Mediator complex (MED) 7: a biomarker associated with good prognosis in invasive breast cancer, especially ER+ luminal subtypes

Background: Mediator complex (MED) proteins have a key role in transcriptional regulation, some interacting with the oestrogen receptor (ER). Interrogation of the METABRIC cohort suggested that MED7 may regulate lymphovascular invasion (LVI). Thus MED7 expression was assessed in large breast cancer (BC) cohorts to determine clinicopathological significance. Methods: MED7 gene expression was investigated in the METABRIC cohort (n = 1980) and externally validated using bc-GenExMiner v4.0. Immunohistochemical expression was assessed in the Nottingham primary BC series (n = 1280). Associations with clinicopathological variables and patient outcome were evaluated. Results: High MED7 mRNA and protein expression was associated with good prognostic factors: low grade, smaller tumour size, good NPI, positive hormone receptor status (p < 0.001), and negative LVI (p = 0.04) status. Higher MED7 protein expression was associated with improved BC-specific survival within the whole cohort and ER+/luminal subgroup. Pooled MED7 gene expression data in the external validation cohort confirmed association with better survival, corroborating with the protein expression. On multivariate analysis, MED7 protein was independently predictive of longer BC-specific survival in the whole cohort and Luminal A subtype (p < 0.001). Conclusions: MED7 is an important prognostic marker in BC, particularly in ER+luminal subtypes, associated with improved survival and warrants future functional analysis

The multifunctional solute carrier 3A2 (SLC3A2) confers a poor prognosis in the highly proliferative breast cancer subtypes

Background: Breast cancer (BC) is a heterogeneous disease characterised by variant biology, metabolic activity and patient outcome. This study aimed to evaluate the biological and prognostic value of the membrane solute carrier, SLC3A2 in BC with emphasis on the intrinsic molecular subtypes. Methods: SLC3A2 was assessed at the genomic level, using METABRIC data (n=1,980), and proteomic level, using immunohistochemistry on TMA sections constructed from a large well-characterised primary BC cohort (n=2,500). SLC3A2 expression was correlated with clinicopathological parameters, molecular subtypes, and patient outcome. Results: SLC3A2 mRNA and protein expression were strongly correlated with higher tumour grade and poor Nottingham prognostic index (NPI). High expression of SLC3A2 was observed in triple negative (TN), HER2+, and ER+ high proliferation subtypes. SLC3A2 mRNA and protein expression were significantly associated with the expression of c-MYC in all BC subtypes (p<0.001). High expression of SLC3A2 protein was associated with poor patient outcome (p<0.001)), but only in the ER+ high proliferation (p=0.01) and triple negative (p=0.04) subtypes. In multivariate analysis SLC3A2 protein was an independent risk factor for shorter breast cancer specific survival (p<0.001). Conclusions: SLC3A2 appears to play a role in the aggressive BC subtypes driven by MYC and could act as a potential prognostic marker. Functional assessment is necessary to reveal its potential therapeutic value in the different BC subtypes

Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification

Accurate distant metastasis (DM) prediction is critical for risk stratification and effective treatment decisions in breast cancer (BC). Many prognostic markers/models based on tissue marker studies are continually emerging using conventional statistical approaches analysing complex/dimensional data association with DM/poor prognosis. However, few of them have fulfilled satisfactory evidences for clinical application. This study aimed at building DM risk assessment algorithm for BC patients. A well-characterised series of early invasive primary operable BC (n=1902), with immunohistochemical (IHC) expression of a panel of biomarkers (n=31) formed the material of this study. Decision tree algorithm was computed using WEKA software, utilising quantitative biomarkers’ expression and the absence/presence of distant metastases. Fifteen biomarkers were significantly associated with DM, with six temporal subgroups characterised based on time-to-development of DM ranging from 15 years of follow-up. Of these 15 biomarkers, 10 had a significant expression pattern where Ki67LI, HER2, p53, N-cadherin, P-cadherin, PIK3CA and TOMM34 showed significantly higher expressions with earlier development of DM. In contrast, higher expressions of ER, PR, and BCL2, were associated with delayed occurrence of DM. DM prediction algorithm was built utilising cases informative for the 15 significant markers. Four risk groups of patients were characterised. Three markers; p53, HER2 and BCL2 predicted the probability of DM, based on software-generated cut-offs, with a precision rate of 81.1% for positive predictive value and 77.3%, for the negative predictive value. This algorithm reiterates the reported prognostic values of these three markers and underscores their central biologic role in BC progression. Further independent validation of this pruned panel of biomarkers is therefore warranted

MYC functions are specific in biological subtypes of breast cancer and confers resistance to endocrine therapy in luminal tumours.

