• Shirley Liu, GV20 Therapeutics

Despite the exciting clinical benefits of immune checkpoint inhibitors, only a minority of cancer patients respond to treatment. Addressing resistance to immune checkpoint inhibitors is an urgent unmet need and requires new approaches for target identification and drug discovery.

GV20 Therapeutics adopts an interdisciplinary approach integrating functional genomics, big data AI, and cancer immunology for IO target identification and drug discovery. We use proprietary CRISPR screens to identify IO targets of interest and bioinformatics on big data for target validation. We previously published a computational algorithm TRUST to extract the tumor-infiltrating antibody sequences from tumor RNA-seq profiles. Using an AI approach trained on hundreds of millions of tumor-infiltrating antibodies and billions of circulating antibodies, we could de novo design antibodies against targets without any known antibody sequences against the targets. This approach not only designs antibodies enriched in functional binders and good developability profiles, but also provides insights on target identification and validation.

Using this approach, we identified a novel IO target, its antibodies, and its mechanism. Through multiple syngeneic tumor models, we also demonstrated its single-agent efficacy as well as its synergy with anti-PD1 in controlling tumor growth. We are submitting an Investigational New Drug (IND) Application to the U.S. Food and Drug Administration (FDA) to test this new drug in the clinic. Our efforts represent just the beginning of combining genomics and AI to unlock the hidden gems from patient tumors to develop cancer immunotherapeutics.

• Soumya Raychaudhuri, Bigham and Women's Hospital, Harvard Medical School, Broad Institute, United States

TBD

• Ferhat Ay

Virtual

• Sara Mostafavi

TBD

• Duygu Ucar

Differences in immune function and responses contribute to health- and life-span disparities between sexes. However, the role of sex in immune system aging is not well understood. Here, we characterize peripheral blood mononuclear cells from 172 healthy adults 22–93 years of age using ATAC-seq, RNA-seq and flow cytometry. These data reveal a shared epigenomic signature of aging including declining naïve T cells and increasing monocyte and cytotoxic cell functions. These changes are greater in magnitude in men and accompanied by a male-specific decline in B-cell-specific loci. Interestingly, genomic differences between sexes increase after age 65, with men having higher innate and pro-inflammatory activity and lower adaptive activity. In a separate cohort (n=39) we studied how older adults respond to pneumococcal vaccines: Prevnar and Pneumovax. Both age and sex have a significant impact on vaccine responses.

• Yuri Pritykin

TBD

• John Tsang

• Christina Leslie

We will present recent computational work from our group on decoding the immune response to cancer at single cell resolution. We will describe a semi-supervised deep learning method for automatically annotating immune cell types and functional states while disentangling batch effects in scRNA-seq profiles of the tumor immune microenvironment. We will also describe collaborative work with Emily Cheng on engineering immunogenic cancer cell death as a strategy to enhance cancer immunotherapy, where we adopt a topic model approach to characterize the different T cell responses to immunogenic vs non-immunogenic cell death.

• Bo Li

Similarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

• Ken Hoehn

B cells are an evolutionary system, undergoing rapid somatic hypermutation and antigen-driven selection as part of the adaptive immune response. B cell lineage trees inferred from B cell receptor sequencing data represent the history of mutations in a lineage, and can also link multiple forms of B cell diversity. For example, B cell lineage trees sampled from multiple tissues from the same subject could represent B cell migration between tissues. Trees sampled at different timepoints in the same subject represent clonal persistence over time. Here, we introduce new phylogenetic methods that use lineage trees to understand patterns of B cell migration, differentiation, and evolution over time. We demonstrate how this framework has been used to understand the role of B cell migration and differentiation in multiple immune conditions. Further, we show how longitudinally sampled data can be used to test for ongoing evolution in B cell lineages. Using large, publicly available AIRR-seq datasets, we demonstrate how different infections, vaccinations, and other conditions differ significantly in their ability to stimulate evolution over time in B cells. Some conditions, such as HIV infection, stimulate high strong signatures of B cell evolution, while others such as influenza vaccination produce a more compartmentalized response detectable only with direct germinal center sequencing. These methods are widely applicable and implemented in the R package dowser, available at https://bitbucket.org/kleinstein/dowser.

• Linglin Huang, Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
• Alexandra Schnell, Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA 02115, USA
• Meromit Singer, Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
• Aviv Regev, Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
• Vijay Kuchroo, Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA 02115, USA

While intestinal Th17 cells are critical for maintaining tissue homeostasis, recent studies have implicated their roles in the development of extra-intestinal autoimmune diseases including multiple sclerosis. However, the mechanisms by which tissue Th17 cells mediate these dichotomous functions remain unknown. Here, we characterized the heterogeneity, plasticity, and migratory phenotypes of tissue Th17 cells in vivo by combined fate mapping with profiling of the transcriptomes and TCR clonotypes of over 84,000 Th17 cells at homeostasis and during CNS autoimmune inflammation. Inter- and intra-organ single-cell analyses revealed a homeostatic, stem-like TCF1+ IL-17+ SLAMF6+ population that traffics to the intestine where it is maintained by the microbiota, providing a ready-reservoir for the IL-23-driven generation of encephalitogenic GM-CSF+ IFNγ+ CXCR6+ T cells. Our study defines a direct in vivo relationship between IL-17+ non-pathogenic and GM-CSF+ and IFNγ+ pathogenic Th17 populations and provides a mechanism by which homeostatic intestinal Th17 cells direct extra-intestinal autoimmune disease.

• Chuan Xu, Wellcome Sanger Institute, United Kingdom
• Cecilia Dominguez-Conde, Wellcome Sanger Institute, United Kingdom
• Tomás Gomes, Wellcome Sanger Institute, United Kingdom
• Sarah Teichmann, Wellcome Sanger Institute, United Kingdom

The ever-increasing single-cell RNA-sequencing (scRNA-seq) datasets are largely expanding the compendium of cell types in the field of immunology, while at the same time raising the need to rapidly and precisely annotate the diverse cell types in order to fully utilize their information. This problem is particularly prominent when tackling large-scale or homogenous cell populations. To assist in the accurate classification of different cell types and subtypes, we assembled a comprehensive immune cell atlas from 19 studies, and developed CellTypist (https://github.com/Teichlab/celltypist), an automated cell type annotation tool for scRNA-seq datasets based on logistic regression classifiers optimized by the stochastic gradient descent algorithm. Prediction models in CellTypist are derived from fine-tuned procedures including feature selection and mini-batch training. Combined with the majority voting refinement, CellTypist can assign cell identities to hundreds of thousands of cells with more than 90% accuracy within minutes. Given the high scalability of CellTypist, it can be adapted to various use cases in addition to the current immune atlas, contributing to the community endeavors of cell type standards.