# Bioinformatics Technology to Characterize Tumor Infiltrating Immune Repertoires

> **NIH NIH U01** · DANA-FARBER CANCER INST · 2020 · $423,633

## Abstract

PROJECT SUMMARY
The repertoires of tumor-infiltrating T cells and B cells are rich sources of information about cancer-immune
interactions and provide insights on cancer immunotherapy targets. Efforts have been made to characterize B/T
cell repertoires in solid tumors using cell sorting followed by targeted deep sequencing. However, these
approaches may produce biased estimates during tissue disaggregation and can be expensive when applied to
large sample cohorts. Massively parallel mRNA sequencing (RNA-seq) technology has become the mainstream
method to profile gene expression and thousands of solid tumor RNA-seq profiles are available in the public
domain. The rich collection of tumor RNA-seq datasets provides an alternative approach to study tumor-infiltrating
B/T cell repertoires in solid tumors. Our team has recently developed a statistical method TIMER for deconvolving
different immune components in the tumor microenvironment, and TRUST for inferring the hypervariable
complementarity determining regions (CDRs) of the tumor infiltrating T cell receptor (TCR) repertoire from bulk
tumor RNA-seq data in the public domain.
Our preliminary analysis indicated that there are approximately ten times as many B cell receptor (BCR) reads
and TCR reads, suggesting that extracting the BCR repertoires from bulk tumor RNA-seq could reveal important
insights on B cell mediated tumor immunity. The aims of this proposal are: to extend our TRUST algorithm to
extract B cell receptor (BCR) repertoires from tumor RNA-seq data, and identify somatic hypermutations and
immunoglobin class switches (Aim 1); to systematically analyze TCR and BCR repertoires from large scale tumor
RNA-seq cohorts, and develop a user friendly web interface to allow cancer immunologists or immuno-oncologists
to investigate tumor-immune associations (Aim 2); to promote the utility of our tumor immune resource through
collaborations, cloud sharing, and outreach (Aim 3).
We will deliver a robust bioinformatics algorithm to systematically identify BCR / TCR repertoires from bulk tumor
RNA-seq data and a user-friendly resource for cancer immunologists or immuno-oncologists to explore tumor-
immune interactions from large tumor profiling cohorts in the public as well as their unpublished data. The
successful execution of this proposal has the potential to inform clinical practice of cancer immunotherapies,
including adoptive T cell transfer, therapeutic cancer vaccines or antibodies. Our proposed cancer immunology
algorithm and resource will be a unique addition to the array of bioinformatics tools developed by the Information
Technology for Cancer Research at the National Cancer Institute.

## Key facts

- **NIH application ID:** 9888343
- **Project number:** 5U01CA226196-03
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Heng Li
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $423,633
- **Award type:** 5
- **Project period:** 2018-04-06 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9888343

## Citation

> US National Institutes of Health, RePORTER application 9888343, Bioinformatics Technology to Characterize Tumor Infiltrating Immune Repertoires (5U01CA226196-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9888343. Licensed CC0.

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