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Cells of the Adaptive Immune Response

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Updated: Jan 17, 2026

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

Time-Series Clustering Captures Patterns of Early Immune Effector Cell-Associated Hematotoxicity That Are Predictable

Emily C Liang^1,2, Yein Jeon^1,2, Yang Qiao¹

¹Fred Hutch Cancer Center, Seattle, WA.

JCO Clinical Cancer Informatics

|January 14, 2026

Summary

This summary is machine-generated.

Unsupervised clustering identified distinct hematotoxicity patterns after CAR T-cell therapy, outperforming current grading systems. A random forest model accurately predicts these patterns using limited early data.

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Area of Science:

Hematology
Immunotherapy
Data Science

Background:

Immune effector cell-associated hematotoxicity (ICAHT) is a significant cause of mortality post-chimeric antigen receptor (CAR) T-cell therapy.
Current grading systems for early ICAHT (eICAHT) may not fully capture the complexity of hematotoxicity patterns.

Purpose of the Study:

To investigate if unsupervised time-series clustering can identify distinct patterns of early hematotoxicity after CAR T-cell therapy.
To compare the effectiveness of clustering patterns versus the eICAHT grading system in identifying patient outcomes.

Main Methods:

Applied k-means time-series clustering to longitudinal absolute neutrophil count (ANC) data from 691 patients.
Trained a random forest (RF) model using early ANC values (days +3, +4, +5, +26, +27) to predict cluster assignments.
Validated the RF model's predictive accuracy and compared cluster separation with eICAHT criteria.

Main Results:

Identified four distinct ANC recovery clusters: very good, good, poor, and very poor.
RF-predicted clusters showed better separation and compactness than eICAHT criteria.
The RF model identified patients in the 'good' recovery cluster with intermediate overall survival, a finding missed by grade 2 eICAHT.

Conclusions:

Unsupervised time-series clustering effectively identifies clinically relevant hematotoxicity patterns post-CAR T-cell therapy.
A trained RF model accurately predicts these patterns using only five ANC measurements.
An online web application is available for generating predictions.