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Understanding the evolutionary pathways to metastasis and resistance to immune-checkpoint inhibitors (ICI) in melanoma is critical for improving outcomes. Here, we present the most comprehensive intrapatient metastatic melanoma dataset assembled to date as part of the Posthumous Evaluation of Advanced Cancer Environment (PEACE) research autopsy program, including 222 exome sequencing, 493 panel-sequenced, 161 RNA sequencing, and 22 single-cell whole-genome sequencing samples from 14 ICI-treated patients. We observed frequent whole-genome doubling and widespread loss of heterozygosity, often involving antigen-presentation machinery. We found KIT extrachromosomal DNA may have contributed to the lack of response to KIT inhibitors of a KIT-driven melanoma. At the lesion-level, MYC amplifications were enriched in ICI nonresponders. Single-cell sequencing revealed polyclonal seeding of metastases originating from clones with different ploidy in one patient. Finally, we observed that brain metastases that diverged early in molecular evolution emerge late in disease. Overall, our study illustrates the diverse evolutionary landscape of advanced melanoma.Significance:Despite treatment advances, melanoma remains a deadly disease at stage IV. Through research autopsy and dense sampling of metastases combined with extensive multiomic profiling, our study elucidates the many mechanisms that melanomas use to evade treatment and the immune system, whether through mutations, widespread copy-number alterations, or extrachromosomal DNA.

Supplementary figure 1: Cohort overview. Number of samples sequenced with whole exome, panel or whole RNA sequencing. Supplementary figure 2: Phylogeny and WGD events in CRUKP1047. Supplementary figure 3: Ploidy and SCNA burden. Supplementary figure 4: Overview of each case. Supplementary figure 5: MEDICC2 copy number sample trees. Supplementary figure 6: MEDICC tree of all exome samples demonstrating that samples cluster together by patient, and not by melanoma subtype. Supplementary figure 7: SCNA frequency of cutaneous (a), acral (b) and melanoma of unknown primary (MUP, c). Supplementary figure 8: Correlation between liver copy number distance to other sites and time of emergence after stage IV diagnosis. Supplementary figure 9: Examination of tumour heterogeneity of alterations to antigen-presentation machinery genes, with site and patient annotation. Supplementary figure 10: Boxplots indicating the proportion of losses in the cohort for each segment. Supplementary figure 11: Balance of expression between nonsynonymous mutations that were not predicted to be neoantigens and clonal predicted neoantigens. Supplementary figure 12: Barplot of TIL infiltration score frequencies, determined by pathologist assessment of histology, across all samples. Supplementary figure 13: Number of samples per patient that are classified as either none-low in terms of TILs or moderate-heavy. Supplementary figure 14: Histogram of purity for samples with RNA-seq data. Supplementary figure 15: TME deconvolution. Supplementary figure 16: The effect of metastatic site on transcriptional profiles. Supplementary figure 17: Association of PHF3 copy number with expression. Supplementary figure 18: Overview of gene fusions identified in RNA-seq data. Supplementary figure 19: Comparison of ploidy estimates from panel sequencing data, exome data and FISH. Supplementary figure 20: Comparison of ploidy estimates in panel, exome, FISH and single cell data. Supplementary figure 21: FACs sort plot for CRUKP2567 diaphragmatic metastasis. Supplementary figure 22: Ploidy and wGII values from single cell sequencing of FACS-sorted tumour cells. Supplementary figure 23: Copy number profiles on chromosome 5 for bulk samples from primary and DI_2_R2, a diaphragmatic metastasis. Supplementary figure 24: Histogram of purity of samples for which RNA-seq was performed faceted by patient. Supplementary figure 25: Histogram of purity of samples for which RNA-seq was performed faceted by tissue site.

