BACKGROUND: High-grade serous tubo-ovarian cancer (HGSTOC) is characterised by extensive inter- and intratumour heterogeneity, resulting in persistent therapeutic resistance and poor disease outcome. Molecular subtype classification based on bulk RNA sequencing facilitates a more accurate characterisation of this heterogeneity, but the lack of strong prognostic or predictive correlations with these subtypes currently hinders their clinical implementation. Stromal admixture profoundly affects the prognostic impact of the molecular subtypes, but the contribution of stromal cells to each subtype has poorly been characterised. Increasing the transcriptomic resolution of the molecular subtypes based on single-cell RNA sequencing (scRNA-seq) may provide insights in the prognostic and predictive relevance of these subtypes. METHODS: We performed scRNA-seq of 18,403 cells unbiasedly collected from 7 treatment-naive HGSTOC tumours. For each phenotypic cluster of tumour or stromal cells, we identified specific transcriptomic markers. We explored which phenotypic clusters correlated with overall survival based on expression of these transcriptomic markers in microarray data of 1467 tumours. By evaluating molecular subtype signatures in single cells, we assessed to what extent a phenotypic cluster of tumour or stromal cells contributes to each molecular subtype. RESULTS: We identified 11 cancer and 32 stromal cell phenotypes in HGSTOC tumours. Of these, the relative frequency of myofibroblasts, TGF-β-driven cancer-associated fibroblasts, mesothelial cells and lymphatic endothelial cells predicted poor outcome, while plasma cells correlated with more favourable outcome. Moreover, we identified a clear cell-like transcriptomic signature in cancer cells, which correlated with worse overall survival in HGSTOC patients. Stromal cell phenotypes differed substantially between molecular subtypes. For instance, the mesenchymal, immunoreactive and differentiated signatures were characterised by specific fibroblast, immune cell and myofibroblast/mesothelial cell phenotypes, respectively. Cell phenotypes correlating with poor outcome were enriched in molecular subtypes associated with poor outcome. CONCLUSIONS: We used scRNA-seq to identify stromal cell phenotypes predicting overall survival in HGSTOC patients. These stromal features explain the association of the molecular subtypes with outcome but also the latter's weakness of clinical implementation. Stratifying patients based on marker genes specific for these phenotypes represents a promising approach to predict prognosis or response to therapy.

BACKGROUND: Multimorbidities greatly increase the global health burdens, but the landscapes of their genetic risks have not been systematically investigated. METHODS: We used the hospital inpatient data of 385,335 patients in the UK Biobank to investigate the multimorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify multimorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for the networks of genetically interpretable multimorbidities to detect the hub diseases and the involved molecules and functions in each module. RESULTS: In total, 11,285 multimorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture levels. Multimorbidities affecting the same and different physiological systems displayed different patterns of the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by multimorbidities converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable multimorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the multimorbidity modules could help provide useful insights for the genetic contributors of multimorbidities. CONCLUSIONS: Our results provide a systematic resource for understanding the genetic predispositions of multimorbidities and indicate that hub diseases and converged molecules and functions may be the key for treating multimorbidities. We have created an online database that facilitates researchers and physicians to browse, search, or download these multimorbidities ( https://multimorbidity.comp-sysbio.org ).

BACKGROUND: Candidemia is one of the most common nosocomial bloodstream infections in the United States, causing significant morbidity and mortality in hospitalized patients, but the breadth of the host response to Candida infections in human patients remains poorly defined. METHODS: In order to better define the host response to Candida infection at the transcriptional level, we performed RNA sequencing on serial peripheral blood samples from 48 hospitalized patients with blood cultures positive for Candida species and compared them to patients with other acute viral, bacterial, and non-infectious illnesses. Regularized multinomial regression was utilized to develop pathogen class-specific gene expression classifiers. RESULTS: Candidemia triggers a unique, robust, and conserved transcriptomic response in human hosts with 1641 genes differentially upregulated compared to healthy controls. Many of these genes corresponded to components of the immune response to fungal infection, heavily weighted toward neutrophil activation, heme biosynthesis, and T cell signaling. We developed pathogen class-specific classifiers from these unique signals capable of identifying and differentiating candidemia, viral, or bacterial infection across a variety of hosts with a high degree of accuracy (auROC 0.98 for candidemia, 0.99 for viral and bacterial infection). This classifier was validated on two separate human cohorts (auROC 0.88 for viral infection and 0.87 for bacterial infection in one cohort; auROC 0.97 in another cohort) and an in vitro model (auROC 0.94 for fungal infection, 0.96 for bacterial, and 0.90 for viral infection). CONCLUSIONS: Transcriptional analysis of circulating leukocytes in patients with acute Candida infections defines novel aspects of the breadth of the human immune response during candidemia and suggests promising diagnostic approaches for simultaneously differentiating multiple types of clinical illnesses in at-risk, acutely ill patients.

