PurposeAt least 94 common single nucleotide polymorphisms (SNPs) are associated with breast cancer. The extent to which an SNP panel can refine risk in women who receive preventive therapy has not been directly assessed previously.Materials and MethodsA risk score on the basis of 88 SNPs (SNP88) was investigated in a nested case-control study of women enrolled in the International Breast Intervention Study (IBIS-I) or the Royal Marsden study. A total of 359 women who developed cancer were matched to 636 controls by age, trial, follow-up time, and treatment arm. Genotyping was done using the OncoArray. Conditional logistic regression and matched concordance indices (mC) were used to measure the performance of SNP88 alone and with other breast cancer risk factors assessed using the Tyrer-Cuzick (TC) model.ResultsSNP88 was predictive of breast cancer risk overall (interquartile range odds ratio [IQ-OR], 1.37; 95% CI, 1.14 to 1.66; mC, 0.55), but mainly for estrogen receptor–positive disease (IQ-OR, 1.44; 95% CI, 1.16 to 1.79; P for heterogeneity = .10) versus estrogen receptor–negative disease. However, the observed risk of SNP88 was only 46% (95% CI, 19% to 74%) of expected. No significant interaction was observed with treatment arm (placebo IQ-OR, 1.46; 95% CI, 1.13 to 1.87; tamoxifen IQ-OR, 1.25; 95% CI, 0.96 to 1.64; P for heterogeneity = .5). The predictive power was similar to the TC model (IQ-OR, 1.45; 95% CI, 1.21 to 1.73; mC, 0.55), but SNP88 was independent of TC (Spearman rank-order correlation, 0.012; P = .7), and when combined multiplicatively, a substantial improvement was seen (IQ-OR, 1.64; 95% CI, 1.36 to 1.97; mC, 0.60).ConclusionA polygenic risk score may be used to refine risk from the TC or similar models in women who are at an elevated risk of breast cancer and considering preventive therapy. Recalibration may be necessary for accurate risk assessment.

Pre-surgical studies allow study of the relationship between mutations and response of oestrogen receptor-positive (ER+) breast cancer to aromatase inhibitors (AIs) but have been limited to small biopsies. Here in phase I of this study, we perform exome sequencing on baseline, surgical core-cuts and blood from 60 patients (40 AI treated, 20 controls). In poor responders (based on Ki67 change), we find significantly more somatic mutations than good responders. Subclones exclusive to baseline or surgical cores occur in ∼30% of tumours. In phase II, we combine targeted sequencing on another 28 treated patients with phase I. We find six genes frequently mutated: PIK3CA, TP53, CDH1, MLL3, ABCA13 and FLG with 71% concordance between paired cores. TP53 mutations are associated with poor response. We conclude that multiple biopsies are essential for confident mutational profiling of ER+ breast cancer and TP53 mutations are associated with resistance to oestrogen deprivation therapy.

Recent studies have provided strong evidence for the emergence of substantial numbers of constitutively active ESR1 mutations in estrogen receptor–positive metastatic breast cancer that are undetected in primary disease. Some of these mutants remain partially sensitive to current anti-estrogen therapies but effective therapeutics targeted at them may require new approaches. Clin Cancer Res; 20(7); 1724–6. ©2014 AACR.

IntroductionPIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit α) somatic mutations are the most common genetic alteration in breast cancer (BC). Their prognostic value and that of the phosphatidylinositol 3-kinase (PI3K) pathway in BC remains only partly defined. The effect of PIK3CA mutations and alterations of the PI3K pathway on the antiproliferative response to aromatase inhibitor treatment was determined.MethodsThe Sequenom MassARRAY System was used to determine the presence of 20 somatic mutations across the PIK3CA gene in 85 oestrogen receptor–positive (ER+) BC patients treated with 2 weeks of anastrozole before surgery. Whole-genome expression profiles were used to interrogate gene signatures (GSs) associated with the PI3K pathway. Antiproliferative activity was assessed by the change in Ki67 staining between baseline and surgery. Three GSs representing the PI3K pathway were assessed (PIK3CA-GS (Loi), PI3K-GS (Creighton) and PTEN-loss-GS (Saal)).ResultsIn our study sample, 29% of tumours presented with either a hotspot (HS, 71%) or a nonhotspot (non-HS, 29%) PIK3CA mutation. Mutations were associated with markers of good prognosis such as progesterone receptor positivity (PgR+) (P = 0.006), low grade (P = 0.028) and luminal A subtype (P = 0.039), with a trend towards significance with degree of ER positivity (P = 0.051) and low levels of Ki67 (P = 0.051). Non-HS mutations were associated with higher PgR (P = 0.014) and ER (P < 0.001) expression than both wild-type (WT) and HS-mutated samples, whereas neither biomarker differed significantly between WT and HS mutations or between HS and non-HS mutations. An inverse correlation was found between the Loi signature and both the Creighton and Saal signatures, and a positive correlation was found between the latter signatures. Lower pretreatment Ki67 levels were observed in mutation compared with WT samples (P = 0.051), which was confirmed in an independent data set. Mutation status did not predict change in Ki67 in response to 2 weeks of anastrozole treatment; there was no significant difference between HS and non-HS mutations in this regard.ConclusionsPIK3CA mutations are associated with classical markers of good prognosis and signatures of PI3K pathway activity. The presence of a PIK3CA mutation does not preclude a response to neoadjuvant anastrozole treatment.

