Quantitative analysis of breast cancer tissue composition and associations with tumor subtype

Abstract

The tumor microenvironment is an important determinant of breast cancer progression, but standard methods for describing the tumor microenvironment are lacking. Measures of microenvironment composition such as stromal area and immune infiltrate are labor-intensive and few large studies have systematically collected this data. However, digital histologic approaches are becoming more widely available, allowing high-throughput, quantitative estimation. We applied such methods to tissue microarrays of tumors from 1687 women (mean 4 cores per case) in the Carolina Breast Cancer Study Phase 3. Tumor composition was quantified as percentage of epithelium, stroma, adipose, and lymphocytic infiltrate (with the latter as presence/absence using a ≥1% cutoff). Composition proportions and presence/absence were evaluated in association with clinical and molecular features of breast cancer (intrinsic subtype and RNA-based risk of recurrence [ROR] scores) using multivariable linear and logistic regression. Lower stromal content was associated with aggressive tumor phenotypes, including triple-negative (31.1% vs. 41.6% in HR+/HER2-; RFD [95% CI]: -10.5%, [-13.1, -7.9]), Basal-like subtypes (29.0% vs. 44.0% in Luminal A; RFD [95% CI]: -14.9%, [-17.8, -12.0]), and high RNA-based PAM50 ROR scores (27.6% vs. 48.1% in ROR low; RFD [95% CI]: -20.5%, [24.3, 16.7]), after adjusting for age and race. HER2+ tumors also had lower stromal content, particularly among RNA-based HER2-enriched (35.2% vs. 44.0% in Luminal A; RFD [95% CI]: -8.8%, [-13.8, -3.8]). Similar associations were observed between immune infiltrate and tumor phenotypes. Quantitative digital image analysis of the breast cancer microenvironment showed significant associations with demographic characteristics and biological indicators of aggressive behavior.

Keywords: Breast cancer; Digital histology; Microenvironment; Pathology; Stroma; Tumor-infiltrating lymphocytes (TILs).

Figure 1.

A representative core section (A) without and (B) with the Genie tissue composition algorithm overlaid. All four components – epithelium (green), stroma (red), adipose (yellow), and immune infiltrate (blue) – are visible on this slide. An excess ring of adipose classification is present which will be analytically subtracted from the analysis area (see Figure 2).

Figure 2.

Lower percentages of stroma are associated with more aggressive disease phenotypes such as triple-negative (A) or Basal-like (B) breast cancer subtype and higher grade (C). The presence of immune infiltrate is associated with more aggressive disease phenotypes such as triple-negative (D) or Basal-like (E) breast cancer subtype and higher grade (F).

Figure 2.

Lower percentages of stroma are associated with more aggressive disease phenotypes such as triple-negative (A) or Basal-like (B) breast cancer subtype and higher grade (C). The presence of immune infiltrate is associated with more aggressive disease phenotypes such as triple-negative (D) or Basal-like (E) breast cancer subtype and higher grade (F).

Figure 3.

Exploratory ROC curves to assess the potential ability of stromal proportion to distinguish breast cancer subtypes. The percentage of intratumoral stroma distinguishes between Luminal and non-Luminal tumors (A, AUC = 0.662) and Basal-like and non-Basal-like tumors (B, AUC = 0.680).