Abstract
Despite initial responses1,2,3, most melanoma patients develop resistance4 to immune checkpoint blockade (ICB). To understand the evolution of resistance, we studied 37 tumor samples over 9 years from a patient with metastatic melanoma with complete clinical response to ICB followed by delayed recurrence and death. Phylogenetic analysis revealed co-evolution of seven lineages with multiple convergent, but independent resistance-associated alterations. All recurrent tumors emerged from a lineage characterized by loss of chromosome 15q, with post-treatment clones acquiring additional genomic driver events. Deconvolution of bulk RNA sequencing and highly multiplexed immunofluorescence (t-CyCIF) revealed differences in immune composition among different lineages. Imaging revealed a vasculogenic mimicry phenotype in NGFRhi tumor cells with high PD-L1 expression in close proximity to immune cells. Rapid autopsy demonstrated two distinct NGFR spatial patterns with high polarity and proximity to immune cells in subcutaneous tumors versus a diffuse spatial pattern in lung tumors, suggesting different roles of this neural-crest-like program in different tumor microenvironments. Broadly, this study establishes a high-resolution map of the evolutionary dynamics of resistance to ICB, characterizes a de-differentiated neural-crest tumor population in melanoma immunotherapy resistance and describes site-specific differences in tumor–immune interactions via longitudinal analysis of a patient with melanoma with an unusual clinical course.
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Data availability
All requests for raw and analyzed data and materials will be promptly reviewed by the senior author (G.M.B.) to verify whether the request is subject to any intellectual property or confidentiality obligations. Patient-related data not included in the paper may be subject to patient confidentiality. Any data and materials that can be shared will be released via a material transfer agreement. All analyzed sequencing data are in supplementary data available at the journal website and any additional data made publicly available after publication will be found at https://github.com/davidliu-lab/Pt98. All t-CyCIF tissue images are available for online viewing: https://www.cycif.org/data/liu-lin-2019/. Raw sequencing data have been deposited into dbGAP, phs001427.v2.p1, which is publicly accessible. Matched clinical and sequencing characteristics of tumors are in Supplementary Table 1.
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Acknowledgements
This work was supported by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (M.H.), the Harvard Ludwig Center for Cancer Research, the National Science Foundation (grant no. DGE-1144152, Graduate Research Fellowship no. 2016226995), the Conquer Cancer Foundation Young Investigator Award (D.L.), the Damon Runyon Cancer Research Foundation (Physician Scientist Training Grant (D.L.), Postdoctoral Fellowship (DRQ-03-20, D.S.)), the National Institutes of Health (grants R01CA227388, K08CA222663 (B.I.), K08CA234458 (D.L.) and U54-CA225088 (B.I., J.L., S.W., S.M., D.S. and P.K.S.), P01 CA114046 (M.H.), P50 CA174523 (M.H.), U54 CA224070 (M.H.)), DoD – PRCRP W81XWH-16-1-0119 (CA150619) (M.H.), DoD–CDMRP W81XWH-19-1-0143 (G.M.B.), BroadNext10 (E.M.V.A.), Society for Immunotherapy of Cancer Translational Postdoctoral Fellowship (D.L.), the Burroughs Wellcome Fund Career Award for Medical Scientists (B.I.), the Louis V. Gerstner, Jr Scholars Program (B.I.), the Velocity Fellow Program (B.I.), Abeloff V foundation scholar grant (B.I.), the Barr Award for Innovative Translational Research, Early Postdoc Mobility fellowship (no. P2ZHP3_181475) (D.S.) and the Swiss National Science Foundation (no. P2ZHP3_178022) (M.M).
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D. Liu, J.R.L., M.K., P.K.S. and G.M.B. conceived and designed the overall study. D.T.F., G.G.K., R.F., T.M., D. Lawrence, D.P.C., T.K. and T.S. collected and reviewed all clinical data. D.T.F. and T.S. performed sample processing and shipping. D.P.C., P.K.B., C.A. and J.H.S. provided samples and clinical annotation. A.T. oversaw sample processing and sequencing. D. Liu and E.R. designed and performed mutational and copy-number analyses. D. Liu, A.S., A.S., A.K., K.N., G.Z. and S.M. designed and performed RNA-seq analysis, including quality control, normalization, signature analysis, CIBERSORT and ssGSEA. D. Liu, E.R., A.H. and M.A.N. performed phylogenetic analyses. M.P.H. and C.L. performed IHC and provided pathologic interpretation. L.L.H. performed single-cell RNA-seq sample processing and E.R. and K.B. analyzed single-cell RNA-seq data. J.R.L., Y.A.C. and M.M. performed t-CyCIF normalization, clustering and interpretation. D.S. and S.W. performed neighborhood analysis. D. Liu, J.R.L., E.R., B.I., M.H., M.A.N., E.M.V.A., K.N., K.T.F., R.J.S., M.K., P.K.S. and G.M.B. oversaw all analyses. D. Liu, J.R.L., P.K.S. and G.M.B. wrote the manuscript and all authors reviewed and approved the final manuscript.
