Assessment of the prognostic role of a 94-single nucleotide polymorphisms risk score in early breast cancer in the SIGNAL/PHARE prospective cohort: no correlation with clinico-pathological characteristics and outcomes

Both environmental and genetic factors are involved in breast cancer pathogenesis. Germline mutations in the tumor suppressor genes BRCA1 and BRCA2 are the two main genes involved in hereditary breast cancer, and explain around 15–20% of familial breast cancer [1‐3]; however, less than 10% of all breast cancers occur in patients with BRCA germline mutations [4]. Other rare variants in genes such as PALB2, CHEK2, ATM, NBN, TP53, CDH1, PTEN, STK11 and NF1 [5] confer moderate to high risk of developing breast cancer [6]. Genome-wide association studies (GWAS) have to date identified 94 common genetic variants (single nucleotide polymorphisms (SNPs)) associated with risk of developing breast cancer [7]. If the effect of one SNP on breast cancer risk is low, the combined effect of all known associated SNPs can be of interest for prevention and screening, and SNPs explain 15–20% of familial breast cancer [3, 5, 7]. A score based on the effect of risk variants can be calculated to measure the risk of developing breast cancer conferred by the 94 known SNPs [8]. Rare mutations conferring high risk of breast cancer, for example in BRCA1/2 genes are not included in this score. While SNP scores have been shown to be strongly associated with breast cancer risk, these polygenic SNP scores have not yet been evaluated with respect to clinico-pathological features of breast cancer, prognosis and outcomes.

Clinico-pathological criteria, including patient age, axillary lymph node involvement, tumor size and Scarff-Bloom-Richardson (SBR) grade, are commonly used in the clinical routine as breast cancer prognostic factors; estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status are validated as prognostic and predictive factors [9‐11]. Based on these predictive factors, medical oncologists divide breast cancers into 3 categories according to the management they require [12, 13]: (1) HER2-positive breast cancers are characterized by amplification of the HER2 gene (human epidermal growth factor receptor 2, located at 17q12) associated with gene overexpression and consequently high abundance of HER2 protein. The advent of trastuzumab, a humanized monoclonal antibody specifically targeting the HER2 extracellular domain, has revolutionized the natural history and management of HER2-positive breast cancers [14]; (2) triple-negative breast cancer, with no expression of ER or PR and no HER2 overexpression (amplification) has overall poorer prognosis than other subtypes and requires chemotherapy [15]; (3) HER2-negative breast cancers with ER or PR expression represent the third group, called luminal breast cancers, and are usually treated with endocrine therapy [16]. The SIGNAL/Protocole Herceptin® Adjuvant Réduisant l'Exposition - Herceptin®-based protocol with reduced exposure (PHARE) - prospective cohort benefits from a large, detailed database allowing assessment of pathological subtypes, prognostic factors and outcomes.

We aimed to test the hypothesis that genetic polymorphisms involved in breast cancer risk may also impact the aggressiveness of breast cancer and thus be related to prognostic factors, pathological subtypes and patients’ outcomes. Individually, genetic variants have a small impact on breast cancer risk, and potentially small consequences on outcomes and pathological features of breast cancer. A polygenic 94-SNP score, which has more statistical power than individual SNPs, may also be associated with breast cancer prognostic factors and outcomes. Our objective was to assess if a polygenic 94-SNP risk score was associated with breast cancer outcomes, prognostic factors and pathological subtypes in the PHARE and SIGNAL French prospective case cohort (NCT00381901 – RECF1098).

We have evaluated the prognostic value of a 94-SNP risk score in 8703 patients with early breast cancer included in the PHARE and SIGNAL prospective case cohort (NCT00381901 – RECF1098). This score was not associated with prognostic and predictive factors commonly used in the clinical routine, and was similarly unrelated to breast cancer subtypes. Moreover, the 94-SNP risk score did not predict outcomes. The analysis of this large cohort did not detect any association between iDFS and the 94-SNP score although the study had more than 82% power to detect a HR of 1.02 or higher. A previous GWAS [19] has already suggested that survival may be associated with a different set of SNPs to those that influence breast cancer susceptibility. If we hypothesize that prognosis and subtype of breast cancer are determined by constitutional genetic factors, variants associated with breast cancer subtypes and prognosis may be different from variants involved in the risk of developing breast cancer. Tumoral characteristics and age at diagnosis were superimposable between patients at high and low risk. Even if we assume that patients with family history of breast cancer may have a higher genetic risk score, breast cancer characteristics and outcomes of these high-risk patients are similar to others. Genetic history has already provided such an example: BRCA1 and BRCA2 gene mutations significantly increase the risk of developing breast cancer; however, outcomes of carriers seem to be similar to those with sporadic breast cancer [20‐26]. For each individual, we calculated a 94-SNP score by adding the number of breast cancer risk-increasing alleles across 94 known breast cancer SNPs. All variants are equally weighted. BRCA1/2 variants, which are rare and confer high risk of cancer, are not included in the 94-SNP score. Risk scores are generally calculated this way [3, 7]; however, these points can be considered as limits. Furthermore, we did not apply any quality filtering for imputed SNPs. There may be very minor error in calculating the overall risk score when including poorly imputed SNPs, but this impact should be minor considering the number of SNPs involved.

