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Single-cell RNA sequencing is the reference technology to characterize the composition of the tumor microenvironment and to study tumor heterogeneity at high resolution. Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clonal substructure of tumors from single-cell RNA-seq data. It uses a multichannel segmentation algorithm exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN can automatically and accurately discriminate between malignant and non-malignant cells, resulting in a practical framework to analyze tumors and their microenvironment. We apply SCEVAN to datasets encompassing 106 samples and 93,322 cells from different tumor types and technologies. We demonstrate its application to characterize the intratumor heterogeneity and geographic evolution of malignant brain tumors. The inference of clonal architectures in cancer using single-cell RNA-seq data remains challenging. Here, the authors develop SCEVAN, a variational algorithm for copy number-based clonal structure inference in single-cell RNA-seq data that can characterise evolution and heterogeneity in the tumour and the microenvironment.

Neurofibromatosis type 1 (NF1) is a common tumor predisposition syndrome in which glioma is one of the prevalent tumors. Gliomagenesis in NF1 results in a heterogeneous spectrum of low- to high-grade neoplasms occurring during the entire lifespan of patients. The pattern of genetic and epigenetic alterations of glioma that develops in NF1 patients and the similarities with sporadic glioma remain unknown. Here, we present the molecular landscape of low- and high-grade gliomas in patients affected by NF1 (NF1-glioma). We found that the predisposing germline mutation of the NF1 gene was frequently converted to homozygosity and the somatic mutational load of NF1-glioma was influenced by age and grade. High-grade tumors harbored genetic alterations of TP53 and CDKN2A, frequent mutations of ATRX associated with Alternative Lengthening of Telomere, and were enriched in genetic alterations of transcription/chromatin regulation and PI3 kinase pathways. Low-grade tumors exhibited fewer mutations that were over-represented in genes of the MAP kinase pathway. Approximately 50% of low-grade NF1-gliomas displayed an immune signature, T lymphocyte infiltrates, and increased neo-antigen load. DNA methylation assigned NF1-glioma to LGm6, a poorly defined Isocitrate Dehydrogenase 1 wild-type subgroup enriched with ATRX mutations. Thus, the profiling of NF1-glioma defined a distinct landscape that recapitulates a subset of sporadic tumors.

Background Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. Results We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCO high TAMs induce a phenotypic shift towards mesenchymal cellular state of glioma stem cells, promoting both invasive and proliferative activities, as well as therapeutic resistance to irradiation. MARCO high TAMs also significantly accelerate tumor engraftment and growth in vivo. Moreover, both MA-TAM master regulators and their target genes are significantly correlated with poor clinical outcomes and are often associated with genomic aberrations in neurofibromin 1 (NF1) and phosphoinositide 3-kinases/mammalian target of rapamycin/Akt pathway (PI3K-mTOR-AKT)-related genes. We further demonstrate the origination of MA-TAMs from peripheral blood, as well as their potential association with tumor-induced polarization states and immunosuppressive environments. Conclusions Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy.

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting. Glioma tumours are known to be heterogenous in mutation and gene expression patterns, but sampling limitations can lead to inaccurate detection of evolutionary events. Here, the authors carry out multi-omics analysis of multi-regional biopsies from 68 patients and show differential mutations in non-enhancing regions.

Background Glioblastoma (GBM), the most common primary malignant brain tumor in adults, remains uniformly fatal due to the lack of effective targeted therapies. The epidermal growth factor receptor (EGFR) is the most frequently altered receptor tyrosine kinase oncogene in GBM with most alterations impacting the receptor ectodomain function, including gene amplification, mutation, rearrangement, and splicing site changes, which occur in approximately 50% of GBM tumors. Depatuxizumab mafodotin (Depatux-M; ABT-414), an antibody-drug conjugate composed of an EGFR-specific antibody (ABT-806) that recognizes the EGFR ectodomain linked to the cytotoxic agent monomethyl auristatin F, initially showed clinical promise. However, it failed to improve survival in phase III trials, highlighting an urgent need to understand mechanisms of resistance. Methods We generated in vivo ABT-414 resistant GBM models using patient-derived xenografts (PDXs) and performed genomics and transcriptomic profiling, including whole exome sequencing, bulk RNA sequencing, and single-cell RNA sequencing. Results ABT-414-resistant tumors exhibited transcriptional reprogramming characterized by upregulation of synaptic and developmental gene networks and downregulation of biosynthetic processes, indicative of a plastic, therapy-adaptive state. Whole-exome sequencing revealed novel mutations exclusive to resistant tumors, including a recurrent TEK (TIE2) S466I point mutation present in all ABT-414 resistant GBM12 PDX tumors. Functional validation demonstrated that ectopic expression of TEK S466I and TEK WT in PDX models reduced EGFR levels, suggesting a novel feedback mechanism linking TEK signaling to EGFR downregulation and contributes to resistance. Conclusion Our findings demonstrate that resistance to ABT-414 arises through both adaptive transcriptional remodeling and newly acquired genetic alterations. TEK-mediated suppression of EGFR represents a previously unrecognized mechanism of resistance, with potential implications for overcoming antibody-drug conjugate failure in GBM.

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states. Deconvolution methods infer levels of immune infiltration from bulk expression of tumour samples. Here, authors assess 6 published and 22 community-contributed methods via a DREAM Challenge using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells.

