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

Gene expression profiling of carcinomas has been widely used for molecular subtyping and prognostic prediction1,2,3,4,5,6,7,8,9 producing a diverse library of gene classifiers. However, these signatures may be limited by the particular dataset used to produce the signature10 and by the inherent cellular heterogeneity of tumours and the practical considerations of how samples are collected – in short the heterogeneity of the cohort and the heterogeneity of sampling. A more informative and considered approach when performing molecular studies is to use matched biopsies from the same patient, allowing for both inter-patient variation to be controlled and changes occurring within a given tumour or organism to be more accurately modelled11,12,13. Matched sample pairs coupled with careful cohort selection should ensure that changes related to a given drug represent the largest source of variation, avoiding any unwanted contribution from confounding factors known or unknown and allow for greater statistical power with a smaller sample size14,15. Acquisition of multiple biopsies from an individual patient has been simplified by the more common use of neoadjuvant therapy for breast cancer, an increasingly popular treatment option for initially large, inoperable or locally advanced breast tumours, as well as operable cancers susceptible to specific treatments16. Pre-operative treatment with chemotherapy, or endocrine therapy in ER+ disease, not only increases rates of breast conserving surgery17, but also allows a unique in vivo observation of tumour response to treatment12,18. This so-called ‘window of opportunity’ permits sequential biopsies of the same cancer to be taken at different time points during the course of the pre-operative treatment, allowing assessment of molecular changes in the tumour long before clinical evidence of response can be determined11,13. This has allowed the molecular effects of an administered drug to be studied and enabled biomarkers predictive of response or resistance to therapy to be identified at an increased rate. Recently our own group has demonstrated the added value of additional on-treatment measurements of gene expression to characterise and accurately predict the response to treatment13,19. Whilst the benefits of this ‘window of opportunity’ approach are certainly attractive for translational research, matched samples have commonly been collected under the assumption that variation observed between pairs will have occurred as a result of treatment – i.e. the results apparent are due to the drug alone. A control group is often not included in these studies and those that do are commonly limited to a handful of samples (n = 820, n = 1521) or are confounded by concurrent treatments. Conversely, studies that have compared multiple biopsies from the same patient in lieu of treatment are limited to only a fraction of the total molecular repertoire, most often focussing on hormone receptor status by IHC and not full transcriptomic profiling22,23,24,25. Whilst oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her2) exhibit high concordance between sample pairs in these studies, the growing understanding of breast cancer as an increasingly heterogeneous and polygenic disease necessitates a high-throughput approach. Previous work26 that has utilised larger-scale assays (a panel of 147 cancer related genes) investigated molecular variation under conditions of no-intervening treatment (NIT) in 21 paired core needle biopsy (CB) and excision biopsy (EB) samples. Here the diagnostic core biopsy was implicated in initiating an immune response, hypothesised to then be detected in a later surgically extracted excision biopsy. Potential stimulation of tumour-associated macrophages (TAMs) in response to CB was also reported, itself associated with poor prognosis in human breast cancer, raising concerns of taking multiple repeated biopsies from the same patient, underlining the importance of considering the full repertoire of genetic expression under conditions of no treatment. Here we present the largest dataset to-date of untreated patient-matched breast cancer samples to determine whether, and to what extent, sample pairs exhibit molecular heterogeneity independent of treatment, and what the implications of any variation are in terms of the interpretation of patient-matched gene expression profiling studies. We explore possible causes of consistent differential expression and whether these reflect a wounding or immune response to the first biopsy, a hypoxia- or stress-induced response following blood supply interruption27 or the normal growth and evolution of tumours over time28.