Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics

Abstract

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

Fig. 1. Overview of clinical timeline and response.

a Treatment (colored boxes) and biopsy (red stars) timeline, in months, following disease recurrence in the metastatic setting. b Timeline of blood biomarker levels (U/ml, solid lines) and lesion sizes (in millimeters) for four selected hepatic lesions (hatched lines). Study biopsy #1 (Bx#1) = Liver 3 segment 2 (L3 seg2); Study biopsy #2 (Bx#2) = Liver 4 segment 5/6 (L4 seg5/6).

Fig. 2. HER2 immunohistochemistry (IHC) comparison of Study biopsies.

Strong HER2 IHC staining in the first hepatic biopsy (Study biopsy #1, left) and negative HER2 IHC staining in the second hepatic biopsy (Study biopsy #2, right). Images are shown at ×20 magnification.

Fig. 3. Pathway analysis of protein and phosphoproteins within Study biopsy #2 by the Intracellular Signaling Protein Panel.

Box and Whisker plots Y-axis showing the distribution of antibody levels (log batch correct counts) of Study biopsy #2 (red asterisks) compared to a cohort of 32 metastatic breast cancers of all subtypes (BC) or 15 metastatic TNBC specimens, X-axis. Cohort consists of predominantly HER2 negative samples. Boxes show the 25th, 50th (median line), and 75th percentiles of the cohorts. Protein and phosphoproteins are grouped under pathways including “PI3K-AKT”, “mTOR”, and “MAPK-RAS”, and the categories “Proliferation” and “Other”. ND = Not detectable or below level of background, measured with non-specific antibody.

Fig. 4. RNA profiling analysis of Study biopsy #2 showing intrinsic subtyping and gene expression of hormone receptors and identified copy alterations.

a Hormone receptor gene expression: distribution of batch corrected gene expression values, z-scaled within each cohort, filtered by subtype, and depicted as a density histogram. Y-axis shows the frequency, X-axis represents the log transformed transcripts per million (TPM), mean centered and scaled within each cohort: TCGA Breast Cancer cohort (n = 1227), in blue, and a SMMART-program metastatic breast cancer cohort (n = 40), in orange. Black line marks the patient’s RNA expression for ERBB2 (HER2), ESR1 (ER), PGR (PR), and AR. b PAM50 intrinsic subtyping analysis of RNA expression related to five breast cancer subtypes: Basal, HER2, Luminal A (LumA), Luminal B (LumB), and Normal. Y-axis is the Spearman Correlation. c TNBC-specific intrinsic subtyping: analysis of RNA expression that identifies four basal subtypes: basal-like immune-activated (BLIA), basal-like immunosuppressed (BLIS), mesenchymal (MES) and luminal androgen receptor (LAR). Y-axis is the Spearman Correlation. d Additional RNA expression profiles of genes found to be altered by genomic or proteomic assays, processed and displayed as in (a).

Fig. 5. Reverse Phase Protein Array (RPPA) profiling by subtype clustering and pathway analysis of proteins and phosphoproteins in Study biopsy #2.

Protein expression values were normalized within the TCGA breast cancer cohort. a The heat map represents a rank sum ordering of the protein expression across TCGA (red), 31 SMMART-program samples (purple), and Study biopsy #2 (arrow). The red and blue colors represent higher and lower expression proteins, respectively. b Pathway profiling by RPPA. The data were z-scored and pathway activity was assessed using pathway scores calculated as described previously. The histogram represents the distribution of the pathway’s activity (Y-axis = density) of the TCGA basal breast cancer cohort (white) and SMMART-program cohort (gray) as well as the pathway activity of Study biopsy #2 (black line). See Supplementary Table 1 for proteins comprising each RPPA pathway.