SARS-CoV-2 and influenza coinfection throughout the COVID-19 pandemic: an assessment of coinfection rates, cohort characteristics, and clinical outcomes

Case reports of patients infected with COVID-19 and influenza virus ("flurona") have raised questions around the prevalence and severity of coinfection. Using data from HHS Protect Public Data Hub, NCBI Virus, and CDC FluView, we analyzed trends in SARS-CoV-2 and influenza hospitalized coinfection cases and strain prevalences. We also characterized coinfection cases across the Mayo Clinic Enterprise from January 2020 to April 2022. We compared expected and observed coinfection case counts across different waves of the pandemic and assessed symptoms and outcomes of coinfection and COVID-19 monoinfection cases after propensity score matching on clinically relevant baseline characteristics. From both the Mayo Clinic and nationwide datasets, the observed coinfection rate for SARS-CoV-2 and influenza has been higher during the Omicron era (2021 December 14 to 2022 April 2) compared to previous waves, but no higher than expected assuming infection rates are independent. At the Mayo Clinic, only 120 coinfection cases were observed among 197,364 SARS-CoV-2 cases. Coinfected patients were relatively young (mean age: 26.7 years) and had fewer serious comorbidities compared to monoinfected patients. While there were no significant differences in 30-day hospitalization, ICU admission, or mortality rates between coinfected and matched COVID-19 monoinfection cases, coinfection cases reported higher rates of symptoms including congestion, cough, fever/chills, headache, myalgia/arthralgia, pharyngitis, and rhinitis. While most coinfection cases observed at the Mayo Clinic occurred among relatively healthy individuals, further observation is needed to assess outcomes among subpopulations with risk factors for severe COVID-19 such as older age, obesity, and immunocompromised status.

Quantifying the immunological distinctiveness of emerging SARS-CoV-2 variants in the context of prior regional herd exposure

The COVID-19 pandemic has seen the persistent emergence of immune-evasive SARS-CoV-2 variants under the selection pressure of natural and vaccination-acquired immunity. However, it is currently challenging to quantify how immunologically distinct a new variant is compared to all the prior variants to which a population has been exposed. Here, we define "Distinctiveness" of SARS-CoV-2 sequences based on a proteome-wide comparison with all prior sequences from the same geographical region. We observe a correlation between Distinctiveness relative to contemporary sequences and future change in prevalence of a newly circulating lineage (Pearson r = 0.75), suggesting that the Distinctiveness of emergent SARS-CoV-2 lineages is associated with their epidemiological fitness. We further show that the average Distinctiveness of sequences belonging to a lineage, relative to the Distinctiveness of other sequences that occur at the same place and time (n = 944 location/time data points), is predictive of future increases in prevalence (Area Under the Curve, AUC = 0.88 [95% confidence interval 0.86 to 0.90]). By assessing the Delta variant in India versus Brazil, we show that the same lineage can have different Distinctiveness-contributing positions in different geographical regions depending on the other variants that previously circulated in those regions. Finally, we find that positions that constitute epitopes contribute disproportionately (20-fold higher than the average position) to Distinctiveness. Overall, this study suggests that real-time assessment of new SARS-CoV-2 variants in the context of prior regional herd exposure via Distinctiveness can augment genomic surveillance efforts.

Continuous genomic diversification of long polynucleotide fragments drives the emergence of new SARS-CoV-2 variants of concern

