Elevated levels of tumour apolipoprotein D independently predict poor outcome in breast cancer patients.

AIMS: Apolipoprotein D (ApoD) is a protein that is regulated by androgen and oestrogen, and is a major constituent of breast cysts. Although ApoD has been reported to be a marker of breast cancer, its prognostic importance in invasive breast cancer is unclear. The aim of this study was to investigate the relationship between ApoD protein expression, oestrogen receptor-α (ERα) expression and androgen receptor (AR) expression in predicting breast cancer outcome. METHODS AND RESULTS: ApoD levels were measured by the use of immunohistochemistry and video image analysis on tissue sections from a breast cancer cohort (n = 214). We assessed the associations of ApoD expression with disease-free survival (DFS), metastasis-free survival (MFS), and overall survival (OS). We also assessed the relationship between ApoD expression, AR expression and ERα expression in predicting OS. ApoD expression (>1% ApoD positivity) was found in 72% (154/214) of tissues. High ApoD positivity (≥20.7%, fourth quartile) was an independent predictor of MFS and OS, and conferred a 2.2-fold increased risk of developing metastatic disease and a 2.1-fold increased risk of breast cancer-related death. ApoD positivity was not associated with AR or ERα nuclear positivity. However, patients with (≥1%) ERα-positive cancers with low (<20.7%) ApoD positivity, or those showing high (≥78%) AR positivity and low (<20.7%) ApoD positivity had better OS than other patient groups. CONCLUSIONS: ApoD expression could be used to predict breast cancer prognosis independently of ERα and AR expression.

ELF5 Drives Lung Metastasis in Luminal Breast Cancer through Recruitment of Gr1+ CD11b+ Myeloid-Derived Suppressor Cells.

During pregnancy, the ETS transcription factor ELF5 establishes the milk-secreting alveolar cell lineage by driving a cell fate decision of the mammary luminal progenitor cell. In breast cancer, ELF5 is a key transcriptional determinant of tumor subtype and has been implicated in the development of insensitivity to anti-estrogen therapy. In the mouse mammary tumor virus-Polyoma Middle T (MMTV-PyMT) model of luminal breast cancer, induction of ELF5 levels increased leukocyte infiltration, angiogenesis, and blood vessel permeability in primary tumors and greatly increased the size and number of lung metastasis. Myeloid-derived suppressor cells, a group of immature neutrophils recently identified as mediators of vasculogenesis and metastasis, were recruited to the tumor in response to ELF5. Depletion of these cells using specific Ly6G antibodies prevented ELF5 from driving vasculogenesis and metastasis. Expression signatures in luminal A breast cancers indicated that increased myeloid cell invasion and inflammation were correlated with ELF5 expression, and increased ELF5 immunohistochemical staining predicted much shorter metastasis-free and overall survival of luminal A patients, defining a group who experienced unexpectedly early disease progression. Thus, in the MMTV-PyMT mouse mammary model, increased ELF5 levels drive metastasis by co-opting the innate immune system. As ELF5 has been previously implicated in the development of antiestrogen resistance, this finding implicates ELF5 as a defining factor in the acquisition of the key aspects of the lethal phenotype in luminal A breast cancer.

MCL-1 inhibition provides a new way to suppress breast cancer metastasis and increase sensitivity to dasatinib.

BACKGROUND: Metastatic disease is largely resistant to therapy and accounts for almost all cancer deaths. Myeloid cell leukemia-1 (MCL-1) is an important regulator of cell survival and chemo-resistance in a wide range of malignancies, and thus its inhibition may prove to be therapeutically useful. METHODS: To examine whether targeting MCL-1 may provide an effective treatment for breast cancer, we constructed inducible models of BIMs2A expression (a specific MCL-1 inhibitor) in MDA-MB-468 (MDA-MB-468-2A) and MDA-MB-231 (MDA-MB-231-2A) cells. RESULTS: MCL-1 inhibition caused apoptosis of basal-like MDA-MB-468-2A cells grown as monolayers, and sensitized them to the BCL-2/BCL-XL inhibitor ABT-263, demonstrating that MCL-1 regulated cell survival. In MDA-MB-231-2A cells, grown in an organotypic model, induction of BIMs2A produced an almost complete suppression of invasion. Apoptosis was induced in such a small proportion of these cells that it could not account for the large decrease in invasion, suggesting that MCL-1 was operating via a previously undetected mechanism. MCL-1 antagonism also suppressed local invasion and distant metastasis to the lung in mouse mammary intraductal xenografts. Kinomic profiling revealed that MCL-1 antagonism modulated Src family kinases and their targets, which suggested that MCL-1 might act as an upstream modulator of invasion via this pathway. Inhibition of MCL-1 in combination with dasatinib suppressed invasion in 3D models of invasion and inhibited the establishment of tumors in vivo. CONCLUSION: These data provide the first evidence that MCL-1 drives breast cancer cell invasion and suggests that MCL-1 antagonists could be used alone or in combination with drugs targeting Src kinases such as dasatinib to suppress metastasis.

