Empirical evaluation of language modeling to ascertain cancer outcomes from clinical text reports.

BACKGROUND: Longitudinal data on key cancer outcomes for clinical research, such as response to treatment and disease progression, are not captured in standard cancer registry reporting. Manual extraction of such outcomes from unstructured electronic health records is a slow, resource-intensive process. Natural language processing (NLP) methods can accelerate outcome annotation, but they require substantial labeled data. Transfer learning based on language modeling, particularly using the Transformer architecture, has achieved improvements in NLP performance. However, there has been no systematic evaluation of NLP model training strategies on the extraction of cancer outcomes from unstructured text. RESULTS: We evaluated the performance of nine NLP models at the two tasks of identifying cancer response and cancer progression within imaging reports at a single academic center among patients with non-small cell lung cancer. We trained the classification models under different conditions, including training sample size, classification architecture, and language model pre-training. The training involved a labeled dataset of 14,218 imaging reports for 1112 patients with lung cancer. A subset of models was based on a pre-trained language model, DFCI-ImagingBERT, created by further pre-training a BERT-based model using an unlabeled dataset of 662,579 reports from 27,483 patients with cancer from our center. A classifier based on our DFCI-ImagingBERT, trained on more than 200 patients, achieved the best results in most experiments; however, these results were marginally better than simpler "bag of words" or convolutional neural network models. CONCLUSION: When developing AI models to extract outcomes from imaging reports for clinical cancer research, if computational resources are plentiful but labeled training data are limited, large language models can be used for zero- or few-shot learning to achieve reasonable performance. When computational resources are more limited but labeled training data are readily available, even simple machine learning architectures can achieve good performance for such tasks.

Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset.

To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for participants with any of 7 solid tumors in a precision oncology study. Outcomes are extracted from 305,151 imaging reports for 13,130 patients and 233,517 oncologist notes for 13,511 patients, including patients with 6 additional cancer types. NLP models recapitulate outcome annotation from these documents, including the presence of cancer, progression/worsening, response/improvement, and metastases, with excellent discrimination (AUROC > 0.90). Models generalize to cancers excluded from training and yield outcomes correlated with survival. Among patients receiving checkpoint inhibitors, we confirm that high tumor mutation burden is associated with superior progression-free survival ascertained using NLP. Here, we show that deep NLP can accelerate annotation of molecular cancer datasets with clinically meaningful endpoints to facilitate discovery.

Biologically informed deep neural network for prostate cancer discovery.

The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge1,2. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics3-5. Here we developed P-NET-a biologically informed deep learning model-to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types.

Clinical Inflection Point Detection on the Basis of EHR Data to Identify Clinical Trial-Ready Patients With Cancer.

PURPOSE: To inform precision oncology, methods are needed to use electronic health records (EHRs) to identify patients with cancer who are experiencing clinical inflection points, consistent with worsening prognosis or a high propensity to change treatment, at specific time points. Such patients might benefit from real-time screening for clinical trials. METHODS: Using serial unstructured imaging reports for patients with solid tumors or lymphoma participating in a single-institution precision medicine study, we trained a deep neural network natural language processing (NLP) model to dynamically predict patients' prognoses and propensity to start new palliative-intent systemic therapy within 30 days. Model performance was evaluated using Harrell's c-index (for prognosis) and the area under the receiver operating characteristic curve (AUC; for new treatment and new clinical trial enrollment). Associations between model outputs and manual annotations of cancer progression were also evaluated using the AUC. RESULTS: A deep NLP model was trained and evaluated using 302,688 imaging reports for 16,780 patients. In a held-out test set of 34,770 reports for 1,952 additional patients, the model predicted survival with a c-index of 0.76 and initiation of new treatment with an AUC of 0.77. Model-generated prognostic scores were associated with annotation of cancer progression on the basis of manual EHR review (n = 1,488 reports for 110 patients with lung or colorectal cancer) with an AUC of 0.78, and predictions of new treatment were associated with annotation of cancer progression on the basis of manual EHR review with an AUC of 0.84. CONCLUSION: Training a deep NLP model to identify clinical inflection points among patients with cancer is feasible. This approach could identify patients who may benefit from real-time targeted clinical trial screening interventions at health system scale.

Systematic auditing is essential to debiasing machine learning in biology.

Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences. We use this auditing framework to examine biases in three ML applications of therapeutic interest and identify unrecognized biases that hinder the ML process and result in substantially reduced model performance on new datasets. Ultimately, we show that ML models tend to learn primarily from data biases when there is insufficient signal in the data to learn from. We provide detailed protocols, guidelines, and examples of code to enable tailoring of the auditing framework to other biomedical applications.

Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology.

