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Updated: Jul 14, 2026
Monitoring PD-1-Blocking Antibodies Bound to T Cells Derived from a Drop of Peripheral Blood
Published on: February 5, 2020
Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade
Theinmozhi Arulraj1, Hanwen Wang1, Atul Deshpande2,3,4
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205.
Abstract:
Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.
Insights
Predicting response to PD-1 inhibitors in metastatic triple-negative breast cancer (TNBC) is challenging. Early on-treatment biomarkers, particularly blood-based ones, show promise for improved patient selection and treatment efficacy.
Area of Science:
- Oncology
- Immunotherapy
- Computational Biology
Background:
- Metastatic triple-negative breast cancer (TNBC) patients exhibit varied responses to PD-1 inhibition.
- Current predictive biomarkers for PD-1 therapy in TNBC have limited performance, hindering effective patient selection.
- Quantitative systems pharmacology (QSP) modeling offers a novel approach to explore biomarker strategies.
Purpose of the Study:
- To leverage in silico TNBC patient cohorts to identify improved predictive biomarkers for PD-1 inhibition.
- To evaluate the performance of a wide range of potential biomarker candidates.
Main Methods:
- Generation of in silico metastatic TNBC patient cohorts using a QSP model.
- Integration of transcriptomic and clinical data to inform the QSP model.
- Evaluation of 90 biomarker candidates using a cutoff-based testing algorithm and machine learning feature selection.
Main Results:
- Combinations of pre-treatment biomarkers enhanced specificity but reduced sensitivity compared to single biomarkers.
- Early on-treatment biomarkers, such as relative change in tumor diameter at two weeks, demonstrated superior sensitivity and specificity.
- Blood-based biomarkers showed comparable efficacy to tumor- or lymph node-based biomarkers in identifying responders.
Conclusions:
- Early on-treatment and blood-based biomarkers represent promising strategies for improving patient selection in TNBC immunotherapy.
- Less invasive, blood-based biomarkers could facilitate more accessible patient stratification for PD-1 inhibitors.
