Search

Cancers assume a variety of distinct histologies and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision making based on consensus guidelines such as NCCN is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings—in addition to ambiguous clinical presentations such as recurrence versus new primary—a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for CUP patients, with a median survival of 8-11 months. Here we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-seq-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. We show that the Tempus TO model is 91% accurate when assessed on retrospectively and prospectively held out cohorts of containing 9,210 samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.

Supplementary Figures 1-9. Suppl. Fig. 1 contains quantification of Western blots. Suppl. Fig. 2 demonstrates the effect of PI3K/mTOR/Akt inhibitors on regulation of endogenous genes by Dex in CEM and Granta cells. Suppl. Fig. 3 shows cytotoxic effects of LY294002, Wortmannin and AZD8055 on CEM and Granta cells. Suppl. Fig. 4 demonstrates cytotoxic effects of LY294002, Wortmannin and AZD8055 on CEM cells, Granta cells and normal human monocytes. Suppl. Fig. 5 shows anti-lymphoma effect of Rapamycin and Dex in CEM and Granta cells. Suppl. Fig. 6 demonstrates the effect of LY294002, Rapamycin and Dex on animal body weight in xenograft study. Suppl. Fig. 7 shows anti-tumor effect of Dex, Rapa, LY294002 on Granta xenografts. Suppl. Fig. 8 demonstrates the effect of LY294002, Rapamycin and Dex on animal body weight in Dexamethasone-induced osteoporosis study. Suppl. Fig. 9 shows the data on Q-PCR analysis of Col1a1 and Col2a1 mRNA expression in bone tissue.

Glucocorticoids are widely used for therapy of hematologic malignancies. Unfortunately, chronic treatment with glucocorticoids commonly leads to adverse effects including skin and muscle atrophy and osteoporosis. We found recently that REDD1 (regulated in development and DNA damage 1) plays central role in steroid atrophy. Here, we tested whether REDD1 suppression makes glucocorticoid-based therapy of blood cancer safer. Unexpectedly, approximately 50% of top putative REDD1 inhibitors selected by bioinformatics screening of Library of Integrated Network-Based Cellular Signatures database (LINCS) were PI3K/Akt/mTOR inhibitors. We selected Wortmannin, LY294002, and AZD8055 for our studies and showed that they blocked basal and glucocorticoid-induced REDD1 expression. Moreover, all PI3K/mTOR/Akt inhibitors modified glucocorticoid receptor function shifting it toward therapeutically important transrepression. PI3K/Akt/mTOR inhibitors enhanced anti-lymphoma effects of Dexamethasone in vitro and in vivo, in lymphoma xenograft model. The therapeutic effects of PI3K inhibitor+Dexamethasone combinations ranged from cooperative to synergistic, especially in case of LY294002 and Rapamycin, used as a previously characterized reference REDD1 inhibitor. We found that coadministration of LY294002 or Rapamycin with Dexamethasone protected skin against Dexamethasone-induced atrophy, and normalized RANKL/OPG ratio indicating a reduction of Dexamethasone-induced osteoporosis. Together, our results provide foundation for further development of safer and more effective glucocorticoid-based combination therapy of hematologic malignancies using PI3K/Akt/mTOR inhibitors.

Small cell lung cancer (SCLC) is an aggressive neuroendocrine subtype of lung cancer with high mortality. We used a systematic drug repositioning bioinformatics approach querying a large compendium of gene expression profiles to identify candidate U.S. Food and Drug Administration (FDA)–approved drugs to treat SCLC. We found that tricyclic antidepressants and related molecules potently induce apoptosis in both chemonaïve and chemoresistant SCLC cells in culture, in mouse and human SCLC tumors transplanted into immunocompromised mice, and in endogenous tumors from a mouse model for human SCLC. The candidate drugs activate stress pathways and induce cell death in SCLC cells, at least in part by disrupting autocrine survival signals involving neurotransmitters and their G protein–coupled receptors. The candidate drugs inhibit the growth of other neuroendocrine tumors, including pancreatic neuroendocrine tumors and Merkel cell carcinoma. These experiments identify novel targeted strategies that can be rapidly evaluated in patients with neuroendocrine tumors through the repurposing of approved drugs.Significance: Our work shows the power of bioinformatics-based drug approaches to rapidly repurpose FDA-approved drugs and identifies a novel class of molecules to treat patients with SCLC, a cancer for which no effective novel systemic treatments have been identified in several decades. In addition, our experiments highlight the importance of novel autocrine mechanisms in promoting the growth of neuroendocrine tumor cells. Cancer Discov; 3(12); 1364–77. ©2013 AACR.See related commentary by Wang and Byers, p. 1333This article is highlighted in the In This Issue feature, p. 1317

PDF file 980K, Expression levels of the ligands of the GPCRs targeted by the TCAs and the enzymes required for their biosynthesis in SCLC cells

PDF file 94K, Top 100 FDA-approved drugs identified by a bioinformatics drug- repositioning approach

PDF file 779K, Induction of cell death in SCLC cells after treatment with related TCAs and with specific GPCR inhibitors

PDF file 745K, Expression levels of the GPCRs that are the targets of tricyclic antidepressants

PDF file 2897K, Molecular mechanisms leading to the cell death induced by the drugs

Supplementary Tables 1-3. Suppl. Table 1 contains the list of REDD1 inhibitors identified by computational screen and selected for study. Suppl. Table 2 contains primer sets for Q-PCR analysis. Suppl. Table 3 contains IC50 values of WM, LY294002 and AZD8055 after 24 h of incubation.

