Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology-Genomic Integration Analysis

Abstract

Background: The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.

Methods and findings: We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10-4, hazard ratio = 1.47, 95% CI 1.17-1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26-2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.

Conclusions: To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.

Fig 1. In silico tumor dissection pipeline for quantifying spatial diversity in the tumor ecosystem.

(A) Flow diagram depicting the overall study design. (B) Schematic of our pipeline for quantifying spatial diversity in pathological samples. H&E sections are morphologically classified and divided into regions to be spatially scored. The number of clusters k in the regional scores is indicative of the number of sub-populations of cell types in the tumor regions. (C) Examples of tumor regions with low and high diversity scores using the Shannon diversity index, accounting for cancer cells (outlined in green), lymphocytes (blue), and stromal cells (red). Cell classification is automated by image analysis. (D) The 3-D landscape of cell diversity scores on an example H&E section; the x- and y-axes are the geometric axes of the image, and the z-axis is cell diversity computed on a region-by-region basis. (E) The distribution of regional scores in a tumor from the METABRIC study with two regional clusters identified using Gaussian mixture clustering (grey shading: histogram; dashed black line: density; solid black lines: mixture components/clusters).

Fig 2. Application of EDI to 1,026 breast tumors from the METABRIC study.

(A) The frequencies of EDI scores in breast tumors. (B) H&E staining, distribution of classified cells (green: cancer; blue: lymphocyte; red: stromal cells), and the heatmap of regional diversity scores for a tumor with the highest EDI score (EDI = 5). (C) Representative regions from each of the clusters k1–k5 are shown in a tumor with EDI = 5, with cluster k1 having the lowest diversity score and k5 the highest. By mapping regional clusters to the H&E image, we can begin to interpret these clusters with different cell diversity. We observed predominantly cancer cells in k1, increasingly more stromal cells and ductal in situ carcinoma cells (DCIS) in k2, and a vessel in k3. Cluster k4 features extensive stromal lymphocytes between ductal in situ carcinoma components, while k5 shows tumor-infiltrating lymphocytes (TIL) associated with invasive carcinoma cells.

Fig 3. Reproducibility, stability, and independence of the EDI-high group in 507 grade 3 breast tumors.

(A) Kaplan–Meier curves of disease-specific survival to illustrate the prognosis of EDI-high samples compared to other grade 3 samples in two independent patient cohorts. Shown below the graph are the number of patients (the number of disease-specific events) per group for EDI-low (grey) and EDI-high (red). (B) Agreement of the EDI subtyping between 100% data and resampling with progressively fewer tumor regions in 200 repeats. (C) Distribution of known subtypes in grade 3 tumors stratified by EDI; asterisks mark subtypes enriched in the EDI-high group. (D) Kaplan–Meier curves illustrating the duration of disease-specific survival according to tumor size (left) and improvement of stratification with the addition of EDI information (right).

Fig 4. Accumulated prognostic value of microenvironmental heterogeneity and cancer TP53 mutation in high-grade breast tumors.

(A) Kaplan–Meier curves illustrating the duration of disease-specific survival according to microenvironmental heterogeneity (EDI, left panel), TP53 mutation (middle panel), and both (right panel) in 507 grade 3 breast tumors. MUT, mutant; WT, wild-type. (B) Schematic diagram to illustrate the hypothesized temporal progression of invasive breast cancer with loss of TP53 tumor suppressor functions as an early-stage event and spatial diversification of microenvironment as late-stage event.

Fig 5. The relationship between ecological heterogeneity and cancer genomic aberrations in 507 grade 3 tumors.

(A) Genome-wide copy number aberrations in grade 3 breast tumors and genomic coordinates of genes with copy number aberrations enriched in the EDI-high group. Lengths of black lines denote level of enrichment significance with copy number gains (above the horizontal line) or losses (below the horizontal line). (B) Kaplan–Meier curves illustrating the duration of disease-specific survival in grade 3 breast cancer patients according to copy number loss of the 4p14 region (left) and the EDI-high group with additional information of 4p14 copy number loss (right). (C) Kaplan–Meier curves illustrating the duration of disease-specific survival according to copy number loss of the 5q13 region (left) and the EDI-high group with additional information of 5q13 copy number loss (right).