Assessment of DNA methylation profiling and copy number variation as indications of clonal relationship in ipsilateral and contralateral breast cancers to distinguish recurrent breast cancer from a second primary tumour.

BACKGROUND: Patients with breast cancer have an increased risk of developing subsequent breast cancers. It is important to distinguish whether these tumours are de novo or recurrences of the primary tumour in order to guide the appropriate therapy. Our aim was to investigate the use of DNA methylation profiling and array comparative genomic hybridization (aCGH) to determine whether the second tumour is clonally related to the first tumour. METHODS: Methylation-sensitive high-resolution melting was used to screen promoter methylation in a panel of 13 genes reported as methylated in breast cancer (RASSF1A, TWIST1, APC, WIF1, MGMT, MAL, CDH13, RARß, BRCA1, CDH1, CDKN2A, TP73, and GSTP1) in 29 tumour pairs (16 ipsilateral and 13 contralateral). Using the methylation profile of these genes, we employed a Bayesian and an empirical statistical approach to estimate clonal relationship. Copy number alterations were analysed using aCGH on the same set of tumour pairs. RESULTS: There is a higher probability of the second tumour being recurrent in ipsilateral tumours compared with contralateral tumours (38 % versus 8 %; p <0.05) based on the methylation profile. Using previously reported recurrence rates as Bayesian prior probabilities, we classified 69 % of ipsilateral and 15 % of contralateral tumours as recurrent. The inferred clonal relationship results of the tumour pairs were generally concordant between methylation profiling and aCGH. CONCLUSION: Our results show that DNA methylation profiling as well as aCGH have potential as diagnostic tools in improving the clinical decisions to differentiate recurrences from a second de novo tumour.

BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion.

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients. We employ multiple self-supervised feature extractors (DINO and MoCoV3) pretrained on histopathological patches to capture detailed image features. These features are then fused by a variational autoencoder and fed to a self-attention network generating patient-level features. A co-dual-cross-attention mechanism combines the histopathological features with genetic data, enabling the model to capture the interplay between them. Additionally, clinical data is incorporated using a feed-forward network, further enhancing predictive performance and achieving comprehensive multimodal feature integration. Furthermore, we introduce a weighted Cox loss function, specifically designed to handle imbalanced survival data, which is a common challenge. Our model achieves a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84, outperforming state-of-the-art methods. It predicts risk (high versus low) with prognostic significance for overall survival in univariate analysis (HR=2.99, 95% CI: 1.88-4.78, p 0.005), and maintains independent significance in multivariate analysis incorporating standard clinicopathological variables (HR=2.91, 95% CI: 1.80-4.68, p 0.005).

Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.

Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in large-scale clinical oncology studies. Predicting gene expression from hematoxylin and eosin stained histology images provides a more affordable alternative for such studies. Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data. Using this framework, we trained and evaluated four distinct state-of-the-art deep learning architectures, which include ResNet101, Inception-v3, EfficientNet (with six different variants), and vision transformer (with two different variants), all without utilizing pretrained weights for the prediction of 250 genes. To enhance the generalisation performance of the main network, we introduce an auxiliary network into the framework. Our methodology outperforms previous studies, with 237 genes identified with positive correlation, including 24 genes with a median correlation coefficient greater than 0.50. This is a notable improvement over previous studies, which could predict only 102 genes with positive correlation, with the highest correlation values ranging from 0.29 to 0.34.

hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images.