BACKGROUND: MYC is amplified in approximately 15% of breast cancers (BCs) and is associated with poor outcome. c-MYC protein is multi-faceted and participates in many aspects of cellular function and is linked with therapeutic response in BCs. We hypothesised that the functional role of c-MYC differs between molecular subtypes of BCs. METHODS: We therefore investigated the correlation between c-MYC protein expression and other proteins involved in different cellular functions together with clinicopathological parameters, patients' outcome and treatments in a large early-stage molecularly characterised series of primary invasive BCs (n=1106) using immunohistochemistry. The METABRIC BC cohort (n=1980) was evaluated for MYC mRNA expression and a systems biology approach utilised to identify genes associated with MYC in the different BC molecular subtypes. RESULTS: High MYC and c-MYC expression was significantly associated with poor prognostic factors, including grade and basal-like BCs. In luminal A tumours, c-MYC was associated with ATM (P=0.005), Cyclin B1 (P=0.002), PIK3CA (P=0.009) and Ki67 (P<0.001). In contrast, in basal-like tumours, c-MYC showed positive association with Cyclin E (P=0.003) and p16 (P=0.042) expression only. c-MYC was an independent predictor of a shorter distant metastases-free survival in luminal A LN+ tumours treated with endocrine therapy (ET; P=0.013). In luminal tumours treated with ET, MYC mRNA expression was associated with BC-specific survival (P=0.001). In ER-positive tumours, MYC was associated with expression of translational genes while in ER-negative tumours it was associated with upregulation of glucose metabolism genes. CONCLUSIONS: c-MYC function is associated with specific molecular subtypes of BCs and its overexpression confers resistance to ET. The diverse mechanisms of c-MYC function in the different molecular classes of BCs warrants further investigation particularly as potential therapeutic targets

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

Impact of intratumoural heterogeneity on the assessment of Ki67 expression in breast cancer

In breast cancer (BC), the prognostic value of Ki67 expression is well-documented. Intratumoural heterogeneity (ITH) of Ki67 expression is amongst the several technical issues behind the lag of its inclusion into BC prognostic work-up. The immunohistochemical (IHC) expression of anti-Ki67 antibody (MIB1 clone) was assessed in four full-face (FF) sections from different primary tumour blocks and their matched axillary nodal (LN) metastases in a series of 55 BC. Assessment was made using the highest expression hot spots (HS), lowest expression (LS), and overall/average expression scores (AS) in each section. Heterogeneity score (Hes), co-efficient of variation, and correlation co-efficient were used to assess the levels of Ki67 ITH. Ki67 HS, LS, and AS scores were highly variable within the same section and between different sections of the primary tumour, with maximal variation observed in the LS (P\0.001). The least variability between the different slides was observed with HS scoring. Although the associations between Ki67 and clinicopathological and molecular variables were similar when using HS or AS, the best correlation between AS and HS was observed in tumours with high Ki67 expression only. Ki67 expression in LN deposits was less heterogeneous than in the primary tumours and was perfectly correlated with the HS Ki67 expression in the primary tumour sections (r = 0.98, P\0.001). In conclusion, assessment of Ki67 expression using HS scoring method on a full-face BC tissue section can represent the primary tumour growth fraction that is likely to metastasise. The association between Ki67 expression pattern in the LN metastasis and the HS in the primary tumour may reflect the temporal heterogeneity through clonal expansion

Clinical and biological significance of RAD51 expression in breast cancer: a key DNA damage response protein

Impaired DNA damage response (DDR) may play a fundamental role in the pathogenesis of breast cancer (BC). RAD51 is a key player in DNA double-strand break repair. In this study, we aimed to assess the biological and clinical significance of RAD51 expression with relevance to different molecular classes of BC and patients’ outcome. The expression of RAD51 was assessed immunohistochemically in a well-characterised annotated series (n = 1184) of early-stage invasive BC with long-term follow-up. A subset of cases of BC from patients with known BRCA1 germline mutations was included as a control group. The results were correlated with clinicopathological and molecular parameters and patients’ outcome. RAD51 protein expression level was also assayed in a panel of cell lines using reverse phase protein array (RPPA). RAD51 was expressed in the nuclei (N) and cytoplasm (C) of malignant cells. Subcellular colocalisation phenotypes of RAD51 were significantly associated with clinicopathological features and patient outcome. Cytoplasmic expression (RAD51C+) and lack of nuclear expression (RAD51 N-) were associated with features of aggressive behaviour, including larger tumour size, high grade, lymph nodal metastasis, basal-like, and triple-negative phenotypes, together with aberrant expression of key DDR biomarkers including BRCA1. All BRCA1-mutated tumours had RAD51C+/N- phenotype. RPPA confirmed IHC results and showed differential expression of RAD51 in cell lines based on ER expression and BRCA1 status. RAD51 N+ and RAD51C+ tumours were associated with longer and shorter breast cancer-specific survival (BCSS), respectively. The RAD51 N+ was an independent predictor of longer BCSS (P<0.0001). Lack of RAD51 nuclear expression is associated with poor prognostic parameters and shorter survival in invasive BC patients. The significant associations between RAD51 subcellular localisation and clinicopathological features, molecular subtype and patients’ outcome suggest that the trafficking of DDR proteins between the nucleus and cytoplasm might play a role in the development and progression of BC

