Molecular and structural basis for Lewis glycan recognition by a cancer-targeting antibody

Immunotherapy has been successful in treating many tumour types. The development of additional tumour-antigen binding monoclonal antibodies (mAbs) will help expand the range of immunotherapeutic targets. Lewis histo-blood group and related glycans are overexpressed on many carcinomas, including those of the colon, lung, breast, prostate and ovary, and can therefore be selectively targeted by mAbs. Here we examine the molecular and structural basis for recognition of extended Lea and Lex containing glycans by a chimeric mAb. Both the murine (FG88.2) IgG3 and a chimeric (ch88.2) IgG1 mAb variants showed reactivity to colorectal cancer cells leading to significantly reduced cell viability. We determined the X-ray structure of the unliganded ch88.2 fragment antigen-binding (Fab) containing two Fabs in the unit cell. A combination of molecular docking, glycan grafting and molecular dynamics simulations predicts two distinct subsites for recognition of Lea and Lex trisaccharides. While light chain residues were exclusively used for Lea binding, recognition of Lex involved both light and heavy chain residues. An extended groove is predicted to accommodate the Lea–Lex hexasaccharide with adjoining subsites for each trisaccharide. The molecular and structural details of the ch88.2 mAb presented here provide insight into its cross-reactivity for various Lea and Lex containing glycans. Furthermore, the predicted interactions with extended epitopes likely explains the selectivity of this antibody for targeting Lewis-positive tumours

Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease

Congenital heart disease (CHD) is a rare structural defect that occurs in ∼1% of live births. Studies on CHD genetic architecture have identified pathogenic single-gene mutations in less than 30% of cases. Single-gene mutations often show incomplete penetrance and variable expressivity. Therefore, we hypothesize that genetic background may play a role in modulating disease expression. Polygenic risk scores (PRSs) aggregate effects of common genetic variants to investigate whether, cumulatively, these variants are associated with disease penetrance or severity. However, the major limitations in this field have been in generating sufficient sample sizes for these studies. Here we used CHD-phenotype matched genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) as our base study and whole-genome sequencing data from the CHD cohort (n1 = 711 trios, n2 = 362 European trios) of the Gabriella Miller Kids First dataset as our target study to develop PRSs for CHD. PRSs estimated using a GWAS for heart valve problems and heart murmur explain 2.5% of the variance in case-control status of CHD (all SNVs, p = 7.90 × 10-3; fetal cardiac SNVs, p = 8.00 × 10-3) and 1.8% of the variance in severity of CHD (fetal cardiac SNVs, p = 6.20 × 10-3; all SNVs, p = 0.015). These results show that common variants captured in CHD phenotype-matched GWASs have a modest but significant contribution to phenotypic expression of CHD. Further exploration of the cumulative effect of common variants is necessary for understanding the complex genetic etiology of CHD and other rare diseases

A reference set of curated biomedical data and metadata from clinical case reports.

Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials

Metadata Extraction Guide

This file provides a guide to the process performed in assembly of the Metadata Acquired from Clinical Case Reports (MACCR) data set

MACCRs.tsv

This file contains 3,100 sets of metadata extracted from clinical case reports. Each metadata record includes information identifying the source report, text corresponding to high-level medical concepts, and funding details

MACCR_RMD_ICD10_Categories

This file contains a set of scores indicating presence of ICD-10-CM codes, grouped into categories, as determined by a panel of domain experts reading clinical case reports describing presentations of rare mitochondrial diseases. These reports are a subset of those used in assembly of the MACCR set. Each row represents a single report, while each column contains a value of 0 (denoting the material corresponding to any code in the category named in the header was not observed) or 1 (denoting at least once code for material within the category named in the header was described in the report text). Reports are identified using their PubMed IDs

MACCR_RMD_ICD10

This file contains a set of scores indicating presence of ICD-10-CM codes, as determined by a panel of domain experts reading clinical case reports describing presentations of rare mitochondrial diseases. These reports are a subset of those used in assembly of the MACCR set. Each row represents a single report, while each column contains a value of 0 (denoting the material corresponding to the code in the header was not observed) or 1 (denoting material corresponding to the code was described in the report text). Reports are identified using their PubMed IDs

MACCR_mesh.tsv

This file contains the MeSH descriptors associated with all source clinical case reports used in assembly of the MACCR set. Each row contains a single descriptor, followed by its single-letter category. All terms are unique and are not repeated. Modifiers are not included. Terms correspond to the 2018 version of MeSH. Please note that the U.S. National Library of Medicine is the creator, maintainer, and provider of MeSH. No proprietary rights to any MeSH content are claimed

MACCR_citations

This BibTeX file contains citations for the source clinical case reports from which metadata were extracted in the assembly of the MACCR set. No abstract text is included