PDF file 2159K, Inhibitory effects of imipramine, promethazine, and bepridil on SCLC cells in culture

Supplementary Figure S2 - PDF file 1676K, Induction of cell death and decreased proliferation following imipramine and promethazine treatment

PDF file 114K, This document provides detailed information on some of the methods employed in our manuscript

Purpose:Somatic mutations in cancer cells can give rise to novel protein sequences that can be presented by antigen-presenting cells as neoantigens to the host immune system. Tumor neoantigens represent excellent targets for immunotherapy, due to their specific expression in cancer tissue. Despite the widespread use of immunomodulatory drugs and immunotherapies that recharge T and NK cells, there has been no direct evidence that neoantigen-specific T-cell responses are elicited in multiple myeloma.Experimental Design:Using next-generation sequencing data we describe the landscape of neo-antigens in 184 patients with multiple myeloma and successfully validate neoantigen-specific T cells in patients with multiple myeloma and support the feasibility of neoantigen-based therapeutic vaccines for use in cancers with intermediate mutational loads such as multiple myeloma.Results:In this study, we demonstrate an increase in neoantigen load in relapsed patients with multiple myeloma as compared with newly diagnosed patients with multiple myeloma. Moreover, we identify shared neoantigens across multiple patients in three multiple myeloma oncogenic driver genes (KRAS, NRAS, and IRF4). Next, we validate neoantigen T-cell response and clonal expansion in correlation with clinical response in relapsed patients with multiple myeloma. This is the first study to experimentally validate the immunogenicity of predicted neoantigens from next-generation sequencing in relapsed patients with multiple myeloma.Conclusions:Our findings demonstrate that somatic mutations in multiple myeloma can be immunogenic and induce neoantigen-specific T-cell activation that is associated with antitumor activity in vitro and clinical response in vivo. Our results provide the foundation for using neoantigen targeting strategies such as peptide vaccines in future trials for patients with multiple myeloma.

Supplementary Figures and Tables

While a range of methods for stool collection exist, many require complicated, self-directed protocols and stool transfer. In this study, we introduce and validate a novel, wipe-based approach to fecal sample collection and stabilization for metagenomics analysis. A total of 72 samples were collected across four different preservation types: freezing at -20°C, room temperature storage, a commercial DNA preservation kit, and a dissolvable wipe used with DESS (dimethyl sulfoxide, ethylenediaminetetraacetic acid, sodium chloride) solution. These samples were sequenced and analyzed for taxonomic abundance metrics, bacterial metabolic pathway classification, and diversity analysis. Overall, the DESS wipe results validated the use of a wipe-based capture method to collect stool samples for microbiome analysis, showing an R2 of 0.96 for species across all kingdoms, as well as exhibiting a maintenance of Shannon diversity (3.1-3.3) and species richness (151-159) compared to frozen samples. Moreover, DESS showed comparable performance to the commercially available preservation kit (R2 of 0.98), and samples consistently clustered by subject across each method. These data support that the DESS wipe method can be used for stable, room temperature collection and transport of human stool specimens.

Background Differences in autonomic nervous system function, measured by heart rate variability (HRV), have been observed between patients with inflammatory bowel disease and healthy control patients and have been associated in cross-sectional studies with systemic inflammation. High HRV has been associated with low stress. Methods Patients with ulcerative colitis (UC) were followed for 9 months. Their HRV was measured every 4 weeks using the VitalPatch, and blood was collected at baseline and every 12 weeks assessing cortisol, adrenocorticotropin hormone, interleukin-1β, interleukin-6, tumor necrosis factor-α, and C-reactive protein (CRP). Stool was collected at enrollment and every 6 weeks for fecal calprotectin. Surveys assessing symptoms, stress, resilience, quality of life, anxiety, and depression were longitudinally collected. Results Longitudinally evaluated perceived stress was significantly associated with systemic inflammation (CRP, P = 0.03) and UC symptoms (P = 0.02). There was a significant association between HRV and stress (low-frequency to high-frequency power [LFHF], P = 0.04; root mean square of successive differences [RMSSD], P = 0.04). The HRV was associated with UC symptoms (LFHF, P = 0.03), CRP (high frequency, P < 0.001; low frequency, P < 0.001; RMSSD, P < 0.001), and fecal calprotectin (high frequency, P < 0.001; low frequency, P < 0.001; RMSSD, P < 0.001; LFHF, P < 0.001). Significant changes in HRV indices from baseline developed before the identification of a symptomatic or inflammatory flare (P < 0.001). Conclusions Longitudinally evaluated HRV was associated with UC symptoms, inflammation, and perceived and physiological measures of stress. Significant changes in HRV were observed before the development of symptomatic or inflammatory flare.