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D.L. reports funding by a postdoctoral fellowship from the Society for Immunotherapy of Cancers which is funded in part by an educational grant from Bristol-Meyers Squibb (BMS). BMS has had no input into the conception, conduct or reporting of the submitted work. D.C. has received consulting (GSK, Lilly, Boston Pharmaceuticals) and travel/speaking (Merck) support outside the scope of the present work. G.M.B. had sponsored research agreements with Takeda Oncology, Palleon Pharmaceuticals and Olink Proteomics, which were not used to support this work. She served as a speaker for Novartis and on scientific advisory boards for Nektar Therapeutics and Novartis and consults for Merck, all of which are outside the scope of this work. P.K.B has consulted for Angiochem, Genentech-Roche, Lilly, Tesaro, Voyager Therapeutics, ElevateBio, Pfizer (Array), Pfizer, SK Life Sciences and Dantari, received grant/research support (to Massachusetts General Hospital) from Merck, BMS and Lilly and honoraria from Merck, Pfizer, Genentech-Roche and Lilly, outside the scope of this present work. R.J.S. has served as consultant and/or on Scientific Advisory Boards for Asana Biosciences, AstraZeneca, BMS, Eisai, Iovance, Merck, Novartis, Oncosec, Pfizer, Replimune and reports research funding from Merck outside the scope of this present work. A.S. has received consulting (Lead Pharma, Checkmate and C-reveal Therapeutics) support outside the scope of the present work. K.T.F. has/had served on the Board of Directors of Loxo Oncology, Clovis Oncology, Strata Oncology, Vivid Biosciences, Checkmate Pharmaceuticals and Kinnate Pharmaceuticals; Corporate Advisory Board of X4 Pharmaceuticals; Scientific Advisory Boards of PIC Therapeutics, Sanofi, Amgen, Asana, Adaptimmune, Aeglea, Shattuck Labs, Tolero, Apricity, Oncoceutics, Fog Pharma, Neon, Tvardi, xCures, Monopteros and Vibliome; and as consultant to Lilly, Novartis, Genentech, BMS, Merck, Takeda, Verastem, Boston Biomedical, Pierre Fabre and Debiopharm; and received research funding from Novartis and Sanofi. B.I. is a consultant for Merck and Volastra Therapeutics. A.S. has received consulting (Lead Pharma, Checkmate and C-reveal Therapeutics) support outside the scope of the present work. P.K.S. is a member of the Board of Directors of Applied Biomath and Glencoe Software and member of the SAB for RareCyte and NanoString; he has equity in the first three of these companies. In the last 5 years the Sorger laboratory has received research funding from Novartis and Merck. E.M.V.A. has consulted or advised for Tango Therapeutics, Genome Medical, Invitae, Enara Bio, Janssen, Manifold Bio and Monte Rosa and holds equity in Tango Therapeutics, Genome Medical, Syapse, Enara Bio, Manifold Bio, Microsoft and Monte Rosa; has received research support from Novartis and BMS, travel reimbursement from Roche/Genentech and has Institutional patents filed on chromatin mutations and immunotherapy response and methods for clinical interpretation. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Mutation Load and Mutations in Significant Melanoma Genes.
221 nonsynonymous clonal mutations were found in all tumors, including hotspot mutations in IDH1 (p.R132C) and MAP2K1 (p.E203K), and additional missense mutations in cancer driver genes CTNNB1 (p.R582W) and ARID2 (p.P1664S).
Extended Data Fig. 2 Mutational Spectrum Profile of Common Ancestor.
489 common single nucleotide variants (including non-coding mutations) found in all tumors with mutations called individually in each sample are represented in their tri-nucleotide context; this mutational spectrum has cosine similarity of 0.965 with the ultraviolet DNA damage signature (Signature 7; https://cancer.sanger.ac.uk/cosmic/signatures). A similar analysis with 548 common ancestor mutations inferred jointly by PyClone generates similar results with cosine similarity of 0.962 to the UV signature (not shown).
