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Updated: Dec 26, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Predictive Immune Modeling of Solid Tumors.

Natalie A LaFranzo¹, Kevin C Flanagan², Danielle Quintanilha²

¹Cofactor Genomics; natalie_lafranzo@cofactorgenomics.com.

Journal of Visualized Experiments : Jove

|March 17, 2020

Summary

This summary is machine-generated.

A novel assay enables precise immune cell profiling from small, degraded tumor RNA samples. This method aids in predicting immunotherapy response and identifying biomarkers for oncology patients.

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Area of Science:

Oncology
Immunology
Biotechnology

Background:

Tumor microenvironment heterogeneity complicates immunotherapy response prediction.
Traditional methods like flow cytometry and immunohistochemistry (IHC) have limitations for analyzing archived tumor tissues.
Accurate immune cell profiling is crucial for understanding treatment efficacy.

Purpose of the Study:

To introduce a novel assay for quantitative immuno-profiling of tumor tissues.
To overcome limitations of traditional techniques in analyzing small or degraded RNA samples.
To provide a comprehensive view of the tumor microenvironment for improved treatment response prediction.

Main Methods:

Utilizes a reagent kit and cloud-based informatics for high-throughput analysis on Illumina sequencing platforms.
Accommodates small amounts (20-40 ng) of total RNA or FFPE tissue sections.
Quantifies eight immune cell types and ten immune escape genes.

Main Results:

Generates an end-to-end quantitative immune profile report without requiring additional bioinformatics analysis.
Provides a complete view of the tumor microenvironment.
Enables identification of statistically significant biomarkers in patient cohorts.

Conclusions:

The ImmunoPrism assay offers a powerful solution for immuno-profiling archived tumor samples.
This technique facilitates a deeper understanding of the tumor microenvironment and immunotherapy response.
The assay has potential for biomarker discovery in oncology research.