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ breast cancer, which is costly, tissue destructive, requires specialised platforms, and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA sequencing techniques to predict the expression of 138 genes (incorporated from 6 commercially available molecular profiling tests), including luminal PAM50 subtype, from hematoxylin and eosin (H&E)-stained whole slide images (WSIs). The training phase involves the aggregation of extracted features for each patient from a pretrained model to predict gene expression at the patient level using annotated H&E images from The Cancer Genome Atlas (TCGA, n = 335). We demonstrate successful gene prediction on a held-out test set (n = 160, corr = 0.82 across patients, corr = 0.29 across genes) and perform exploratory analysis on an external tissue microarray (TMA) dataset (n = 498) with known IHC and survival information. Our model is able to predict gene expression and luminal PAM50 subtype (Luminal A versus Luminal B) on the TMA dataset with prognostic significance for overall survival in univariate analysis (c-index = 0.56, hazard ratio = 2.16 (95% CI 1.12-3.06), p < 5 × 10-3), and independent significance in multivariate analysis incorporating standard clinicopathological variables (c-index = 0.65, hazard ratio = 1.87 (95% CI 1.30-2.68), p < 5 × 10-3). The proposed strategy achieves superior performance while requiring less training time, resulting in less energy consumption and computational cost compared to patch-based models. Additionally, hist2RNA predicts gene expression that has potential to determine luminal molecular subtypes which correlates with overall survival, without the need for expensive molecular testing.

MCP-1 expression in breast cancer and its association with distant relapse.

BACKGROUND: Distant relapse of breast cancer complicates management of the disease and accounts for 90% of breast cancer-related deaths. Monocyte chemoattractant protein-1 (MCP-1) has critical roles in breast cancer progression and is widely accepted as a pro-metastatic chemokine. METHODS: This study explored MCP-1 expression in the primary tumour of 251 breast cancer patients. A simplified 'histoscore' was used to determine if each tumour had high or low expression of MCP-1. Patient breast cancers were retrospectively staged based on available patient data. p < 0.05 was used to determine significance and changes in hazard ratios between models were considered. RESULTS: Low MCP-1 expression in the primary tumour was associated with breast cancer-related death with distant relapse in ER- breast cancers (p < 0.01); however, this was likely a result of most low MCP-1-expressing ER- breast cancers being Stage III or Stage IV, with high MCP-1 expression in the primary tumour significantly correlated with Stage I breast cancers (p < 0.05). Expression of MCP-1 in the primary ER- tumours varied across Stage I, II, III and IV and we highlighted a switch in MCP-1 expression from high in Stage I ER- cancers to low in Stage IV ER- cancers. CONCLUSION: This study has emphasised a critical need for further investigation into MCP-1's role in breast cancer progression and improved characterisation of MCP-1 in breast cancers, particularly in light of the development of anti-MCP-1, anti-metastatic therapies.

Prognostic interaction between expression of p53 and estrogen receptor in patients with node-negative breast cancer: results from IBCSG Trials VIII and IX.

INTRODUCTION: The prognostic significance of p53 protein expression in early breast cancer remains uncertain, with some but not all studies finding an association with poorer outcomes. Estrogen receptor (ER) expression is both a positive prognostic marker and predictive of response to endocrine therapies. The relationship between these biomarkers is unknown. METHODS: We constructed tissue microarrays (TMAs) from available pathological material from 1113 patients participating in two randomized clinical trials comparing endocrine therapy alone versus chemo-endocrine therapy in node-negative breast cancer. Expression of p53 defined as >10% positive nuclei was analyzed together with prior immunohistochemical assays of ER performed at central pathological review of whole tumor sections. RESULTS: ER was present (i.e. >1% positive tumor cell nuclei) in 80.1% (880/1092). p53 expression was significantly more frequent when ER was absent, 125/212 (59%) than when ER was present, 171/880 (19%), p <0.0001. A significant qualitative interaction was observed such that p53 expression was associated with better disease-free survival (DFS) and overall survival (OS) among patients whose tumors did not express ER, but worse DFS and OS among patients whose tumors expressed ER. The interaction remained significant after allowance for pathologic variables, and treatment. Similar effects were seen when luminal and non-luminal intrinsic subtypes were compared. CONCLUSIONS: Interpretation of the prognostic significance of p53 expression requires knowledge of concurrent expression of ER. The reason for the interaction between p53 and ER is unknown but may reflect qualitatively different p53 mutations underlying the p53 expression in tumors with or without ER expression. TRIAL REGISTRATION: Current Controlled Trials ACTRN12607000037404 (Trial VIII) and ACTRN12607000029493 (Trial IX).

