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Principal Investigator: Li Zhang
Organization: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
Fiscal Year: 2024
Award: $342,414
Funding agency: National Library of Medicine
PROJECT SUMMARY
The adaptive immune system is responsible for the specific recognition and elimination of antigens originating
from infection and disease. It recognizes antigens via an immense array of antigen-binding antibodies (B-cell
receptors, BCRs) and T-cell receptors (TCRs), the immune repertoire. Because of the enormous breadth of
epitopes recognized by immune repertoires, immune repertoires are extremely diverse and dynamic. Advances
in immune receptor sequencing (Rep-seq), such as next generation sequencing, have driven the quantitative
and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the
immune receptor sequence landscape. However, the current analysis tools lack the ability to track and examine
the dynamic nature of the repertoire across serial time points or correlate with clinical outcomes. We propose to
use network analysis and formulate a novel ensemble feature selection approach, along with other
advanced machine learning techniques and statistical approaches (e.g., Bayesian nonparametric approach
and shrinkage estimation method), to interrogate and measure immune repertoire architecture longitudinally
and in a clinical context. Network analysis is a powerful approach that can help us identify TCRs sharing antigen
specificity and highly mutable BCR, which can help to develop or improve existing immunotherapeutics and
immunodiagnostics. To integrate gene expression data and scRep-seq data in single-cell setting, we propose to
apply the multitable mixed-membership approach to construct a network to increase the resolution of T and
B cell clusters. In addition, we assess the importance of shared clusters by introducing Bayes factor to
incorporate clonal generation probability and real data abundance. B and T cell responses develop in parallel
and influence one another, thus we will further study how BCR/TCR network properties interact, in addition to
assessing their individual response separately. We will implement the proposed methods on multiple studies to
better illustrate the diversity and richness of the data to demonstrate the flexibility and power of the proposed
tools. These studies are unique and generalizable, because they include three cancer types spanning from
immunogenic to non-immunogenic in both metastatic and localized settings with different
immunotherapeutic modalities. In addition, the proposed methods can be used to study immune response to
diseases besides cancer, including respiratory coronaviruses, such as SARS-CoV-2. Therefore, first, we will
investigate the landscape of bulk Rep-seq changes over serial timepoints for prostate cancer patients who
received Sipuleucel-T and COVID-19 patients. We will develop prognostic/prediction model based on network
properties with clinical outcome/characteristics for durvalumab-treated lung cancer patients to elucidate the
clinically prognostic features of the network as well classify SARS-CoV-2 infected patients from healthy donors.
Moreover, based on unique features of single-cell RNA sequencing, we will classify the immune cells and study
the T and B cell responses to immunotherapy (CD40 agonist antibody) for esophageal and gastroesophageal
junction cancer patients. Furthermore, we will develop bioinformatics software by incorporating the proposed
methods and techniques to tackle the complexity of the immunosequencing data in a translational fashion and
provide a comprehensive platform with user-friendly visualization tools.
Terms: <2019 novel corona virus><2019 novel coronavirus><2019-nCoV><Adaptive Immune System><Agonist><Antibodies><Antigenic Determinants><Antigens><Architecture><B blood cells><B cell><B cell receptor><B cell repertoire><B cells><B-Cell Antigen Receptor><B-Cells><B-Lymphocytes><B-cell><Bar Codes><Binding Determinants><Bio-Informatics><Bioinformatics><Blood><Blood Reticuloendothelial System><Bp50><CD40><CDW40><COVID infected patient><COVID patient><COVID positive patient><COVID-19 infected patient><COVID-19 patient><COVID-19 positive patient><COVID-19 virus><COVID19 patient><COVID19 positive patient><COVID19 virus><Cancer Patient><Cancers><Cell Body><Cells><Characteristics><Classification><Clinical><CoV-2><CoV2><Computational Technique><Computer Analysis><Computer software><Coronaviridae><Coronavirus><Data><Development><Disease><Disorder><Engineering / Architecture><Environment><Epitopes><Esophagogastric Junction><Esophagus><Evaluation><Evolution><Future><Gastroesophageal Junction><Gene Expression><Generalized Growth><Generations><Genetic Heterogeneity><Goals><Growth><Immune><Immune Globulins><Immune mediated therapy><Immune response><Immunes><Immunodiagnostics><Immunoglobulins><Immunologic Receptors><Immunological Receptors><Immunological response><Immunologically Directed Therapy><Immunotherapeutic agent><Immunotherapy><Infection><Investigation><Joints><MGC9013><MHC Receptor><Machine Learning><Major Histocompatibility Complex Receptor><Malignant Neoplasms><Malignant Tumor><Malignant Tumor of the Lung><Malignant Tumor of the Prostate><Malignant neoplasm of lung><Malignant neoplasm of prostate><Malignant prostatic tumor><Measures><Methods><Modality><Modeling><Molecular><NGS Method><NGS system><Nature><Network Analysis><Outcome><Pathway Analysis><Pattern><Probability><Process><Property><Prostate CA><Prostate Cancer><Prostate malignancy><Prostatic Cancer><Provenge><Pulmonary Cancer><Pulmonary malignant Neoplasm><Pythons><Reaction><Research Specimen><Resolution><Role><SARS corona virus 2><SARS-CO-V2><SARS-COVID-2><SARS-CoV-2><SARS-CoV-2 infected patient><SARS-CoV-2 patient><SARS-CoV-2 positive patient><SARS-CoV2><SARS-associated corona virus 2><SARS-associated coronavirus 2><SARS-coronavirus-2><SARS-related corona virus 2><SARS-related coronavirus 2><SARSCoV2><Severe Acute Respiratory Coronavirus 2><Severe Acute Respiratory Distress Syndrome CoV 2><Severe Acute Respiratory Distress Syndrome Corona Virus 2><Severe Acute Respiratory Distress Syndrome Coronavirus 2><Severe Acute Respiratory Syndrome CoV 2><Severe Acute Respiratory Syndrome-associated coronavirus 2><Severe Acute Respiratory Syndrome-related coronavirus 2><Severe acute respiratory syndrome associated corona virus 2><Severe acute respiratory syndrome coronavirus 2><Severe acute respiratory syndrome related corona virus 2><Sipuleucel-T><Software><Specificity><Specimen><Statistical Methods><Systematics><T cell response><T-Cell Antigen Receptors><T-Cell Receptor><T-Cells><T-Lymphocyte><TNFRSF5><TNFRSF5 gene><Techniques><Time><Tissue Growth><Tumor Immunity><Tumor Necrosis Factor Receptor Superfamily Member 5 Gene><Viral Diseases><Virus Diseases><Visualization><Visualization software><Wuhan coronavirus><acquired immune system><adaptive immune response><analysis pipeline><analytical tool><anti-cancer immunotherapy><anti-tumor immunity><antibody and antigen binding><anticancer immunotherapy><antitumor immunity><barcode><bio-informatics tool><bioinformatics tool><biomarker discovery><cancer immunity><cancer immunotherapy><cancer type><cell type><clinical prognostic><clinical relevance><clinically relevant><computational analyses><computational analysis><computer analyses><computer based prediction><corona virus><coronavirus disease 2019 infected patient><coronavirus disease 2019 patient><coronavirus disease 2019 positive patient><coronavirus disease 2019 virus><coronavirus disease infected patient><coronavirus disease patient><coronavirus 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sequencing><novel><ontogeny><open source><p50><patient infected with COVID><patient infected with COVID-19><patient infected with SARS-CoV-2><patient infected with coronavirus disease><patient infected with coronavirus disease 2019><patient infected with severe acute respiratory syndrome coronavirus 2><patient with COVID><patient with COVID-19><patient with COVID19><patient with SARS-CoV-2><patient with coronavirus disease><patient with coronavirus disease 2019><patient with severe acute respiratory distress syndrome coronavirus 2><predictive modeling><prognostic><public repository><publicly accessible repository><publicly available repository><resolutions><respiratory><responders and non-responders><responders from non-responders><responders or non-responders><responders versus non-responders><responders vs non-responders><responders/nonresponders><response><scRNA-seq><severe acute respiratory syndrome coronavirus 2 infected patient><severe acute respiratory syndrome coronavirus 2 patient><severe acute respiratory syndrome coronavirus 2 positive patient><single cell RNA-seq><single cell RNAseq><single cell expression profiling><single cell transcriptomic profiling><single-cell RNA sequencing><social role><statistic methods><thymus derived lymphocyte><tool><transcriptome profiling><transcriptomic profiling><tumor><user-friendly><vaccine discovery><viral infection><virus infection><virus-induced disease><visualization tool>