Insight into the heterogeneity of breast cancer through next-generation sequencing

Abstract

Rapid and sophisticated improvements in molecular analysis have allowed us to sequence whole human genomes as well as cancer genomes, and the findings suggest that we may be approaching the ability to individualize the diagnosis and treatment of cancer. This paradigmatic shift in approach will require clinicians and researchers to overcome several challenges including the huge spectrum of tumor types within a given cancer, as well as the cell-to-cell variations observed within tumors. This review discusses how next-generation sequencing of breast cancer genomes already reveals insight into tumor heterogeneity and how it can contribute to future breast cancer classification and management.

Figure 1. Different study designs for array-based gene expression studies.

(A) Studies aimed at identifying different subgroups investigate a mixed population of patients to group tumors with similar alterations together, and markers that recognize each type can then be identified. (B) This in contrast to studies that search for markers for prediction of therapy response or outcome; here, selected groups of patients are analyzed to identify the most discriminating alterations.

Figure 2. Subtypes of breast cancer.

Hypothetically, subtypes of breast cancer can be viewed as a spectrum of more or less related entities. The majority are classified through histopathology as IDC NOS, but some types have defined histopathological traits. Such groups have tumors that are frequently either ER^–/HER2^– or ER⁺/HER2^–, which also corresponds to the outer part of a spectrum of intrinsic subtypes, namely the basal-like and luminal A types of breast cancer. NGS of a basal-like (top), a HER2-related (second from top), a luminal B (third from top), and a luminal A tumor (bottom) show distinct structural characteristics. The circos plots show intrachromosomal rearrangements in green and interchromosomal rearrangements in purple (circos plots used with permission from Nature; ref. 43).

Figure 3. Hypothetical models explaining intratumor heterogeneity.

(A–C) Different models of tumor progression can give rise to distinct types of intratumor heterogeneity, exemplified here by the clonal evolution (A), the cancer stem cell (B), and the mutator phenotype (C) models. (D) The different models can result in distinct spatial distributions of subpopulations.

Figure 4. A multilevel approach for a dynamic classification system.

The first level is defined by tumor and patient characteristics. The second level includes detailed genomic and translational analyses of tumor to define molecular type and selection of appropriate tests. Parallel to that, tumor-specific serum markers can be assessed. The third level determines intratumor heterogeneity and is crucial for selection of appropriate markers for micrometastatic disease detection in serum, bone marrow, or lymph nodes. MRD, minimal residual disease. The fourth level integrates all available information to produce a diagnosis, prognostication, prediction of therapy, and program for disease monitoring.