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

Gao, Qiong; López-Knowles, Elena; Cheang, Maggie C.U.; Morden, James P.; Ribas, Ricardo; Sidhu, Kiran; Evans, David M.; Martins, Vera; Dodson, Andrew; Skene, Anthony; Holcombe, Chris; Mallon, Elizabeth; Evans, Abigail; Bliss, Judith; Robertson, J. F. R.; Smith, Ian; Martin, Lesley-Ann; Dowsett, Mitch; Trialists,

doi:10.1093/jncics/pky005

Cited by 12 publications

(19 citation statements)

References 6 publications

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“…It appears that the upregulation of these genes may be related to longer time to fixation for the surgical samples rather than study-related as four of these genes were also up regulated in our studies of changes in gene expression in the absence of drug-treatment. 15,16 These five genes were excluded from further analysis, as any changes in their expression between windows could not be ascribed with confidence to menstrual cycle effects.…”

Section: Resultsmentioning

confidence: 99%

“…Tumour samples taken in the same window of the menstrual cycle provided a control group and indicated that the expression of five of our selected genes increased between diagnosis and surgery irrespective of cycle window. The upregulation of these genes does not appear to be study-related as four of these genes were also upregulated in studies of changes in gene expression in the absence of drug-treatment 15,16 where the upregulation was ascribed to differences in the time taken to fixation for the samples at the two time points. These five genes were thus excluded from further analysis, as any changes in their expression between different windows would not be attributable to menstrual cycle effects.…”

Section: Discussionmentioning

confidence: 99%

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Menstrual cycle associated changes in hormone-related gene expression in oestrogen receptor positive breast cancer

et al. 2019

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The major changes in hormone levels that occur through the menstrual cycle have been postulated to affect the expression of hormone-regulated and proliferation-associated genes (PAGs) in premenopausal ER+ breast cancer. Whilst previous studies have demonstrated differences in gene expression, here, we investigated if there are within patient changes in the expression of oestrogen- and progesterone-regulated genes (ERGs and PRGs) and PAGs in ER+ breast cancer during the menstrual cycle. Samples from 96 patients in two independent prospective studies of the effect of menstrual cycle on ER+ breast cancer were used. Plasma hormone measurements were used to assign tumours to one of three pre-defined menstrual cycle windows: W1 (days 27–35 and 1–6; low oestradiol and low progesterone), W2 (days 7–16; high oestradiol and low progesterone) and W3 (days 17–26; intermediate oestradiol and high progesterone). RNA expression of 50 genes, including 27 ERGs, 11 putative PRGs and seven PAGs was measured. The AvERG (geomean of PGR, GREB1, TFF1 and PDZK1) was used as a composite measure of ERG expression and showed significant changes between the three windows of the menstrual cycle increasing over 2.2-fold between W1 and W2 and decreasing between W2 and W3 and between W3 and W1. Proliferation gene expression also varied significantly, following the same pattern of changes as ERG expression, but the changes were of lower magnitude (1.4-fold increase between W1 and W2). Significant changes in the expression of eight individual ERGs, including GREB1, PGR and TFF1, and two PAGs were observed between W1 and either W2 or W3 with all genes showing higher levels in W2 or W3 (1.3–2.4-fold; FDR 0.016–0.05). The AvProg, a composite measure of PRG expression, increased significantly (1.5-fold) in W3 compared to W1 or W2 but no significant changes were observed for individual PRGs. In conclusion, we observed significant changes in ERG, PRG and PAG expression in ER+ breast tumours during the menstrual cycle that may affect the assessment and interpretation of prominent biomarkers (e.g. PgR) and commonly used multigene prognostic signatures in premenopausal ER+ breast cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Menstrual cycle associated changes in hormone-related gene expression in oestrogen receptor positive breast cancer

et al. 2019

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“…In addition, and importantly, previous reports have not included controls that can identify artefacts that result from the experimental design of pre-surgical studies; we have recently reported that the changes in gene expression of the greatest magnitude in AI-treated patients in a short-term presurgical study are entirely artefactual. This makes the inclusion of a control set of tumours critical for eliminating these artefacts [13]. In the following analyses, we have utilised a study design that avoids these limitations by accessing samples from the PeriOperative Endocrine Therapy-Individualising Care (POETIC, CRUK/07/015) trial [14].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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BackgroundEndocrine 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.MethodsGlobal 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.ResultsHigh 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.ConclusionsThere 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 registrationISRCTN, ISRCTN63882543, registered on 18 December 2007.

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“…Given the central role gene expression analyses play in the investigation and management of breast cancer, the report by Gao et al (17), describing artifactual gene expression changes in serially sampled specimens collected as part of a randomized controlled trial, are particularly relevant to the design and interpretation of gene expression biomarker studies. In their accompanying article, the authors build on previous reports describing alterations in gene expression related to delays in tissue processing following sampling, including substantial changes in early response genes between diagnostic core biopsies and surgical specimens (18–20).…”

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confidence: 99%

“…This would result in substantial differences in ischemic time that could produce cellular responses and affect sample integrity. Gao et al showed that many of the genes whose expression is most altered between baseline and surgery in the AI-treated group are also those affected in the control arm (Figure 1) (17). This is a striking finding as it indicates that the genes that could have been attributed to AI treatment were actually due to a confounding factor.…”

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confidence: 99%

Gene Expression Analyses in Breast Cancer: Sample Matters

Haibe‐Kains

Cescon

2018

JNCI Cancer Spectrum

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

Cited by 12 publications

References 6 publications

Menstrual cycle associated changes in hormone-related gene expression in oestrogen receptor positive breast cancer

Menstrual cycle associated changes in hormone-related gene expression in oestrogen receptor positive breast cancer

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

Gene Expression Analyses in Breast Cancer: Sample Matters

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