The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease.

BACKGROUND: Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions. METHODS: MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology. FINDINGS: MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases. CONCLUSIONS: MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO). FUNDING: NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04

Randomized Noninferiority Trial of Telephone vs In-Person Genetic Counseling for Hereditary Breast and Ovarian Cancer: A 12-Month Follow-Up

Background: Telephone delivery of genetic counseling is an alternative to in-person genetic counseling because it may extend the reach of genetic counseling. Previous reports have established the noninferiority of telephone counseling on short-term psychosocial and decision-making outcomes. Here we examine the long-term impact of telephone counseling (TC) vs inperson counseling (usual care [UC]). Methods: We recruited high-risk women for a noninferiority trial comparing TC with UC. Of 1057 potentially eligible women, 669 were randomly assigned to TC (n = 335) or UC (n = 334), and 512 completed the 12-month follow-up. Primary outcomes were patient-reported satisfaction with genetic testing decision, distress, and quality of life. Secondary outcomes were uptake of cancer risk management strategies. Results: TC was noninferior to UC on all primary outcomes. Satisfaction with decision (d = 0.13, lower bound of 97.5% confidence interval [CI] = -0.34) did not cross its one-point noninferiority limit, cancer-specific distress (d = -2.10, upper bound of 97.5% CI = -0.07) did not cross its four-point noninferiority limit, and genetic testing distress (d = -0.27, upper bound of 97.5% CI = 1.46), physical function (d = 0.44, lower bound of 97.5% CI = -0.91) and mental function (d = -0.04, lower bound of 97.5% CI = -1.44) did not cross their 2.5-point noninferiority limit. Bivariate analyses showed no differences in risk-reducing mastectomy or oophorectomy across groups; however, when combined, TC had significantly more risk-reducing surgeries than UC (17.8% vs 10.5%; chi(2) = 4.43, P = .04). Conclusions: Findings support telephone delivery of genetic counseling to extend the accessibility of this service without long-termadverse outcomes.This study was supported by grants (R01 CA108933 and P30 CA051008) from the National Cancer Institute and by the Jess and Mildred Fisher Center for Hereditary Cancer and Clinical Genomics Research.Peer Reviewe

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven\u27t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO\u27s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology.

MOTIVATION: Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation. RESULTS: In this paper, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use all concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a distantly supervised training dataset for machine learning. Next, a cutting-edge deep learning model is trained to classify each candidate phrase (n-gram from input sentence) into a corresponding concept label. Finally, the dictionary and machine learning-based prediction results are combined for improved performance. Our method is validated with two HPO corpora, and the results show that PhenoTagger compares favorably to previous methods. In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ontologies. Experimental results on the NCBI disease corpus show that PhenoTagger without requiring manually annotated training data achieves competitive performance as compared with state-of-the-art supervised methods. AVAILABILITY: The source code, API information and data for PhenoTagger are freely available at https://github.com/ncbi-nlp/PhenoTagger. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology.

The study aims at developing a neural network model to improve the performance of Human Phenotype Ontology (HPO) concept recognition tools. We used the terms, definitions, and comments about the phenotypic concepts in the HPO database to train our model. The document to be analyzed is first split into sentences and annotated with a base method to generate candidate concepts. The sentences, along with the candidate concepts, are then fed into the pre-trained model for re-ranking. Our model comprises the pre-trained BlueBERT and a feature selection module, followed by a contrastive loss. We re-ranked the results generated by three robust HPO annotation tools and compared the performance against most of the existing approaches. The experimental results show that our model can improve the performance of the existing methods. Significantly, it boosted 3.0% and 5.6% in F1 score on the two evaluated datasets compared with the base methods. It removed more than 80% of the false positives predicted by the base methods, resulting in up to 18% improvement in precision. Our model utilizes the descriptive data in the ontology and the contextual information in the sentences for re-ranking. The results indicate that the additional information and the re-ranking model can significantly enhance the precision of HPO concept recognition compared with the base method

Patient and Genetic Counselor Perceptions of In-Person versus Telephone Genetic Counseling for Hereditary Breast/ovarian Cancer.

Telephone genetic counseling (TC) for high-risk women interested in BRCA1/2 testing has been shown to yield positive outcomes comparable to usual care (UC; in-person) genetic counseling. However, little is known about how genetic counselors perceive the delivery of these alternate forms of genetic counseling. As part of a randomized trial of TC versus UC, genetic counselors completed a 5-item genetic counselor process questionnaire (GCQ) assessing key elements of pre-test sessions (information delivery, emotional support, addressing questions and concerns, tailoring of session, and facilitation of decision- making) with the 479 female participants (TC, N=236; UC, N=243). The GCQ scores did not differ for TC vs. UC sessions (t (477) = 0.11, p = 0.910). However, multivariate analysis showed that participant race/ethnicity significantly predicted genetic counselor perceptions (β = 0.172, p<0.001) in that the GCQ scores were lower for minorities in TC and UC. Exploratory analyses suggested that GCQ scores may be associated with patient preference for UC versus TC (t (79) = 2.21, p=0.030). Additionally, we found that genetic counselor ratings of session effectiveness were generally concordant with patient perceptions of the session. These data indicate that genetic counselors perceive that key components of TC can be delivered as effectively as UC, and that these elements may contribute to specific aspects of patient satisfaction. However, undefined process differences may be present which account for lower counselor perceptions about the effectiveness of their sessions with minority women (i.e., those other than non-Hispanic Whites). We discuss other potential clinical and research implications of our findings

SASH3 variants cause a novel form of X-linked combined immunodeficiency with immune dysregulation

Sterile alpha motif (SAM) and Src homology-3 (SH3) domain-containing 3 (SASH3), also called SH3-containing lymphocyte protein (SLY1), is a putative adaptor protein that is postulated to play an important role in the organization of signaling complexes and propagation of signal transduction cascades in lymphocytes. The SASH3 gene is located on the X-chromosome. Here, we identified 3 novel SASH3 deleterious variants in 4 unrelated male patients with a history of combined immunodeficiency and immune dysregulation that manifested as recurrent sinopulmonary, cutaneous, and mucosal infections and refractory autoimmune cytopenias. Patients exhibited CD4+ T-cell lymphopenia, decreased T-cell proliferation, cell cycle progression, and increased T-cell apoptosis in response to mitogens. In vitro T-cell differentiation of CD34+ cells and molecular signatures of rearrangements at the T-cell receptor α (TRA) locus were indicative of impaired thymocyte survival. These patients also manifested neutropenia and B-cell and natural killer (NK)-cell lymphopenia. Lentivirus-mediated transfer of the SASH3 complementary DNA–corrected protein expression, in vitro proliferation, and signaling in SASH3-deficient Jurkat and patient-derived T cells. These findings define a new type of X-linked combined immunodeficiency in humans that recapitulates many of the abnormalities reported in mice with Sly1–/– and Sly1Δ/Δ mutations, highlighting an important role of SASH3 in human lymphocyte function and survival.</p