BACKGROUND: Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations. METHODS: We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2. RESULTS: The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing. CONCLUSIONS: Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts.

BACKGROUND: Gene expression is highly variable across tissues of multi-cellular organisms, influencing the codon usage of the tissue-specific transcriptome. Cancer disrupts the gene expression pattern of healthy tissue resulting in altered codon usage preferences. The topic of codon usage changes as they relate to codon demand, and tRNA supply in cancer is of growing interest. METHODS: We analyzed transcriptome-weighted codon and codon pair usage based on The Cancer Genome Atlas (TCGA) RNA-seq data from 6427 solid tumor samples and 632 normal tissue samples. This dataset represents 32 cancer types affecting 11 distinct tissues. Our analysis focused on tissues that give rise to multiple solid tumor types and cancer types that are present in multiple tissues. RESULTS: We identified distinct patterns of synonymous codon usage changes for different cancer types affecting the same tissue. For example, a substantial increase in GGT-glycine was observed in invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and mixed invasive ductal and lobular carcinoma (IDLC) of the breast. Change in synonymous codon preference favoring GGT correlated with change in synonymous codon preference against GGC in IDC and IDLC, but not in ILC. Furthermore, we examined the codon usage changes between paired healthy/tumor tissue from the same patient. Using clinical data from TCGA, we conducted a survival analysis of patients based on the degree of change between healthy and tumor-specific codon usage, revealing an association between larger changes and increased mortality. We have also created a database that contains cancer-specific codon and codon pair usage data for cancer types derived from TCGA, which represents a comprehensive tool for codon-usage-oriented cancer research. CONCLUSIONS: Based on data from TCGA, we have highlighted tumor type-specific signatures of codon and codon pair usage. Paired data revealed variable changes to codon usage patterns, which must be considered when designing personalized cancer treatments. The associated database, CancerCoCoPUTs, represents a comprehensive resource for codon and codon pair usage in cancer and is available at https://dnahive.fda.gov/review/cancercocoputs/ . These findings are important to understand the relationship between tRNA supply and codon demand in cancer states and could help guide the development of new cancer therapeutics.

If each cancer cell produces on average more than one cancer cell, we see a net growth of the tumors and metastases and vice versa. We review recent clinical results for microsatellite stable metastatic colorectal cancer (MSS-mCRC) suggesting immunotherapy combinations with personalized vaccines, checkpoint inhibitors, targeted therapies, multikinase inhibitors, chemotherapies, and radiation that simultaneously slow cancer cell growth rate and enhance T cell killing rate of cancer cells may in future synergize to control the disease.

