The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination.

Introduction: Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study. Methods: Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools. Results: Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05). Discussion: The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.

Upregulation of Cyclin B2 (CCNB2) in breast cancer contributes to the development of lymphovascular invasion.

Lymphovascular invasion (LVI) is a key step in breast cancer (BC) metastasis. Targeting the molecular drivers of LVI can improve BC patients' management. However, the underlying molecular mechanisms of LVI are complex and interconnected with various carcinogenesis pathways. This study aimed to identify the key regulatory gene associated with LVI and to investigate its mechanisms of action and prognostic significance. Artificial neural network (ANN) was applied to two large transcriptomic datasets of BC with well-characterised LVI status. Cyclin B2 (CCNB2) was identified in the top genes associated with LVI positivity. In vitro functional assays were carried out to assess the role of CCNB2 in tumour cell behaviour and their interactions with endothelial cells using a panel of BC cell lines. Large annotated BC cohorts were used to assess the clinical and prognostic role of CCNB2 at the transcriptomic and protein levels. Knockdown (KD) of CCNB2 mRNA reduced BC cell migration, inhibited proliferation, blocked the G2/M transition during the cell cycle and increased the number of apoptotic cells. Importantly, KD of CCNB2 reduced BC cell lines adherence and transmigration across endothelial cell lines. High CCNB2 protein expression was independently associated with LVI positivity in addition to other features of aggressive behaviour, including larger tumour size, higher histological grade, hormonal receptor-negativity, and HER2-positivity, and with shorter survival. We conclude that CCNB2 plays a crucial role in LVI development in BC, implying that CCNB2 could confer a promising therapeutic target to inhibit LVI and reduce metastatic events.

Association of Sperm-Associated Antigen 5 and Treatment Response in Patients With Estrogen Receptor-Positive Breast Cancer.

Importance: There is no proven test that can guide the optimal treatment, either endocrine therapy or chemotherapy, for estrogen receptor-positive breast cancer. Objective: To investigate the associations of sperm-associated antigen 5 (SPAG5) transcript and SPAG5 protein expressions with treatment response in systemic therapy for estrogen receptor-positive breast cancer. Design, Settings, and Participants: This retrospective cohort study included patients with estrogen receptor-positive breast cancer who received 5 years of adjuvant endocrine therapy with or without neoadjuvant anthracycline-based combination chemotherapy (NACT) derived from 11 cohorts from December 1, 1986, to November 28, 2019. The associations of SPAG5 transcript and SPAG5 protein expression with pathological complete response to NACT were evaluated, as was the association of SPAG5 mRNA expression with response to neoadjuvant endocrine therapy. The associations of distal relapse-free survival with SPAG5 transcript or SPAG5 protein expressions were analyzed. Data were analyzed from September 9, 2015, to November 28, 2019. Main Outcomes and Measures: The primary outcomes were breast cancer-specific survival, distal relapse-free survival, pathological complete response, and clinical response. Outcomes were examined using Kaplan-Meier, multivariable logistic, and Cox regression models. Results: This study included 12 720 women aged 24 to 78 years (mean [SD] age, 58.46 [12.45] years) with estrogen receptor-positive breast cancer, including 1073 women with SPAG5 transcript expression and 361 women with SPAG5 protein expression of locally advanced disease stage IIA through IIIC. Women with SPAG5 transcript and SPAG5 protein expressions achieved higher pathological complete response compared with those without SPAG5 transcript or SPAG5 protein expressions (transcript: odds ratio, 2.45 [95% CI, 1.71-3.51]; P < .001; protein: odds ratio, 7.32 [95% CI, 3.33-16.22]; P < .001). Adding adjuvant anthracycline chemotherapy to adjuvant endocrine therapy for SPAG5 mRNA expression in estrogen receptor-positive breast cancer was associated with prolonged 5-year distal relapse-free survival in patients without lymph node involvement (hazard ratio, 0.34 [95% CI, 0.14-0.87]; P = .03) and patients with lymph node involvement (hazard ratio, 0.35 [95% CI, 0.18-0.68]; P = .002) compared with receiving 5-year endocrine therapy alone. Mean (SD) SPAG5 transcript was found to be downregulated after 2 weeks of neoadjuvant endocrine therapy compared with pretreatment levels in 68 of 92 patients (74%) (0.23 [0.18] vs 0.34 [0.24]; P < .001). Conclusions and Relevance: These findings suggest that SPAG5 transcript and SPAG5 protein expressions could be used to guide the optimal therapies for estrogen receptor-positive breast cancer. Retrospective and prospective clinical trials are warranted.

