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Abstract Background Genome‐wide measures of genetic disruption such as tumour mutation burden (TMB) and mutation signatures are emerging as useful biomarkers to stratify patients for treatment. Clinicians commonly use cancer gene panels for tumour mutation burden estimation, and whole genome sequencing is the gold standard for mutation signature analysis. However, the accuracy and cost associated with these assays limits their utility at scale. Methods WGS data from 560 breast cancer patients was used for in silico library simulations to evaluate the accuracy of an FDA approved cancer gene panel as well as restriction enzyme associated DNA sequencing (RADseq) libraries for TMB estimation and mutation signature analysis. We also transfected a mouse mammary cell line with APOBEC enzymes and sequenced resulting clones to evaluate the efficacy of RADseq in an experimental setting. Results RADseq had improved accuracy of TMB estimation and derivation of mutation profiles when compared to the FDA approved cancer panel. Using simulated immune checkpoint blockade (ICB) trials, we show that inaccurate TMB estimation leads to a reduction in power for deriving an optimal TMB cutoff to stratify patients for immune checkpoint blockade treatment. Additionally, prioritisation of APOBEC hypermutated tumours in these trials optimises TMB cutoff determination for breast cancer. The utility of RADseq in an experimental setting was also demonstrated, based on characterisation of an APOBEC mutation signature in an APOBEC3A transfected mouse cell line. Conclusion In conclusion, our work demonstrates that RADseq has the potential to be used as a cost‐effective, accurate solution for TMB estimation and mutation signature analysis by both clinicians and basic researchers.

Abstract Background Oestrogen receptor-positive (ER+) breast cancer is commonly treated using endocrine therapies such as aromatase inhibitors which block synthesis of oestradiol, but the influence of this therapy on the immune composition of breast tumours has not been fully explored. Previous findings suggest that tumour infiltrating lymphocytes and immune-related gene expression may be altered by treatment with aromatase inhibitors. However, whether these changes are a direct result of impacts on the host immune system or mediated through tumour cells is not known. We aimed to investigate the effect of oestrogen deprivation on the expression of chemokines and immune infiltration in vitro and in an ER+ immunocompetent mouse model. Methods RT-qPCR and a bead-based Bioplex system were used to investigate the expression of chemokines in MCF-7 breast cancer cells deprived of oestrogen. A migration assay and flow cytometry were used to measure the migration of human peripheral blood mononuclear cells (PBMCs) to MCF-7 cells grown without the main biologically active oestrogen, oestradiol. Using flow cytometry and immunohistochemistry, we examined the immune cell infiltrate into tumours created by injecting SSM3 ER+ breast cancer cells into wild-type, immunocompetent 129/SvEv mice. Results This study demonstrates that oestrogen deprivation increases breast cancer secretion of TNF, CCL5, IL-6, IL-8, and CCL22 and alters total human peripheral blood mononuclear cell migration in an in vitro assay. Oestrogen deprivation of breast cancer cells increases migration of CD4+ T cells and decreases migration of CD11c+ and CD14+ PBMC towards cancer cells. PBMC migration towards breast cancer cells can be reduced by treatment with the non-steroidal anti-inflammatory drugs, aspirin and celecoxib. Treatment with endocrine therapy using the aromatase inhibitor letrozole increases CD4+ T cell infiltration into ER+ breast cancer tumours in immune competent mice. Conclusions These results suggest that anti-oestrogen treatment of ER+ breast cancer cells can alter cytokine production and immune cells in the area surrounding the cancer cells. These findings may have implications for the combination and timing of anti-oestrogen therapies with other therapies.

Abstract Background Altered cellular metabolism is a hallmark of cancer but the association between utilisation of particular metabolic pathways in tumours and patient outcome is poorly understood. We sought to investigate the association between fatty acid metabolism and outcome in breast and other cancers. Methods Cox regression analysis and Gene Set Enrichment Analysis (GSEA) of a gene expression dataset from primary breast tumours with well annotated clinical and survival information was used to identify genesets associated with outcome. A geneset representing fatty acid oxidation (FAO) was then examined in other datasets. A doxycycline-inducible breast cancer cell line model overexpressing the rate-limiting enzyme in FAO, carnitine palmitoyl transferase 1A (CPT1A) was generated and analysed to confirm the association between FAO and cancer-associated characteristics in vitro. Results We identified a gene expression signature composed of 19 genes associated with fatty acid oxidation (FAO) that was significantly associated with patient outcome. We validated this observation in eight independent breast cancer datasets, and also observed the FAO signature to be prognostic in other cancer types. Furthermore, the FAO signature expression was significantly downregulated in tumours, compared to normal tissues from a variety of anatomic origins. In breast cancer, the expression of CPT1A was higher in oestrogen receptor (ER)-positive, compared to ER-negative tumours and cell lines. Importantly, overexpression of CPT1A significantly decreased the proliferation and wound healing migration rates of MDA-MB231 breast cancer cells, compared to basal expression control. Conclusions Our findings suggest that FAO is downregulated in multiple tumour types, and activation of this pathway may lower cancer cell proliferation, and is associated with improved outcomes in some cancers.

Approximately 80% of human breast carcinomas present as oestrogen receptor α-positive (ER+ve) disease, and ER status is a critical factor in treatment decision-making. Recently, single nucleotide polymorphisms (SNPs) in the region immediately upstream of the ER gene (ESR1) on 6q25.1 have been associated with breast cancer risk. Our investigation of factors associated with the level of expression of ESR1 in ER+ve tumours has revealed unexpected associations between genes in this region and ESR1 expression that are important to consider in studies of the genetic causes of breast cancer risk. RNA from tumour biopsies taken from 104 postmenopausal women before and after 2 weeks treatment with an aromatase (oestrogen synthase) inhibitor was analyzed on Illumina 48K microarrays. Multiple-testing corrected Spearman correlation revealed that three previously uncharacterized open reading frames (ORFs) located immediately upstream of ESR1, C6ORF96, C6ORF97, and C6ORF211 were highly correlated with ESR1 (Rs = 0.67, 0.64, and 0.55 respectively, FDR

Purpose: Aromatase inhibitors (AIs) have an established role in the treatment of breast cancer. Response rates are only 50% to 70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy., Patients and Methods: Pretreatment and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 postmenopausal women who had estrogen receptor-alpha positive breast cancer and were receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed with use of three-dimensional ultrasound measurements., Results: The molecular response to letrozole was characterized and a four-gene classifier of clinical response was established (accuracy of 96%) on the basis of the level of two genes before treatment (one gene [IL6ST] was associated with immune signaling, and the other [NGFRAP1] was associated with apoptosis) and the level of two proliferation genes (ASPM, MCM4) after 2 weeks of therapy. The four-gene signature was found to be 91% accurate in a blinded, completely independent validation data set of patients treated with anastrozole. Matched 2-week on-treatment biopsies were associated with improved predictive power as compared with pretreatment biopsies alone. This signature also significantly predicted recurrence-free survival (P = .029) and breast cancer -specific survival (P = .009). We demonstrate that the test can also be performed with use of quantitative polymerase chain reaction or immunohistochemistry., Conclusion: A four-gene predictive model of clinical response to AIs by 2 weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and cell proliferation that is not reduced 2 weeks after initiation of treatment are functional characteristics of breast tumors that do not respond to AIs., (© 2015 by American Society of Clinical Oncology.)