Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Oct 7, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Transcriptomic-Assisted Immune and Neoantigen Profiling in Premalignancy.

Kyle Chang^1,2, Florencia McAllister^1,2,3,4, Eduardo Vilar^5,6,7,8

¹Departments of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Methods in Molecular Biology (Clifton, N.J.)

|January 7, 2022

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Euphol targets key oncogenic processes and promotes chemoprevention in organoid and In Vivo models of colorectal cancers.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2026

Same author

Tumor microbial burden drives immune responses through regulation of Interferon signaling.

Cancer discovery·2026

Same author

Aerobic Exercise Activates S1PR1 Signaling in Tumor Endothelial Cells to Promote Vascular Function in Pancreatic Cancer.

Cancer research·2026

Same author

Design and preliminary report of a randomized phase IIb clinical trial of multitargeted recombinant adenovirus 5 vaccines against CEA, MUC1, and brachyury (Tri-Ad5) and the IL-15 receptor superagonist nogapendekin alfa inbakicept in Lynch syndrome (TRIAD5-Plus): the first cross-network trial of the Cancer Prevention Clinical Trials Network (CP-CTNet).

Frontiers in immunology·2026

Same author

Dietary Fat Content Influences PanIN Progression and Pancreatic Cancer Development in Mice.

Cancer research communications·2026

Same author

Genomic analysis of T Cell receptors reveals lynch syndrome specific immune signatures.

Nature communications·2026

Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

This summary is machine-generated.

This study introduces a streamlined RNA sequencing (RNA-seq) workflow for comprehensive cancer immune profiling. It enables simultaneous analysis of mutational burden, neoantigen load, and immune infiltration, even with limited premalignant samples.

Area of Science:

Oncology
Immunology
Genomics

Background:

Immune-based cancer therapies, including checkpoint inhibitors (CPI) and vaccines, show promise but treatment response varies.
Therapy efficacy depends on factors like mutational burden, neoantigen load, and tumor-infiltrating lymphocytes.
Next-generation sequencing (NGS) offers a powerful approach for large-scale immune profiling, neoantigen discovery, and immune infiltration analysis.

Purpose of the Study:

To address the challenge of limited sample material in premalignant specimens for immune profiling.
To explore the feasibility of a single RNA sequencing (RNA-seq) workflow for comprehensive profiling.
To enable simultaneous analysis of mutational burden, neoantigen load, and immune expression profiles.

Main Methods:

Keywords:

Immune checkpoints Mutational rate Neoantigens Premalignancy RNA-seq

More Related Videos

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

Published on: November 17, 2018

High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research

High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research

Published on: June 10, 2025

Related Experiment Videos

Last Updated: Oct 7, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

Published on: November 17, 2018

High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research

High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research

Published on: June 10, 2025

Development and validation of an RNA-seq-based workflow.

Utilizing RNA-seq for simultaneous assessment of mutational burden, neoantigen prediction, and immune infiltration.

Comparison with traditional methods like whole-exome sequencing (WES) and immunohistochemistry (IHC) where applicable.

Main Results:

Demonstrated the capability of RNA-seq to accurately profile mutational burden, neoantigen load, and immune expression from a single sample.
Showcased the utility of this approach for smaller, premalignant specimens where traditional dual-sequencing methods are challenging.
Provided a unified workflow for interrogating key features relevant to immune response.

Conclusions:

A single RNA-seq workflow is viable for comprehensive immune, neoantigen, and mutation profiling.
This approach overcomes sample limitations in premalignant research and clinical settings.
RNA-seq offers a scalable and efficient method for advancing cancer immunotherapy research.