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Updated: Jan 9, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Toward a better pan-tumor predictive signature for unleashing precision immuno-oncology
Jason R Brown1,2, Guru Sonpavde3,4
1University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Abstract:
While immune checkpoint inhibition (ICI) has revolutionized therapy across solid tumors, resistance remains an issue. Programmed death ligand-1 immunohistochemistry has limited clinical utility, whereas tumor mutation burden and microsatellite instability are only valuable for a minority of patients and leave room for improvement. Multiomic gene signatures have enhanced prediction of immune response by incorporating interferon-gamma signaling, T-cell dysfunction and exhaustion genes, and myeloid signatures. Single-cell RNA technology has been adopted to further optimize prediction of response to immunotherapy. A novel Immune Profile Score is presented by Zander et al that builds on prior immune signatures, using DNA and RNA profiling to predict outcomes across solid tumors receiving ICIs. While this assay is promising, further prospective validation and refinements will be necessary to realize its full potential in our quest to develop precision immuno-oncology. The incorporation of readily available clinical factors (eg, sites of metastasis), host genetics, orthogonal molecular platforms (microbiome, computational pathology, spatial transcriptomics, epigenetics, proteomics, radiomics) and investigating biomarkers to predict primary refractory disease and severe toxicities may further facilitate precision medicine.
Insights
A new Immune Profile Score uses DNA and RNA data to predict solid tumor responses to immune checkpoint inhibitors (ICI), offering a promising tool for precision immuno-oncology. Further validation is needed to optimize its clinical utility.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Immune checkpoint inhibition (ICI) has transformed cancer therapy but faces challenges with resistance.
- Current biomarkers like PD-L1, tumor mutation burden, and microsatellite instability have limitations in predicting ICI response.
- Multiomic gene signatures and single-cell RNA sequencing have improved prediction by integrating various biological signals.
Purpose of the Study:
- To introduce a novel Immune Profile Score for predicting outcomes in solid tumors treated with ICIs.
- To leverage DNA and RNA profiling for enhanced prediction of immunotherapy response.
- To advance precision immuno-oncology by developing more accurate predictive biomarkers.
Main Methods:
- Development of a novel Immune Profile Score integrating DNA and RNA profiling data.
- Building upon existing multiomic gene signatures and single-cell RNA technologies.
- Application of the score across diverse solid tumors receiving ICIs.
Main Results:
- The novel Immune Profile Score demonstrates promise in predicting patient outcomes with ICIs.
- The score integrates multiple molecular layers to enhance predictive accuracy.
- The study highlights the potential of multiomic approaches in immuno-oncology.
Conclusions:
- The presented Immune Profile Score is a promising advancement for predicting ICI response in solid tumors.
- Prospective validation and further refinements are essential for its full clinical implementation.
- Integrating clinical factors and orthogonal molecular platforms may further enhance precision medicine in immuno-oncology.
