Douglas Hoen, Tyler J. Alban, Richa Verma, Raghvendra M. Srivastava, Xiao Zhang & Timothy A. Chan

  3. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Nadeem Riaz, Xin Pei, Jiapeng Chen & Timothy A. Chan

  4. Department of Radiation Oncology, Mount Sinai Hospital, New York, NY, USA

    Robert M. Samstein

  5. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Robert M. Samstein & Diego Chowell

  6. Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Mark Lee, Cristina Valero, Conall W. R. Fitzgerald, Jingming Wang & Luc G. T. Morris

  7. The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Diego Chowell

  8. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Diego Chowell

  9. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Fengshen Kuo, Hui Jiang & Lynda Vuong

  10. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Hui Jiang, Jonathan Alektiar, Erich Y. Sabio, Lynda Vuong & Wei Yang

  11. Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Lawrence K. Chu

  12. Medical Scientist Training Program, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Stephen L. Wang

  13. Technology Division, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Daniel W. Kelly

  14. Gastrointestinal Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Rona Yaeger

  15. Gynecologic Medical Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Dmitriy Zamarin

  16. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ahmet Zehir

  17. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Mithat Gönen

  18. Taussig Cancer Institute, Cleveland Clinic Foundation, Cleveland, OH, USA

    Timothy A. Chan

  19. National Center for Regenerative Medicine, Cleveland, OH, USA

    Timothy A. Chan

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Contributions

X.M. and T.A.C. conceptualized the study. X.M., V.M., F.K., D.H., N.R. and T.A.C. developed the methodology. X.M., V.M, F.K., R.M.S., D.H., F.K., H.J., C.W.R.F., J.A., P.B.P., Y.Z., L.K.C., S.L.W., D.C., E.Y.S., M.L., J.W., R.V., L.V., W.Y., X.Z., C.V., L.G.T.M., N.R. and T.A.C. performed the investigaion. X.M., V.M., F.K., D.H., N.R. and T.A.C. wrote the original draft of the manuscript. X.M., R.M.S., D.C., T.J.A., I.J., L.G.T.M., N.R. and T.A.C. reviewed and edited the manuscript. M.G. carried out the biostatistical review. T.A.C. acquired funding. T.A.C. supervised the project. All authors critically discussed and revised the manuscript for important intellectual content.

Corresponding author

Correspondence to Timothy A. Chan.

Ethics declarations

Competing interests

T.A.C. is a cofounder of Gritstone Oncology and holds equity. T.A.C. holds equity in An2H. T.A.C. acknowledges grant funding from Bristol-Myers Squibb, AstraZeneca, Illumina, Pfizer, An2H and Eisai. T.A.C. has served as an advisor for Bristol-Myers, MedImmune, Squibb, Illumina, Eisai, AstraZeneca and An2H. T.A.C., L.G.T.M., R.M.S. and D.C. are inventors on intellectual property held by MSKCC on using tumor mutation burden to predict immunotherapy response, with pending patent, which has been licensed to PGDx. C.V. acknowledges research grant funding from Fundación Alfonso Martín Escudero. D.Z. received consulting fees from Agenus, Hookipa Biotech, Targovax, AstraZeneca, Synthekine, Mana Therapeutics, Xencor, Crown Biosciences and Memgen. D.Z. receives grant/research support from AstraZeneca, Roche and Plexxikon. D.Z. holds stock options for Immunos Therapeutics, Calidi Biotherapeutics, Mana Therapeutics and Accurius. D.Z. has a patent related to use of Newcastle Disease Virus for cancer therapy with royalties paid from Merck. R.Y. has served as an advisor for Natera, Array BioPharma/Pfizer and Mirati Therapeutics and has received research support to her institution from Array BioPharma/Pfizer, Boehringer Ingelheim and Mirati Therapeutics. The remaining authors declare no competing interests.

Peer review

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Nature Genetics thanks Shiaw-Yih Lin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Mouse tumors harboring Pole/d1 functional mutations are sensitive to immunotherapy.