Fecal DNA Virome Is Associated with the Development of Colorectal Neoplasia in a Murine Model of Colorectal Cancer.

Alteration of the gut virome has been associated with colorectal cancer (CRC); however, when and how the alteration takes place has not been studied. Here, we employ a longitudinal study in mice to characterize the gut virome alteration in azoxymethane (AOM)-induced colorectal neoplasia and identify important viruses associated with tumor growth. The number and size of the tumors increased as the mice aged in the AOM treated group, as compared to the control group. Tumors were first observed in the AOM group at week 12. We observed a significantly lower alpha diversity and shift in viral profile when tumors first appeared. In addition, we identified novel viruses from the genera Brunovirus, Hpunavirus that are positively associated with tumor growth and enriched at a late time point in AOM group, whereas members from Lubbockvirus show a negative correlation with tumor growth. Moreover, network analysis revealed two clusters of viruses in the AOM virome, a group that is positively correlated with tumor growth and another that is negatively correlated with tumor growth, all of which are bacteriophages. Our findings suggest that the gut virome changes along with tumor formation and provides strong evidence of a potential role for bacteriophage in the development of colorectal neoplasia.

Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images.

Computational pathology is a rapidly expanding area for research due to the current global transformation of histopathology through the adoption of digital workflows. Survival prediction of breast cancer patients is an important task that currently depends on histopathology assessment of cancer morphological features, immunohistochemical biomarker expression and patient clinical findings. To facilitate the manual process of survival risk prediction, we developed a computational pathology framework for survival prediction using digitally scanned haematoxylin and eosin-stained tissue microarray images of clinically aggressive triple negative breast cancer. Our results show that the model can produce an average concordance index of 0.616. Our model predictions are analysed for independent prognostic significance in univariate analysis (hazard ratio = 3.12, 95% confidence interval [1.69,5.75], p < 0.005) and multivariate analysis using clinicopathological data (hazard ratio = 2.68, 95% confidence interval [1.44,4.99], p < 0.005). Through qualitative analysis of heatmaps generated from our model, an expert pathologist is able to associate tissue features highlighted in the attention heatmaps of high-risk predictions with morphological features associated with more aggressive behaviour such as low levels of tumour infiltrating lymphocytes, stroma rich tissues and high-grade invasive carcinoma, providing explainability of our method for triple negative breast cancer.

ALTEN: A High-Fidelity Primary Tissue-Engineering Platform to Assess Cellular Responses Ex Vivo.

To fully investigate cellular responses to stimuli and perturbations within tissues, it is essential to replicate the complex molecular interactions within the local microenvironment of cellular niches. Here, the authors introduce Alginate-based tissue engineering (ALTEN), a biomimetic tissue platform that allows ex vivo analysis of explanted tissue biopsies. This method preserves the original characteristics of the source tissue's cellular milieu, allowing multiple and diverse cell types to be maintained over an extended period of time. As a result, ALTEN enables rapid and faithful characterization of perturbations across specific cell types within a tissue. Importantly, using single-cell genomics, this approach provides integrated cellular responses at the resolution of individual cells. ALTEN is a powerful tool for the analysis of cellular responses upon exposure to cytotoxic agents and immunomodulators. Additionally, ALTEN's scalability using automated microfluidic devices for tissue encapsulation and subsequent transport, to enable centralized high-throughput analysis of samples gathered by large-scale multicenter studies, is shown.

Recruitment of regulatory T cells is correlated with hypoxia-induced CXCR4 expression, and is associated with poor prognosis in basal-like breast cancers.

