Tumour sampling method can significantly influence gene expression profiles derived from neoadjuvant window studies

Abstract

Patient-matched transcriptomic studies using tumour samples before and after treatment allow inter-patient heterogeneity to be controlled, but tend not to include an untreated comparison. Here, Illumina BeadArray technology was used to measure dynamic changes in gene expression from thirty-seven paired diagnostic core and surgically excised breast cancer biopsies obtained from women receiving no treatment prior to surgery, to determine the impact of sampling method and tumour heterogeneity. Despite a lack of treatment and perhaps surprisingly, consistent changes in gene expression were identified during the diagnosis-surgery interval (48 up, 2 down; Siggenes FDR 0.05) in a manner independent of both subtype and sampling-interval length. Instead, tumour sampling method was seen to directly impact gene expression, with similar effects additionally identified in six published breast cancer datasets. In contrast with previous findings, our data does not support the concept of a significant wounding or immune response following biopsy in the absence of treatment and instead implicates a hypoxic response following the surgical biopsy. Whilst sampling-related gene expression changes are evident in treated samples, they are secondary to those associated with response to treatment. Nonetheless, sampling method remains a potential confounding factor for neoadjuvant study design.

Figure 1. Evidence of treatment independent variation between breast cancer diagnostic core biopsies and surgical excision samples.

(a) Hierarchical clustering of the 37 patient-matched diagnostic core and excision biopsy samples using the 500 most variable genes (upper) and a SAM derived signature of 50 genes consistently differentially expressed between core and excision biopsies (lower). Bars represent IHC status (ER+/Her2− = Blue; ER+/Her2+ = Pink; ER-/Her2− = Red) or biopsy method. Lower-most bar indicates where sample pairs have co-aggregated. Two-thirds (25/37) of the pairs cluster at the first level of the upper dendrogram, whereas pairwise association is lost in 31/37 cases for the lower. (b) There is a significantly stronger correlation between biopsy pairs (intra-tumour) than between different tumours (mean inter-tumour). ***p < 0.001. (c) Discordance in molecular subtype assignment between core and excision biopsies. Patients are ranked left to right by pairwise correlation. Colours represent SSP subtypes (Luminal A = Dark blue; Luminal B = Light Blue; Her2 = Pink; Basal = Red; Normal = Green). (d) Sample pairs were called discordant when Biopsy A ≠Biopsy B for at least 4/5 classifiers. Comparison of concordant vs. discordant pairwise correlations then revealed an inverse relationship between correlation and discordancy.

Figure 2. Factors associated with consistent gene expression changes between diagnostic core biopsy and surgical excision of breast tumours in the absence of treatment.

(a) Pairwise correlations between biopsy pairs are not explained as either a function of time between biopsy (p = 0.32) or IHC status (p = 0.43). ER+/Her2− = Blue; ER+/Her2+ = Pink; ER-/Her2− = Red. (b) Heatmap showing differential expression of NIT signature genes in NIT and letrozole treated cohorts. Colours represent gene expression fold changes (up = yellow; down = blue) between samples and their subsequent patient-matched biopsies. Samples are ordered by increasing time between biopsies and reveal a pattern associated with either extraction method - CB (grey) or EB (dark grey) - or time. (c) Frequency distribution of biopsy time intervals. For further analysis, the letrozole treated data was split into three subsets – 2-week CB (2wCB), 3-month CB (3 mCB) and 3-month EB (3 mEB) – to investigate the effects of biopsy method and/or time on gene expression. (d) Mean expression fold changes since previous biopsy for 2 week, 3 month and NIT samples. 3 month subsets closely resemble NIT expression changes, though SAM analysis revealed a greater intersection of differentially expressed genes between NIT and 3 mEB samples than between NIT and 3 mCB samples.

Figure 3. Multiple patient-matched datasets demonstrate shared changes in NIT early growth response genes.

Pairwise analysis of four early growth response genes among the NIT signature in six validation datasets. These genes potentially represent an association between gene expression and sampling method, with surgically excised samples (EB) showing greater expression fold-change than their core biopsied (CB) counterparts. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; − = not significant.