a, Strategy to introduce PoleP286R into murine cell lines. b, Scheme of in vitro culture and WES (whole exome sequencing) of parental and PoleP286R mutant cell lines. c, Total SNV (single nucleotide variant) counts of the PoleP286R mutant and parental cell lines. P values by two-sided Student’s t-tests. d, B16F10 PoleP286R clone2 cell line with ICB therapies (N = 15 mice per group). P values (anti-CTLA4, P = 0.0003; anti-PD1, P = 0.0002; Combo, P = 0.0001). e, Survival analysis of mice bearing the B16F10 parental (anti-CTLA4, P = 0.14; anti-PD1, P = 0.13; Combo, P = 0.0003), the B16F10 PoleP286R (anti-CTLA4, P < 0.0001; anti-PD1, P < 0.0001; Combo, P < 0.0001) or the B16F10 PoleP286R clone2 tumors (anti-CTLA4, P < 0.0001; anti-PD1, P < 0.0001; Combo, P < 0.0001) after ICB (N = 15 mice per group). P values indicate log-rank test significance. f, CT-26- PoleP286R clone2 cell line with ICB therapies (N = 15 mice per group). P values (CTLA4 P = 1.1e-8, PD-1 P = 0.0015, Combo P = 3.2e-9). g, CT-26 PoleWT single cell clone1&2 tumors with ICB therapy (N = 15 mice per group). P values (Clone1 P = 0.023, Clone2 P = 0.053). h, Tumor inhibition rates of the CT-26 parental, PoleWT single clones and PoleP286R clone1 and clone2 tumors with anti-PD1 therapy at the last time point. (N = 15). Dots represent individual biological replicates. P values by two-sided Student’s t-tests. i, Growth curves of the B16F10 PoleV411L clone2 with anti-PD1 therapy (N = 15 mice per group, P = 0.0001). For all growth curves related panels (d,f,g,i), P values by two-sided Student’s t-tests at the end time points. For all panels, data are presented as mean values ± s.e.m. (n.s., no statistical significance, * P < 0.05, ** P < 0.01, *** P < 0.005, **** P < 0.0001). No multiple comparisons adjustment was performed.

Extended Data Fig. 2 The baseline immune microenvironment of the Pole mutant tumors.

a, Heatmap of 1298 DEGs (differentially regulated genes) from RNA-seq analysis of the B16F10 parental and PoleP286R mutant tumors 14 days post implantation. Color sale indicates normalized z-score. b, GSEA (gene set enrichment assay) indicating enrichment of gene sets related to interferon gamma response, T cell and NK cell activation, inflammation, antigen presenting pathway and PD1 signaling in the mutant tumors versus parental tumors. c, Heatmap showing DEGs (FDR P < = 0.05) between parental and mutant tumors from the Hallmark interferon gamma response pathway, PID CD8 TCR pathway and KEGG natural killer cell mediated cytotoxicity pathway. d, Ptprc TPM of parental and mutant tumors from the RNA-seq data showed in Fig. 2c (N = 3 biological replicates). P = 0.0008, two-sided Student’s t-tests (n.s., no statistical significance, * P < 0.05, ** P < 0.01, *** P < 0.005). Data are presented as mean values + /- SEM. e, Flow cytometry analysis of the percentage of Cd8 T cells expressing Pd-1 (P = 0.026), Tigit (P = 0.48) and Lag3 (P = 0.78) in the parental and mutant tumors 14 days post implantation (N = 6 biological replicates). P values by two-sided Student’s t-tests (n.s., no statistical significance, * P < 0.05, ** P < 0.01, *** P < 0.005). Data are presented as mean values + /- SEM. f, Flow cytometry analysis of expression intensity of Pd-l1 (P = 0.045), Cd204 (P = 0.043) and Cd206 (P = 0.0087) on tumor associated macrophages in the parental and mutant tumors 14 days post implantation (N = 6 biological replicates). P values by two-sided Student’s t-tests at the end time points (n.s., no statistical significance, * P < 0.05, ** P < 0.01, *** P < 0.005). Data are presented as mean values + /- s.e.m.

Extended Data Fig. 3 Immune microenvironment of the post-treatment Pole mutant tumors.

a, Differentially expressed genes up-regulated or down-regulated in the post-ICB PoleP286R tumors versus the parental tumors. There are 82 DEGs that are consistently upregulated in all ICB treatment arms, while there are 59 genes are consistently downregulated in mutant tumors of all immune checkpoint blockade (ICB) arms. b, Top 10 KEGG pathways that were significantly enriched in the consistently up-regulated DEGs. c, Top 10 KEGG pathways that were enriched in the consistently down-regulated DEGs across all ICB arms. No pathway is statistical significantly enriched, as determined by q value <0.05. d, GSEA analysis of PoleP286R tumor in the combo arm versus the IgG arm. Only pathways with nominal p value <0.05 were shown. e, GSEA plot of enriched gene sets related with inflammatory response in combo ICB arm versus IgG arm of the PoleP286R tumors.

