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Updated: Aug 4, 2025
Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
Published on: February 7, 2021
Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant
Rhea Chitalia1,2, Marios Miliotis3,4, Nariman Jahani2
1Department of Bioengineering, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA.
Background:
Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS).
Methods:
A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components.
Results:
We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002).
Conclusions:
These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.