Tumor molecular profiling of single gene-variant ('first-order') genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (for example, mutational signatures) are increasingly associated with clinical outcomes, but these 'second-order' alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomic data for point-of-care decision making and translational-hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles.

Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma.

Importance: Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection. Objective: To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer. Design, Setting, and Participants: A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017. Exposures: Germline variant detection using standard or deep learning methods. Main Outcomes and Measures: The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach. Results: The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, -1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, -2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]). Conclusions and Relevance: Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.

Natural Language Processing to Ascertain Cancer Outcomes From Medical Oncologist Notes.

PURPOSE: Cancer research using electronic health records and genomic data sets requires clinical outcomes data, which may be recorded only in unstructured text by treating oncologists. Natural language processing (NLP) could substantially accelerate extraction of this information. METHODS: Patients with lung cancer who had tumor sequencing as part of a single-institution precision oncology study from 2013 to 2018 were identified. Medical oncologists' progress notes for these patients were reviewed. For each note, curators recorded whether the assessment/plan indicated any cancer, progression/worsening of disease, and/or response to therapy or improving disease. Next, a recurrent neural network was trained using unlabeled notes to extract the assessment/plan from each note. Finally, convolutional neural networks were trained on labeled assessments/plans to predict the probability that each curated outcome was present. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) among a held-out test set of 10% of patients. Associations between curated response or progression end points and overall survival were measured using Cox models among patients receiving palliative-intent systemic therapy. RESULTS: Medical oncologist notes (n = 7,597) were manually curated for 919 patients. In the 10% test set, NLP models replicated human curation with AUROCs of 0.94 for the any-cancer outcome, 0.86 for the progression outcome, and 0.90 for the response outcome. Progression/worsening events identified using NLP models were associated with shortened survival (hazard ratio [HR] for mortality, 2.49; 95% CI, 2.00 to 3.09); response/improvement events were associated with improved survival (HR, 0.45; 95% CI, 0.30 to 0.67). CONCLUSION: NLP models based on neural networks can extract meaningful outcomes from oncologist notes at scale. Such models may facilitate identification of clinical and genomic features associated with response to cancer treatment.

Author Correction: Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma.

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

ATM Loss Confers Greater Sensitivity to ATR Inhibition Than PARP Inhibition in Prostate Cancer.

Alterations in DNA damage response (DDR) genes are common in advanced prostate tumors and are associated with unique genomic and clinical features. ATM is a DDR kinase that has a central role in coordinating DNA repair and cell-cycle response following DNA damage, and ATM alterations are present in approximately 5% of advanced prostate tumors. Recently, inhibitors of PARP have demonstrated activity in advanced prostate tumors harboring DDR gene alterations, particularly in tumors with BRCA1/2 alterations. However, the role of alterations in DDR genes beyond BRCA1/2 in mediating PARP inhibitor sensitivity is poorly understood. To define the role of ATM loss in prostate tumor DDR function and sensitivity to DDR-directed agents, we created a series of ATM-deficient preclinical prostate cancer models and tested the impact of ATM loss on DNA repair function and therapeutic sensitivities. ATM loss altered DDR signaling, but did not directly impact homologous recombination function. Furthermore, ATM loss did not significantly impact sensitivity to PARP inhibition but robustly sensitized to inhibitors of the related DDR kinase ATR. These results have important implications for planned and ongoing prostate cancer clinical trials and suggest that patients with tumor ATM alterations may be more likely to benefit from ATR inhibitor than PARP inhibitor therapy. SIGNIFICANCE: ATM loss occurs in a subset of prostate tumors. This study shows that deleting ATM in prostate cancer models does not significantly increase sensitivity to PARP inhibition but does sensitize to ATR inhibition.See related commentary by Setton and Powell, p. 2085.

Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma.

Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.

Harmonization of Tumor Mutational Burden Quantification and Association With Response to Immune Checkpoint Blockade in Non-Small-Cell Lung Cancer.

