An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma.

Diverse genetic, epigenetic, and developmental programs drive glioblastoma, an incurable and poorly understood tumor, but their precise characterization remains challenging. Here, we use an integrative approach spanning single-cell RNA-sequencing of 28 tumors, bulk genetic and expression analysis of 401 specimens from the The Cancer Genome Atlas (TCGA), functional approaches, and single-cell lineage tracing to derive a unified model of cellular states and genetic diversity in glioblastoma. We find that malignant cells in glioblastoma exist in four main cellular states that recapitulate distinct neural cell types, are influenced by the tumor microenvironment, and exhibit plasticity. The relative frequency of cells in each state varies between glioblastoma samples and is influenced by copy number amplifications of the CDK4, EGFR, and PDGFRA loci and by mutations in the NF1 locus, which each favor a defined state. Our work provides a blueprint for glioblastoma, integrating the malignant cell programs, their plasticity, and their modulation by genetic drivers.

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.

Hair Cell Mechanotransduction Regulates Spontaneous Activity and Spiral Ganglion Subtype Specification in the Auditory System.

Type I spiral ganglion neurons (SGNs) transmit sound information from cochlear hair cells to the CNS. Using transcriptome analysis of thousands of single neurons, we demonstrate that murine type I SGNs consist of subclasses that are defined by the expression of subsets of transcription factors, cell adhesion molecules, ion channels, and neurotransmitter receptors. Subtype specification is initiated prior to the onset of hearing during the time period when auditory circuits mature. Gene mutations linked to deafness that disrupt hair cell mechanotransduction or glutamatergic signaling perturb the firing behavior of SGNs prior to hearing onset and disrupt SGN subtype specification. We thus conclude that an intact hair cell mechanotransduction machinery is critical during the pre-hearing period to regulate the firing behavior of SGNs and their segregation into subtypes. Because deafness is frequently caused by defects in hair cells, our findings have significant ramifications for the etiology of hearing loss and its treatment.

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain.

The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.

Sensory Neuron Diversity in the Inner Ear Is Shaped by Activity.

In the auditory system, type I spiral ganglion neurons (SGNs) convey complex acoustic information from inner hair cells (IHCs) to the brainstem. Although SGNs exhibit variation in physiological and anatomical properties, it is unclear which features are endogenous and which reflect input from synaptic partners. Using single-cell RNA sequencing, we derived a molecular classification of mouse type I SGNs comprising three subtypes that express unique combinations of Ca2+ binding proteins, ion channel regulators, guidance molecules, and transcription factors. Based on connectivity and susceptibility to age-related loss, these subtypes correspond to those defined physiologically. Additional intrinsic differences among subtypes and across the tonotopic axis highlight an unexpectedly active role for SGNs in auditory processing. SGN identities emerge postnatally and are disrupted in a mouse model of deafness that lacks IHC-driven activity. These results elucidate the range, nature, and origins of SGN diversity, with implications for treatment of congenital deafness.

Germline genetic contribution to the immune landscape of cancer.

Understanding the contribution of the host's genetic background to cancer immunity may lead to improved stratification for immunotherapy and to the identification of novel therapeutic targets. We investigated the effect of common and rare germline variants on 139 well-defined immune traits in ∼9000 cancer patients enrolled in TCGA. High heritability was observed for estimates of NK cell and T cell subset infiltration and for interferon signaling. Common variants of IFIH1, TMEM173 (STING1), and TMEM108 were associated with differential interferon signaling and variants mapping to RBL1 correlated with T cell subset abundance. Pathogenic or likely pathogenic variants in BRCA1 and in genes involved in telomere stabilization and Wnt-ß-catenin also acted as immune modulators. Our findings provide evidence for the impact of germline genetics on the composition and functional orientation of the tumor immune microenvironment. The curated datasets, variants, and genes identified provide a resource toward further understanding of tumor-immune interactions.

Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions.

Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1, and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative-intent lung resection, and these individuals require adjuvant platinum-etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum-etoposide chemotherapy. Patients with metastatic (extensive-stage) disease are treated with a combination of platinum-etoposide chemotherapy plus immunotherapy with an anti-programmed death-ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum-based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development.

Structural insights into assembly and function of GluN1-2C, GluN1-2A-2C, and GluN1-2D NMDARs.

