Genomic instability in breast and ovarian cancers: translation into clinical predictive biomarkers

Abstract

Breast and ovarian cancer are among the most common malignancies diagnosed in women worldwide. Together, they account for the majority of cancer-related deaths in women. These cancer types share a number of features, including their association with hereditary cancer syndromes caused by heterozygous germline mutations in BRCA1 or BRCA2. BRCA-associated breast and ovarian cancers are hallmarked by genomic instability and high sensitivity to DNA double-strand break (DSB) inducing agents due to loss of error-free DSB repair via homologous recombination (HR). Recently, poly(ADP-ribose) polymerase inhibitors, a new class of drugs that selectively target HR-deficient tumor cells, have been shown to be highly active in BRCA-associated breast and ovarian cancers. This finding has renewed interest in hallmarks of HR deficiency and the use of other DSB-inducing agents, such as platinum salts or bifunctional alkylators, in breast and ovarian cancer patients. In this review we discuss the similarities between breast and ovarian cancer, the hallmarks of genomic instability in BRCA-mutated and BRCA-like breast and ovarian cancers, and the efforts to search for predictive markers of HR deficiency in order to individualize therapy in breast and ovarian cancer.

Fig. 1

Predictive markers are needed to adequately select those patients benefiting from DSB-inducing agents (such as bifunctional alkylators, platinum salts, or PARPi) out of the general breast or ovarian cancer populations. In general, three ways of predictive marker studies can be distinguished. Examples are given for all three options (in example 2, aCGH plots are depicted, with on the x-axis the chromosomes, and on the y-axis the log2-ratio of tumor DNA over normal DNA). IHC immunohistochemistry, CNA copy number aberration, HR homologous recombination, hrs hours, DSB double-strand break

Fig. 2

a A prognostic marker predicts outcome of the natural history of a disease, regardless of treatment and therefore tells you whom to treat. (In this example: Marker A is a prognostic marker, as it predicts for a worse survival for Marker A-positive cases compared to Marker A-negative cases, irrespective of treatment. Consequently, Marker A-positive cases should be treated as these are more likely to die of disease. However, the marker does not tell how to treat). b A predictive marker predicts outcome in the presence of a specific therapy only but not in the absence of that specific treatment. It therefore tells you how (with what specific therapy) the patient should be treated (in this example: Marker B is a predictive marker, as it predicts an improved outcome to treatment X over no treatment in Marker B-positive cases, while no such benefit is seen in Marker B-negative cases; This difference in treatment effect between Marker B-positive and -negative cases should be significant on a test of interaction)

Fig. 3

In the future, therapy choice might be guided by combinations of predictive biomarkers. In the presented example, the presence of one feature and the absence of a second feature might guide treatment with DSB-inducing agents. HR homologous recombination, DSB double-strand break