# A Statistical Physics Framework for Understanding the Role of Repeat RNA in Tumor Immunity

> **NIH NIH U01** · SLOAN-KETTERING INST CAN RESEARCH · 2020 · $688,005

## Abstract

PROJECT SUMMARY
Transcriptional dysregulation in tumors can induce the abundant expression of repetitive elements in
cancerous cells compared to normal tissues, where they are often transcriptionally silent. Such transcripts
have been associated with better outcomes to cancer immunotherapies, as they can modulate the tumor
immune microenvironment and generate an under-quantified source of tumor neoantigens. Therefore, it has
been hypothesized that the aberrant transcription of repeat RNA is both a critical mechanism for initiating the
immune response in the tumor microenvironment and an untapped source of potential therapeutic targets.
Using a set of approaches from statistical physics, our team predicted repetitive element RNA directly
stimulates receptors of the innate immune system, confirmed this hypothesis in a key subset of immune cells,
and showed repeat expression can correlate with response to checkpoint blockade immunotherapies. Repeat
RNA is therefore both a novel biomarker for the innate immune response in cancer and a potential therapeutic
target to modulate tumor immunity.
We will utilize a set of tools, developed by our team, from statistical physics to characterize repeat RNA
recognition by innate immune receptors in silico and their role in tumor-immune co-evolution, both with and
without the application of immunotherapy (Aim 1). Next, we will characterize the spatial context of repeat
RNAs in the tumor immune microenvironment and the co-localization of predicted immunostimulatory RNA with
activation of immune signaling, along with in depth immune-phenotyping of the state of the immune
microenvironment in vivo (Aim 2). Finally, we will perform functional validation of our predictions on human
immune cells to validate mechanisms of recognition and the specific immune subsets responsible for repeat
recognition via a set of in vitro assays (Aim 3). Our goal is to use approaches from statistical physics to
quantify the role of repetitive elements in tumor immunology, their rules of recognition by innate immune
receptors and their part in facilitating cytolytic T cell activity. In doing so we will combine novel RNA detection
technologies to study their spatial distribution and localization in cancers; state of the art immune-phenotyping;
and mathematical models to characterize their direct role in tumor evolution. We hypothesize that our approach
from statistical physics will identify the key structural and sequence features of repeat mediated immune
activation in solid tumors and shed light on their specific consequences for tumor evolution and therapeutic
efficacy.

## Key facts

- **NIH application ID:** 9858026
- **Project number:** 1U01CA228963-01A1
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Benjamin Greenbaum
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $688,005
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9858026

## Citation

> US National Institutes of Health, RePORTER application 9858026, A Statistical Physics Framework for Understanding the Role of Repeat RNA in Tumor Immunity (1U01CA228963-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9858026. Licensed CC0.

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