Highly transmissible or immuno-evasive SARS-CoV-2 variants have intermittently emerged, resulting in repeated COVID-19 surges. With over 6 million SARS-CoV-2 genomes sequenced, there is unprecedented data to decipher the evolution of fitter SARS-CoV-2 variants. Much attention has been directed to studying the functional importance of specific mutations in the Spike protein, but there is limited knowledge of genomic signatures shared by dominant variants. Here, we introduce a method to quantify the genome-wide distinctiveness of polynucleotide fragments (3- to 240-mers) that constitute SARS-CoV-2 sequences. Compared to standard phylogenetic metrics and mutational load, the new metric provides improved separation between Variants of Concern (VOCs; Reference = 89, IQR: 65-108; Alpha = 166, IQR: 149-181; Beta 131, IQR: 114-149; Gamma = 164, IQR: 150-178; Delta = 235, IQR: 217-255; and Omicron = 459, IQR: 395-521). Omicron's high genomic distinctiveness may confer an advantage over prior VOCs and the recently emerged and highly mutated B.1.640.2 (IHU) lineage. Evaluation of 883 lineages highlights that genomic distinctiveness has increased over time (R ² = 0.37) and that VOCs score significantly higher than contemporary non-VOC lineages, with Omicron among the most distinctive lineages observed. This study demonstrates the value of characterizing SARS-CoV-2 variants by genome-wide polynucleotide distinctiveness and emphasizes the need to go beyond a narrow set of mutations at known sites on the Spike protein. The consistently higher distinctiveness of each emerging VOC compared to prior VOCs suggests that monitoring of genomic distinctiveness would facilitate rapid assessment of viral fitness.

Building a best-in-class automated de-identification tool for electronic health records through ensemble learning

The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries.

Inference from longitudinal laboratory tests characterizes temporal evolution of COVID-19-associated coagulopathy (CAC)

Temporal inference from laboratory testing results and triangulation with clinical outcomes extracted from unstructured electronic health record (EHR) provider notes is integral to advancing precision medicine. Here, we studied 246 SARS-CoV-2 PCR-positive (COVID_pos) patients and propensity-matched 2460 SARS-CoV-2 PCR-negative (COVID_neg) patients subjected to around 700,000 lab tests cumulatively across 194 assays. Compared to COVID_neg patients at the time of diagnostic testing, COVID_pos patients tended to have higher plasma fibrinogen levels and lower platelet counts. However, as the infection evolves, COVID_pos patients distinctively show declining fibrinogen, increasing platelet counts, and lower white blood cell counts. Augmented curation of EHRs suggests that only a minority of COVID_pos patients develop thromboembolism, and rarely, disseminated intravascular coagulopathy (DIC), with patients generally not displaying platelet reductions typical of consumptive coagulopathies. These temporal trends provide fine-grained resolution into COVID-19 associated coagulopathy (CAC) and set the stage for personalizing thromboprophylaxis.

Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis

Understanding temporal dynamics of COVID-19 symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVIDpos; n = 2,317) versus COVID-19-negative (COVIDneg; n = 74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVIDpos over COVIDneg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVIDpos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.

Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis

Understanding temporal dynamics of COVID-19 symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVID_pos; n = 2,317) versus COVID-19-negative (COVID_neg; n = 74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVID_pos over COVID_neg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVID_pos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.

Abstract 2452: Patient segmentation using machine-learning based literature and genomic data synthesis uncovers novel cohorts of NSCLC and mesothelioma patients

Current unbiased approaches to mine the large amounts of patient-level data on mutations, structural variations and gene expression result in an unwieldy amount of interactions and correlations, which cannot be parsed to identify disease drivers. Here we present an approach to encode mutational and structural variant data at a patient level in a semantic association space. This approach transforms the presence of a mutation (or other feature) in each patient into the quantitative semantic association score of the corresponding gene and the phenotype of interest, which we have trained on all publicly available literature using word-embedding neural networks. Using data from The Cancer Genome Atlas (TCGA), we encoded the mutation or structural variant status (incl. copy number, fusion and chromothripsis) of all patients in the Lung Adenocarcinoma and Mesothelioma cohorts into our semantic space. For each cancer, we first defined the set of genes that are most associated to it according to the literature. To project each patient into this semantic space, we next determined if each patient had a mutation in the genes representing the disease semantic vector (e.g. NSCLC). For TCGA data we only counted non-Silent mutations and represented them as a binary number for each gene, i.e. 0 if the patient had no mutations in that gene and 1 if the patient had a non-Silent mutation in the gene. Each patient was then encoded in a binary vector with each member corresponding to a gene from the disease semantic vector. For example, lung adenocarcinoma was associated to 1,367 genes in our semantic space. A lung adenocarcinoma patient’s vector would them be composed of 1,367 binary numbers dictating if the gene is mutated or not in that patient. We then multiply these binary vectors with the semantic disease vector to obtain the patient’s projection in the disease space, which in effect replaces the binary number with the Semantic Association Score between the gene and the disease. Contrary to clustering patient samples by their mutation or structural variant data alone, our projected patient vectors clustered patients together into 22 groups with high patient-to-patient similarity. These clusters recapitulate canonical knowledge about the disease, e.g. Lung Adenocarcinoma patients form clusters that include EGFR-driven and KRAS-driven cohorts. We also see novel groups of patients driven by genes such as MET, STK11 and MALAT1. These clusters can be further stratified by their survival status and other clinical features. We validated our approach with a non-TCGA Mesothelioma cohort, revealing similarities in patient stratification regardless of the data source. This approach represents a dramatic shift in patient segmentation, delivering real-time grouping of patients and biomarker identification, which can accelerate clinical trial design and therapeutic development strategy.