Extended Data Fig. 4 Both adaptive and innate immune cell types contribute to the distinct immune profiles of the post-treatment Pole mutant tumors, compared to that of the parental tumors.

a, Heatmap of immune-cell-type-signatures enrichment in the post-ICB samples. Color scale indicates normalized enrichment scores. b, Screen plot of the principal component analysis. Bars indicate the explained variations for each PC. The red line and dots indicate the accumulatively explained variations from PC1 to each other PC. c, Contribution of each immune-cell-type-signature to PC1. Red indicates the enrichment score of the immune-cell-type-signatures aligned to the same direction with the PC1 axle, blue indicates the enrichment score of the immune-cell-type-signatures aligned to the opposite direction with the PC1 axle. d, Normalized enrichment score of monocytes and macrophages in post-ICB tumors (N = 3 biological replicates). P values (monocytes P = 3.98e-5; macrophage P = 4.1e-6) were derived from two-way ANOVA test. e, Normalized enrichment score of Treg (regulatory T cell) and NKT cells in post-immunotherapy tumors (N = 3 biological replicates). P values (Treg P = 0.030; NKT P = 0.022) were derived from two-way ANOVA test. i, z-score transformed fraction of the TCR-beta CDR3 clone types in the post-treated parental and PoleP286R tumors. Note that no CDR3 clone type is successfully extracted from two of the parental tumors treated with IgG. f, Chao1 index and richness score of the TCR-beta CDR3 sequence of post-treated tumors (N = 3 biological replicates). g-h, Chao1, richness, evenness and clonality scores of the TCR-beta CDR3 sequences of post-treated tumors (N = 3 biological replicates). P values (Chao1 P = 0.012; Richness P = 0.051; Evenness P = 0.26; Clonality P = 0.26) were derived from two-way ANOVA test. For all boxplots (d-e, g-h), the minima (0% percentile), maxima (100% percentile) were plotted as the whiskers, 25% percentile and 75% percentile were plotted as the bounds of the boxes, medians were plotted as the center bar.

Extended Data Fig. 5 Mutation signatures of the Pole mutant cell lines.

a, Schematic explanation of the baseline and de novo SNVs in parental and mutant cell lines. b, Transcriptional strand bias (TSB) of the six base substitution categories of the baseline and de novo mutations from the B16F10 parental and PoleP286R mutant cell lines. De novo mutations in the parental and the PoleP286R mutation cell lines showed distinct transcriptional strand bias, indicating that these mutations are generated by different biological processes. c, The 192 TSB base substitutions in tri-nucleotide sequence contexts of the three NMF extracted de novo TSB mutational signatures from the baseline and de novo SNVs of the B16F10 parental and PoleP286R mutant cell lines. d, Contribution of the three NMF extracted de novo TSB mutation signatures to the baseline and de novo mutations in the B16F10 parental and PoleP286R mutant cell lines showed that the TSB-Sig.B is exclusively contributed to the de novo mutations discovered in the B16F10 PoleP286R mutant cell lines. e, Cosine similarity of the de novo mutational TSB signatures with COSMIC TSB-SBS signatures v3. The TSB-Sig.B is highly similar to the POLE/D1 function-assocaited signature TSB-SBS10b (cosine similarity of 0.84). f, COSMIC SBS signatures extracted from B16F10 parental and PoleP286R mutant cell lines by NNLS method. The POLE/D1 function-associated signature SBS10b can only be extracted from the de novo SNVs of the PoleP286R mutant samples.

Extended Data Fig. 6 Statistical models based on function-associated signatures can be used to identify tumors with POLE/D1 functional mutations.

a, TCGA data set summary. Wild-type, tumors are wild-type for POLE/D1; Functional, tumors harboring known POLE/D1 functional mutations; Mutated, tumors with only POLE/D1 VUS; SNV, SNV count by WES sequencing. b, the optimal Youden Index point and corresponding probability cutoff value. c, ICGC/CCLE test set summary. d, Heatmap of the Non-negative least squares (NNLS) extracted COSMIC SBS signatures of false negative predictions. e, Reconstitution accuracy of the non-negative matrix factorization (NMF) extracted signatures on TCGA samples with POLE/D1 functional mutations. f, Cosine similarity of the three NMF extracted mutational signatures to the COSMIC SBS signatures. g-h, Contribution of the three NMF de novo mutational signatures to the samples with known POLE/D1 functional mutations in the training set (g) and test set (h). TMB, SNV count in the exome region by WES sequencing. Functional mutation, whether the sample contain POLE or POLD1 functional mutations. i, Fisher exact test on the MSI status of the true-positive and false negative samples from the training and test sets when MSI status is available. j. Tumor allele frequencies of the POLE/D1 functional mutations from the false negative predictions (FN, N = 5), True-positive predictions (TP, N = 77) with available allele frequencies. P value was calculated with two-sided Wilcoxon Rank Sum Test. k, SNV count distribution of the false positive samples from the WES training set. l, Distribution of the true-positive samples (top) or VUS samples (bottom) that were predicted as samples with functional mutations. The green dash lines indicate cutoff for SNVlow (3.6 SNV/Mb exome), the blue dash lines indicate cutoff for SNVint/hi (10 SNV/Mb exome) and the red dash lines indicate cutoff for SNVhyper (50SNV/Mb exome). m. Unsupervised clustering of the SNVint/hi FP samples from the TCGA training set based on the extracted COSMIC SBS signatures.

