Brouwer, E. | Search bioRxiv

bioRxiv2021

Genetic, environmental and intrinsic determinants of the human antibody epitope repertoire

Phage-displayed immunoprecipitation sequencing (PhIP-Seq) has successfully enabled high-throughput profiling of human antibody profiles. However, a comprehensive overview of environmental and genetic determinants shaping human adaptive immunity is currently lacking. In this study, we aimed to investigate the effects of genetic, environmental and intrinsic factors on the variation in human antibody repertoires. We characterized serological antibody repertoires against 344,000 peptides using PhIP-Seq libraries from a wide range of microbial and environmental antigens in 1,443 participants from a population cohort. We demonstrate individual-specificity, temporal consistency and co-housing similarities in antibody repertoire. Genetic analyses showed involvement of the HLA, IGHV and FUT2 regions. Furthermore, we uncovered associations between 48 phenotypic factors and 544 antibody-bound peptides, including age, cell counts, sex, smoking behavior and allergies, among others. Overall, our results indicate that human antibody epitope repertoires are shaped by both host genetics and environmental exposures and highlight unique signatures of distinct phenotypes and genotypes.

immunology↗

bioRxiv2020

piNET: An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

In this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and wholeslide images (WSI), representing a diverse multicentre dataset for evaluating Ki67 quantification. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefficient R = 0.927. The success of the system can be attributed to a number of innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images - and it was posed as a detection problem to mimic pathologists workflow which improves accuracy and efficiency. Secondly, the system is trained purely on tumour cells, which reduces false positives from non-tumour cells without needing the usual pre-requisite tumour segmentation step for Ki67 quantification. Thirdly, the concept of learning background regions through weak supervision is introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain "significant" activity.

bioinformatics↗