Global absence and targeting of protective immune states in severe COVID-19.

Although infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has pleiotropic and systemic effects in some individuals1-3, many others experience milder symptoms. Here, to gain a more comprehensive understanding of the distinction between severe and mild phenotypes in the pathology of coronavirus disease 2019 (COVID-19) and its origins, we performed a whole-blood-preserving single-cell analysis protocol to integrate contributions from all major immune cell types of the blood-including neutrophils, monocytes, platelets, lymphocytes and the contents of the serum. Patients with mild COVID-19 exhibit a coordinated pattern of expression of interferon-stimulated genes (ISGs)3 across every cell population, whereas these ISG-expressing cells are systemically absent in patients with severe disease. Paradoxically, individuals with severe COVID-19 produce very high titres of anti-SARS-CoV-2 antibodies and have a lower viral load compared to individuals with mild disease. Examination of the serum from patients with severe COVID-19 shows that these patients uniquely produce antibodies that functionally block the production of the ISG-expressing cells associated with mild disease, by activating conserved signalling circuits that dampen cellular responses to interferons. Overzealous antibody responses pit the immune system against itself in many patients with COVID-19, and perhaps also in individuals with other viral infections. Our findings reveal potential targets for immunotherapies in patients with severe COVID-19 to re-engage viral defence.

Uncovering axes of variation among single-cell cancer specimens.

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed 'phenotypic earth mover's distance' (PhEMD). PhEMD is a general method for embedding a 'manifold of manifolds', in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.

Exploring single-cell data with deep multitasking neural networks.

It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that combines parallelization and scalability offered by neural networks, with the deep representation of data that can be learned by them to perform many single-cell data analysis tasks. Our regularizations (penalties) render features learned in hidden layers of the neural network interpretable. On large, multi-patient datasets, SAUCIE's various hidden layers contain denoised and batch-corrected data, a low-dimensional visualization and unsupervised clustering, as well as other information that can be used to explore the data. We analyze a 180-sample dataset consisting of 11 million T cells from dengue patients in India, measured with mass cytometry. SAUCIE can batch correct and identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue.

Genomic and clinical characterization of stromal infiltration markers in prostate cancer.

BACKGROUND: The progression of prostate cancer is a complex, multistep process that involves molecular alterations in cells of the tumor and the microenvironment, with associated interactions between the stroma and epithelium. Genomic expression analyses of stromal infiltration markers were performed to determine the significance thereof in prostate cancer. METHODS: Genome-wide expression profiles of formalin-fixed, paraffin-embedded radical prostatectomy samples were evaluated from a prospective registry cohort (n = 5239) and 3 retrospective institutional cohorts (n = 1135). Two independent stromal gene expression signatures implied stromal infiltration. Cox proportional hazards regression defined the association between stromal infiltration expression and metastasis-free survival (MFS). RESULTS: Stromal expression scores were correlated with stromal signature genes and with other key stromal markers (CAV1, VIM, and TAGLN), basal activity, and CD3 and CD4 immune biomarkers (r > 0.5 for all). The top decile of stromal expression was associated with high genomic risk scores (Decipher ≥ 0.6) , high Cancer of the Prostate Risk Assessment-Postsurgical scores, Gleason 9 to 10 disease, and a higher risk for metastasis (hazard ratio, 2.35; 95% CI, 1.37-4.02; P = .001). A higher stromal infiltration score was also associated with decreased expression of DNA repair genes and higher radiation sensitivity genomic scores. Postoperative radiation therapy (RT) was associated with an MFS benefit for patients with high stromal scores, but not for patients with low stromal scores (Pinteraction  = .02). CONCLUSIONS: Expression of stromal infiltration markers is correlated with prostate cancer aggressiveness/progression and may be predictive of a response to RT. Stromal infiltration markers should be studied and considered for incorporation into clinical prognostication and decision making.

Akt deficiency impairs normal cell proliferation and suppresses oncogenesis in a p53-independent and mTORC1-dependent manner.

