Key Points PCNSLs and PTLs have a defining genetic signature that differs from other LBCLs and suggests rational targeted therapies. PCNSLs and PTLs frequently exhibit 9p24.1/PD-L1/PD-L2 copy number alterations and translocations, likely genetic bases of immune evasion.

Combination anti-cytotoxic T lymphocyte antigen 4 (CTLA-4) and anti-programmed cell death protein 1 (PD-1) therapy promotes antitumor immunity and provides superior benefit to patients with advanced-stage melanoma compared with either therapy alone. T cell immunity requires recognition of antigens in the context of major histocompatibility complex (MHC) class I and class II proteins by CD8 and CD4 T cells, respectively. We examined MHC class I and class II protein expression on tumor cells from previously untreated melanoma patients and correlated the results with transcriptional and genomic analyses and with clinical response to anti-CTLA-4, anti-PD-1, or combination therapy. Most (>50% of cells) or complete loss of melanoma MHC class I membrane expression was observed in 78 of 181 cases (43%), was associated with transcriptional repression of ,, , and, and predicted primary resistance to anti-CTLA-4, but not anti-PD-1, therapy. Melanoma MHC class II membrane expression on >1% cells was observed in 55 of 181 cases (30%), was associated with interferon-γ (IFN-γ) and IFN-γ-mediated gene signatures, and predicted response to anti-PD-1, but not anti-CTLA-4, therapy. We conclude that primary response to anti-CTLA-4 requires robust melanoma MHC class I expression. In contrast, primary response to anti-PD-1 is associated with preexisting IFN-γ-mediated immune activation that includes tumor-specific MHC class II expression and components of innate immunity when MHC class I is compromised. The benefits of combined checkpoint blockade may be attributable, in part, to distinct requirements for melanoma-specific antigen presentation to initiate antitumor immunity.

Signaling between programmed cell death protein 1 (PD-1) and the PD-1 ligands (PD-L1, PD-L2) is essential for malignant Hodgkin Reed-Sternberg (HRS) cells to evade antitumor immunity in classical Hodgkin lymphoma (cHL). Copy number alterations of 9p24.1/()() contribute to robust PD-L1 and PD-L2 expression by HRS cells. PD-L1 is also expressed by nonmalignant tumor-associated macrophages (TAMs), but the relationships among PD-L1 HRS cells, PD-L1 TAMs, and PD-1 T cells remain undefined. We used multiplex immunofluorescence and digital image analysis to examine the topography of PD-L1 and PD-1 cells in the tumor microenvironment (TME) of cHL. We find that the majority of PD-L1 in the TME is expressed by the abundant PD-L1 TAMs, which physically colocalize with PD-L1 HRS cells in a microenvironmental niche. PD-L1 TAMs are enriched for contacts with T cells, and PD-L1 HRS cells are enriched for contacts with CD4 T cells, a subset of which are PD-1 Our data define a unique topology of cHL in which PD-L1 TAMs surround HRS cells and implicate CD4 T cells as a target of PD-1 blockade.

BackgroundWhile immune checkpoint blockade has greatly improved clinical outcomes in diseases such as melanoma, there remains a need for predictive biomarkers to determine who will likely benefit most from which therapy. To date, most biomarkers of response have been identified in the tumors themselves. Biomarkers that could be assessed from peripheral blood would be even more desirable, because of ease of access and reproducibility of sampling.MethodsWe used mass cytometry (CyTOF) to comprehensively profile peripheral blood of melanoma patients, in order to find predictive biomarkers of response to anti-CTLA-4 or anti-PD-1 therapy. Using a panel of ~ 40 surface and intracellular markers, we performed in-depth phenotypic and functional immune profiling to identify potential predictive biomarker candidates.ResultsImmune profiling of baseline peripheral blood samples using CyTOF revealed that anti-CTLA-4 and anti-PD-1 therapies have distinct sets of candidate biomarkers. The distribution of CD4+ and CD8+ memory/non-memory cells and other memory subsets was different between responders and non-responders to anti-CTLA-4 therapy. In anti-PD-1 (but not anti-CTLA-4) treated patients, we discovered differences in CD69 and MIP-1β expressing NK cells between responders and non-responders. Finally, multivariate analysis was used to develop a model for the prediction of response.ConclusionsOur results indicate that anti-CTLA-4 and anti-PD-1 have distinct predictive biomarker candidates. CD4+ and CD8+ memory T cell subsets play an important role in response to anti-CTLA-4, and are potential biomarker candidates. For anti-PD-1 therapy, NK cell subsets (but not memory T cell subsets) correlated with clinical response to therapy. These functionally active NK cell subsets likely play a critical role in the anti-tumor response triggered by anti-PD-1.Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0328-8) contains supplementary material, which is available to authorized users.