INTRODUCTION: Basal-like breast cancers behave more aggressively despite the presence of a dense lymphoid infiltrate. We hypothesised that immune suppression in this subtype may be due to T regulatory cells (Treg) recruitment driven by hypoxia-induced up-regulation of CXCR4 in Treg. METHODS: Immunoperoxidase staining for FOXP3 and CXCL12 was performed on tissue microarrays from 491 breast cancers. The hypoxia-associated marker carbonic anhydrase IX (CA9) and double FOXP3/CXCR4 staining were performed on sections from a subset of these cancers including 10 basal-like and 11 luminal cancers matched for tumour grade. RESULTS: High Treg infiltration correlated with tumour CXCL12 positivity (OR 1.89, 95% CI 1.22 to 2.94, P = 0.004) and basal phenotype (OR 3.14, 95% CI 1.08 to 9.17, P = 0.004) in univariate and multivariate analyses. CXCL12 positivity correlated with improved survival (P = 0.005), whereas high Treg correlated with shorter survival for all breast cancers (P = 0.001), luminal cancers (P < 0.001) and basal-like cancers (P = 0.040) that were confirmed in a multivariate analysis (OR 1.61, 95% CI 1.02 to 2.53, P = 0.042). In patients treated with hormone therapy, high Treg were associated with a shorter survival in a multivariate analysis (OR 1.78, 95% CI 1.01 to 3.15, P = 0.040). There was a tendency for luminal cancers to show CXCL12 expression (102/138, 74%) compared to basal-like cancers (16/27, 59%), which verged on statistical significance (P = 0.050). Up-regulation of CXCR4 in Treg correlated with the basal-like phenotype (P = 0.029) and tumour hypoxia, as indicated by CA9 expression (P = 0.049). CONCLUSIONS: Our data show that in the setting of hypoxia and CXCR4 up-regulation in Treg, CXCL12 expression may have the negative consequence of enhancing Treg recruitment and suppressing the anti-tumour immune response.

The expression of the ubiquitin ligase SIAH2 (seven in absentia homolog 2) is mediated through gene copy number in breast cancer and is associated with a basal-like phenotype and p53 expression.

INTRODUCTION: The seven in absentia homolog 2 (SIAH2) protein plays a significant role in the hypoxic response by regulating the abundance of hypoxia-inducible factor-α; however, its role in breast carcinoma is unclear. We investigated the frequency and expression pattern of SIAH2 in two independent cohorts of sporadic breast cancers. METHODS: Immunohistochemical evaluation of SIAH2protein expression was conducted in normal breast tissues and in tissue microarrays comprising ductal carcinoma in situ (DCIS) and a cohort of invasive breast carcinomas. Correlation analysis was performed between SIAH2 and clinicopathological variables and intrinsic breast cancer subgroups and validated in a cohort of 293 invasive ductal carcinomas. Promoter methylation, gene copy number and mRNA expression of SIAH2 were determined in a panel of basal-like tumors and cell lines. RESULTS: There was a significant increase in nuclear SIAH2 expression from normal breast tissues through to DCIS and progression to invasive cancers. A significant inverse correlation was apparent between SIAH2 and estrogen receptor and progesterone receptor and a positive association with tumor grade, HER2, p53 and an intrinsic basal-like subtype. Logistic regression analysis confirmed the significant positive association between SIAH2 expression and the basal-like phenotype. No SIAH2 promoter methylation was identified, yet there was a significant correlation between SIAH2 mRNA and gene copy number. SIAH2-positive tumors were associated with a shorter relapse-free survival in univariate but not multivariate analysis. CONCLUSIONS: SIAH2 expression is upregulated in basal-like breast cancers via copy number changes and/or transcriptional activation by p53 and is likely to be partly responsible for the enhanced hypoxic drive through abrogation of the prolyl hydroxylases.

Multiplexed immunofluorescence identifies high stromal CD68+PD-L1+ macrophages as a predictor of improved survival in triple negative breast cancer.

Triple negative breast cancer (TNBC) comprises 10-15% of all breast cancers and has a poor prognosis with a high risk of recurrence within 5 years. PD-L1 is an important biomarker for patient selection for immunotherapy but its cellular expression and co-localization within the tumour immune microenvironment and associated prognostic value is not well defined. We aimed to characterise the phenotypes of immune cells expressing PD-L1 and determine their association with overall survival (OS) and breast cancer-specific survival (BCSS). Using tissue microarrays from a retrospective cohort of TNBC patients from St George Hospital, Sydney (n = 244), multiplexed immunofluorescence (mIF) was used to assess staining for CD3, CD8, CD20, CD68, PD-1, PD-L1, FOXP3 and pan-cytokeratin on the Vectra Polaris™ platform and analysed using QuPath. Cox multivariate analyses showed high CD68+PD-L1+ stromal cell counts were associated with improved prognosis for OS (HR 0.56, 95% CI 0.33-0.95, p = 0.030) and BCSS (HR 0.47, 95% CI 0.25-0.88, p = 0.018) in the whole cohort and in patients receiving chemotherapy, improving incrementally upon the predictive value of PD-L1+ alone for BCSS. These data suggest that CD68+PD-L1+ status can provide clinically useful prognostic information to identify sub-groups of patients with good or poor prognosis and guide treatment decisions in TNBC.