Akt contributes to tumorigenesis by inhibiting apoptosis. Here we establish that Akt is required for normal cell proliferation and susceptibility to oncogenesis independently of its antiapoptotic activity. Partial ablation of Akt activity by deleting Akt1 inhibits cell proliferation and oncogenesis. These effects are compounded by deleting both Akt1 and Akt2. In vivo, Akt1 null mice are resistant to MMTV-v-H-Ras-induced tumors and to skin carcinogenesis. Thus, partial ablation of Akt activity is sufficient to suppress tumorigenesis in vitro and in vivo. The effect of Akt deficiency on cell proliferation and oncogenesis is p53 independent but mTORC1 dependent. Surprisingly, upon mTORC1 hyperactivation, the reduction in Akt activity does not impair cell proliferation and susceptibility to oncogenic transformation; thus, Akt may mediate these processes exclusively via mTORC1.

Akt1 regulates pathological angiogenesis, vascular maturation and permeability in vivo.

Akt kinases control essential cellular functions, including proliferation, apoptosis, metabolism and transcription, and have been proposed as promising targets for treatment of angiogenesis-dependent pathologies, such as cancer and ischemic injury. But their precise roles in neovascularization remain elusive. Here we show that Akt1 is the predominant isoform in vascular cells and describe the unexpected consequences of Akt1 knockout on vascular integrity and pathological angiogenesis. Angiogenic responses in three distinct in vivo models were enhanced in Akt1(-/-) mice; these enhanced responses were associated with impairment of blood vessel maturation and increased vascular permeability. Although impaired vascular maturation in Akt1(-/-) mice may be attributed to reduced activation of endothelial nitric oxide synthase (eNOS), the major phenotypic changes in vascular permeability and angiogenesis were linked to reduced expression of two endogenous vascular regulators, thrombospondins 1 (TSP-1) and 2 (TSP-2). Re-expression of TSP-1 and TSP-2 in mice transplanted with wild-type bone marrow corrected the angiogenic abnormalities in Akt1(-/-) mice. These findings establish a crucial role of an Akt-thrombospondin axis in angiogenesis.

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer.

Analysis of DNA methylation is a valuable tool to understand disease progression and is increasingly being used to create diagnostic and prognostic clinical biomarkers. While conversion of cytosine to 5-methylcytosine (5mC) commonly results in transcriptional repression, further conversion to 5-hydroxymethylcytosine (5hmC) is associated with transcriptional activation. Here we perform the first study integrating whole-genome 5hmC with DNA, 5mC, and transcriptome sequencing in clinical samples of benign, localized, and advanced prostate cancer. 5hmC is shown to mark activation of cancer drivers and downstream targets. Furthermore, 5hmC sequencing revealed profoundly altered cell states throughout the disease course, characterized by increased proliferation, oncogenic signaling, dedifferentiation, and lineage plasticity to neuroendocrine and gastrointestinal lineages. Finally, 5hmC sequencing of cell-free DNA from patients with metastatic disease proved useful as a prognostic biomarker able to identify an aggressive subtype of prostate cancer using the genes TOP2A and EZH2, previously only detectable by transcriptomic analysis of solid tumor biopsies. Overall, these findings reveal that 5hmC marks epigenomic activation in prostate cancer and identify hallmarks of prostate cancer progression with potential as biomarkers of aggressive disease. SIGNIFICANCE: In prostate cancer, 5-hydroxymethylcytosine delineates oncogene activation and stage-specific cell states and can be analyzed in liquid biopsies to detect cancer phenotypes. See related article by Wu and Attard, p. 3880.

An integrated functional and clinical genomics approach reveals genes driving aggressive metastatic prostate cancer.