PURPOSE: Heterogeneity in tumor mutational burden (TMB) quantification across sequencing platforms limits the application and further study of this potential biomarker of response to immune checkpoint inhibitors (ICI). We hypothesized that harmonization of TMB across platforms would enable integration of distinct clinical datasets to better characterize the association between TMB and ICI response. METHODS: Cohorts of NSCLC patients sequenced by one of three targeted panels or by whole exome sequencing (WES) were compared (total n=7297). TMB was calculated uniformly and compared across cohorts. TMB distributions were harmonized by applying a normal transformation followed by standardization to z-scores. In sub-cohorts of patients treated with ICIs (DFCI n=272; MSKCC n=227), the association between TMB and outcome was assessed. Durable clinical benefit (DCB) was defined as responsive/stable disease lasting ≥6 months. RESULTS: TMB values were higher in the panel cohorts than the WES cohort. Average mutation rates per gene were highly concordant across cohorts (Pearson coefficient 0.842-0.866). Subsetting the WES cohort by gene panels only partially reproduced the observed differences in TMB. Standardization of TMB into z-scores harmonized TMB distributions and enabled integration of the ICI-treated sub-cohorts. Simulations indicated that cohorts >900 are necessary for this approach. TMB did not associate with response in never smokers or patients harboring targetable driver alterations, although these analyses were under-powered. Increasing TMB thresholds increased DCB rate, but DCB rates within deciles varied. Receiver operator curves yielded an area under the curve of 0.614 with no natural inflection point. CONCLUSION: Z-score conversion harmonizes TMB values and enables integration of datasets derived from different sequencing panels. Clinical and biologic features may provide context to the clinical application of TMB, and warrant further study.

Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports.

IMPORTANCE: A rapid learning health care system for oncology will require scalable methods for extracting clinical end points from electronic health records (EHRs). Outside of clinical trials, end points such as cancer progression and response are not routinely encoded into structured data. OBJECTIVE: To determine whether deep natural language processing can extract relevant cancer outcomes from radiologic reports, a ubiquitous but unstructured EHR data source. DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study evaluated 1112 patients who underwent tumor genotyping for a diagnosis of lung cancer and participated in the Dana-Farber Cancer Institute PROFILE study from June 26, 2013, to July 2, 2018. EXPOSURES: Patients were divided into curation and reserve sets. Human abstractors applied a structured framework to radiologic reports for the curation set to ascertain the presence of cancer and changes in cancer status over time (ie, worsening/progressing vs improving/responding). Deep learning models were then trained to capture these outcomes from report text and subsequently evaluated in a 10% held-out test subset of curation patients. Cox proportional hazards regression models compared human and machine curations of disease-free survival, progression-free survival, and time to improvement/response in the curation set, and measured associations between report classification and overall survival in the curation and reserve sets. MAIN OUTCOMES AND MEASURES: The primary outcome was area under the receiver operating characteristic curve (AUC) for deep learning models; secondary outcomes were time to improvement/response, disease-free survival, progression-free survival, and overall survival. RESULTS: A total of 2406 patients were included (mean [SD] age, 66.5 [10.8] years; 1428 female [59.7%]; 2170 [90.2%] white). Radiologic reports (n = 14 230) were manually reviewed for 1112 patients in the curation set. In the test subset (n = 109), deep learning models identified the presence of cancer, improvement/response, and worsening/progression with accurate discrimination (AUC >0.90). Machine and human curation yielded similar measurements of disease-free survival (hazard ratio [HR] for machine vs human curation, 1.18; 95% CI, 0.71-1.95); progression-free survival (HR, 1.11; 95% CI, 0.71-1.71); and time to improvement/response (HR, 1.03; 95% CI, 0.65-1.64). Among 15 000 additional reports for 1294 reserve set patients, algorithm-detected cancer worsening/progression was associated with decreased overall survival (HR for mortality, 4.04; 95% CI, 2.78-5.85), and improvement/response was associated with increased overall survival (HR, 0.41; 95% CI, 0.22-0.77). CONCLUSIONS AND RELEVANCE: Deep natural language processing appears to speed curation of relevant cancer outcomes and facilitate rapid learning from EHR data.

Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine.

Immune checkpoint blockade (ICB) therapies, which potentiate the body's natural immune response against tumor cells, have shown immense promise in the treatment of various cancers. Currently, tumor mutational burden (TMB) and programmed death ligand 1 (PD-L1) expression are the primary biomarkers evaluated for clinical management of cancer patients across histologies. However, the wide range of responses has demonstrated that the specific molecular and genetic characteristics of each patient's tumor and immune system must be considered to maximize treatment efficacy. Here, we review the various biological pathways and emerging biomarkers implicated in response to PD-(L)1 and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) therapies, including oncogenic signaling pathways, human leukocyte antigen (HLA) variability, mutation and neoantigen burden, microbiome composition, endogenous retroviruses (ERV), and deficiencies in chromatin remodeling and DNA damage repair (DDR) machinery. We also discuss several mechanisms that have been observed to confer resistance to ICB, such as loss of phosphatase and tensin homolog (PTEN), loss of major histocompatibility complex (MHC) I/II expression, and activation of the indoleamine 2,3-dioxygenase 1 (IDO1) and transforming growth factor beta (TGFß) pathways. Clinical trials testing the combination of PD-(L)1 or CTLA-4 blockade with molecular mediators of these pathways are becoming more common and may hold promise for improving treatment efficacy and response. Ultimately, some of the genes and molecular mechanisms highlighted in this review may serve as novel biological targets or therapeutic vulnerabilities to improve clinical outcomes in patients.

