Genomic Instability and <i>TP53</i> Genomic Alterations Associate With Poor Antiproliferative Response and Intrinsic Resistance to Aromatase Inhibitor Treatment.

Purpose Although aromatase inhibitor (AI) treatment is effective in estrogen receptor-positive postmenopausal breast cancer, resistance is common and incompletely explained. Genomic instability, as measured by somatic copy number alterations (SCNAs), is important in breast cancer development and prognosis. SCNAs to specific genes may drive intrinsic resistance, or high genomic instability may drive tumor heterogeneity, which allows differential response across tumors and surviving cells to evolve resistance to treatment rapidly. We therefore evaluated the relationship between SCNAs and intrinsic resistance to treatment as measured by a poor antiproliferative response.Patients and methods SCNAs were determined by single nucleotide polymorphism array in baseline and surgery core-cuts from 73 postmenopausal patients randomly assigned to receive 2 weeks of preoperative AI or no AI in the Perioperative Endocrine Therapy-Individualizing Care (POETIC) trial. Fifty-six samples from the AI group included 28 poor responders (PrRs, less than 60% reduction in protein encoded by the MKI67 gene [Ki-67]) and 28 good responders (GdRs, greater than 75% reduction in Ki-67). Exome sequencing was available for 72 pairs of samples.Results Genomic instability correlated with Ki-67 expression at both baseline (P P P = .048). The SCNA with the largest difference between GdRs and PrRs was loss of heterozygosity observed at 17p (false discovery rate, 0.08), which includes TP53. Nine of 28 PrRs had loss of wild-type TP53 as a result of mutations and loss of heterozygosity compared with three of 28 GdRs. In PrRs, somatic alterations of TP53 were associated with higher genomic instability, higher baseline Ki-67, and greater resistance to AI treatment compared with wild-type TP53.Conclusion We observed that primary tumors with high genomic instability have an intrinsic resistance to AI treatment and do not require additional evolution to develop resistance to estrogen deprivation therapy

Major Impact of Sampling Methodology on Gene Expression in Estrogen Receptor-Positive Breast Cancer.

To investigate the impact of sampling methodology on gene expression data from primary estrogen receptor-positive (ER+) breast cancer biopsies, global gene expression was measured in core-cut biopsies at baseline and surgery from patients randomly assigned to receive either two weeks of presurgical aromatase inhibitor (AI; n = 157) or no presurgical treatment (n = 56). Those genes most markedly altered in the AI group (eg, FOS, DUSP1, RGS1, FOSB) were similarly altered in the no treatment group; some widely investigated genes that were apparently unaffected in the AI group (eg, MYC) were counter-altered in the control group, masking actual AI-dependent changes. In the absence of a control group, these artefactual changes would likely lead to the most affected genes being the erroneous focus of research. The findings are likely relevant to all archival collections of ER+ breast cancer

Impact of aromatase inhibitor treatment on global gene expression and its association with antiproliferative response in ER+ breast cancer in postmenopausal patients.

BACKGROUND:Endocrine therapy reduces breast cancer mortality by 40%, but resistance remains a major clinical problem. In this study, we sought to investigate the impact of aromatase inhibitor (AI) therapy on gene expression and identify gene modules representing key biological pathways that relate to early AI therapy resistance. METHODS:Global gene expression was measured on pairs of core-cut biopsies taken at baseline and at surgery from 254 patients with ER-positive primary breast cancer randomised to receive 2-week presurgical AI (n = 198) or no presurgical treatment (control n = 56) from the POETIC trial. Data from the AI group was adjusted to eliminate artefactual process-related changes identified in the control group. The response was assessed by changes in the proliferation marker, Ki67. RESULTS:High baseline ESR1 expression associated with better AI response in HER2+ tumours but not HER2- tumours. In HER2- tumours, baseline expression of 48 genes associated with poor antiproliferative response (p < 0.005) including PERP and YWHAQ, the two most significant, and the transcription co-regulators (SAP130, HDAC4, and NCOA7) which were among the top 16 most significant. Baseline gene signature scores measuring cell proliferation, growth factor signalling (ERBB2-GS, RET/GDNF-GS, and IGF-1-GS), and immune activity (STAT1-GS) were significantly higher in poor AI responders. Two weeks of AI caused downregulation of genes involved in cell proliferation and ER signalling, as expected. Signature scores of E2F activation and TP53 dysfunction after 2-week AI were associated with poor AI response in both HER2- and HER2+ patients. CONCLUSIONS:There is a high degree of heterogeneity in adaptive mechanisms after as little as 2-week AI therapy; however, all appear to converge on cell cycle regulation. Our data support the evaluation of whether an E2F signatures after short-term exposure to AI may identify those patients most likely to benefit from the early addition of CDK4/6 inhibitors. TRIAL REGISTRATION:ISRCTN, ISRCTN63882543, registered on 18 December 2007

Impact of mutational profiles on response of primary oestrogen receptor-positive breast cancers to oestrogen deprivation

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.</p