S. Table 1: List of PEACE Consortium members. S. Table 2: Sample database used for the study for Panel, Exome and RNASeq samples. S. Table 3: Overview of lines of treatment given to each patient. S. Table 4: Lesions and patients included in the lesion-level response to ICI analysis. S. Table 5: Hallmark genesets significantly upregulated in normal tissue vs tumor tissue. Table shows p-values for tissue type, purity; T value for tissue type, purity; q-value for tissue type, purity (FDR corrected). S. Table 6: Hallmark genesets significantly upregulated in tumor tissue vs normal tissue. Table shows p-values for tissue type, purity; T value for tissue type, purity; q-value for tissue type, purity (FDR corrected). S. Table 7: Genes included in the target panel. S. Table 8: Tree topology comparisons between manually constructed trees and pairtree-constructed trees.

Purpose:Immune dysregulation is described in multiple myeloma. While preclinical models suggest a role for altered T-cell immunity in disease progression, the contribution of immune dysfunction to clinical outcomes remains unclear. We aimed to characterize marrow-infiltrating T cells in newly diagnosed patients and explore associations with outcomes of first-line therapy.Experimental Design:We undertook detailed characterization of T cells from bone marrow (BM) samples, focusing on immune checkpoints and features of immune dysfunction, correlating with clinical features and progression-free survival.Results:We found that patients with multiple myeloma had greater abundance of BM regulatory T cells (Tregs) which, in turn, expressed higher levels of the activation marker CD25 compared with healthy donors. Patients with higher frequencies of Tregs had shorter PFS and a distinct Treg immune checkpoint profile (increased PD-1, LAG-3) compared with patients with lower frequencies of Tregs. Analysis of CD4 and CD8 effectors revealed that low CD4effector (CD4eff):Treg ratio and increased frequency of PD-1–expressing CD4eff cells were independent predictors of early relapse over and above conventional risk factors, such as genetic risk and depth of response. Ex vivo functional analysis and RNA sequencing revealed that CD4 and CD8 cells from patients with greater abundance of CD4effPD-1+ cells displayed transcriptional and secretory features of dysfunction.Conclusions:BM-infiltrating T-cell subsets, specifically Tregs and PD-1–expressing CD4 effectors, negatively influence clinical outcomes in newly diagnosed patients. Pending confirmation in larger cohorts and further mechanistic work, these immune parameters may inform new risk models, and present potential targets for immunotherapeutic strategies.

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Patients with blood cancer continue to have a greater risk of inadequate immune responses following three COVID-19 vaccine doses and risk of severe COVID-19 disease. In the context of the CAPTURE study (NCT03226886), we report immune responses in 80 patients with blood cancer who received a fourth dose of BNT162b2. We measured neutralizing antibody titers (NAbTs) using a live virus microneutralization assay against wild-type (WT), Delta, and Omicron BA.1 and BA.2 and T cell responses against WT and Omicron BA.1 using an activation-induced marker (AIM) assay. The proportion of patients with detectable NAb titers and T cell responses after the fourth vaccine dose increased compared with that after the third vaccine dose. Patients who received B cell-depleting therapies within the 12 months before vaccination have the greatest risk of not having detectable NAbT. In addition, we report immune responses in 57 patients with breakthrough infections after vaccination.

Checkpoint inhibitors (CPIs) augment adaptive immunity. Systematic pan-tumor analyses may reveal the relative importance of tumour cell intrinsic and microenvironmental features underpinning CPI sensitization. Here we collated whole-exome and transcriptomic data for >1000 CPI-treated patients across eight tumor-types, utilizing standardized bioinformatics-workflows and clinical outcome-criteria to validate multivariate predictors of CPI-sensitization. Clonal-TMB was the strongest predictor of CPI response, followed by TMB and CXCL9 expression. Subclonal-TMB, somatic copy alteration burden and HLA-evolutionary divergence failed to attain significance. Discovery analysis identified two additional determinants of CPI-response supported by prior functional evidence: 9q34.3 (TRAF2) loss and CCND1 amplification, both independently validated in >1600 CPI-treated patients. We find evidence for collateral sensitivity, likely mediated through selection for CDKN2A-loss, with 9q34.3 loss as a passenger event leading to CPI-sensitization. Finally, scRNA sequencing of clonal neoantigen-reactive CD8-TILs, combined with bulk RNAseq analysis of CPI responding tumors, identified CCR5 and CXCL13 as T cell-intrinsic mediators of CPI-sensitisation.