PYK2 promotes HER2-positive breast cancer invasion.

BACKGROUND: Metformin, a biguanide, is one of the most commonly prescribed treatments for type 2 diabetes and has recently been recommended as a potential drug candidate for advanced cancer therapy. Although Metformin has antiproliferative and proapoptotic effects on breast cancer, the heterogenous nature of this disease affects the response to metformin leading to the activation of pro-invasive signalling pathways that are mediated by the focal adhesion kinase PYK2 in pure HER2 phenotype breast cancer. METHODS: The effect of metformin on different breast cancer cell lines, representing the molecular heterogenicity of the disease was investigated using in vitro proliferation and apoptosis assays. The activation of PYK2 by metformin in pure HER2 phenotype (HER2+/ER-/PR-) cell lines was investigated by microarrays, quantitative real time PCR and immunoblotting. Cell migration and invasion PYK2-mediated and in response to metformin were determined by wound healing and invasion assays using HER2+/ER-/PR- PYK2 knockdown cell lines. Proteomic analyses were used to determine the role of PYK2 in HER2+/ER-/PR- proliferative, migratory and invasive cellular pathways and in response to metformin. The association between PYK2 expression and HER2+/ER-/PR- patients' cancer-specific survival was investigated using bioinformatic analysis of PYK2 expression from patient gene expression profiles generated by the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study. The effect of PYK2 and metformin on tumour initiation and invasion of HER2+/ER-/PR- breast cancer stem-like cells was performed using the in vitro stem cell proliferation and invasion assays. RESULTS: Our study showed for the first time that pure HER2 breast cancer cells are more resistant to metformin treatment when compared with the other breast cancer phenotypes. This drug resistance was associated with the activation of PTK2B/PYK2, a well-known mediator of signalling pathways involved in cell proliferation, migration and invasion. The role of PYK2 in promoting invasion of metformin resistant HER2 breast cancer cells was confirmed through investigating the effect of PYK2 knockdown and metformin on cell invasion and by proteomic analysis of associated cellular pathways. We also reveal a correlation between high level of expression of PYK2 and reduced survival in pure HER2 breast cancer patients. Moreover, we also report a role of PYK2 in tumour initiation and invasion-mediated by pure HER2 breast cancer stem-like cells. This was further confirmed by demonstrating a correlation between reduced survival in pure HER2 breast cancer patients and expression of PYK2 and the stem cell marker CD44. CONCLUSIONS: We provide evidence of a PYK2-driven pro-invasive potential of metformin in pure HER2 cancer therapy and propose that metformin-based therapy should consider the molecular heterogeneity of breast cancer to prevent complications associated with cancer chemoresistance, invasion and recurrence in treated patients.

A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.

Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytogenetic abnormalities and gene mutations are important predictors of outcome, 50% to 70% of AMLs harbor normal or risk-indeterminate karyotypes. Therefore, identifying more effective biomarkers predictive of treatment success and failure is essential for informing tailored therapeutic decisions. We applied an artificial neural network (ANN)-based machine learning approach to a publicly available data set for a discovery cohort of 593 adults with nonpromyelocytic AML. ANN analysis identified a parsimonious 3-gene expression signature comprising CALCRL, CD109, and LSP1, which was predictive of event-free survival (EFS) and overall survival (OS). We computed a prognostic index (PI) using normalized gene-expression levels and ß-values from subsequently created Cox proportional hazards models, coupled with clinically established prognosticators. Our 3-gene PI separated the adult patients in each European LeukemiaNet cytogenetic risk category into subgroups with different survival probabilities and identified patients with very high-risk features, such as those with a high PI and either FLT3 internal tandem duplication or nonmutated nucleophosmin 1. The PI remained significantly associated with poor EFS and OS after adjusting for established prognosticators, and its ability to stratify survival was validated in 3 independent adult cohorts (n = 905 subjects) and 1 cohort of childhood AML (n = 145 subjects). Further in silico analyses established that AML was the only tumor type among 39 distinct malignancies for which the concomitant upregulation of CALCRL, CD109, and LSP1 predicted survival. Therefore, our ANN-derived 3-gene signature refines the accuracy of patient stratification and the potential to significantly improve outcome prediction.

Management and 5-year outcomes in 9938 women with screen-detected ductal carcinoma in situ: the UK Sloane Project.