Background We developed an integrated multi-omics analysis in metastatic melanoma (MM) cohorts to associate DNA methylation profiles with tumor progression, survival, response to adjuvant immunotherapy, structure of the tumor immune microenvironment and transcriptional programs of immunity and melanoma differentiation. Methods Lesions ( n  = 191) from a fully annotated, retrospective cohort of 165 AJCC 8th Stage III and IV melanoma patients (EPICA cohort) were characterized by reduced representation bisulfite sequencing, RNA sequencing, whole exome sequencing, quantitative immunohistochemistry and multiplex immunofluorescence analysis. The TCGA melanoma datasets were used for validation. Pre-therapy lesions ( n  = 28) from a cohort of MM patients treated with adjuvant immune checkpoint blockade were characterized for the DNA methylation profile. Impact of a DNMT inhibitor on DNA methylation and transcriptomic profiles of melanoma cell lines was investigated by EPIC arrays and Clariom S arrays. Results Four tumor subsets (i.e. DEMethylated, LOW, INTermediate and CIMP) with progressively increasing levels of DNA methylation were identified in EPICA, TCGA MM and TCGA primary melanoma cohorts. EPICA patients with LOW methylation tumors exhibited a significantly longer survival and a lower progression rate to more advanced AJCC stages, compared to patients with CIMP tumors. In an adjuvant immune checkpoint blockade cohort, patients with DEM/LOW pre-therapy lesions showed significantly longer relapse-free survival compared to those with INT/CIMP lesions. RNA-seq data analysis revealed that LOW and CIMP EPICA tumors showed opposite activation of master molecules influencing prognostic target genes, and differential expression of immunotherapy response and melanoma differentiation signatures. Compared to CIMP tumors, LOW lesions showed enrichment for CD8 + TCF-1 + PD-1 + TIM-3 − pre-exhausted and CD8 + TCF-1 − PD-1 + TIM-3 + exhausted T cells, more frequent retention of HLA Class I antigens and a de-differentiated melanoma phenotype. The differentiation and immune-related transcriptional features associated with LOW vs CIMP lesions were tumor-intrinsic programs retained in-vitro by melanoma cell lines. Consistently, treatment of differentiated melanoma cell lines with a DNMT inhibitor induced global DNA de-methylation, promoted de-differentiation and upregulated viral mimicry and IFNG predictive signatures of immunotherapy response. Conclusions These results reveal the biological, prognostic and therapeutic relevance of DNA methylation classes in MM and support methylome targeting strategies for precision immunotherapy.

Glioblastoma (GBM) is resistant to multimodality therapeutic approaches. A high burden of tumor-specific mutant peptides (neoantigens) correlates with better survival and response to immunotherapies in selected solid tumors but how neoantigens impact clinical outcome in GBM remains unclear. Here, we exploit the similarity between tumor neoantigens and infectious disease-derived immune epitopes and apply a neoantigen fitness model for identifying high-quality neoantigens in a human pan-glioma dataset. We find that the neoantigen quality fitness model stratifies GBM patients with more favorable clinical outcome and, together with CD8 + T lymphocytes tumor infiltration, identifies a GBM subgroup with the longest survival, which displays distinct genomic and transcriptomic features. Conversely, neither tumor neoantigen burden from a quantitative model nor the isolated enrichment of CD8 + T lymphocytes were able to predict survival of GBM patients. This approach may guide optimal stratification of GBM patients for maximum response to immunotherapy. Jing Zhang et al. analyze glioblastoma patient data with a computational model integrating mutation-derived neoantigens similar to pathogen-specific antigens with enrichment of signatures for individual immune cells. Using a fitness model for identifying high-quality neoantigens and T lymphocyte infiltrates in glioblastoma, they uncover a subset of patients with prolonged survival.

Background Improvement of efficacy of immune checkpoint blockade (ICB) remains a major clinical goal. Association of ICB with immunomodulatory epigenetic drugs is an option. However, epigenetic inhibitors show a heterogeneous landscape of activities. Analysis of transcriptional programs induced in neoplastic cells by distinct classes of epigenetic drugs may foster identification of the most promising agents. Methods Melanoma cell lines, characterized for mutational and differentiation profile, were treated with inhibitors of DNA methyltransferases (guadecitabine), histone deacetylases (givinostat), BET proteins (JQ1 and OTX-015), and enhancer of zeste homolog 2 (GSK126 ). Modulatory effects of epigenetic drugs were evaluated at the gene and protein levels. Master molecules explaining changes in gene expression were identified by Upstream Regulator (UR) analysis. Gene set enrichment and IPA were used respectively to test modulation of guadecitabine-specific gene and UR signatures in baseline and on-treatment tumor biopsies from melanoma patients in the Phase Ib NIBIT-M4 Guadecitabine + Ipilimumab Trial. Prognostic significance of drug-specific immune-related genes was tested with Timer 2.0 in TCGA tumor datasets. Results Epigenetic drugs induced different profiles of gene expression in melanoma cell lines. Immune-related genes were frequently upregulated by guadecitabine, irrespective of the mutational and differentiation profiles of the melanoma cell lines, to a lesser extent by givinostat, but mostly downregulated by JQ1 and OTX-015. GSK126 was the least active drug. Quantitative western blot analysis confirmed drug-specific modulatory profiles. Most of the guadecitabine-specific signature genes were upregulated in on-treatment NIBIT-M4 tumor biopsies, but not in on-treatment lesions of patients treated only with ipilimumab. A guadecitabine-specific UR signature, containing activated molecules of the TLR, NF-kB, and IFN innate immunity pathways, was induced in drug-treated melanoma, mesothelioma and hepatocarcinoma cell lines and in a human melanoma xenograft model. Activation of guadecitabine-specific UR signature molecules in on-treatment tumor biopsies discriminated responding from non-responding NIBIT-M4 patients. Sixty-five % of the immune-related genes upregulated by guadecitabine were prognostically significant and conferred a reduced risk in the TCGA cutaneous melanoma dataset. Conclusions The DNMT inhibitor guadecitabine emerged as the most promising immunomodulatory agent among those tested, supporting the rationale for usage of this class of epigenetic drugs in combinatorial immunotherapy approaches.