Extended Data Fig. 7 Statistical models based on function-associated signatures predict functional mutations and associated immune features.

a, Correlation of the proportion of POLE/D1 function-associated signatures extracted from TCGA WES or IMPACT simulation data. Pearson correlation coefficients were shown. b. Confusion table of the WES model on TCGA-impact data. Pred. wild-type, samples that were predicted as wild-type for POLE/D1 functional mutations; Pred. functional, samples that were predicted harboring POLE/D1 functional mutations. c, Scheme of training a model to identify tumors harboring POLE/D1 functional mutations. d, MSK-IMPACT training set summary. e, Optimal Youden Index point and corresponding probability cutoff. f, Heatmap of the NNLS extracted COSMIC SBS mutational signatures of the false negative predictions from the MSK-IMPACT training set. g, Co-efficiencies of the POLE/D1 functional-associated mutational signatures in the WES and IMPACT models. h. Immune infiltration scores and CYT scores of the samples with known POLE/D1 functional mutations (N = 53) and POLE/D1 variant of unknown significance (VUS) samples with function-associated signatures (N = 7) compared to the POLE/D1 functional mutation/signature-negative tumors (N = 520) from the TCGA-endometrial cohort. VUS samples with function-associated signatures, samples harbor POLE/D1 VUSes and were positive for POLE/D1 function-associated signatures predicted by the function-associated signature-based model, POLE/D1 functional mutation/signature-negative tumors, wild type samples or samples harbor POLE/D1 VUSes and didn’t show POLE/D1 function-associated signature. P values represent two-sided Wilcoxon Rank Sum Test. The minima, maxima were plotted as the whiskers, 25% and 75% percentile were plotted as the bounds of the boxes, medians and mean were plotted as the center bar and black dot. i. Screen plot of the principal component analysis. Bars indicate the explained variations for each PC. The red line and dots indicate the accumulatively explained variations. j, Sample separation plot of the three groups of samples in h based on the first two PCs, P values represent permutational multivariate analysis of variance test. (n.s., no statistical significance, * P < 0.05, ** P < 0.01, *** P < 0.005).

Extended Data Fig. 8 Immune features and the outcome of patients with POLE/D1 functional mutations/signatures.

a, Enrichment scores of the immune cell types of sample populations in Fig. 5d (continued). CD4 Tem P = 0.0042; Th1 P = 6.2e-5; Th2 P = 1.6e-9; Eosinophils P = 0.027; Macrophages P = 4.8e-5; Memory B-cell P = 0.036. b, Log-fold changes of the immune cell type enrichment scores from the human tumors and mouse tumors. Red color indicates cell types that are consistently upregulated or downregulated (P < 0.05) for both human and mouse tumor comparisons. c, Chao1 and clonality of the TCR-beta CDR3 repertoires from the sample population in Fig. 5f. Chao1 P = 8.5e-5, clonality P = 0.045. d, SBS signature profiles of the 24 POLE/D1 functional mutation/signature-positive patients in the ICB cohort. e-f. Comparison of the TMB (e) and copy number alterations (f) between the POLE/D1 functional mutation/signature-positive patients, other POLE/D1 mutated patients, and wild-type patients (N = 24, 148 and 2528 patients). TMB, P = 2.2e-5; P = 0.0021; FGA, P < 2.2e-16; P = 0.0095. g, Kaplan-Meier overall survival plot of the POLE/D1 functional mutation/signature-positive patients versus the histology-matched wild-type patients. Log-Rank P value and hazard ratio shown represents coxph model with cancer-type correction. h, Forest plot of the POLE/D1 functional mutations/signatures in coxph models of overall survival after immunotherapy with cancer type correction for pan-cancer or single cancer type categories that have at least three POLE/D1 functional mutation/signature-positive patients. Horizontal bars represent the 95% confidence interval for the hazard ratios. Each line indicates an individual coxph model. Error bar centers indicate Hazard ratios. i, Estimated tumor size change and mutational signatures composition for the complete responder harbored POLEP286R mutation. For all boxplots (a, c, e), P values by Wilcoxon Rank Sum Test. The minima and maxima were plotted as the whiskers, 25% and 75% were plotted as box bounds, medians were plotted as the center bar. For all tests (n.s., no statistical significance, * P < 0.05, ** P < 0.01, *** P < 0.005).