BACKGROUND: Klebsiella pneumoniae, as a global priority pathogen, is well known for its capability of acquiring mobile genetic elements that carry resistance and/or virulence genes. Its virulence plasmid, previously deemed nonconjugative and restricted within hypervirulent K. pneumoniae (hvKP), has disseminated into classic K. pneumoniae (cKP), particularly carbapenem-resistant K. pneumoniae (CRKP), which poses alarming challenges to public health. However, the mechanism underlying its transfer from hvKP to CRKP is unclear. METHODS: A total of 28 sequence type (ST) 11 bloodstream infection-causing CRKP strains were collected from Ruijin Hospital in Shanghai, China, and used as recipients in conjugation assays. Transconjugants obtained from conjugation assays were confirmed by XbaI and S1 nuclease pulsed-field gel electrophoresis, PCR detection and/or whole-genome sequencing. The plasmid stability of the transconjugants was evaluated by serial culture. Genetically modified strains and constructed mimic virulence plasmids were employed to investigate the mechanisms underlying mobilization. The level of extracellular polysaccharides was measured by mucoviscosity assays and uronic acid quantification. An in silico analysis of 2608 plasmids derived from 814 completely sequenced K. pneumoniae strains available in GenBank was performed to investigate the distribution of putative helper plasmids and mobilizable virulence plasmids. RESULTS: A nonconjugative virulence plasmid was mobilized by the conjugative plasmid belonging to incompatibility group F (IncF) from the hvKP strain into ST11 CRKP strains under low extracellular polysaccharide-producing conditions or by employing intermediate E. coli strains. The virulence plasmid was mobilized via four modes: transfer alone, cotransfer with the conjugative IncF plasmid, hybrid plasmid formation due to two rounds of single-strand exchanges at specific 28-bp fusion sites or homologous recombination. According to the in silico analysis, 31.8% (242) of the putative helper plasmids and 98.8% (84/85) of the virulence plasmids carry the 28-bp fusion site. All virulence plasmids carry the origin of the transfer site. CONCLUSIONS: The nonconjugative virulence plasmid in ST11 CRKP strains is putatively mobilized from hvKP or E. coli intermediates with the help of conjugative IncF plasmids. Our findings emphasize the importance of raising public awareness of the rapid dissemination of virulence plasmids and the consistent emergence of hypervirulent carbapenem-resistant K. pneumoniae (hv-CRKP) strains.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has been associated with neurological and neuropsychiatric illness in many individuals. We sought to further our understanding of the relationship between brain tropism, neuro-inflammation, and host immune response in acute COVID-19 cases. METHODS: Three brain regions (dorsolateral prefrontal cortex, medulla oblongata, and choroid plexus) from 5 patients with severe COVID-19 and 4 controls were examined. The presence of the virus was assessed by western blot against viral spike protein, as well as viral transcriptome analysis covering > 99% of SARS-CoV-2 genome and all potential serotypes. Droplet-based single-nucleus RNA sequencing (snRNA-seq) was performed in the same samples to examine the impact of COVID-19 on transcription in individual cells of the brain. RESULTS: Quantification of viral spike S1 protein and viral transcripts did not detect SARS-CoV-2 in the postmortem brain tissue. However, analysis of 68,557 single-nucleus transcriptomes from three distinct regions of the brain identified an increased proportion of stromal cells, monocytes, and macrophages in the choroid plexus of COVID-19 patients. Furthermore, differential gene expression, pseudo-temporal trajectory, and gene regulatory network analyses revealed transcriptional changes in the cortical microglia associated with a range of biological processes, including cellular activation, mobility, and phagocytosis. CONCLUSIONS: Despite the absence of detectable SARS-CoV-2 in the brain at the time of death, the findings suggest significant and persistent neuroinflammation in patients with acute COVID-19.

BACKGROUND: The development of secondary resistance (SR) in metastatic colorectal cancer (mCRC) treated with anti-epidermal growth factor receptor (anti-EGFR) antibodies is not fully understood at the molecular level. Here we tested in vivo selection of anti-EGFR SR tumors in CRC patient-derived xenograft (PDX) models as a strategy for a molecular dissection of SR mechanisms. METHODS: We analyzed 21 KRAS, NRAS, BRAF, and PI3K wildtype CRC patient-derived xenograft (PDX) models for their anti-EGFR sensitivity. Furthermore, 31 anti-EGFR SR tumors were generated via chronic in vivo treatment with cetuximab. A multi-omics approach was employed to address molecular primary and secondary resistance mechanisms. Gene set enrichment analyses were used to uncover SR pathways. Targeted therapy of SR PDX models was applied to validate selected SR pathways. RESULTS: In vivo anti-EGFR SR could be established with high efficiency. Chronic anti-EGFR treatment of CRC PDX tumors induced parallel evolution of multiple resistant lesions with independent molecular SR mechanisms. Mutations in driver genes explained SR development in a subgroup of CRC PDX models, only. Transcriptional reprogramming inducing anti-EGFR SR was discovered as a common mechanism in CRC PDX models frequently leading to RAS signaling pathway activation. We identified cAMP and STAT3 signaling activation, as well as paracrine and autocrine signaling via growth factors as novel anti-EGFR secondary resistance mechanisms. Secondary resistant xenograft tumors could successfully be treated by addressing identified transcriptional changes by tailored targeted therapies. CONCLUSIONS: Our study demonstrates that SR PDX tumors provide a unique platform to study molecular SR mechanisms and allow testing of multiple treatments for efficient targeting of SR mechanisms, not possible in the patient. Importantly, it suggests that the development of anti-EGFR tolerant cells via transcriptional reprogramming as a cause of anti-EGFR SR in CRC is likely more prevalent than previously anticipated. It emphasizes the need for analyses of SR tumor tissues at a multi-omics level for a comprehensive molecular understanding of anti-EGFR SR in CRC.