SHON expression predicts response and relapse risk of breast cancer patients after anthracycline-based combination chemotherapy or tamoxifen treatment

BACKGROUND: SHON nuclear expression (SHON-Nuc+) was previously reported to predict clinical outcomes to tamoxifen therapy in ERα+ breast cancer (BC). Herein we determined if SHON expression detected by specific monoclonal antibodies could provide a more accurate prediction and serve as a biomarker for anthracycline-based combination chemotherapy (ACT).METHODS: SHON expression was determined by immunohistochemistry in the Nottingham early-stage-BC cohort (n=1,650) who, if eligible, received adjuvant tamoxifen; the Nottingham ERα- early-stage-BC (n=697) patients who received adjuvant ACT; and the Nottingham locally advanced-BC cohort who received pre- operative ACT with/without taxanes (Neo-ACT, n=120) and if eligible, 5-year adjuvant tamoxifen treatment. Prognostic significance of SHON and its relationship with the clinical outcome of treatments were analysed.RESULTS: As previously reported, SHON-Nuc+ in high risk/ERα+ patients was significantly associated with a 48% death risk reduction after exclusive adjuvant tamoxifen treatment compared with SHON-Nuc- [HR(95%CI)=0.52(0.34-0.78), p=0.002]. Meanwhile, in ERα- patients treated with adjuvant ACT, SHON cytoplasmic expression (SHON-Cyto+) was significantly associated with a 50% death risk reduction compared with SHON-Cyto- [HR(95%CI)=0.50(0.34-0.73), p=0.0003]. Moreover, in patients received Neo-ACT, SHON-Nuc- or SHON-Cyto+ was associated with an increased pathological complete response (pCR) compared with SHON-Nuc+ [21% vs 4%; OR(95%CI)=5.88(1.28-27.03), p=0.012], or SHON-Cyto- [20.5% vs 4.5%; OR(95%CI)=5.43(1.18-25.03), p=0.017], respectively. After receiving Neo-ACT, patients with SHON-Nuc+ had a significantly lower distant relapse risk compared to those with SHON-Nuc- [HR(95%CI)=0.41(0.19-0.87), p=0.038], whereas SHON-Cyto+ patients had a significantly higher distant relapse risk compared to SHON-Cyto- patients [HR(95%CI)=4.63(1.05-20.39), p=0.043]. Furthermore, multivariate Cox regression analyses revealed that SHON-Cyto+ was independently associated with a higher risk of distant relapse after Neo-ACT and 5- year tamoxifen treatment [HR(95%CI)=5.08(1.13-44.52), p=0.037]. The interaction term between ERα status and SHON-Nuc+ (p=0.005), and between SHON-Nuc+ and tamoxifen therapy (p=0.007), were both statistically significant.CONCLUSION: SHON-Nuc+ in tumours predicts response to tamoxifen in ERα+ BC while SHON-Cyto+ predicts response to ACT

A novel prognostic two-gene signature for triple negative breast cancer

The absence of a robust risk stratification tool for triple negative breast cancer (TNBC) underlies imprecise and non-selective treatment of these patients with cytotoxic chemotherapy. This study aimed to interrogate transcriptomes of TNBC resected samples using next generation sequencing to identify novel biomarkers associated with disease outcomes. A subset of cases (n=112) from a large, well-characterized cohort of primary TNBC (n=333) were subjected to RNA-sequencing. Reads were aligned to the human reference genome (GRCH38.83) using the STAR aligner and gene expression quantified using HTSEQ. We identified genes associated with distant metastasis-free survival and breast cancer-specific survival by applying supervised artificial neural network analysis with gene selection to the RNA-sequencing data. The prognostic ability of these genes was validated using the Breast Cancer Gene-Expression Miner v4. 0 and Genotype 2 outcome datasets. Multivariate Cox regression analysis identified a prognostic gene signature that was independently associated with poor prognosis. Finally, we corroborated our results from the two-gene prognostic signature by their protein expression using immunohistochemistry. Artificial neural network identified two gene panels that strongly predicted distant metastasis-free survival and breast cancer-specific survival. Univariate Cox regression analysis of 21 genes common to both panels revealed that the expression level of eight genes was independently associated with poor prognosi