The endogenous ligand-activated aryl hydrocarbon receptor (AHR) plays an important role in numerous biologic processes. As the known number of AHR-mediated processes grows, so too does the importance of determining what endogenous AHR ligands are produced, how their production is regulated, and what biologic consequences ensue. Consequently, our studies were designed primarily to determine whether ER−/PR−/Her2− breast cancer cells have the potential to produce endogenous AHR ligands and, if so, how production of these ligands is controlled. We postulated that: 1) malignant cells produce tryptophan-derived AHR ligand(s) through the kynurenine pathway; 2) these metabolites have the potential to drive AHR-dependent breast cancer migration; 3) the AHR controls expression of a rate-limiting kynurenine pathway enzyme(s) in a closed amplification loop; and 4) environmental AHR ligands mimic the effects of endogenous ligands. Data presented in this work indicate that primary human breast cancers, and their metastases, express high levels of AHR and tryptophan-2,3-dioxygenase (TDO); representative ER−/PR−/Her2− cell lines express TDO and produce sufficient intracellular kynurenine and xanthurenic acid concentrations to chronically activate the AHR. TDO overexpression, or excess kynurenine or xanthurenic acid, accelerates migration in an AHR-dependent fashion. Environmental AHR ligands 2,3,7,8-tetrachlorodibenzo[p]dioxin and benzo[a]pyrene mimic this effect. AHR knockdown or inhibition significantly reduces TDO2 expression. These studies identify, for the first time, a positive amplification loop in which AHR-dependent TDO2 expression contributes to endogenous AHR ligand production. The net biologic effect of AHR activation by endogenous ligands, which can be mimicked by environmental ligands, is an increase in tumor cell migration, a measure of tumor aggressiveness.

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org.

CTLA-4 immune checkpoint blockade is clinically effective in a subset of patients with metastatic melanoma. We identify a subcluster of MAGE-A cancer-germline antigens, located within a narrow 75 kb region of chromosome Xq28, that predicts resistance uniquely to blockade of CTLA-4, but not PD-1. We validate this gene expression signature in an independent anti-CTLA-4-treated cohort and show its specificity to the CTLA-4 pathway with two independent anti-PD-1-treated cohorts. Autophagy, a process critical for optimal anti-cancer immunity, has previously been shown to be suppressed by the MAGE-TRIM28 ubiquitin ligase in vitro. We now show that the expression of the key autophagosome component LC3B and other activators of autophagy are negatively associated with MAGE-A protein levels in human melanomas, including samples from patients with resistance to CTLA-4 blockade. Our findings implicate autophagy suppression in resistance to CTLA-4 blockade in melanoma, suggesting exploitation of autophagy induction for potential therapeutic synergy with CTLA-4 inhibitors.

Background Adjuvant dabrafenib plus trametinib reduced the risk of relapse versus placebo in patients with resected, BRAF V600 -mutant, stage III melanoma in the phase 3 COMBI-AD trial. This prespecified exploratory biomarker analysis aimed to evaluate potential prognostic or predictive factors and mechanisms of resistance to adjuvant targeted therapy.Methods COMBI-AD is a randomised, double-blind, placebo-controlled, phase 3 trial comparing dabrafenib 150 mg orally twice daily plus trametinib 2 mg orally once daily versus two matched placebos. Study participants were at least 18 years of age and underwent complete resection of stage IIIA (lymph node metastases >1 mm), IIIB, or IIIC cutaneous melanoma, per American Joint Committee on Cancer 7th edition criteria, with a BRAF V600E or BRAF V600K mutation. Patients were randomly assigned (1:1) to the two treatment groups by an interactive voice response system, stratified by mutation type and disease stage. Patients, physicians, and the investigators who analysed data were masked to treatment allocation. The primary outcome was relapse-free survival, defined as the time from randomisation to disease recurrence or death from any cause. Biomarker assessment was a prespecified exploratory outcome of the trial. We assessed intrinsic tumour genomic features by use of next-generation DNA sequencing and characteristics of the tumour microenvironment by use of a NanoString RNA assay, which might provide prognostic and predictive information. This trial is registered with ClinicalTrials.gov, number NCT01682083, and is ongoing but no longer recruiting participants. FindingsBetween Jan 31, 2013, and Dec 11, 2014, 870 patients were enrolled in the trial. Median follow-up at data cutoff (April 30, 2018) was 44 months (IQR 38-49) in the dabrafenib plus trametinib group and 42 months (21-49) in the placebo group. Intrinsic tumour genomic features were assessed in 368 patients (DNA sequencing set) and tumour microenvironment characteristics were assessed in 507 patients (NanoString biomarker set). MAPK pathway genomic alterations at baseline did not affect treatment benefit or clinical outcome. An IFNγ gene expression signature higher than the median was prognostic for prolonged relapse-free survival in both treatment groups. Tumour mutational burden was independently prognostic for relapse-free survival in the placebo group (high TMB, top third; hazard ratio [HR] 0•56, 95% CI 0•37-0•85, p=0•0056), but not in the dabrafenib plus trametinib group (0•83, 95% CI 0•53-1•32, p=0•44). Patients with tumour mutational burden in the lower two terciles seem to derive a substantial long-term relapse-free survival benefit from targeted therapy (HR [versus placebo] 0•49, 95% CI 0•35-0•68, p<0•0001). However, patients with high tumour mutational burden seem to have a less pronounced benefit with targeted therapy (HR [versus placebo] 0•75, 95% CI 0•44-1•26, p=0•27), especially if they had an IFNγ signature lower than the median (HR 0•88 [95% CI 0•40-1•93], p=0•74). Interpretation Tumour mutationa...