Single-cell genomics is essential to chart tumor ecosystems. Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each requires customization to different tissue and tumor types, posing a barrier to adoption. Here, we have developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 cells and nuclei from 40 samples across 23 specimens spanning eight tumor types of varying tissue and sample characteristics. We evaluated protocols by cell and nucleus quality, recovery rate and cellular composition. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, but at different proportions. Our work provides guidance for studies in a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.

In isocitrate dehydrogenase wildtype glioblastoma (GBM), cellular heterogeneity across and within tumors may drive therapeutic resistance. Here we analyzed 121 primary and recurrent GBM samples from 59 patients using single-nucleus RNA sequencing and bulk tumor DNA sequencing to characterize GBM transcriptional heterogeneity. First, GBMs can be classified by their broad cellular composition, encompassing malignant and nonmalignant cell types. Second, in each cell type we describe the diversity of cellular states and their pathway activation, particularly an expanded set of malignant cell states, including glial progenitor cell-like, neuronal-like and cilia-like. Third, the remaining variation between GBMs highlights three baseline gene expression programs. These three layers of heterogeneity are interrelated and partially associated with specific genetic aberrations, thereby defining three stereotypic GBM ecosystems. This work provides an unparalleled view of the multilayered transcriptional architecture of GBM. How this architecture evolves during disease progression is addressed in the companion manuscript by Spitzer et al.

The evolution of isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) after standard-of-care therapy remains poorly understood. Here we analyzed matched primary and recurrent GBMs from 59 patients using single-nucleus RNA sequencing and bulk DNA sequencing, assessing the longitudinal evolution of the GBM ecosystem across layers of cellular and molecular heterogeneity. The most consistent change was a lower malignant cell fraction at recurrence and a reciprocal increase in glial and neuronal cell types in the tumor microenvironment (TME). The predominant malignant cell state differed between most matched pairs, but no states were exclusive or highly enriched in either time point, nor was there a consistent longitudinal trajectory across the cohort. Nevertheless, specific trajectories were enriched in subsets of patients. Changes in malignant state abundances mirrored changes in TME composition and baseline profiles, reflecting the co-evolution of the GBM ecosystem. Our study provides a blueprint of GBM’s diverse longitudinal trajectories and highlights the treatment and TME modifiers that shape them.

Immune-based therapies have shown limited efficacy in glioma thus far. This might be at least in part due to insufficient numbers of neoantigens, thought to be targets of immune attack. In addition, we hypothesized that dynamic genetic and epigenetic tumor evolution in gliomas might also affect the mutation/neoantigen landscape and contribute to treatment resistance through immune evasion. Here, we investigated changes in the neoantigen landscape and immunologic features during glioma progression using exome and RNA-seq of paired primary and recurrent tumor samples obtained from 25 WHO grade II–IV glioma patients (glioblastoma, IDH-wild-type, n = 8; grade II–III astrocytoma, IDH-mutant, n = 9; and grade II–III oligodendroglioma, IDH-mutant, 1p/19q-codeleted, n = 8). The number of missense mutations, predicted neoantigens, or expressed neoantigens was not significantly different between primary and recurrent tumors. However, we found that in individual patients the ratio of expressed neoantigens to predicted neoantigens, designated the “neoantigen expression ratio,” decreased significantly at recurrence (P = 0.003). This phenomenon was particularly pronounced for “high-affinity,” “clonal,” and “passenger gene–derived” neoantigens. Gene expression and IHC analyses suggested that the decreased neoantigen expression ratio was associated with intact antigen presentation machinery, increased tumor-infiltrating immune cells, and ongoing immune responses. Our findings imply that decreased expression of highly immunogenic neoantigens, possibly due to persistent immune selection pressure, might be one of the immune evasion mechanisms along with tumor clonal evolution in some gliomas.

Recent studies have demonstrated that tumor-driving alterations are often different among gliomas that originated from different brain regions and have underscored the importance of analyzing molecular characteristics of gliomas stratified by brain region. Therefore, to elucidate molecular characteristics of diffuse cerebellar gliomas (DCGs), 27 adult, mostly glioblastoma cases were analyzed. Comprehensive analysis using whole-exome sequencing, RNA sequencing, and Infinium methylation array (n = 17) demonstrated their distinct molecular profile compared to gliomas in other brain regions. Frequent mutations in chromatin-modifier genes were identified including, noticeably, a truncating mutation in SETD2 (n = 4), which resulted in loss of H3K36 trimethylation and was mutually exclusive with H3F3A K27M mutation (n = 3), suggesting that epigenetic dysregulation may lead to DCG tumorigenesis. Alterations that cause loss of p53 function including TP53 mutation (n = 9), PPM1D mutation (n = 2), and a novel type of PPM1D fusion (n = 1), were also frequent. On the other hand, mutations and copy number changes commonly observed in cerebral gliomas were infrequent. DNA methylation profile analysis demonstrated that all DCGs except for those with H3F3A mutations were categorized in the “RTK I (PDGFRA)” group, and those DCGs had a gene expression signature that was highly associated with PDGFRA. Furthermore, compared with the data of 315 gliomas derived from different brain regions, promoter methylation of transcription factors genes associated with glial development showed a characteristic pattern presumably reflecting their tumor origin. Notably, SOX10, a key transcription factor associated with oligodendroglial differentiation and PDGFRA regulation, was up-regulated in both DCG and H3 K27M-mutant diffuse midline glioma, suggesting their developmental and biological commonality. In contrast, SOX10 was silenced by promoter methylation in most cerebral gliomas. These findings may suggest potential tailored targeted therapy for gliomas according to their brain region, in addition to providing molecular clues to identify the region-related cellular origin of DCGs.