Citation Format: Enrique Garcia-Rivera, Aaron S. Mansfield, Karthik Murugadoss, Murali Aravamudan. Patient segmentation using machine-learning based literature and genomic data synthesis uncovers novel cohorts of NSCLC and mesothelioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2452.

Abstract B20: Discovery of combination therapies in a pan-cancer context through functional complementarity and convergence analysis of oncogenic drivers

Inherent genetic alterations and tissue-specific variations in cancer present a range of unique vulnerabilities which can be targeted by precision cancer therapies. An understanding of these alterations is a crucial first step in developing novel therapeutic hypotheses in a personalized context. An unbiased method to correlate responses to treatment with small-molecules is cancer cell line (CCL) sensitivity profiling. This allows the understanding of single-agent therapies and associated mechanisms of resistance by employing unbiased combination screening. However, performing such studies in a principled manner to understand multiple potential combinations is limited by the large scale of the required experiments. The area under the concentration-response curve can be used as a measure of sensitivity and the relationship between the sensitivity profiles for a large number of drugs helps build a network based on similarity of activity. Hence, it would be possible to identify ‘modules' or ‘clusters' that correspond to small molecules with highly correlated response across a number of cell lines. However, these clusters are often non-informative in predicting or determining potential combinatorial treatments. By leveraging recent whole-exome and RNA sequencing efforts across a diverse panel of human cancer cell lines coupled with small-molecule sensitivity information, this study aims at applying a pan-cancer exome-wide approach to identify potentially synergistic drug combinations.

We define a ‘feature' as an alteration resulting from a single nucleotide variant, genomic amplification or deletion. We first perform feature selection to extract a functionally coupled set of genomic alterations using drug sensitivity as the phenotypic readout. This was achieved through an existing information-theoretic framework which iteratively maximizes the conditional information coefficient of the each potential feature with the target phenotype conditioned on prior selected features. We then integrate gene expression profiles into the model through a regression-based approach. Incorporating sensitivity measurements across a set of 545 small molecules allow us to derive functionally complementary genomic alterations unique to each drug. We find that our model is capable of identifying distinct features even for sets of small molecules that are known to have the same oncogenic target thereby revealing the mechanisitic intricacies that underlie drug activity. This knowledge transforms the drug similarity network. We notice that different small molecules functionally correspond to partially overlapping sets of genomic alterations which belong to the same signalling pathway. Therefore, this enables targeted identification of small molecules or their combinations which are specifically effective against a spectrum of genomic or transcriptomic alterations. We further expand the model to discover features that could confer resistance to therapy. For this case, we systematically identify a number of genomic deletions in tumor suppressors, epigenetic modifiers and genes linked to cell death. Interestingly, these events do not converge onto a single oncogenic pathway, thereby indicating potentially distinct and drug-dependent modes of therapeutic resistance. We believe that the proposed framework presents an unbiased method towards revealing crucial relationships and prospective synergies between different classes of targeted therapeutics. We anticipate that this approach will serve as a template for future efforts focusing on discovery of predictive biomarkers of small molecule sensitivity.

Citation Format: Karthik Murugadoss, Manolis Kellis. Discovery of combination therapies in a pan-cancer context through functional complementarity and convergence analysis of oncogenic drivers [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B20.