MicroRNAs are recognized as important regulators of many facets of physiological brain function while also being implicated in the pathogenesis of several neurological disorders. Dysregulation of miR155 is widely reported across a variety of neurodegenerative conditions, including Alzheimer’s disease (AD), Parkinson’s disease, amyotrophic lateral sclerosis, and traumatic brain injury. In previous work, we observed that experimentally validated miR155 gene targets were consistently enriched among genes identified as differentially expressed across multiple brain tissue and disease contexts. In particular, we found that human herpesvirus-6A (HHV-6A) suppressed miR155, recapitulating reports of miR155 inhibition by HHV-6A in infected T-cells, thyrocytes, and natural killer cells. In earlier studies, we also reported the effects of constitutive deletion of miR155 on accelerating the accumulation of Aß deposits in 4-month-old APP/PSEN1 mice. Herein, we complete the cumulative characterization of transcriptomic, electrophysiological, neuropathological, and learning behavior profiles from 4-, 8- and 10-month-old WT and APP/PSEN1 mice in the absence or presence of miR155. We also integrated human post-mortem brain RNA-sequences from four independent AD consortium studies, together comprising 928 samples collected from six brain regions. We report that gene expression perturbations associated with miR155 deletion in mouse cortex are in aggregate observed to be concordant with AD-associated changes across these independent human late-onset AD (LOAD) data sets, supporting the relevance of our findings to human disease. LOAD has recently been formulated as the clinicopathological manifestation of a multiplex of genetic underpinnings and pathophysiological mechanisms. Our accumulated data are consistent with such a formulation, indicating that miR155 may be uniquely positioned at the intersection of at least four components of this LOAD “multiplex”: (i) innate immune response pathways; (ii) viral response gene networks; (iii) synaptic pathology; and (iv) proamyloidogenic pathways involving the amyloid β peptide (Aß).

The tens of thousands of industrial and synthetic chemicals released into the environment have an unknown but potentially significant capacity to interfere with neurodevelopment. Consequently, there is an urgent need for systematic approaches that can identify disruptive chemicals. Little is known about the impact of environmental chemicals on critical periods of developmental neuroplasticity, in large part, due to the challenge of screening thousands of chemicals. Using an integrative bioinformatics approach, we systematically scanned 2001 environmental chemicals and identified 50 chemicals that consistently dysregulate two transcriptional signatures of critical period plasticity. These chemicals included pesticides (e.g., pyridaben), antimicrobials (e.g., bacitracin), metals (e.g., mercury), anesthetics (e.g., halothane), and other chemicals and mixtures (e.g., vehicle emissions). Application of a chemogenomic enrichment analysis and hierarchical clustering across these diverse chemicals identified two clusters of chemicals with one that mimicked an immune response to pathogen, implicating inflammatory pathways and microglia as a common chemically induced neuropathological process. Thus, we established an integrative bioinformatics approach to systematically scan thousands of environmental chemicals for their ability to dysregulate molecular signatures relevant to critical periods of development.

Topical glucocorticoids, well-known anti-inflammatory drugs, induce multiple adverse effects, including skin atrophy. The sex-specific effects of systemic glucocorticoids are known, but sexual dimorphism of therapeutic and side effects of topical steroids has not been studied. We report here that female and male mice were equally sensitive to the anti-inflammatory effect of glucocorticoid fluocinolone acetonide (FA) in ear edema test. At the same time, females were more sensitive to FA-induced skin atrophy. We recently reported that REDD1 (regulated in development and DNA damage 1) plays central role in steroid atrophy. We found that REDD1 was more efficiently activated by FA in females, and that REDD1 knockout significantly protected female but not male mice from skin atrophy. Studies using human keratinocytes revealed that both estradiol and FA induced REDD1 mRNA/protein expression, and cooperated when they were combined at low doses. Chromatin immunoprecipitation analysis confirmed that REDD1 is an estrogen receptor (ER) target gene with multiple estrogen response elements in its promoter. Moreover, experiments with GR and ER inhibitors suggested that REDD1 induction by these hormones was interdependent on functional activity of both receptors. Overall, our results are important for the development of safer GR-targeted therapies suited for female and male dermatological patients.

Purpose:Somatic mutations in cancer cells can give rise to novel protein sequences that can be presented by antigen-presenting cells as neoantigens to the host immune system. Tumor neoantigens represent excellent targets for immunotherapy, due to their specific expression in cancer tissue. Despite the widespread use of immunomodulatory drugs and immunotherapies that recharge T and NK cells, there has been no direct evidence that neoantigen-specific T-cell responses are elicited in multiple myeloma.Experimental Design:Using next-generation sequencing data we describe the landscape of neo-antigens in 184 patients with multiple myeloma and successfully validate neoantigen-specific T cells in patients with multiple myeloma and support the feasibility of neoantigen-based therapeutic vaccines for use in cancers with intermediate mutational loads such as multiple myeloma.Results:In this study, we demonstrate an increase in neoantigen load in relapsed patients with multiple myeloma as compared with newly diagnosed patients with multiple myeloma. Moreover, we identify shared neoantigens across multiple patients in three multiple myeloma oncogenic driver genes (KRAS, NRAS, and IRF4). Next, we validate neoantigen T-cell response and clonal expansion in correlation with clinical response in relapsed patients with multiple myeloma. This is the first study to experimentally validate the immunogenicity of predicted neoantigens from next-generation sequencing in relapsed patients with multiple myeloma.Conclusions:Our findings demonstrate that somatic mutations in multiple myeloma can be immunogenic and induce neoantigen-specific T-cell activation that is associated with antitumor activity in vitro and clinical response in vivo. Our results provide the foundation for using neoantigen targeting strategies such as peptide vaccines in future trials for patients with multiple myeloma.