Genomic sequencing of thousands of tumors has revealed many genes associated with specific types of cancer. Similarly, large scale CRISPR functional genomics efforts have mapped genes required for cancer cell proliferation or survival in hundreds of cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, there are likely a number of undiscovered tumor specific driver genes that may represent potential drug targets. To identify such genetic dependencies, we performed genome-scale CRISPRi screens in metastatic prostate cancer models. We then created a pipeline in which we integrated pan-cancer functional genomics data with our metastatic prostate cancer functional and clinical genomics data to identify genes that can drive aggressive prostate cancer phenotypes. Our integrative analysis of these data reveals known prostate cancer specific driver genes, such as AR and HOXB13, as well as a number of top hits that are poorly characterized. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two top hit genes, KIF4A and WDR62. We demonstrate that both KIF4A and WDR62 drive aggressive prostate cancer phenotypes in vitro and in vivo in multiple models, irrespective of AR-status, and are also associated with poor patient outcome.

Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures.

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.

Germline polymorphisms associated with impaired survival outcomes and somatic tumor alterations in advanced prostate cancer.

INTRODUCTION: Germline variants in androgen metabolism genes may influence clinical response to androgen deprivation therapy (ADT) in advanced prostate cancer. We sought to investigate the prognostic significance of germline variants in androgen metabolism genes with respect to overall survival (OS) after ADT, and to associate germline variants with tumor genomic features. METHODS: Germline and somatic whole-genome sequencing (WGS) data were evaluated in a cohort of 101 men with metastatic castration-resistant prostate cancer (mCRPC). Survival analyses were performed to identify polymorphisms associated with impaired OS after primary ADT. Germline variants found to be prognostic of OS were associated with tumor somatic DNA-sequence alterations based on WGS performed on paired metastasis biopsies from the same 101 patients. Gene set enrichment analysis was performed based on tumor RNA-sequencing data to identify genomic pathways differentially expressed in patients with germline variants. RESULTS: A comprehensive literature review identified 17 candidate polymorphisms in nine androgen metabolism genes that have been previously shown to have an association with response to ADT in prostate cancer. Of these, the variant rs1856888 allele located 13 kb upstream of HSD3B1 was found to be significantly associated with impaired OS (P = 0.029). Variant rs1856888 was commonly co-inherited with the well-characterized HSD3B1(1245A>C) polymorphism, and there was a trend toward shorter median OS in patients with HSD3B1(1245A>C) compared with homozygous wild-type patients (P = 0.052). While HSD3B1 germline variants were not associated with common somatic tumor DNA alterations, they were associated with increased tumor expression of cell proliferation and cell cycle genes. CONCLUSIONS: This study presents a comprehensive assessment of germline variants in androgen metabolism genes and highlights HSD3B1 polymorphisms as prognostic of OS after ADT and associated with an aggressive gene expression tumor profile in mCRPC.

Author Correction: Visualizing structure and transitions in high-dimensional biological data.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Down-regulation of ADRB2 expression is associated with small cell neuroendocrine prostate cancer and adverse clinical outcomes in castration-resistant prostate cancer.

OBJECTIVES: The net oncogenic effect of ß2-adrenergic receptor ADRB2, whose downstream elements induce neuroendocrine differentiation and whose expression is regulated by EZH2, is unclear. ADRB2 expression and associated clinical outcomes in metastatic castration-resistant prostate cancer (mCRPC) are unknown. METHODS AND MATERIALS: This was a retrospective analysis of a multi-center, prospectively enrolled cohort of mCRPC patients. Metastatic biopsies were obtained at progression, and specimens underwent laser capture microdissection and RNA-seq. ADRB2 expression was stratified by histology and clustering based on unsupervised hierarchical transcriptome analysis and correlated with EZH2 expression; an external dataset was used for validation. The association between ADRB2 expression and overall survival (OS) was assessed by log-rank test and a multivariable Cox proportional hazard model. RESULTS: One hundred and twenty-seven patients with progressive mCRPC had sufficient metastatic tumor for RNA-seq. ADRB2 expression was lowest in the small cell-enriched transcriptional cluster (P < 0.01) and correlated inversely with EZH2 expression (r = -0.28, P < 0.01). These findings were validated in an external cohort enriched for neuroendocrine differentiation. Patients with tumors harboring low ADRB2 expression (lowest quartile) had a shorter median OS than those with higher (9.5 vs. 20.5 months, P = 0.02). In multivariable analysis, low ADRB2 expression was associated with a trend toward shorter OS (HR for death = 1.54, 95%CI 0.98-2.44). Conversely, higher expression of upstream transcriptional regulator EZH2 was associated with shortened OS (HR for death = 3.01, 95%CI 1.12-8.09). CONCLUSIONS: Low ADRB2 expression is associated with neuroendocrine differentiation and is associated with shortened survival. EZH2 is a potential therapeutic target for preventing neuroendocrine transdifferentiation and improving outcomes in mCRPC. Further studies of agents targeting ß-adrenergic signaling are warranted.

