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Updated: Feb 6, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

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Strategies for Predicting Response to Checkpoint Inhibitors

Roberta Zappasodi1,2, Jedd D Wolchok1,2,3,4, Taha Merghoub5,6,7

  • 1Ludwig Collaborative and Swim Across America Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.

Current Hematologic Malignancy Reports
|August 31, 2018
PubMed
Abstract

Insights

Identifying biomarkers for immune checkpoint blockade response is crucial for patient treatment. This review categorizes biomarkers into tumor-intrinsic, immune microenvironmental, host-related, and dynamic factors to predict therapy success.

Area of Science:

  • Oncology
  • Immunology
  • Biomarker Discovery

Background:

  • Immune checkpoint blockade (ICB) therapies have shown success in various cancers.
  • However, many patients exhibit primary resistance or develop acquired resistance to ICBs.
  • Understanding the molecular basis of ICB response is essential for personalized treatment strategies.

Purpose of the Study:

  • To review and categorize biomarkers associated with response to immune checkpoint blockade.
  • To provide a framework for assessing biomarkers to predict clinical outcomes.
  • To enhance the rational and personalized application of ICB therapies.

Main Methods:

  • Systematic review and classification of existing biomarkers for ICB activity.
  • Categorization into four major groups: tumor-intrinsic, immune microenvironmental, host-related, and dynamic factors.
  • Proposed model for assessing baseline and dynamic biomarkers in relation to patient outcomes.

Main Results:

  • Identified and classified key biomarkers influencing ICB response.
  • Highlighted the importance of integrating diverse biomarker types for accurate prediction.
  • Emphasized the role of both baseline and dynamic factors in determining treatment efficacy.

Conclusions:

  • Biomarker-driven stratification is critical for optimizing ICB therapy.
  • A comprehensive understanding of tumor-immune interactions and dynamics can guide personalized treatment.
  • Further research and systematic biomarker assessment will improve patient outcomes in cancer immunotherapy.
Keywords:
Gut microbiotaImmune checkpointImmunotherapyNeoantigensResponse biomarkersT cells

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