BACKGROUND: Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects. METHODS: Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a "healthy-like" status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies. RESULTS: Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. CONCLUSIONS: Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

The Global Alliance for Genomics and Health has approved a policy for the return of clinically actionable genomic research results, the first such policy approved by an international body. The policy acknowledges the potential medical benefits to millions of individuals who are participating in genomics research. It ties the pace of implementation to each country's clinical standards, including for the return of secondary findings, and urges funders to set aside resources to support responsible return.

Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods. DeepProg is highly predictive, exemplified by two liver cancer (C-index 0.73-0.80) and five breast cancer datasets (C-index 0.68-0.73). Pan-cancer analysis associates common genomic signatures in poor survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis processes. DeepProg is freely available at https://github.com/lanagarmire/DeepProg.

BACKGROUND: Renal medullary carcinomas (RMCs) are rare kidney cancers that occur in adolescents and young adults of African ancestry. Although RMC is associated with the sickle cell trait and somatic loss of the tumor suppressor, SMARCB1, the ancestral origins of RMC remain unknown. Further, characterization of structural variants (SVs) involving SMARCB1 in RMC remains limited. METHODS: We used linked-read genome sequencing to reconstruct germline and somatic haplotypes in 15 unrelated patients with RMC registered on the Children's Oncology Group (COG) AREN03B2 study between 2006 and 2017 or from our prior study. We performed fine-mapping of the HBB locus and assessed the germline for cancer predisposition genes. Subsequently, we assessed the tumor samples for mutations outside of SMARCB1 and integrated RNA sequencing to interrogate the structural variants at the SMARCB1 locus. RESULTS: We find that the haplotype of the sickle cell mutation in patients with RMC originated from three geographical regions in Africa. In addition, fine-mapping of the HBB locus identified the sickle cell mutation as the sole candidate variant. We further identify that the SMARCB1 structural variants are characterized by blunt or 1-bp homology events. CONCLUSIONS: Our findings suggest that RMC does not arise from a single founder population and that the HbS allele is a strong candidate germline allele which confers risk for RMC. Furthermore, we find that the SVs that disrupt SMARCB1 function are likely repaired by non-homologous end-joining. These findings highlight how haplotype-based analyses using linked-read genome sequencing can be applied to identify potential risk variants in small and rare disease cohorts and provide nucleotide resolution to structural variants.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a heterogeneous cancer. Better knowledge of molecular and cellular intra-tumor heterogeneity throughout the thoracic cavity is required to develop efficient therapies. This study focuses on molecular intra-tumor heterogeneity using the largest series to date in MPM and is the first to report on the multi-omics profiling of a substantial series of multi-site tumor samples. METHODS: Intra-tumor heterogeneity was investigated in 16 patients from whom biopsies were taken at distinct anatomical sites. The paired biopsies collected from apex, side wall, costo-diaphragmatic, or highest metabolic sites as well as 5 derived cell lines were screened using targeted sequencing. Whole exome sequencing, RNA sequencing, and DNA methylation were performed on a subset of the cohort for deep characterization. Molecular classification, recently defined histo-molecular gradients, and cell populations of the tumor microenvironment were assessed. RESULTS: Sequencing analysis identified heterogeneous variants notably in NF2, a key tumor suppressor gene of mesothelial carcinogenesis. Subclonal tumor populations were shared among paired biopsies, suggesting a polyclonal dissemination of the tumor. Transcriptome analysis highlighted dysregulation of cell adhesion and extracellular matrix pathways, linked to changes in histo-molecular gradient proportions between anatomic sites. Methylome analysis revealed the contribution of epigenetic mechanisms in two patients. Finally, significant changes in the expression of immune mediators and genes related to immunological synapse, as well as differential infiltration of immune populations in the tumor environment, were observed and led to a switch from a hot to a cold immune profile in three patients. CONCLUSIONS: This comprehensive analysis reveals patient-dependent spatial intra-tumor heterogeneity at the genetic, transcriptomic, and epigenetic levels and in the immune landscape of the tumor microenvironment. Results support the need for multi-sampling for the implementation of molecular-based precision medicine.