Data from Cancer Cell Intrinsic and Immunologic Phenotypes Determine Clinical Outcomes in Basal-like Breast Cancer
Authors: Li Christopher I. , Zhang Yuping , Cieślik Marcin , Wu Yi-Mi , Xiao Lanbo , Cobain Erin , Tang Mei-Tzu C. , Cao Xuhong , Porter Peggy , Guenthoer Jamie , Robinson Dan R. , Chinnaiyan Arul M. 
Published: April 2023

<div>AbstractPurpose:<p>Basal-like breast cancer (BLBC) is a particularly aggressive intrinsic molecular subtype of breast cancer that lacks targeted therapies. There is also no clinically useful test to risk stratify patients with BLBC. We hypothesized that a transcriptome-based phenotypic characterization of BLBC tumors and their microenvironments may overcome these challenges.</p>Experimental Design:<p>We conducted a retrospective correlative genomic sequencing study using a matched pairs design with validation in five independent cohorts. The study was conducted on a large population-based prospective cohort of the major molecular subtypes of breast cancer conducted in the greater Seattle-Puget Sound metropolitan area. Cases consisted of women 20–69 years of age first diagnosed with invasive breast cancer identified through the population-based Surveillance Epidemiology and End Results program. Patients for this analysis (<i>n</i> = 949) were identified from the 1,408 patients with stage I–III triple-negative breast cancer [estrogen receptor–negative (ER<sup>−</sup>), progesterone receptor–negative (PR<sup>−</sup>), HER2<sup>−</sup>]. Of the 949 women, 248 developed a recurrence after their initial diagnosis. A matched set of 67 recurrent and nonrecurrent BLBC tumors was subjected to transcriptome sequencing. Through RNA sequencing of the matched sets of recurrent and nonrecurrent BLBC tumors, we aimed to identify prognostic phenotypes.</p><p>To identify nonredundant and uncorrelated prognostic genes, we used an ensemble of variable selection algorithms, which resulted in a ranking of genes on the basis of their expected utility in classification. Using leave-one-out cross-validation, we trained a random forest classifier on the basis of the top 21 genes (BRAVO-DX). Validations were performed in five independent triple-negative or BLBC cohorts, and biomarker robustness and transferability were demonstrated by employing real-time PCR.</p>Results:<p>We found that cancer cell intrinsic and immunologic phenotypes are independent predictors of recurrence. By simultaneously interrogating the tumor and its microenvironment, we developed a compound risk model that stratified patients into low-, medium-, and high-risk groups, with a 14%/56%/74% chance of recurrence, respectively. Biologically, the primary tumors of patients who developed a recurrence had increased growth factor signaling and stem-like features, while nonrecurrent tumors showed high lymphocyte infiltration with clonal expansion of T and B cells, as well as antitumor polarization of macrophages. We validated our model in five independent cohorts, including three large cohorts, where BRAVO-DX was highly informative in identifying patients with disease recurrence [HR, 6.79 (95% confidence interval (CI), 1.89–24.37); HR, 3.45 (95% CI, 2.41–4.93); and HR, 1.69 (95% CI, 1.17–2.46)]. A smaller gene set focused on the tumor immunophenotype, BRAVO-IMMUNE, was highly prognostic in all five cohorts.</p>Conclusions:<p>Together, these results indicate that phenotypic characteristics of BLBCs and their microenvironment are associated with recurrence-free survival and demonstrate the utility of intrinsic and extrinsic phenotypes as independent prognostic biomarkers in BLBC. Pending further evaluation and validation, our prognostic model has the potential to inform clinical decision-making for patients with BLBC as it identifies those at high risk of rapidly progressing on standard chemotherapy, as well as those who may benefit from alternative first-line therapies.</p></div>