Autoantibody Landscape in Patients with Advanced Prostate Cancer.

PURPOSE: Autoantibody responses in cancer are of great interest, as they may be concordant with T-cell responses to cancer antigens or predictive of response to cancer immunotherapies. Thus, we sought to characterize the antibody landscape of metastatic castration-resistant prostate cancer (mCRPC). EXPERIMENTAL DESIGN: Serum antibody epitope repertoire analysis (SERA) was performed on patient serum to identify tumor-specific neoepitopes. Somatic mutation-specific neoepitopes were investigated by associating serum epitope enrichment scores with whole-genome sequencing results from paired solid tumor metastasis biopsies and germline blood samples. A protein-based immunome-wide association study (PIWAS) was performed to identify significantly enriched epitopes, and candidate serum antibodies enriched in select patients were validated by ELISA profiling. A distinct cohort of patients with melanoma was evaluated to validate the top cancer-specific epitopes. RESULTS: SERA was performed on 1,229 serum samples obtained from 72 men with mCRPC and 1,157 healthy control patients. Twenty-nine of 6,636 somatic mutations (0.44%) were associated with an antibody response specific to the mutated peptide. PIWAS analyses identified motifs in 11 proteins, including NY-ESO-1 and HERVK-113, as immunogenic in mCRPC, and ELISA confirmed serum antibody enrichment in candidate patients. Confirmatory PIWAS, Identifying Motifs Using Next-generation sequencing Experiments (IMUNE), and ELISA analyses performed on serum samples from 106 patients with melanoma similarly revealed enriched cancer-specific antibody responses to NY-ESO-1. CONCLUSIONS: We present the first large-scale profiling of autoantibodies in advanced prostate cancer, utilizing a new antibody profiling approach to reveal novel cancer-specific antigens and epitopes. Our study recovers antigens of known importance and identifies novel tumor-specific epitopes of translational interest.

Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: The purpose of this study was to measure genomic changes that emerge with enzalutamide treatment using analyses of whole-genome sequencing and RNA sequencing. EXPERIMENTAL DESIGN: One hundred and one tumors from men with metastatic castration-resistant prostate cancer (mCRPC) who had not been treated with enzalutamide (n = 64) or who had enzalutamide-resistant mCRPC (n = 37) underwent whole genome sequencing. Ninety-nine of these tumors also underwent RNA sequencing. We analyzed the genomes and transcriptomes of these mCRPC tumors. RESULTS: Copy number loss was more common than gain in enzalutamide-resistant tumors. Specially, we identified 124 protein-coding genes that were more commonly lost in enzalutamide-resistant samples. These 124 genes included eight putative tumor suppressors located at nine distinct genomic regions. We demonstrated that focal deletion of the 17q22 locus that includes RNF43 and SRSF1 was not present in any patient with enzalutamide-naïve mCRPC but was present in 16% (6/37) of patients with enzalutamide-resistant mCRPC. 17q22 loss was associated with lower RNF43 and SRSF1 expression and poor overall survival from time of biopsy [median overall survival of 19.3 months in 17q22 intact vs. 8.9 months in 17q22 loss, HR, 3.44 95% confidence interval (CI), 1.338-8.867, log-rank P = 0.006]. Finally, 17q22 loss was linked with activation of several targetable factors, including CDK1/2, Akt, and PLK1, demonstrating the potential therapeutic relevance of 17q22 loss in mCRPC. CONCLUSIONS: Copy number loss is common in enzalutamide-resistant tumors. Focal deletion of chromosome 17q22 defines a previously unappreciated molecular subset of enzalutamide-resistant mCRPC associated with poor clinical outcome.