The first studies for identification of variants associated with prognosis in breast cancer investigated polymorphisms of candidate genes involved in oncogenesis, such as Plasminogen activator inhibitor-1 gene [27, 28], VEGF [29], TP53 [30] or Cycline D1 genes [31] and suggested links between some gene variants and breast cancer prognosis. Recently, GWAS have focused on associations between inherited germline genetic variants and breast cancer outcomes. They have identified SNPs that may influence breast cancer prognosis [28, 32‐34]. Around 60 variants have been described to date as potentially correlated with breast cancer outcomes [35]. Most of them are involved in pathways playing fundamental roles in oncogenesis such as cell cycle control, cell adhesion or DNA repair [35, 36]. However, in a cohort of over 37,000 patients with breast cancer, none of the 62 studied variants showed significant association with outcomes [35, 37‐42]. From these 62 variants, only one (rs2981582, in FGFR2 on chromosome 10) is used in our 94-SNP score. It has been identified as possibly associated with outcomes in breast cancer, with a HR (90% CI) of 1.09 (1.04–1.14) [35]. This variant reached nominal significance (p < 0.05) but did not reach genome-wide significance (p < 5 × 10⁻⁸) [35]. Preliminary analyses in our GWAS study do not indicate that this variant is associated with outcomes (unpublished data). This lack of evidence can be explained by limited statistical power, or that germline genetic polymorphisms may not impact the natural history of breast cancer, once the cancer is present.

Regarding breast cancer subtype, there is more evidence that susceptibility loci are associated with specific breast cancer subtypes. In 2011, the Breast Cancer Association Consortium identified six loci associated with ER+ breast cancer, four loci associated with triple-negative tumors and two loci associated with basal-like tumors [43]. These variants were included in the present analyses. The SIGNAL/PHARE cohort confirmed the association between FGFR2 locus and ER+ tumors, further restricting this association with HER2-negative breast tumors [44]. In our study, the 94-SNP risk score was not associated with specific breast cancer subtypes.

In clinical practice, there is a need to identify prognostic factors that can predict the risk of tumor recurrence. To accurately determine the prognosis of a patient is crucial and can also help to stratify patients in clinical trials assessing new therapies. Finding predictive factors that are associated with response or failure to a treatment and thus help to identify the most effective therapy remains the ultimate challenge to provide patients with personalized medicine. With regard to this aim, gene expression signatures assessed on tumor tissue, such as the 21-gene recurrence score assay Oncotype DX®, Mammaprint®, EndoPredict® or PAM50®, are of interest. They estimate the risk of distant recurrence and Oncotype DX® also predicts the magnitude of benefit of adjuvant chemotherapy for patients with early-stage breast cancer [45‐49]. Genes involved in this signature are different from those used in the 94-SNP score. Genetic variants and scores based on SNPs may be of interest in clinical routine if they provide prognostic and predictive information [50, 51]. GWAS in very large case cohorts of patients with available complete clinical data provide the opportunity to identify prognostic and predictive variants usable in clinical practice. The SIGNAL/PHARE database will also allow the investigation of clinical endpoints such as iDFS. The SIGNAL trial is the first large prospective clinical study whose primary objective was to identify prognostic and predictive genetic variants in early breast cancer. We are currently expanding our analyses, in order to search for SNPs associated with prognostic and predictive factors, eventually combined in a polygenic risk score as described for breast cancer risk, which could be of interest in routine clinical practice. Further stratifying patients based on their potential to respond to treatment will help optimize adjuvant regimens, if indeed they are necessary.

A score built with 94 SNPs can be used to stratify women with respect to their risk of developing breast cancer. In a prospective cohort of 8703 patients, this score is not associated with breast cancer characteristics, cancer subtypes or patients’ outcomes (iDFS and OS). If we hypothesize that prognosis and subtypes of breast cancer are determined by constitutional genetic factors, we suggest that inherited variants associated with breast cancer subtypes and prognosis may be different from variants involved in the risk of developing breast cancer.