Extended Data Fig. 9 POLE/D1 function-associated signatures predicts immunotherapy response.

a. Proportion of clinical beneficial cases between the POLE/D1 (POLE or POLD1) functional mutation/signature-positive patients and the histology-matched wild-type patients. Functional mutations/signatures, patients either harbored known POLE/D1 functional mutations, or only harbored POLE/D1 VUSes but were predicted as function-associated signature-positive. b, Kaplan-Meier progression free survival plot of the patients harbored any types of POLE/D1 mutations versus POLE/D1 wild-type patients. c, Kaplan-Meier progression free survival plot of the patient populations in a. d-e, Kaplan-Meier overall survival plot (d) and proportion of clinical beneficial cases (e) of the POLE/D1 functional mutation/signature-positive patients versus the POLE/D1 function-associated signature-negative VUS patients after immunotherapy. f. Proportion of clinical beneficial cases of the FP (false positive) prediction wild-type patients versus the TN (true negative) prediction wild-type patients upon ICB. FP prediction, wild-type patients that were predicted as POLE/D1 functional mutation-positive. TN prediction, wild-type patients that were predicted as wild-type samples. g-h, Kaplan-Meier overall survival (g) and progression free survival plot (h) of FP prediction wild-type patients versus TN prediction wild-type patients. I, A multivariable coxph model comparing the predictive capability of different patient selection strategies on the progression free survival on patients after ICB (N = 1130). Horizontal bars represent the 95% confidence interval of the hazard ratio. Error bar centers indicate hazard ratios. j-k, Kaplan-Meier overall survival plot (j) and progression free survival plot (k) of the POLE/D1 exonuclease domain mutation-positive patients versus the POLE/D1 wild-type patients. l-m, Kaplan-Meier overall survival plot (l) and progression free survival plot (m) of the POLE/D1 functional mutation/signature-positive patients that were not hypermutated, versus the POLE/D1 wild-type patients. For a, e-f, P values, Fisher’s exact t-test. For all Kaplan-Meier plots (b-d, g-h, j-m), Log-Rank P value and hazard ratio shown were calculated from coxph model with cancer type correction.

Extended Data Fig. 10 POLE/D1 function-associated signature-based model predicts ICB outcome and outperforms traditional approaches.

a-b. Kaplan-Meier overall survival (a) and progression free survival plot (b) of the patients with at least one POLE/D1 (POLE or POLD1) mutation classified as damaging mutation by all five in silico algorithms versus all the rest POLE/D1 mutated patients. c, C-index of the progression free survival coxph models generated based on different patient selection strategies and patient population in Fig. 7g. Two-sided P values represent paired student t-tests of coxph model based on POLE/D1 functional mutation/signature-positive against other models without multiple comparison adjustment. d, Multi-variable coxph model of ICB progression free survival for ‘POLE/D1 functional mutation/signature-positive’ and TMB with cancer type correction (N = 1130). Only POLE/D1 functional mutation/signature-positive and TMB are shown in the forest plot, * log-rank P < 0.05. *** log-rank P < 0.005. Error bar indicating 95% CI of the Hazard ratio. e, Kaplan-Meier progression free survival plot of the POLE/D1 functional mutation/signature-positive patients versus the POLE/D1 wild-type patients in the ICB-treated patient cohort with high TMB (TMB > = 10). f, Kaplan-Meier overall survival plot of a random sub-cohort of the POLE/D1 functional mutation/signature-positive patients versus the POLE/D1 wild-type patients with matched median and minimum TMB. g, Kaplan-Meier progression free survival plot of a random sub-cohort of the POLE/D1 functional mutation/signature-positive patients versus the POLE/D1 wild-type patients with matched median and minimum TMB. h-i, Proportion heatmap of the observed association between mutational signatures with each SNV class (h) or each amino acid alteration category (i) in the TCGA pan-cancer cohort. In i, amino acids on the top row are the resulting new allele from the mutation (Post); amino acids on the bottom row are the wild-type allele (Pre). For all Kaplan-Meier plots (a-b, e-g), Log-Rank P value and hazard ratio shown were calculated from coxph model with cancer type correction.

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Ma, X., Riaz, N., Samstein, R.M. et al. Functional landscapes of POLE and POLD1 mutations in checkpoint blockade-dependent antitumor immunity. Nat Genet 54, 996–1012 (2022). https://doi.org/10.1038/s41588-022-01108-w

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