Radiogenomic analysis of cellular tumor-stroma heterogeneity as a prognostic predictor in breast cancer

Abstract

Background: The tumor microenvironment and intercellular communication between solid tumors and the surrounding stroma play crucial roles in cancer initiation, progression, and prognosis. Radiomics provides clinically relevant information from radiological images; however, its biological implications in uncovering tumor pathophysiology driven by cellular heterogeneity between the tumor and stroma are largely unknown. We aimed to identify radiogenomic signatures of cellular tumor-stroma heterogeneity (TSH) to improve breast cancer management and prognosis analysis.

Methods: This retrospective multicohort study included five datasets. Cell subpopulations were estimated using bulk gene expression data, and the relative difference in cell subpopulations between the tumor and stroma was used as a biomarker to categorize patients into good- and poor-survival groups. A radiogenomic signature-based model utilizing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was developed to target TSH, and its clinical significance in relation to survival outcomes was independently validated.

Results: The final cohorts of 1330 women were included for cellular TSH biomarker identification (n = 112, mean age, 57.3 years ± 14.6) and validation (n = 886, mean age, 58.9 years ± 13.1), radiogenomic signature of TSH identification (n = 91, mean age, 55.5 years ± 11.4), and prognostic (n = 241) assessments. The cytotoxic lymphocyte biomarker differentiated patients into good- and poor-survival groups (p < 0.0001) and was independently validated (p = 0.014). The good survival group exhibited denser cell interconnections. The radiogenomic signature of TSH was identified and showed a positive association with overall survival (p = 0.038) and recurrence-free survival (p = 3 × 10^-4).

Conclusion: Radiogenomic signatures provide insights into prognostic factors that reflect the imbalanced tumor-stroma environment, thereby presenting breast cancer-specific biological implications and prognostic significance.

Keywords: Breast cancer; Cell subpopulation; Prognosis; Radiogenomics.

Fig. 1

Study framework. a Cellular tumor-stroma heterogeneity biomarker identification. b Imaging the cellular tumor-stroma heterogeneity. c Prognostic validation. Radiogenomic analysis by associating cellular tumor-stroma biomarkers and imaging features

Fig. 2

Imaging cellular tumor-stroma heterogeneity

Fig. 3

Data collection flowchart

Fig. 4

Distribution of cell subpopulations within and surrounding tumors in the good-survival and poor-survival groups. a The distributions of the abundance of cytotoxic lymphocyte cell subpopulations from tumor and stromal tissues in the good- and poor-survival groups. b Survival curves for the abundance of cytotoxic lymphocyte cell subpopulations. c Boxplots of cell subpopulation abundances in tumors and stromal regions in the poor-survival group. d Boxplots of cell subpopulation abundances in tumors and stromal regions in the good-survival group. In the boxplot, the centerline indicates the median; box limits indicate the 25% and 75% quantiles; whiskers represent the 1.5× interquartile range; and points above or below whiskers represent outliers

Fig. 5

Network topology in the poor-survival and good-survival groups. a The cell subpopulation network in the good-survival group; b The cell subpopulation network in the poor-survival group; c The radar map of the topological parameters (n = 9) of the stroma-based cell subpopulation network for good- and poor-survival groups; d The radar map of the topological parameters (n = 9) of the tumor-based cell subpopulation network for good- and poor-survival groups

Fig. 6

Radiogenomic analysis and predictive model based on differences between the tumor and stroma. a The ROC curve for the predictive model. b The survival curves for the tumor-stromal validation dataset to evaluate the effectiveness of the identified relative cell subtype features

Fig. 7

Imaging feature distribution and Kaplan–Meier survival analyses. a Boxplot of the inverse difference moment normalized (IDMN) features in precontrast images showing significantly higher values in the good survival group than in the poor-survival group (p = 6.386 × 10^–7). An example of a patient aged 54.16 years showed a higher feature value (0.9657) with good survival compared with a patient aged 44.5 years with poor survival (feature value = 0.9494). b Boxplot of the flatness feature showed significantly higher values in the good-survival group than in the poor-survival group (p = 0.01). Examples of a patient aged 62.4 years showed a higher feature value of 0.3086 with good survival than a patient aged 58.89 years with a feature value of 0.2700 with poor survival. The predicted tumor-stroma heterogeneity (TSH) score using radiogenomic signatures (n = 6) separated patients into good- and poor-survival groups for c overall survival (prognostic validation 1 dataset) and d recurrence-free survival (prognostic validation 2 dataset)