Modulation of physical activity represents an important intervention that may delay, slow, or prevent mild cognitive impairment (MCI) or dementia due to Alzheimer’s disease (AD). One mechanism proposed to underlie the beneficial effect of physical exercise involves the apparent stimulation of adult hippocampal neurogenesis (AHN). BCI-838 is a pro-drug whose active metabolite BCI-632 is an antagonist at the group II metabotropic glutamate receptor (mGluR2/3). We previously demonstrated that administration of BCI-838 to a mouse model of cerebrovascular accumulation of oligomeric AβE22Q (APPE693Q= “Dutch APP”) reduced learning behavior impairment and anxiety, both of which are associated with the phenotype of the Dutch APP mice. Here we show that (i) administration of BCI-838, physical exercise, or a combination of BCI-838 and physical exercise enhanced AHN in a four-month old mouse model of AD amyloid pathology (APPKM670/671NL/ PSEN1Δexon9 = APP/PS1), (ii) administration of BCI-838 alone or associated with physical exercise led to stimulation of AHN and improvement in both spatial and recognition memory, (iii) significantly, the hippocampal dentate gyrus transcriptome of APP/PS1 mice following BCI-838 treatment up-regulated brain-derived neurotrophic factor (BDNF), PIK3C2A of the PI3K-MTOR pathway, and metabotropic glutamate receptors, and down-regulated EIF5A of ketamine-modulating mTOR activity, and finally (iv) qPCR findings validate a significantly strong association between increased BDNF levels and BCI-838 treatment. Our study points to BCI-838 as a safe and orally active compound capable of mimicking the beneficial effect of exercise on AHN, learning behavior, and anxiety in a mouse model of AD neuropathology.

Background:The combined impact of immunity and SARS-CoV-2 variants on viral kinetics during infections has been unclear.Methods:We characterized 1,280 infections from the National Basketball Association occupational health cohort identified between June 2020 and January 2022 using serial RT-qPCR testing. Logistic regression and semi-mechanistic viral RNA kinetics models were used to quantify the effect of age, variant, symptom status, infection history, vaccination status and antibody titer to the founder SARS-CoV-2 strain on the duration of potential infectiousness and overall viral kinetics. The frequency of viral rebounds was quantified under multiple cycle threshold (Ct) value-based definitions.Results:Among individuals detected partway through their infection, 51.0% (95% credible interval [CrI]: 48.3–53.6%) remained potentially infectious (Ct Conclusions:SARS-CoV-2 viral kinetics are partly determined by immunity and variant but dominated by individual-level variation. Since booster vaccination protects against infection, longer clearance times for BA.1-infected, boosted individuals may reflect a less effective immune response, more common in older individuals, that increases infection risk and reduces viral RNA clearance rate. The shifting landscape of viral kinetics underscores the need for continued monitoring to optimize isolation policies and to contextualize the health impacts of therapeutics and vaccines.Funding:Supported in part by CDC contract #200-2016-91779, a sponsored research agreement to Yale University from the National Basketball Association contract #21-003529, and the National Basketball Players Association.

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, and often fatal disorder. Two FDA approved anti-fibrotic drugs, nintedanib and pirfenidone, slow the rate of decline in lung function, but responses are variable and side effects are common. Using an in-silico data-driven approach, we identified a robust connection between the transcriptomic perturbations in IPF disease and those induced by saracatinib, a selective Src kinase inhibitor, originally developed for oncological indications. Based on these observations, we hypothesized that saracatinib would be effective at attenuating pulmonary fibrosis. We investigated the anti-fibrotic efficacy of saracatinib relative to nintedanib and pirfenidone in three preclinical models: (i) in vitro in normal human lung fibroblasts (NHLFs); (ii) in vivo in bleomycin and recombinant adenovirus transforming growth factor-beta (Ad-TGF-β) murine models of pulmonary fibrosis; and (iii) ex vivo in precision cut lung slices from these mouse models. In each model, the effectiveness of saracatinib in blocking fibrogenic responses was equal or superior to nintedanib and pirfenidone.

Using a systems biology approach to prioritize potential points of intervention in ovarian cancer, we identified the lysine rich coiled-coil 1 (KRCC1), as a potential target. High-grade serous ovarian cancer patient tumors and cells express significantly higher levels of KRCC1 which correlates with poor overall survival and chemoresistance. We demonstrate that KRCC1 is predominantly present in the chromatin-bound nuclear fraction, interacts with HDAC1, HDAC2, and with the serine-threonine phosphatase PP1CC. Silencing KRCC1 inhibits cellular plasticity, invasive properties, and potentiates apoptosis resulting in reduced tumor growth. These phenotypes are associated with increased acetylation of histones and with increased phosphorylation of H2AX and CHK1, suggesting the modulation of transcription and DNA damage that may be mediated by the action of HDAC and PP1CC, respectively. Hence, we address an urgent need to develop new targets in cancer.