Beacon Editor: Capturing Signal Transduction Pathways Using the Systems Biology Graphical Notation Activity Flow Language.

The Beacon Editor is a cross-platform desktop application for the creation and modification of signal transduction pathways using the Systems Biology Graphical Notation Activity Flow (SBGN-AF) language. Prompted by biologists' requests for enhancements, the Beacon Editor includes numerous powerful features for the benefit of creation and presentation.

A Proposal for a Genome Similarity-Based Taxonomy for Plant-Pathogenic Bacteria that Is Sufficiently Precise to Reflect Phylogeny, Host Range, and Outbreak Affiliation Applied to Pseudomonas syringae sensu lato as a Proof of Concept.

Taxonomy of plant pathogenic bacteria is challenging because pathogens of different crops often belong to the same named species but current taxonomy does not provide names for bacteria below the subspecies level. The introduction of the host range-based pathovar system in the 1980s provided a temporary solution to this problem but has many limitations. The affordability of genome sequencing now provides the opportunity for developing a new genome-based taxonomic framework. We already proposed to name individual bacterial isolates based on pairwise genome similarity. Here, we expand on this idea and propose to use genome similarity-based codes, which we now call life identification numbers (LINs), to describe and name bacterial taxa. Using 93 genomes of Pseudomonas syringae sensu lato, LINs were compared with a P. syringae genome tree whereby the assigned LINs were found to be informative of a majority of phylogenetic relationships. LINs also reflected host range and outbreak association for strains of P. syringae pathovar actinidiae, a pathovar for which many genome sequences are available. We conclude that LINs could provide the basis for a new taxonomic framework to address the shortcomings of the current pathovar system and to complement the current taxonomic system of bacteria in general.

A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis.

Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git.

Potential targets of VIVIPAROUS1/ABI3-LIKE1 (VAL1) repression in developing Arabidopsis thaliana embryos.

Developing Arabidopsis seeds accumulate oils and seed storage proteins synthesized by the pathways of primary metabolism. Seed development and metabolism are positively regulated by transcription factors belonging to the LAFL (LEC1, AB13, FUSCA3 and LEC2) regulatory network. The VAL gene family encodes repressors of the seed maturation program in germinating seeds, although they are also expressed during seed maturation. The possible regulatory role of VAL1 in seed development has not been studied to date. Reverse genetics revealed that val1 mutant seeds accumulated elevated levels of proteins compared with the wild type, suggesting that VAL1 functions as a repressor of seed metabolism; however, in the absence of VAL1, the levels of metabolites, ABA, auxin and jasmonate derivatives did not change significantly in developing embryos. Two VAL1 splice variants were identified through RNA sequencing analysis: a full-length form and a truncated form lacking the plant homeodomain-like domain associated with epigenetic repression. None of the transcripts encoding the core LAFL network transcription factors were affected in val1 embryos. Instead, activation of VAL1 by FUSCA3 appears to result in the repression of a subset of seed maturation genes downstream of core LAFL regulators, as 39% of transcripts in the FUSCA3 regulon were derepressed in the val1 mutant. The LEC1 and LEC2 regulons also responded, but to a lesser extent. Additional 832 transcripts that were not LAFL targets were derepressed in val1 mutant embryos. These transcripts are candidate targets of VAL1, acting through epigenetic and/or transcriptional repression.

Similarity-based codes sequentially assigned to ebolavirus genomes are informative of species membership, associated outbreaks, and transmission chains.

Background. Developing a universal standardized microbial typing and nomenclature system that provides phylogenetic and epidemiological information in real time has never been as urgent in public health as it is today. We previously proposed to use genome similarity as the basis for immediate and precise typing and naming of individual organisms or viruses. In this study, we tested the validity of the proposed system and applied it to the epidemiology of infectious diseases using Ebola virus disease (EVD) outbreaks as the example. Methods. One hundred twenty-eight publicly available ebolavirus genomes were compared with each other, and average nucleotide identity (ANI) was calculated. The ANI was then used to assign unique codes, hereafter referred to as Life Identification Numbers (LINs), to every viral isolate, whereby each LIN consisted of a series of positions reflecting increasing genome similarity. Congruence of LINs with phylogenetic and epidemiological relationships was then determined. Results. Assigned LINs correlate with phylogeny at the species and infraspecies level and can even identify some individual transmission chains during the 2014-2015 EVD epidemic in West Africa. Conclusions. Life Identification Numbers can provide a fast, automated, standardized, and scalable approach to precisely identify and name viral isolates upon genome sequence submission, facilitating unambiguous communication during disease epidemics among clinicians, epidemiologists, and governments.