# Computational approaches to unravel immune receptor sequencing for cancer immunotherapy

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $183,156

## Abstract

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, current analysis tools lack the ability to track
and examine the dynamic nature of the repertoire across serial time points or to identify the common features
across repertoires thoroughly and efficiently. We will develop computationally efficient methods with advanced
machine learning techniques, including network analysis, feature selection and classification, and advanced
statistical approaches, to interrogate and measure immune repertoire architecture longitudinally, to identify
common features across repertoires and to assess their clinical relevance. Network analysis is a powerful
approach that can identify TCRs sharing antigen specificity and highly mutable BCR, which can help to develop
or improve existing immunotherapeutics and immunodiagnostics. However, network construction is
computationally expensive, therefore, we will develop an adaptive subsampling strategy to relieve computation
burden. We will implement the proposed methods on two studies to better illustrate the diversity and richness of
the data to demonstrate the flexibility and power of the proposed tools. Furthermore, we will develop
bioinformatics software by incorporating the proposed methods and techniques to tackle the complexity of the
Rep-seq data in a translational fashion and provide a comprehensive platform with user-friendly visualization
tools.

## Key facts

- **NIH application ID:** 10490312
- **Project number:** 5R21CA264381-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Li Zhang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $183,156
- **Award type:** 5
- **Project period:** 2021-09-17 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10490312

## Citation

> US National Institutes of Health, RePORTER application 10490312, Computational approaches to unravel immune receptor sequencing for cancer immunotherapy (5R21CA264381-02). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10490312. Licensed CC0.

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