Glucocorticoids are the most frequently used anti-inflammatory drugs in dermatology. However, the molecular signature of glucocorticoids and their receptor in human skin is largely unknown. Our validated bioinformatics analysis of human skin transcriptome induced by topical glucocorticoid clobetasol propionate (CBP) in healthy volunteers identified numerous unreported glucocorticoid-responsive genes, including over a thousand noncoding RNAs. We observed sexual and racial dimorphism in the CBP response including a shift toward IFN-α/IFN-γ and IL-6/Jak/Signal transducer and activator of transcription (STAT) 3 signaling in female skin; and a larger response to CBP in African-American skin. Weighted gene coexpression network analysis unveiled a dense skin network of 41 transcription factors including circadian Kruppel-like factor 9 (KLF9), and ∼260 of their target genes enriched for functional pathways representative of the entire CBP transcriptome. Using keratinocytes with Kruppel-like factor 9 knockdown, we revealed a feedforward loop in glucocorticoid receptor signaling, previously unreported. Interestingly, many of the CBP-regulated transcription factors were involved in the control of development, metabolism, circadian clock; and 80% of them were associated with skin aging showing similarities between glucocorticoid-treated and aged skin. Overall, these findings indicate that glucocorticoid receptor acts as an important regulator of gene expression in skin-both at the transcriptional and posttranscriptional level-via multiple mechanisms including regulation of noncoding RNAs and multiple core transcription factors.

Brain tissue gene expression from donors with and without Alzheimer’s disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer’s Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer’s disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors Alzheimer’s disease progression. A severity index was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this data set, severity indexes were found to be very closely correlated with all Alzheimer’s disease neuropathology biomarkers (R ∼ 0.5, P < 1e−11) and global cognitive function (R = −0.68, P < 2.2e−16). We then applied the model to additional transcriptomic data sets from different brain regions (MAYO, n = 266; Mount Sinai Brain Bank, n = 214), and observed that the model remained significantly predictive (P < 1e−3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with Alzheimer’s disease co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types, respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with Alzheimer’s disease, other age-related neurological disorders and controls, and revealed that the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyse heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like Alzheimer’s disease., A pseudo-temporal trajectory was derived for Alzheimer’s disease progression from brain transcriptome in a large clinical cohort. The numeric index of the progression is highly predictive of neuropathology and clinical severity in independent data sets. Network analysis identified genes enriched in different cell types implicated for Alzheimer’s disease’s progression., Graphical Abstract Graphical Abstract

Description, Integrative multiomics analysis of myeloma identifies 12 disease subtypes defined by specific patterns of genetic alterations., The remarkable genetic heterogeneity of multiple myeloma poses a substantial challenge for proper prognostication and clinical management of patients. Here, we introduce MM-PSN, the first multiomics patient similarity network of myeloma. MM-PSN enabled accurate dissection of the genetic and molecular landscape of the disease and determined 12 distinct subgroups defined by five data types generated from genomic and transcriptomic profiling of 655 patients. MM-PSN identified patient subgroups not previously described defined by specific patterns of alterations, enriched for specific gene vulnerabilities, and associated with potential therapeutic options. Our analysis revealed that co-occurrence of t(4;14) and 1q gain identified patients at significantly higher risk of relapse and shorter survival as compared to t(4;14) as a single lesion. Furthermore, our results show that 1q gain is the most important single lesion conferring high risk of relapse and that it can improve on the current International Staging Systems (ISS and R-ISS).

Non-alcoholic steatohepatitis (NASH) is a rising health challenge, with no approved drugs. We used a computational drug repositioning strategy to uncover a novel therapy for NASH, identifying a GABA-B receptor agonist, AZD3355 (Lesogaberan) previously evaluated as a therapy for esophageal reflux. AZD3355’s potential efficacy in NASH was tested in human stellate cells, human precision cut liver slices (hPCLS), and in vivo in a well-validated murine model of NASH. In human stellate cells AZD3355 significantly downregulated profibrotic gene and protein expression. Transcriptomic analysis of these responses identified key regulatory nodes impacted by AZD3355, including Myc, as well as MAP and ERK kinases. In PCLS, AZD3355 down-regulated collagen1α1, αSMA and TNF-α mRNAs as well as secreted collagen1α1. In vivo, the drug significantly improved histology, profibrogenic gene expression, and tumor development, which was comparable to activity of obeticholic acid in a robust mouse model of NASH, but awaits further testing to determine its relative efficacy in patients. These data identify a well-tolerated clinical stage asset as a novel candidate therapy for human NASH through its hepatoprotective, anti-inflammatory and antifibrotic mechanisms of action. The approach validates computational methods to identify novel therapies in NASH in uncovering new pathways of disease development that can be rapidly translated into clinical trials.