Neurotransmission mediated by diverse subtypes of N-methyl-D-aspartate receptors (NMDARs) is fundamental for basic brain functions and development as well as neuropsychiatric diseases and disorders. NMDARs are glycine- and glutamate-gated ion channels that exist as heterotetramers composed of obligatory GluN1 and GluN2(A-D) and/or GluN3(A-B). The GluN2C and GluN2D subunits form ion channels with distinct properties and spatio-temporal expression patterns. Here, we provide the structures of the agonist-bound human GluN1-2C NMDAR in the presence and absence of the GluN2C-selective positive allosteric potentiator (PAM), PYD-106, the agonist-bound GluN1-2A-2C tri-heteromeric NMDAR, and agonist-bound GluN1-2D NMDARs by single-particle electron cryomicroscopy. Our analysis shows unique inter-subunit and domain arrangements of the GluN2C NMDARs, which contribute to functional regulation and formation of the PAM binding pocket and is distinct from GluN2D NMDARs. Our findings here provide the fundamental blueprint to study GluN2C- and GluN2D-containing NMDARs, which are uniquely involved in neuropsychiatric disorders.

Current treatment and recent progress in gastric cancer.

Gastric cancer is not a top-10 malignancy in the United States but represents one of the most common causes of cancer death worldwide. Biological differences between tumors from Eastern and Western countries add to the complexity of identifying standard-of-care therapy based on international trials. Systemic chemotherapy, radiotherapy, surgery, immunotherapy, and targeted therapy all have proven efficacy in gastric adenocarcinoma; therefore, multidisciplinary treatment is paramount to treatment selection. Triplet chemotherapy for resectable gastric cancer is now accepted and could represent a plateau of standard cytotoxic chemotherapy for localized disease. Classification of gastric cancer based on molecular subtypes is providing an opportunity for personalized therapy. Biomarkers, in particular microsatellite instability (MSI), programmed cell death ligand 1 (PD-L1), human epidermal growth factor receptor 2 (HER2), tumor mutation burden, and Epstein-Barr virus, are increasingly driving systemic therapy approaches and allowing for the identification of populations most likely to benefit from immunotherapy and targeted therapy. Significant research opportunities remain for the less differentiated histologic subtypes of gastric adenocarcinoma and those without markers of immunotherapy activity.

Transcriptome- and proteome-wide association studies identify genes associated with renal cell carcinoma.

We performed a series of integrative analyses including transcriptome-wide association studies (TWASs) and proteome-wide association studies (PWASs) of renal cell carcinoma (RCC) to nominate and prioritize molecular targets for laboratory investigation. On the basis of a genome-wide association study (GWAS) of 29,020 affected individuals and 835,670 control individuals and prediction models trained in transcriptomic reference models, our TWAS across four kidney transcriptomes (GTEx kidney cortex, kidney tubules, TCGA-KIRC [The Cancer Genome Atlas kidney renal clear-cell carcinoma], and TCGA-KIRP [TCGA kidney renal papillary cell carcinoma]) identified 38 gene associations (false-discovery rate <5%) in at least two of four transcriptomic panels and identified 12 genes that were independent of GWAS susceptibility regions. Analyses combining TWAS associations across 48 tissues from GTEx identified associations that were replicable in tumor transcriptomes for 23 additional genes. Analyses by the two major histologic types (clear-cell RCC and papillary RCC) revealed subtype-specific associations, although at least three gene associations were common to both subtypes. PWAS identified 13 associated proteins, all mapping to GWAS-significant loci. TWAS-identified genes were enriched for active enhancer or promoter regions in RCC tumors and hypoxia-inducible factor binding sites in relevant cell lines. Using gene expression correlation, common cancers (breast and prostate) and RCC risk factors (e.g., hypertension and BMI) display genetic contributions shared with RCC. Our work identifies potential molecular targets for RCC susceptibility for downstream functional investigation.

The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

Transcription factor expression repertoire basis for epigenetic and transcriptional subtypes of colorectal cancers.