SP142 PD-L1 Scoring Shows High Interobserver and Intraobserver Agreement in Triple-negative Breast Carcinoma But Overall Low Percentage Agreement With Other PD-L1 Clones SP263 and 22C3.

SP142 programmed cell death ligand 1 (PD-L1) status predicts response to atezolizumab in triple-negative breast carcinoma (TNBC). Prevalence of VENTANA PD-L1 (SP142) Assay positivity, concordance with the VENTANA PD-L1 (SP263) Assay and Dako PD-L1 IHC 22C3 pharmDx assay, and association with clinicopathologic features were assessed in 447 TNBCs. SP142 PD-L1 intraobserver and interobserver agreement was investigated in a subset of 60 TNBCs, with scores enriched around the 1% cutoff. The effect of a 1-hour training video on pretraining and posttraining scores was ascertained. At a 1% cutoff, 34.2% of tumors were SP142 PD-L1 positive. SP142 PD-L1 positivity was significantly associated with tumor-infiltrating lymphocytes (P <0.01), and node negativity (P=0.02), but not with tumor grade (P=0.35), tumor size (P=0.58), or BRCA mutation (P=0.53). Overall percentage agreement (OPA) for intraobserver and interobserver agreement was 95.0% and 93.7%, respectively, among 5 pathologists trained in TNBC SP142 PD-L1 scoring. In 5 TNBC SP142 PD-L1-naive pathologists, significantly higher OPA to the reference score was achieved after video training (posttraining OPA 85.7%, pretraining OPA 81.5%, P<0.05). PD-L1 status at a 1% cutoff was assessed by SP142 and SP263 in 420 cases, and by SP142 and 22C3 in 423 cases, with OPA of 88.1% and 85.8%, respectively. The VENTANA PD-L1 (SP142) Assay is reproducible for classifying TNBC PD-L1 status by trained observers; however, it is not analytically equivalent to the VENTANA PD-L1 (SP263) Assay and Dako PD-L1 IHC 22C3 pharmDx assay.

PI3K pathway activation in breast cancer is associated with the basal-like phenotype and cancer-specific mortality.

Breast cancer is a common malignancy with current biological therapies tailored to steroid hormone (ER, PR) and HER2 receptor status. Understanding the biological basis of resistance to current targeted therapies and the identification of new potential therapeutic targets is an ongoing challenge. The PI3K pathway is altered in a high proportion of breast cancers and may contribute to therapeutic resistance. We undertook an integrative study of mutational, copy number and expression analyses of key regulators of the PI3K pathway in a cohort of 292 invasive breast cancer patients with known treatment outcomes. The alterations identified in this cohort included PIK3CA mutations (12/168, i.e. 7%), PIK3CA copy number gain (28/209, i.e. 14%), PTEN loss (73/258, i.e. 28%) and AKT activation (62/258, i.e. 24%). Overall at least 1 parameter was altered in 72% (139/193) of primary breast cancers. PI3K pathway activation was significantly associated with ER negative (p = 0.0008) and PR negative (p = 0.006) status, high tumor grade (p = 0.032) and a "basal-like" phenotype (p = 0.01), where 92% (25/27) of tumors had an altered pathway. In univariate analysis, PI3K pathway aberrations were associated with death from breast cancer; however, this relationship was not maintained in multivariate analysis. No association was identified between an activated pathway and outcome in tamoxifen- or chemotherapy-treated patients. We concluded that >70% of breast cancers have an alteration in at least 1 component of the PI3K pathway and this might be exploited to therapeutic advantage especially in "basal-like" cancers.

Loss of STARD10 expression identifies a group of poor prognosis breast cancers independent of HER2/Neu and triple negative status.