Abstract A14: Convergence analysis of regulatory mutations into immuno-modulatory pathways across 14 tumor types

Evasion of immune system surveillance is a hallmark of cancer. Cancer cells may secrete cytokines like TGF-β which hampers cytotoxic T-cells and natural killer cells in addition to recruiting tumor-infiltrating regulatory T-cells endowed with immunosuppressive potential. Little is known about the genomic basis for immune evasion especially in the context of dysregulation and rewiring of the immune-related circuitry of tumor cells. A vast majority of mutations in cancer frequently occur in non-coding regions. The functional impact of these mutations in mediating interactions with the tumor microenvironment have largely been unexplored. Given that recent efforts have implicated non-coding elements in various disease association studies, it can be expected that a significant number of recurrent non-coding mutations in cancer have a regulatory effect. Pan-Cancer analysis has improved the discovery and analysis of these regulatory mutations while avoiding the type I and type II errors made in several tissue-specific cancer projects. By leveraging recent pan-cancer whole-genome sequencing efforts, we have been able to characterize the non-coding mutational profiles of 505 samples, spread across 14 tumor types. These methods amplify the power to detect heterogeneous signals of positive selection thereby enhancing our ability to distinguish ‘driver’ from ‘passenger’ alterations. This study aims at applying a pan-cancer genome-wide approach towards identifying regulatory mutations that potentially impact immune modulation and evasion. We first infer the genome-wide position- and sample-specific probabilities of mutation from somatic mutations calls. A multinomial logistic regression model describes the relationship between the mutation rate and a set of explanatory variables such as the sample ID, replication timing, the genomic context and the local mutation rate. The predicted site-specific probabilities, when overlaid with tissue-specific annotations from the Roadmap Epigenomics consortium and GENCODE, allow us to derive a test statistic for each cis non-coding region that yields information regarding the likelihood that the region of interest is a “driver region”. By utilizing published databases on enhancer-gene links, we extend this framework to comprehensively characterize distal enhancer regions mapped with their target genes. We then systematically identify key immunomodulatory networks enriched for non-coding mutations and evaluate these in a pan-cancer context. A number of immune-related genes are found to harbor an excess of non-coding mutations suggesting higher-order regulatory convergence. We assign a significance score to this convergence by factoring the proximity to the target gene as well as the genomic instability modeled through local copy number changes. Therefore, we integrate individual low-frequency alterations into high-frequency recurrent events across different tumor types. We believe that the proposed model presents an unbiased method towards characterizing the impact of regulatory mutations towards immunomodulation, immune suppression and evasion. We anticipate that this approach will serve as a template for future functional non-coding mutational dissections in tumor-related studies.

Citation Format: Karthik Murugadoss, Malene Rasmussen, Alvin Shi, Manolis Kellis. Convergence analysis of regulatory mutations into immuno-modulatory pathways across 14 tumor types. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A14.

Advancing multiscale structural mapping of the brain through fluorescence imaging and analysis across length scales

Brain function emerges from hierarchical neuronal structure that spans orders of magnitude in length scale, from the nanometre-scale organization of synaptic proteins to the macroscopic wiring of neuronal circuits. Because the synaptic electrochemical signal transmission that drives brain function ultimately relies on the organization of neuronal circuits, understanding brain function requires an understanding of the principles that determine hierarchical neuronal structure in living or intact organisms. Recent advances in fluorescence imaging now enable quantitative characterization of neuronal structure across length scales, ranging from single-molecule localization using super-resolution imaging to whole-brain imaging using light-sheet microscopy on cleared samples. These tools, together with correlative electron microscopy and magnetic resonance imaging at the nanoscopic and macroscopic scales, respectively, now facilitate our ability to probe brain structure across its full range of length scales with cellular and molecular specificity. As these imaging datasets become increasingly accessible to researchers, novel statistical and computational frameworks will play an increasing role in efforts to relate hierarchical brain structure to its function. In this perspective, we discuss several prominent experimental advances that are ushering in a new era of quantitative fluorescence-based imaging in neuroscience along with novel computational and statistical strategies that are helping to distil our understanding of complex brain structure.