Multigene prognostic tests in breast cancer: past, present, future

Balázs Győrffy, Christos Hatzis, Tara Sanft, Erin Hofstatter, Bilge Aktas, Lajos Pusztai, Balázs Győrffy, Christos Hatzis, Tara Sanft, Erin Hofstatter, Bilge Aktas, Lajos Pusztai

Abstract

There is growing consensus that multigene prognostic tests provide useful complementary information to tumor size and grade in estrogen receptor (ER)-positive breast cancers. The tests primarily rely on quantification of ER and proliferation-related genes and combine these into multivariate prediction models. Since ER-negative cancers tend to have higher proliferation rates, the prognostic value of current multigene tests in these cancers is limited. First-generation prognostic signatures (Oncotype DX, MammaPrint, Genomic Grade Index) are substantially more accurate to predict recurrence within the first 5 years than in later years. This has become a limitation with the availability of effective extended adjuvant endocrine therapies. Newer tests (Prosigna, EndoPredict, Breast Cancer Index) appear to possess better prognostic value for late recurrences while also remaining predictive of early relapse. Some clinical prediction problems are more difficult to solve than others: there are no clinically useful prognostic signatures for ER-negative cancers, and drug-specific treatment response predictors also remain elusive. Emerging areas of research involve the development of immune gene signatures that carry modest but significant prognostic value independent of proliferation and ER status and represent candidate predictive markers for immune-targeted therapies. Overall metrics of tumor heterogeneity and genome integrity (for example, homologue recombination deficiency score) are emerging as potential new predictive markers for platinum agents. The recent expansion of high-throughput technology platforms including low-cost sequencing of circulating and tumor-derived DNA and RNA and rapid reliable quantification of microRNA offers new opportunities to build extended prediction models across multiplatform data.

Figures

Figure 1
Figure 1
Prognostic and predictive relationship between multigene signatures and prognostic and predictive features in breast cancer. ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; LVI, lymphovascular invasion; PR, progesterone receptor.
Figure 2
Figure 2
Conceptual framework for risk stratification and currently available prognostic and predictive tools. Aces, Adjuvant chemotherapy and endocrine therapy sensitivity signature [29]; BCI, Breast Cancer Index; ER, estrogen receptor; GGI, Genomic Grade Index; HER2, human epidermal growth factor receptor 2.

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