The phospholipid transfer protein STARD10 cooperates with c-erbB signaling and is overexpressed in Neu/ErbB2 breast cancers. We investigated if STARD10 expression provides additional prognostic information to HER2/neu status in primary breast cancer. A published gene expression dataset was used to determine relationships between STARD10 and HER2 mRNA levels and patient outcome. The central findings were independently validated by immunohistochemistry in a retrospective cohort of 222 patients with breast cancer with a median follow-up of 64 months. Kaplan-Meier and Cox proportional hazards analyses were used for univariate and multivariate analyses. Patients with low STARD10 or high HER2 tumor mRNA levels formed discrete groups each associated with a poor disease-specific survival (p = 0.0001 and p = 0.0058, respectively). In the immunohistochemical study low/absent STARD10 expression i.e. < or = 10% positive cells was observed in 24 of 222 (11%) tumors. In a univariate model, low/absent STARD10 expression was significantly associated with decreased patient survival (p = 0.0008). In multivariate analyses incorporating tumor size, tumor grade, lymph node status, ER, PR and HER2 status, low STARD10 expression was an independent predictor of death from breast cancer (HR: 2.56 (95% CI: 1.27-5.18), p = 0.0086). Furthermore, low/absent STARD10 expression, HER2 amplification and triple negative status were independent prognostic variables. Loss of STARD10 expression may provide an additional marker of poor outcome in breast cancer identifying a subgroup of patients with a particularly adverse prognosis, which is independent of HER2 amplification and the triple negative phenotype.

High Notch1 protein expression is an early event in breast cancer development and is associated with the HER-2 molecular subtype.

AIMS: Activation of Notch signalling results in hyperplasia and tumorigenesis in murine mammary epithelium. However, there is little information regarding the expression of Notch1 in premalignant lesions and early breast cancer. We investigated expression of Notch1 in breast cancer development and its association with molecular subtypes. METHODS AND RESULTS: Immunohistochemical expression of Notch1 was determined in a murine model of mammary carcinogenesis and in breast tissue from two cohorts of breast cancer patients, the first (n=222) comprising a histological progression series and the second an outcome series of 228 patients with operable invasive ductal carcinoma. Enhanced expression of Notch1 protein was an early event in both murine and human breast cancer development with progressive increases in expression with the development of hyperplasia and malignancy. High Notch1 was not prognostic in the outcome cohort. There was, however, a highly significant association of high Notch1 protein with the HER-2 molecular subtype of breast cancer (P=0.008). CONCLUSIONS: These data demonstrate that aberrant Notch regulation is an early event in mammary carcinogenesis and is associated with the HER-2 molecular subtype of breast cancer, and suggest the Notch signalling pathway may be a potential therapeutic target worthy of further investigation.

Id Proteins Promote a Cancer Stem Cell Phenotype in Mouse Models of Triple Negative Breast Cancer via Negative Regulation of Robo1.

Breast cancers display phenotypic and functional heterogeneity and several lines of evidence support the existence of cancer stem cells (CSCs) in certain breast cancers, a minor population of cells capable of tumor initiation and metastatic dissemination. Identifying factors that regulate the CSC phenotype is therefore important for developing strategies to treat metastatic disease. The Inhibitor of Differentiation Protein 1 (Id1) and its closely related family member Inhibitor of Differentiation 3 (Id3) (collectively termed Id) are expressed by a diversity of stem cells and are required for metastatic dissemination in experimental models of breast cancer. In this study, we show that ID1 is expressed in rare neoplastic cells within ER-negative breast cancers. To address the function of Id1 expressing cells within tumors, we developed independent murine models of Triple Negative Breast Cancer (TNBC) in which a genetic reporter permitted the prospective isolation of Id1+ cells. Id1+ cells are enriched for self-renewal in tumorsphere assays in vitro and for tumor initiation in vivo. Conversely, depletion of Id1 and Id3 in the 4T1 murine model of TNBC demonstrates that Id1/3 are required for cell proliferation and self-renewal in vitro, as well as primary tumor growth and metastatic colonization of the lung in vivo. Using combined bioinformatic analysis, we have defined a novel mechanism of Id protein function via negative regulation of the Roundabout Axon Guidance Receptor Homolog 1 (Robo1) leading to activation of a Myc transcriptional programme.