BACKGROUND: Management of screen-detected ductal carcinoma in situ (DCIS) remains controversial. METHODS: A prospective cohort of patients with DCIS diagnosed through the UK National Health Service Breast Screening Programme (1st April 2003 to 31st March 2012) was linked to national databases and case note review to analyse patterns of care, recurrence and mortality. RESULTS: Screen-detected DCIS in 9938 women, with mean age of 60 years (range 46-87), was treated by mastectomy (2931) or breast conserving surgery (BCS) (7007; 70%). At 64 months median follow-up, 697 (6.8%) had further DCIS or invasive breast cancer after BCS (7.8%) or mastectomy (4.5%) (p < 0.001). Breast radiotherapy (RT) after BCS (4363/7007; 62.3%) was associated with a 3.1% absolute reduction in ipsilateral recurrent DCIS or invasive breast cancer (no RT: 7.2% versus RT: 4.1% [p < 0.001]) and a 1.9% absolute reduction for ipsilateral invasive breast recurrence (no RT: 3.8% versus RT: 1.9% [p < 0.001]), independent of the excision margin width or size of DCIS. Women without RT after BCS had more ipsilateral breast recurrences (p < 0.001) when the radial excision margin was <2 mm. Adjuvant endocrine therapy (1208/9938; 12%) was associated with a reduction in any ipsilateral recurrence, whether RT was received (hazard ratio [HR] 0.57; 95% confidence interval [CI] 0.41-0.80) or not (HR 0.68; 95% CI 0.51-0.91) after BCS. Women who developed invasive breast recurrence had a worse survival than those with recurrent DCIS (p < 0.001). Among 321 (3.2%) who died, only 46 deaths were attributed to invasive breast cancer. CONCLUSION: Recurrent DCIS or invasive cancer is uncommon after screen-detected DCIS. Both RT and endocrine therapy were associated with a reduction in further events but not with breast cancer mortality within 5 years of diagnosis. Further research to identify biomarkers of recurrence risk, particularly as invasive disease, is indicated.

Multicentre study of patient-reported and clinical outcomes following immediate and delayed Autologous Breast Reconstruction And Radiotherapy (ABRAR study).

BACKGROUND: Timing of autologous breast reconstruction in patients requiring adjuvant radiotherapy remains contentious. The primary objective of this study was to assess clinical and patient reported outcomes in immediate reconstruction with radiotherapy compared to delayed reconstruction after radiotherapy, the two relevant clinical pathways for patients who need radiotherapy. METHODS: This retrospective UK multi-centre study grouped patients into three categories: immediate reconstruction with post-operative radiotherapy (IBR); delayed reconstruction after radiotherapy (DBR); control group of immediate reconstruction without radiotherapy (noRT). Data collection utilised clinician questionnaire, patient questionnaire (BreastQ) and medical examination. Examination assessed fat necrosis, texture, symmetry and overall result. RESULTS: 412 patients were recruited (IBR 104; DBR 119; noRT 189) with median follow-up time of 57 months. Post-operative complications were higher in IBR & noRT (p <0.001). Total number of operations for completion of reconstruction was similar in all groups. Completion of reconstruction after mastectomy was three years longer in DBR versus IBR. BreastQ domain scores were lower in IBR versus DBR and noRT (p <0.01) but all scores were within acceptable range (satisfaction with outcome: IBR 71; DBR 85; noRT 81). Examination scores were similar for IBR and DBR but lower than noRT (p <0.01). Correlation between BreastQ and examination scores was poor. CONCLUSIONS: Acceptable results are observed with either IBR or DBR, with high rates of patient and clinician satisfaction, low rates of complications, and a similar number of operations to complete reconstruction in all groups suggesting all options should be considered for patients.

Clinicopathological and Functional Significance of RECQL1 Helicase in Sporadic Breast Cancers.