No abstract

Purpose A case-control study from two randomised breast cancer prevention trials of tamoxifen and raloxifene (P-1 and P-2) identified single nucleotide polymorphisms (SNPs) in or near genes ZNF423 and CTSO as factors which predict which women will derive most anti-cancer benefit from selective oestrogen receptor modulator (SERM) therapy. In this article we further examine this question by using blood samples from two randomised tamoxifen prevention trials: the International Breast Cancer Intervention Study I (IBIS-I), and the Royal Marsden trial (Marsden). Methods A nested case-control study was designed with 2:1 matching in IBIS-I and 1:1 matching in Marsden. The OncoArray was used for genotyping, and included two SNPs previously identified (rs8060157 in ZNF423 and rs10030044 near CTSO), and 102 further SNPs within the same regions. Overall there were 369 cases and 662 controls, with 148 cases and 268 controls from the tamoxifen arms. Odds ratios were estimated by conditional logistic regression, with Wald 95% confidence intervals. Results In the tamoxifen arms the per-allele odds ratio for rs8060157 was 0.99 (95%CI 0.73–1.34), and 1.00 (95%CI 0.76–1.33) for rs10030044. In the placebo arm, the odds ratio was 1.10 (95%CI 0.87-1.40) for rs8060157 and 1.01 (95%CI 0.79-1.29) for rs10030044. There was no evidence to suggest other SNPs in the surrounding regions of these SNPs might predict response to tamoxifen. Conclusions Results from these two prevention trials do not support the earlier findings. rs8060157 in ZNF423 and rs10030044 near CTSO do not appear to predict response to tamoxifen.

Background: PIK3CA is the single most commonly mutated gene in breast cancer, with highest incidence reported in ER positive and HER2 negative breast cancer. Substantial data now suggests that breast cancers show intra-tumoural genetic heterogeneity, with apparently clonal tumours composed of multiple populations of tumour cells that, in addition to the founder genetic events common to all cells, harbour private genetic alterations. Tumours with mutations that are sub-clonal may respond less well to therapies targeting these mutations than cancers with clonal mutations. To assess how frequently PIK3CA mutations are clonal founder mutations, or may be subclonal, we assessed the abundance on PIK3CA mutation using digital PCR. Methods: DNA was extracted from frozen sections of 119 primary breast cancers, following macrodissection to achieve tumour cell content of &gt;70%. PIK3CA mutations c.1624G&gt;A (E542K), c.1633G&gt;A (E545K), c.3140A&gt;T (H1047L) and c.3140A&gt;G (H1047R) were assessed by droplet digital PCR on a BioRad QX100 system. Exon 9 mutation assays were optimised to not amplify the PIK3CA pseudogene. Mutational abundance was calculated from the Poisson distribution, expressed as the portion of PIK3CA DNA in the sample that was mutant, and compared between breast cancer subtypes. A mutational abundance of &lt;20% was predefined to represent low abundance mutation, that may be subclonal. Results: PIK3CA mutations were detected with abundance ranging from 80.4% to 0.0063%, with 26 cancers with an abundance &gt;20% and 19 cancers with low abundance &lt;20% (5 cancers with abundance 1-20%, and 14 cancer with abundance &lt;1%). There was highly correlation between repeat experiments r2 = 0.98, p&lt;0.0001, with 100% concordance for low abundance mutations in repeat analysis. High abundance mutations were numerically more common in ER positive HER2 negative cancers (18/65, 28%) than HER2 positive or triple negative (TN) cancers (7/54, 14% p = 0.07 Fishers exact test). Conversely, low abundance mutations were less common in ER positive HER2 negative cancers (4/65, 6%) than in HER2 positive or TN cancers (10/54, 19% p = 0.047). In cancers with a detectable PIK3CA mutation, mutational abundance was higher in ER positive cancer than ER negative cancers (p = 0.023 Mann-Whitney U test), and higher in ER positive HER2 negative cancers compared to HER2 positive or TN cancers (p = 0.0024). In ER positive HER2 negative cancers 82% (18/22) mutations were of high abundance, and likely clonal, whereas in TN or HER2 positive cancers 39% (7/18, p = 0.009) were of high abundance. Conclusion: Our data suggests that hotspot PIK3CA mutations are frequently of low abundance in HER2 positive or TN breast cancer, and may be subclonal. However, we cannot exclude the possibility that these findings represent contamination. If confirmed on an independent data set, our data suggest that identification of mutational abundance may be an important component of PIK3CA mutation assessment and the potential targeting of these mutations with PI3 kinase inhibitors. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-08-01.

No abstract