Extended Data Fig. 3 Copy Number Alterations by Lineage and Tumor.
Each bar represents a tumor, with numbers indicating chromosomes and copy number alterations indicated by shade. Red arrows indicate the chromosomal segment loss of heterozygosity with corresponding loss of mutations in that segment that characterize the lineage, that is 2q for Lineage 2, 15q in Lineage 3, 19q in Lineage 5. Genome doubling is indicated by *.
Extended Data Fig. 4 Inferred Lineage-Defining Mutational Clusters and Cancer Cell Fractions by Tumor.
a, Inferred Mutational Clusters and Cluster Cancer Cell Fraction by Tumor. Mutational clusters representing subclones and the proportion of cancer cells in each tumor sample with each mutational cluster was inferred using PyClone. The x-axis shows each tumor, the y-axis is the proportion of cancer cells in each sample containing the cluster, and the legend (n=xx) refers to the number of mutations for each inferred cluster. Only clusters containing more than 3 mutations were included (9 clusters excluded) in subsequent analyses. b, Mutational Cluster Defining Lineage 3. This pattern demonstrates the loss of mutations in a common ancestor of the seventeen tumors in Lineage 3 (T11, T21, the early escape lesions (R1 and R2) and late-emerging resistant lesions (R2 and R3.2) and the post-autopsy lesions). These mutations are all found in chromosome 15, with a corresponding LOH in chromosome 15q. c, Mutational Cluster Defining Early Resistant Small Bowel Metastasis. This cluster represents the acquired mutations shared between the small bowel metastasis (R1) and the other late resistant tumors which also share a bi-allelic CDKN2A deletion. 2/4 mutations were inferred to have multiplicity of 2, and 2/4 multiplicity of 1, consistent with a unique genome doubling event just prior to the emergence of this tumor in lineage 3, and present in all subsequent resistant tumors. These mutations were manually reviewed and showed no evidence of artifact, although MYO7A is detectable at a lower level in P4 than the subsequent resistant tumors.
Extended Data Fig. 5 Hierarchical Clustering of Mutational Clusters CCFs and Copy Number Alterations define concordant tumor lineages.
Top: Hierarchically clustered heatmap of inferred cancer cell fractions (CCFs) for each mutation cluster (columns) for each tumor (rows), demonstrating 7 different lineages. Bottom: Hierarchically clustered heatmap of large copy number alterations (columns) for each tumor (rows), demonstrating concordance with lineages derived from mutational clusters. Complete allelic deletions are dark blue, and copy number gains and losses are light blue.
Extended Data Fig. 6 Aneuploidy in Genome-Doubled vs. non-Genome-Doubled tumors.
Aneuploidy here is defined as the proportion of the genome with copy number gain or loss (compared to the ‘baseline’ allelic copy number, which is 1 for non-genome doubled tumors and 2 for genome doubled tumors). a, Allelic copy number ratios for a representative non-genome doubled tumor (T13 from lineage 1, upper), and a genome-doubled tumor (R1, the jejunal metastasis from lineage 3, lower). The x axis is the genome (chromosomes in increasing number), and y represents the relative inferred copy number at that genomic location. b, A different representation of the inferred allelic copy from T13 and R1 demonstrating increased aneuploidy in the genome-doubled tumor. c, The genome doubled tumors (n=19) had evidence of chromosomal instability, with higher proportion of genome with aneuploidy (two-sided Mann-Whitney p=2.2e-07, Methods) compared to non-genome doubled tumors (n=18) (upper panel). Late resistant tumors (D1500+) had the higher aneuploidy compared to all other tumors, (lower panel). Boxplots: box limits indicate the IQR (25th to 75th percentiles), with a center line indicating the median. Whiskers show the value ranges up to 1.5 × IQR above the 75th or below the 25th percentiles, with outliers beyond those ranges shown as individual points.
Extended Data Fig. 7 Tumor Immune Microenvironment.
a, Tumors from the patient (n=20) had lower overall immune signature score compared to a large cohort of PD-1 treated melanoma patients (n=121) (MWW nominal two-sided p = 0.036);. b, overall Immune signature scores in tumors biopsied within the first 120 days after immunotherapy initiation decreased after initiation of immune checkpoint blockade (linear regression p = 0.020). c, T cell signature scores in the same tumors decrease after initiation of immune checkpoint blockade (linear regression p = 0.008). d, All immune cell signature scores in the same tumors and their association with time after treatment. Negative coefficients are associated with a decrease in score with time after treatment. Boxplots: box limits indicate the IQR (25th to 75th percentiles), with a center line indicating the median. Whiskers show the value ranges up to 1.5 × IQR above the 75th or below the 25th percentiles, with outliers beyond those ranges shown as individual points.
