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Updated: Jun 17, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Transcriptomic profiling identifies immunotherapy-responsive phenotypes in microsatellite-stable metastatic

Tomas Konecny^1,2, Nate Zadirako^1,3, Arpine Grigoryan¹

¹Armenian Bioinformatics Institute (ABI), Yerevan, Armenia.

|June 15, 2026

Summary

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Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...

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This study reveals four molecular subtypes in microsatellite-stable metastatic colorectal cancer (MSS mCRC), identifying specific tumor microenvironments that predict response to novel immunotherapy. These findings may help expand treatment efficacy for patients with MSS mCRC.

Area of Science:

Oncology
Immunology
Genomics

Background:

Microsatellite-stable metastatic colorectal cancer (MSS mCRC) shows resistance to conventional immune checkpoint inhibitors (ICIs).
Tumor immunogenicity and heterogeneity contribute to therapeutic resistance in MSS mCRC.
Botensilimab (Fc-enhanced anti-CTLA-4) and balstilimab (anti-PD-1) are novel immunotherapies being investigated.

Purpose of the Study:

To analyze transcriptomic profiles of MSS mCRC tumors treated with botensilimab ± balstilimab.
To identify molecular subtypes and their correlation with immunotherapy response.
To understand the tumor microenvironment's role in treatment resistance and efficacy.

Main Methods:

Comprehensive transcriptomics analysis of primary and metastatic MSS mCRC tumor biopsies.

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Last Updated: Jun 17, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Application of Self-Organizing Map (SOM) machine learning for tumor stratification.

Correlation of molecular subtypes with clinical response and survival data.

Main Results:

Four molecular tumor types (liver-like, proliferative, inflammatory, mesenchymal) were identified.
Tumor types showed distinct cellular compositions and microenvironment states.
Inflammatory and mesenchymal types demonstrated improved clinical responses and survival compared to liver-like and proliferative types.
Treatment induced transcriptomic shifts towards immune-enriched states.

Conclusions:

Distinct tumor microenvironment states in MSS mCRC are associated with immunotherapy response.
Molecular subtypes identified can inform patient stratification for novel immunotherapies.
Findings provide insights into expanding ICI efficacy in previously unresponsive MSS mCRC tumors.