Global Absence and Targeting of Protective Immune States in Severe COVID-19.

While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a wholeblood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferonstimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and autodirected antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense.

Global Absence and Targeting of Protective Immune States in Severe COVID-19.

While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a whole-blood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferon-stimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and auto-directed antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense. ONE SENTENCE SUMMARY: In severe COVID-19 patients, the immune system fails to generate cells that define mild disease; antibodies in their serum actively prevents the successful production of those cells.

The DNA methylation landscape of advanced prostate cancer.

Although DNA methylation is a key regulator of gene expression, the comprehensive methylation landscape of metastatic cancer has never been defined. Through whole-genome bisulfite sequencing paired with deep whole-genome and transcriptome sequencing of 100 castration-resistant prostate metastases, we discovered alterations affecting driver genes that were detectable only with integrated whole-genome approaches. Notably, we observed that 22% of tumors exhibited a novel epigenomic subtype associated with hypermethylation and somatic mutations in TET2, DNMT3B, IDH1 and BRAF. We also identified intergenic regions where methylation is associated with RNA expression of the oncogenic driver genes AR, MYC and ERG. Finally, we showed that differential methylation during progression preferentially occurs at somatic mutational hotspots and putative regulatory regions. This study is a large integrated study of whole-genome, whole-methylome and whole-transcriptome sequencing in metastatic cancer that provides a comprehensive overview of the important regulatory role of methylation in metastatic castration-resistant prostate cancer.

Large-scale assessment of needs in low vision individuals using the Aira assistive technology.

PURPOSE: To systematically evaluate the needs of low vision individuals through call data obtained through the Aira assistive technology system. PATIENTS AND METHODS: Aira (Aira Tech Corporation, La Jolla, CA, USA) is an on-demand assistive wearable technology designed for individuals with low vision. The user wears glasses with an integrated front-facing video camera that connects with a remote human agent who assists the user with the specified task. Call types, temporal characteristics, and duration of call were compared by gender and vision status (low vision, light perception, and blind). Chi-square tests, t-tests, ANOVA, linear regression and Poisson regression analyses were performed. RESULTS: 878 subscribers placed 10,022 total calls (4759 female, 5263 male) over 3 months. The most common categories were reading (35%), navigation (33%), and home management (16%). The distribution of categories (χ2=49.3, p<0.001), duration (t=-7.59, p<0.0001) and time of call (χ2=37.4, p<0.001) differed by gender. The distribution of categories (χ2=61, p<0.001), duration (F=13.7, p<0.0001), and time of call (χ2=36.9, p<0.001) differed by vision status. Blind [adjusted IRR=1.68 (95% CI: 1.56-1.79)] and light perception users [adjusted IRR=1.43 (95% CI: 1.32-1.53)] had increased usage compared to low vision users. Women had higher usage than men [adjusted IRR=1.09 (95% CI: 1.04-1.13)]. CONCLUSION: To our knowledge, this is the first large-scale needs assessment of 878 low vision individuals over 10,022 calls. The most common categories were reading, navigation, and home management. Distribution of call types, duration, and time of call differed significantly by gender and vision status. Blind and light perception users had higher usage rates than those with low vision. Women had higher usage rates than men. This large-scale needs analysis of low vision individuals provides insight into utilization patterns across varying levels of vision loss and gender, which will guide future evolutions of assistive technology by tailoring future hardware and software upgrades.