Colorectal cancers (CRCs) form a heterogenous group classified into epigenetic and transcriptional subtypes. The basis for the epigenetic subtypes, exemplified by varying degrees of promoter DNA hypermethylation, and its relation to the transcriptional subtypes is not well understood. We link cancer-specific transcription factor (TF) expression alterations to methylation alterations near TF-binding sites at promoter and enhancer regions in CRCs and their premalignant precursor lesions to provide mechanistic insights into the origins and evolution of the CRC molecular subtypes. A gradient of TF expression changes forms a basis for the subtypes of abnormal DNA methylation, termed CpG-island promoter DNA methylation phenotypes (CIMPs), in CRCs and other cancers. CIMP is tightly correlated with cancer-specific hypermethylation at enhancers, which we term CpG-enhancer methylation phenotype (CEMP). Coordinated promoter and enhancer methylation appears to be driven by downregulation of TFs with common binding sites at the hypermethylated enhancers and promoters. The altered expression of TFs related to hypermethylator subtypes occurs early during CRC development, detectable in premalignant adenomas. TF-based profiling further identifies patients with worse overall survival. Importantly, altered expression of these TFs discriminates the transcriptome-based consensus molecular subtypes (CMS), thus providing a common basis for CIMP and CMS subtypes.

Coexistence within one cell of microvillous and ciliary phototransductions across M1- through M6-IpRGCs.

Intrinsically photosensitive retinal ganglion cells (ipRGCs) serve as primary photoceptors by expressing the photopigment, melanopsin, and also as retinal relay neurons for rod and cone signals en route to the brain, in both cases for the purpose of non-image vision as well as aspects of image vision. So far, six subtypes of ipRGCs (M1 through M6) have been characterized. Regarding their phototransduction mechanisms, we have previously found that, unconventionally, rhabdomeric (microvillous) and ciliary signaling motifs co-exist within a given M1-, M2-, and M4-ipRGC, with the first mechanism involving PLCß4 and TRPC6,7 channels and the second involving cAMP and HCN channels. We have now examined M3-, M5-, and M6-cells and found that each cell likewise uses both signaling pathways for phototransduction, despite differences in the percentage representation by each pathway in a given ipRGC subtype for bright-flash responses (and saturated except for M6-cells). Generally, M3- and M5-cells show responses quite similar in kinetics to M2-responses, and M6-cell responses resemble broadly those of M1-cells although much lower in absolute sensitivity and amplitude. Therefore, similar to rod and cone subtypes in image vision, ipRGC subtypes possess the same phototransduction mechanism(s) even though they do not show microvilli or cilia morphologically.

Optogenetic Determination of Dynamic and Cell-Type-Specific Inhibitory Reversal Potentials.

The reversal potential refers to the membrane potential at which the net current flow through a channel reverses direction. The reversal potential is determined by transmembrane ion gradients and, in turn, determines how the channel's activity will affect the membrane potential. Traditional investigation into the reversal potential of inhibitory ligand-gated ion channels (EInh) has relied upon the activation of endogenous receptors, such as the GABA-A receptor (GABAAR). There are, however, challenges associated with activating endogenous receptors, including agonist delivery, isolating channel responses, and the effects of receptor saturation and desensitization. Here, we demonstrate the utility of using a light-gated anion channel, stGtACR2, to probe EInh in the rodent brain. Using mice of both sexes, we demonstrate that the properties of this optically activated channel make it a suitable proxy for studying GABAAR receptor-mediated inhibition. We validate this agonist-independent optogenetic strategy in vitro and in vivo and further show how it can accurately capture differences in EInh dynamics following manipulations of endogenous ion fluxes. This allows us to explore distinct resting EInh differences across genetically defined neuronal subpopulations. Using this approach to challenge ion homeostasis mechanisms in neurons, we uncover cell-specific EInh dynamics that are supported by the differential expression of endogenous ion handling mechanisms. Our findings therefore establish an effective optical strategy for revealing novel aspects of inhibitory reversal potentials and thereby expand the repertoire of optogenetics.

Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes.

Family-based sequencing studies are increasingly used to find rare genetic variants of high risk for disease traits with familial clustering. In some studies, families with multiple disease subtypes are collected and the exomes of affected relatives are sequenced for shared rare variants (RVs). Since different families can harbor different causal variants and each family harbors many RVs, tests to detect causal variants can have low power in this study design. Our goal is rather to prioritize shared variants for further investigation by, for example, pathway analyses or functional studies. The transmission-disequilibrium test prioritizes variants based on departures from Mendelian transmission in parent-child trios. Extending this idea to families, we propose methods to prioritize RVs shared in affected relatives with two disease subtypes, with one subtype more heritable than the other. Global approaches condition on a variant being observed in the study and assume a known probability of carrying a causal variant. In contrast, local approaches condition on a variant being observed in specific families to eliminate the carrier probability. Our simulation results indicate that global approaches are robust to misspecification of the carrier probability and prioritize more effectively than local approaches even when the carrier probability is misspecified.

Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives.

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1987 ovarian cancer patients spanning 26 independent Gene Expression Omnibus cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust ten-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

Exon-Skipping-Based Subtyping of Colorectal Cancers.

BACKGROUND & AIMS: The identification of colorectal cancer (CRC) molecular subtypes has prognostic and potentially diagnostic value for patients, yet reliable subtyping remains unavailable in the clinic. The current consensus molecular subtype (CMS) classification in CRCs is based on complex RNA expression patterns quantified at the gene level. The clinical application of these methods, however, is challenging due to high uncertainty of single-sample classification and associated costs. Alternative splicing, which strongly contributes to transcriptome diversity, has rarely been used for tissue type classification. Here, we present an AS-based CRC subtyping framework sensitive to differential exon use that can be adapted for clinical application. METHODS: Unsupervised clustering was used to measure the strength of association between different categories of alternative splicing and CMS. To build a classifier, the ground truth for CMS labels was derived from expression data quantified at the gene level. Feature selection was achieved through bootstrapping and L1-penalized estimation. The resulting feature space was used to construct a subtype prediction framework applicable to single and multiple samples. The performance of the models was evaluated on unseen CRCs from 2 independent sources (Indivumed, n = 129; The Cancer Genome Atlas, n = 99). RESULTS: We developed a CRC subtype identifier based on 29 exon-skipping events that accurately classifies unseen tumors and enables more precise differentiation of subtypes characterized by distinct biological and prognostic features as compared to classifiers based on gene expression. CONCLUSIONS: Here, we demonstrate that a small number of exon-skipping events can reliably classify CRC subtypes using individual patient specimens in a manner suitable to clinical application.

Proteomic Characterization Identifies Clinically Relevant Subgroups of Gastrointestinal Stromal Tumors.

BACKGROUND & AIMS: Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the gastrointestinal tract, and it has high metastatic and recurrence rates. We aimed to characterize the proteomic features of GIST to understand biological processes and treatment vulnerabilities. METHODS: Quantitative proteomics and phosphoproteomics analyses were performed on 193 patients with GIST to reveal the biological characteristics of GIST. Data-driven hypotheses were tested by performing functional experiments using both GIST cell lines and xenograft mouse models. RESULTS: Proteomic analysis revealed differences in the molecular features of GISTs from different locations or with different histological grades. MAPK7 was identified and functionally proved to be associated with tumor cell proliferation in GIST. Integrative analysis revealed that increased SQSTM1 expression inhibited the patient response to imatinib mesylate. Proteomics subtyping identified 4 clusters of tumors with different clinical and molecular attributes. Functional experiments confirmed the role of SRSF3 in promoting tumor cell proliferation and leading to poor prognosis. CONCLUSIONS: Our study provides a valuable data resource and highlights potential therapeutic approaches for GIST.

Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping.

Driven by multi-omics data, some multi-view clustering algorithms have been successfully applied to cancer subtypes prediction, aiming to identify subtypes with biometric differences in the same cancer, thereby improving the clinical prognosis of patients and designing personalized treatment plan. Due to the fact that the number of patients in omics data is much smaller than the number of genes, multi-view spectral clustering based on similarity learning has been widely developed. However, these algorithms still suffer some problems, such as over-reliance on the quality of pre-defined similarity matrices for clustering results, inability to reasonably handle noise and redundant information in high-dimensional omics data, ignoring complementary information between omics data, etc. This paper proposes multi-view spectral clustering with latent representation learning (MSCLRL) method to alleviate the above problems. First, MSCLRL generates a corresponding low-dimensional latent representation for each omics data, which can effectively retain the unique information of each omics and improve the robustness and accuracy of the similarity matrix. Second, the obtained latent representations are assigned appropriate weights by MSCLRL, and global similarity learning is performed to generate an integrated similarity matrix. Third, the integrated similarity matrix is used to feed back and update the low-dimensional representation of each omics. Finally, the final integrated similarity matrix is used for clustering. In 10 benchmark multi-omics datasets and 2 separate cancer case studies, the experiments confirmed that the proposed method obtained statistically and biologically meaningful cancer subtypes.