Extended Data Fig. 8 Quantification of Selected Immune and Tumor Populations from CyCIF.
Selected immune cell and NGFR-high subset proportions over time by spatial compartment. (a) CD4 + Treg cells; (b) NGFR-high tumor cells. Error bars represent standard error of the mean (S.E.M.). Sample numbers for each days are: 1(day -62), 1(day -22), 4(day 4), 4(day 39), 2(day 62), 3(day 76), 3(day 92), 1(day 109), 1(day 1028), 1(day 1849), 1(day 1862), 10(day 2065).
Extended Data Fig. 9 Gaussian Mixture Modelling of CyCIF data.
a, Heatmap demonstrating clusters of cells from Gaussian Mixture Model clustering characterized by a range of CyCIF quantitative fluorescence (Methods) from the 19 tumors in Batch 1. b, Heatmap demonstrating clusters of cells from Gaussian Mixture Model clustering characterized by a range of CyCIF quantitative fluorescence (Methods) from the pre- and post-TLR9 + antiPD1 therapy tumors (Batch 2). (Top) There is a distinct NGFR-Hi tumor cell cluster, which is high in PD-L1 protein expression, a MITF-Hi/NGFR-lo tumor cell cluster, and an immune cell cluster. Several non-specific (that is non-NGFR-Hi, non-MITF-Hi) tumor cell clusters are also seen. (Bottom) There is a strong association between NGFR and PD-L1 expression among S100 + gated tumor cells (Pearson correlation coefficient r = 0.66 and p-value = 0, calculated by the default function in MATLAB).
Extended Data Fig. 10 Expression of class I and class II MHC and of clonal ancestral neoantigens in lineage 3 and over time.
a, MHC-I and –II scores were generated from bulk RNAseq and compared between Lineage 3 tumors (n=3) and other tumors (n=17). Scores for each sample were calculated using an averaged standardized z-score of 6 MHC-I genes (HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2) and 13 MHC-II genes (HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRA, HLA-DRB1, HLA-DRB5). (Left) Lineage 3 has lower MHC-I score compared to other tumors (t-test p = 0.01). (Right) Lineage 3 tumors do not have a statistically significant difference in MHC-II score compared to other tumors (t-test p = 0.13). b, Expression of clonal ancestral neoantigens in lineage 3 and over time. Neoantigens were inferred using NetMHCPan with inputs of the patient’s HLA and mutations. 174 genes with clonal ancestral mutations that coded for neoantigens were identified, and their RNAseq expression (TPM) in each tumor calculated. Overall expression, Chr15 neoantigen expression (that is the expression of the 3 genes with clonal ancestral mutations lost with LOH of Chr15 in Lineage 3 tumors), and the proportion of the overall neoantigen expression that Chr15 neoantigen genes represented were calculated. Left: Lineage 3 vs other tumors. Overall expression was not different, but there was a trend towards lower expression and proportion of expression of Chr15 neoantigen genes in Lineage 3 tumors. Right: Expression over time in the on-treatment time period (D27-D109). Overall neoantigen gene expression was not different by time, but Chr15 neoantigen gene expression and the proportion of Chr15 neoantigen gene expression trended towards decreasing with time. Boxplots: box limits indicate the IQR (25th to 75th percentiles), with a center line indicating the median. Whiskers show the value ranges up to 1.5 × IQR above the 75th or below the 25th percentiles, with outliers beyond those ranges shown as individual points.
Supplementary information
Supplementary Information
Supplementary Figs. 1–9.
Supplementary Tables
Supplementary Tables 1–4
Supplementary Data 1
MAF (mutation annotation format) of called mutations per sample.
Supplementary Data 2
MAF (mutation annotation format) of union of all called mutations across samples with cancer cell fractions per sample used for clustering and phylogenetic analysis.
Supplementary Data 3
TPM (transcripts per kilobase million) normalized transcriptomes of tumor samples.
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Liu, D., Lin, JR., Robitschek, E.J. et al. Evolution of delayed resistance to immunotherapy in a melanoma responder. Nat Med 27, 985–992 (2021). https://doi.org/10.1038/s41591-021-01331-8
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DOI: https://doi.org/10.1038/s41591-021-01331-8
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