RECQL1, a key member of the RecQ family of DNA helicases, is required for DNA replication and DNA repair. Two recent studies have shown that germline RECQL1 mutations are associated with increased breast cancer susceptibility. Whether altered RECQL1 expression has clinicopathologic significance in sporadic breast cancers is unknown. We evaluated RECQL1 at the transcriptomic level (METABRIC cohort, n = 1,977) and at the protein level [cohort 1, n = 897; cohort 2, n = 252; cohort 3 (BRCA germline deficient), n = 74]. In RECQL1-depleted breast cancer cells, we investigated anthracycline sensitivity. High RECQL1 mRNA was associated with intClust.3 (P = 0.026), which is characterized by low genomic instability. On the other hand, low RECQL1 mRNA was linked to intClust.8 [luminal A estrogen receptor-positive (ER+) subgroup; P = 0.0455] and intClust.9 (luminal B ER+ subgroup; P = 0.0346) molecular phenotypes. Low RECQL1 expression was associated with shorter breast cancer-specific survival (P = 0.001). At the protein level, low nuclear RECQL1 level was associated with larger tumor size, lymph node positivity, high tumor grade, high mitotic index, pleomorphism, dedifferentiation, ER negativity, and HER-2 overexpression (P < 0.05). In ER+ tumors that received endocrine therapy, low RECQL1 was associated with poor survival (P = 0.008). However, in ER- tumors that received anthracycline-based chemotherapy, high RECQL1 was associated with poor survival (P = 0.048). In RECQL1-depleted breast cancer cell lines, we confirmed doxorubicin sensitivity, which was associated with DNA double-strand breaks accumulation, S-phase cell-cycle arrest, and apoptosis. We conclude that RECQL1 has prognostic and predictive significance in breast cancers. Mol Cancer Ther; 16(1); 239-50. ©2016 AACR.

The localization of pre mRNA splicing factor PRPF38B is a novel prognostic biomarker that may predict survival benefit of trastuzumab in patients with breast cancer overexpressing HER2.

Cancer biomarkers that can define disease status and provide a prognostic insight are essential for the effective management of patients with breast cancer (BC). The prevalence, clinicopathological and prognostic significance of PRPF38B expression in a consecutive series of 1650 patients with primary invasive breast carcinoma were examined using immunohistochemistry. Furthermore, the relationship(s) between clinical outcome and PRPF38B expression was explored in 627 patients with ER-negative (oestrogen receptor) disease, and 322 patients with HER2-overexpressing disease. Membranous expression of PRPF38B was observed in 148/1388 (10.7%) cases and was significantly associated with aggressive clinicopathological features, including high grade, high mitotic index, pleomorphism, invasive ductal carcinoma of no specific type (IDC-NST), ER-negative, HER2-overexpression and p53 mutational status (all p < 0.01). In patients with ER-negative disease receiving chemotherapy, nuclear expression of PRPF38B was significantly associated with a reduced risk of relapse (p = 0.0004), whereas membranous PRPF38B expression was significantly associated with increased risk of relapse (p = 0.004; respectively) at a 5 year follow-up. When patients were stratified according to ER-negative/HER2-positive status, membranous PRPF38B expression was associated with a higher risk of relapse in those patients that did not receive trastuzumab therapy (p = 0.02), whereas in those patients with ER-negative/HER2-positive disease that received trastuzumab adjuvant therapy, membranous PRPF38B expression associated with a lower risk of relapse (p = 0.00018). Nuclear expression of PRPF38B is a good prognostic indicator in both ER-negative patients and ER-negative/HER2-positive BC (breast cancer) patients, whereas membranous localisation of PRPF38B is a poor prognostic biomarker that predicts survival benefit from trastuzumab therapy in patients with ER-negative/HER2-overexpressing BC.

Glucolipotoxicity initiates pancreatic ß-cell death through TNFR5/CD40-mediated STAT1 and NF-κB activation.

Type 2 diabetes is a chronic metabolic disorder, where failure to maintain normal glucose homoeostasis is associated with, and exacerbated by, obesity and the concomitant-elevated free fatty acid concentrations typically found in these patients. Hyperglycaemia and hyperlipidaemia together contribute to a decline in insulin-producing ß-cell mass through activation of the transcription factors nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and signal transducer and activator of transcription (STAT)-1. There are however a large number of molecules potentially able to modulate NF-κB and STAT1 activity, and the mechanism(s) by which glucolipotoxicity initially induces NF-κB and STAT1 activation is currently poorly defined. Using high-density microarray analysis of the ß-cell transcritptome, we have identified those genes and proteins most sensitive to glucose and fatty acid environment. Our data show that of those potentially able to activate STAT1 or NF-κB pathways, tumour necrosis factor receptor (TNFR)-5 is the most highly upregulated by glucolipotoxicity. Importantly, our data also show that the physiological ligand for TNFR5, CD40L, elicits NF-κB activity in ß-cells, whereas selective knockdown of TNFR5 ameliorates glucolipotoxic induction of STAT1 expression and NF-κB activity. This data indicate for the first time that TNFR5 signalling has a major role in triggering glucolipotoxic islet cell death.

Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series.