A more complete understanding of phenomic space is critical for elucidating genome-phenome relationships and for assessing disease risk from genome sequencing. To incorporate knowledge of phenomic variability into variant interpretation, we developed a new genome interpretation metric called, Pleiotropic Variability Score (PVS). PVS uses ontologies of human diseases and medical phenotypes, namely human phenotype ontology (HPO) and disease ontology (DO), to compute the similarities of disease and clinical phenotypes associated with a genetic variant based on semantic reasoning algorithms. We tested 78 unique semantic similarity methods and integrated six robust metrics to define the pleiotropy score of SNPs. We computed PVS for 12, 541 SNPs (10, 021 SNPs mapped to DO phenotype and 8, 569 SNPs mapped to HPO phenotypes) using a repertoire of 382 HPO and 317 DO unique phenotype terms compiled from genotype-phenotype catalog. We validated the utility of PVS by computing pleiotropy using an electronic health record linked genomic database (BioME, n=11,210) and generated "allele-specific pleiotropy". Further we demonstrate the application of PVS in personalized medicine using "personalized pleiotropy score" reports for individuals with genomic data that could potentially aid in variant interpretation. We further developed a software framework to incorporate PVS into VCF files and to consolidate pleiotropy assessment as part of genome interpretation pipelines. As the genome-phenome catalogs are growing, PVS will be a useful metric to assess genetic variation to find SNPs with highly pleiotropic effects. Additionally, variants with varying degree of pleiotropy can be prioritized for explorative studies to understand specific roles of SNPs and pleiotropic hubs in mediating novel phenotypes and drug development.

Brain tissue gene expression from donors with and without Alzheimer’s disease (AD) have been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyze RNA-seq data from 1,114 brain donors from the AMP-AD consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles, and clinical severity in multiple AD dementia populations. Starting from the cross-sectional data in the ROSMAP cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors AD progression. A severity index (SI) was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this dataset, SIs were found to be very closely correlated with all AD neuropathology biomarkers (R ∼ 0.5, p < 1e-11) and global cognitive function (R = -0.68, p < 2.2e-16). We then applied the model to additional transcriptomic datasets from different brain regions (MAYO, n = 266; MSBB, n = 214), and observed that the model remained significantly predictive (p < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with AD co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with AD, other age-related neurological disorders and controls; and revealed the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyze heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like AD.

Background Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy. Methods FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8–10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers. Findings Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism. Interpretation In this pilot study, transcriptomic models could predict if FCT increased following 8–10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.

Non-alcoholic steatohepatitis (NASH) is a rising health challenge, with no approved drugs. We used a computational drug repositioning strategy to uncover a novel therapy for NASH, identifying a GABA-B receptor agonist, AZD3355 (lesogaberan) previously evaluated as a therapy for esophageal reflux. AZD3355’s potential efficacy in NASH was tested in human stellate cells, human precision cut liver slices (hPCLS), and in vivo in a well-validated murine model of NASH. In human stellate cells AZD3355 significantly downregulated profibrotic gene and protein expression. Transcriptomic analysis of these responses identified key regulatory nodes impacted by AZD3355, including Myc, as well as MAP and ERK kinases. In PCLS, AZD3355 down-regulated collagen1α1, aSMA and TNF-a mRNA as well as secreted collagen1a1. In vivo, the drug significantly improved histology, profibrogenic gene expression, and tumor development in a robust murine model of NASH, which was comparable to activity of obeticholic acid, an advanced investigational therapy for this disease. These data identify a well-tolerated clinical stage asset as a novel therapy for human NASH through its hepatoprotective, anti-inflammatory and antifibrotic mechanisms of action. The approach validates computational methods to identify novel therapies in NASH in uncovering new pathways of disease development that can be rapidly translated into clinical trials.

Summary Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts., Highlights • Automated framework for disease phenotyping from electronic health records • Unsupervised learning to identify cohorts for any disease of interest • Comparable or superior performance with that of widely adopted expert-based standards, The bigger picture Electronic health record (EHR)-based research is central to fulfill the vision of personalized medicine. However, due to EHRs being structured for billing purposes, reliably identifying patients with a phenotype of interest in a clinical data warehouse is difficult. Phe2vec uses unsupervised learning to derive medical concept embeddings and build phenotype definitions to identify patient cohorts. Pre-training embeddings leads to a flexible solution which is applicable to any disease by simply defining a seed concept. This method showed performance comparable or superior to that of other widely adopted EHR phenotyping approaches. Phe2vec aims to contribute to the next generation of clinical systems that use machine learning to effectively support clinicians in their activities. These systems capable of scaling to a large number of diseases, patients, and health data promise to offer a more holistic way to examine disease complexity and to improve clinical practice and medical research., De Freitas et al. present Phe2vec, an automated framework for disease phenotyping based on unsupervised learning. Phe2vec derives embeddings of medical concepts from electronic health records and uses them to define phenotypes and measure the association between diseases and patients. The authors demonstrate the method's effectiveness via comparison with both rule-based algorithms and standard automated methods. Phe2vec can be used to identify patient cohorts by simply specifying a seed concept associated to any disease of interest.

With the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants that may increase transmissibility and/or cause escape from immune responses, there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant, first detected in the United Kingdom, could be serendipitously detected by the Thermo Fisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike Δ69–70, would cause a “spike gene target failure” (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern (VOC) that lack spike Δ69–70, such as B.1.351 (also 501Y.V2), detected in South Africa, and P.1 (also 501Y.V3), recently detected in Brazil. We identified a deletion in the ORF1a gene (ORF1a Δ3675–3677) in all 3 variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a Δ3675–3677 as the primary target and spike Δ69–70 to differentiate, we designed and validated an open-source PCR assay to detect SARS-CoV-2 VOC. Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence and spread of B.1.1.7, B.1.351, and P.1., Surveillance for SARS-CoV-2 variants is very important, but sequencing is not always practical or affordable. This study presents a multiplex qPCR that is able to distinguish among different SARS-CoV-2 variants of concern that are currently circulating.

BackgroundThe alpha and delta SARS-CoV-2 variants have been responsible for major recent waves of COVID-19 despite increasing vaccination rates. The reasons for the increased transmissibility of these variants and for the reduced transmissibility of vaccine breakthrough infections are unclear.MethodsWe quantified the course of viral proliferation and clearance for 173 individuals with acute SARS-CoV-2 infections using longitudinal quantitative RT-PCR tests conducted using anterior nares/oropharyngeal samples (n = 199,941) as part of the National Basketball Association’s (NBA) occupational health program between November 28th, 2020, and August 11th, 2021. We measured the duration of viral proliferation and clearance and the peak viral concentration separately for individuals infected with alpha, delta, and non-variants of interest/variants of concern (non-VOI/VOC), and for vaccinated and unvaccinated individuals.ResultsThe mean viral trajectories of alpha and delta infections resembled those of non-VOI/VOC infections. Vaccine breakthrough infections exhibited similar proliferation dynamics as infections in unvaccinated individuals (mean peak Ct: 20.5, 95% credible interval [19.0, 21.0] vs. 20.7 [19.8, 20.2], and mean proliferation time 3.2 days [2.5, 4.0] vs. 3.5 days [3.0, 4.0]); however, vaccinated individuals exhibited faster clearance (mean clearance time: 5.5 days [4.6, 6.6] vs. 7.5 days [6.8, 8.2]).ConclusionsAlpha, delta, and non-VOI/VOC infections feature similar viral trajectories. Acute infections in vaccinated and unvaccinated people feature similar proliferation and peak Ct, but vaccinated individuals cleared the infection more quickly. Viral concentrations do not fully explain the differences in infectiousness between SARS-CoV-2 variants, and mitigation measures are needed to limit transmission from vaccinated individuals.

SummaryThe emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

With the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants that may increase transmissibility and/or cause escape from immune responses, there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant, first detected in the United Kingdom, could be serendipitously detected by the Thermo Fisher TaqPath Coronavirus Disease 2019 (COVID-19) PCR assay because a key deletion in these viruses, spike Δ69-70, would cause a "spike gene target failure" (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern (VOC) that lack spike Δ69-70, such as B.1.351 (also 501Y.V2), detected in South Africa, and P.1 (also 501Y.V3), recently detected in Brazil. We identified a deletion in the ORF1a gene (ORF1a Δ3675-3677) in all 3 variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a Δ3675-3677 as the primary target and spike Δ69-70 to differentiate, we designed and validated an open-source PCR assay to detect SARS-CoV-2 VOC. Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence and spread of B.1.1.7, B.1.351, and P.1.

Background While Alzheimer’s disease (AD) remains one of the most challenging diseases to tackle, genome-wide genetic/epigenetic studies reveal many disease-associated risk loci, which sheds new light onto disease heritability, provides novel insights to understand its underlying mechanism and potentially offers easily measurable biomarkers for early diagnosis and intervention. Methods We analyzed whole-genome DNA methylation data collected from peripheral blood in a cohort (n = 649) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compared the DNA methylation level at baseline among participants diagnosed with AD (n = 87), mild cognitive impairment (MCI, n = 175) and normal controls (n = 162), to identify differentially methylated regions (DMRs). We also leveraged up to 4 years of longitudinal DNA methylation data, sampled at approximately 1 year intervals to model alterations in methylation levels at DMRs to delineate methylation changes associated with aging and disease progression, by linear mixed-effects (LME) modeling for the unchanged diagnosis groups (AD, MCI and control, respectively) and U-shape testing for those with changed diagnosis (converters). Results When compared with controls, patients with MCI consistently displayed promoter hypomethylation at methylation QTL (mQTL) gene locus PM20D1. This promoter hypomethylation was even more prominent in patients with mild to moderate AD. This is in stark contrast with previously reported hypermethylation in hippocampal and frontal cortex brain tissues in patients with advanced-stage AD at this locus. From longitudinal data, we show that initial promoter hypomethylation of PM20D1 during MCI and early stage AD is reversed to eventual promoter hypermethylation in late stage AD, which helps to complete a fuller picture of methylation dynamics. We also confirm this observation in an independent cohort from the Religious Orders Study and Memory and Aging Project (ROSMAP) Study using DNA methylation and gene expression data from brain tissues as neuropathological staging (Braak score) advances. Conclusions Our results confirm that PM20D1 is an mQTL in AD and demonstrate that it plays a dynamic role at different stages of the disease. Further in-depth study is thus warranted to fully decipher its role in the evolution of AD and potentially explore its utility as a blood-based biomarker for AD.

Cystic fibrosis (CF), caused by mutations to CFTR, leads to severe and progressive lung disease. The most common mutant, ΔF508-CFTR, undergoes proteasomal degradation, extinguishing its anion channel function. Numerous in vitro interventions have been identified to partially rescue ΔF508-CFTR function yet remain poorly understood. Improved understanding of both the altered state of CF cells and the mechanisms of existing rescue strategies could reveal novel therapeutic strategies. Toward this aim, we measured transcriptional profiles of established temperature, genetic, and chemical interventions that rescue ΔF508-CFTR and also re-analyzed public datasets characterizing transcription in human CF vs. non-CF samples from airway and whole blood. Meta-analysis yielded a core disease signature and two core rescue signatures. To interpret these through the lens of prior knowledge, we compiled a “CFTR Gene Set Library” from literature. The core disease signature revealed remarkably strong connections to genes with established effects on CFTR trafficking and function and suggested novel roles of EGR1 and SGK1 in the disease state. Our data also revealed an unexpected mechanistic link between several genetic rescue interventions and the unfolded protein response. Finally, we found that C18, an analog of the CFTR corrector compound Lumacaftor, induces almost no transcriptional perturbation despite its rescue activity.

MICU1 is a mitochondrial inner membrane protein that inhibits mitochondrial calcium entry; elevated MICU1 expression is characteristic of many cancers, including ovarian cancer. MICU1 induces both glycolysis and chemoresistance and is associated with poor clinical outcomes. However, there are currently no available interventions to normalize aberrant MICU1 expression. Here, we demonstrate that microRNA‐195‐5p (miR‐195) directly targets the 3′ UTR of the MICU1 mRNA and represses MICU1 expression. Additionally, miR‐195 is under‐expressed in ovarian cancer cell lines, and restoring miR‐195 expression reestablishes native MICU1 levels and the associated phenotypes. Stable expression of miR‐195 in a human xenograft model of ovarian cancer significantly reduces tumor growth, increases tumor doubling times, and enhances overall survival. In conclusion, miR‐195 controls MICU1 levels in ovarian cancer and could be exploited to normalize aberrant MICU1 expression, thus reversing both glycolysis and chemoresistance and consequently improving patient outcomes.

Background Mapping disease-associated genetic variants to complex disease pathophysiology is a major challenge in translating findings from genome-wide association studies into novel therapeutic opportunities. The difficulty lies in our limited understanding of how phenotypic traits arise from non-coding genetic variants in highly organized biological systems with heterogeneous gene expression across cells and tissues. Results We present a novel strategy, called GWAS component analysis, for transferring disease associations from single-nucleotide polymorphisms to co-expression modules by stacking models trained using reference genome and tissue-specific gene expression data. Application of this method to genome-wide association studies of blood cell counts confirmed that it could detect gene sets enriched in expected cell types. In addition, coupling of our method with Bayesian networks enables GWAS components to be used to discover drug targets. Conclusions We tested genome-wide associations of four disease phenotypes, including age-related macular degeneration, Crohn’s disease, ulcerative colitis and rheumatoid arthritis, and demonstrated the proposed method could select more functional genes than S-PrediXcan, the previous single-step model for predicting gene-level associations from SNP-level associations.

The remarkable genetic heterogeneity of Multiple Myeloma (MM) poses a significant challenge for proper prognostication and clinical management of patients. Accurate dissection of the genetic and molecular landscape of the disease and the robust identification of homogeneous classes of patients are essential steps to reliable risk stratification and the development of novel precision medicine strategies. Here we introduce MM-PSN, the first multi-omics Patient Similarity Network of newly diagnosed MM. MM-PSN has enabled the identification of three broad patient groups and twelve distinct sub-groups defined by five data types generated from genomic and transcriptomic patient profiling of 655 patients. The MM-PSN classification uncovered novel associations between distinct MM hallmarks with significant prognostic implications and allowed further refinement of risk stratification. Our analysis revealed that gain of 1q is the most important single lesion conferring high risk of relapse, and its association with an MMSET translocation is the most significant determinant of poor outcome. We developed a classifier and validated these results in an independent dataset of 559 pts. Our findings suggest that gain of 1q should be incorporated in routine staging systems and risk assessment tools. The MM-PSN classifier is available as a free resource to allow for an easy implementation in most clinical settings.

FKBP51 (FK506-binding protein 51) is a known co-chaperone and regulator of the glucocorticoid receptor (GR), which usually attenuates its activity. FKBP51 is one of the major GR target genes in skin, but its role in clinical effects of glucocorticoids is not known. Here, we used FKBP51 knockout (KO) mice to determine FKBP51's role in the major adverse effect of topical glucocorticoids, skin atrophy. Unexpectedly, we found that all skin compartments (epidermis, dermis, dermal adipose and CD34+ stem cells) in FKBP51 KO animals were much more resistant to glucocorticoid-induced hypoplasia. Furthermore, despite the absence of inhibitory FKBP51, the basal level of expression and glucocorticoid activation of GR target genes were not increased in FKBP51 KO skin or CRISPR/Cas9-edited FKBP51 KO HaCaT human keratinocytes. FKBP51 is known to negatively regulate Akt and mTOR. We found a significant increase in AktSer473 and mTORSer2448 phosphorylation and downstream pro-growth signaling in FKBP51-deficient keratinocytes in vivo and in vitro. As Akt/mTOR-GR crosstalk is usually negative in skin, our results suggest that Akt/mTOR activation could be responsible for the lack of increased GR function and resistance of FKBP51 KO mice to the steroid-induced skin atrophy.