The Nottingham Prognostic Index Plus (NPI+) is a clinical decision making tool in breast cancer (BC) that aims to provide improved patient outcome stratification superior to the traditional NPI. This study aimed to validate the NPI+ in an independent series of BC. Eight hundred and eighty five primary early stage BC cases from Edinburgh were semi-quantitatively assessed for 10 biomarkers [Estrogen Receptor (ER), Progesterone Receptor (PgR), cytokeratin (CK) 5/6, CK7/8, epidermal growth factor receptor (EGFR), HER2, HER3, HER4, p53, and Mucin 1] using immunohistochemistry and classified into biological classes by fuzzy logic-derived algorithms previously developed in the Nottingham series. Subsequently, NPI+ Prognostic Groups (PGs) were assigned for each class using bespoke NPI-like formulae, previously developed in each NPI+ biological class of the Nottingham series, utilising clinicopathological parameters: number of positive nodes, pathological tumour size, stage, tubule formation, nuclear pleomorphism and mitotic counts. Biological classes and PGs were compared between the Edinburgh and Nottingham series using Cramer's V and their role in patient outcome prediction using Kaplan-Meier curves and tested using Log Rank. The NPI+ biomarker panel classified the Edinburgh series into seven biological classes similar to the Nottingham series (p > 0.01). The biological classes were significantly associated with patient outcome (p < 0.001). PGs were comparable in predicting patient outcome between series in Luminal A, Basal p53 altered, HER2+/ER+ tumours (p > 0.01). The good PGs were similarly validated in Luminal B, Basal p53 normal, HER2+/ER- tumours and the poor PG in the Luminal N class (p > 0.01). Due to small patient numbers assigned to the remaining PGs, Luminal N, Luminal B, Basal p53 normal and HER2+/ER- classes could not be validated. This study demonstrates the reproducibility of NPI+ and confirmed its prognostic value in an independent cohort of primary BC. Further validation in large randomised controlled trial material is warranted.

SPAG5 as a prognostic biomarker and chemotherapy sensitivity predictor in breast cancer: a retrospective, integrated genomic, transcriptomic, and protein analysis.

BACKGROUND: Proliferation markers and profiles have been recommended for guiding the choice of systemic treatments for breast cancer. However, the best molecular marker or test to use has not yet been identified. We did this study to identify factors that drive proliferation and its associated features in breast cancer and assess their association with clinical outcomes and response to chemotherapy. METHODS: We applied an artificial neural network-based integrative data mining approach to data from three cohorts of patients with breast cancer (the Nottingham discovery cohort (n=171), Uppsala cohort (n=249), and Molecular Taxonomy of Breast Cancer International Consortium [METABRIC] cohort; n=1980). We then identified the genes with the most effect on other genes in the resulting interactome map. Sperm-associated antigen 5 (SPAG5) featured prominently in our interactome map of proliferation and we chose to take it forward in our analysis on the basis of its fundamental role in the function and dynamic regulation of mitotic spindles, mitotic progression, and chromosome segregation fidelity. We investigated the clinicopathological relevance of SPAG5 gene copy number aberrations, mRNA transcript expression, and protein expression and analysed the associations of SPAG5 copy number aberrations, transcript expression, and protein expression with breast cancer-specific survival, disease-free survival, distant relapse-free survival, pathological complete response, and residual cancer burden in the Nottingham discovery cohort, Uppsala cohort, METABRIC cohort, a pooled untreated lymph node-negative cohort (n=684), a multicentre combined cohort (n=5439), the Nottingham historical early stage breast cancer cohort (Nottingham-HES; n=1650), Nottingham early stage oestrogen receptor-negative breast cancer adjuvant chemotherapy cohort (Nottingham-oestrogen receptor-negative-ACT; n=697), the Nottingham anthracycline neoadjuvant chemotherapy cohort (Nottingham-NeoACT; n=200), the MD Anderson taxane plus anthracycline-based neoadjuvant chemotherapy cohort (MD Anderson-NeoACT; n=508), and the multicentre phase 2 neoadjuvant clinical trial cohort (phase 2 NeoACT; NCT00455533; n=253). FINDINGS: In the METABRIC cohort, we detected SPAG5 gene gain or amplification at the Ch17q11.2 locus in 206 (10%) of 1980 patients overall, 46 (19%) of 237 patients with a PAM50-HER2 phenotype, and 87 (18%) of 488 patients with PAM50-LumB phenotype. Copy number aberration leading to SPAG5 gain or amplification and high SPAG5 transcript and SPAG5 protein concentrations were associated with shorter overall breast cancer-specific survival (METABRIC cohort [copy number aberration]: hazard ratio [HR] 1·50, 95% CI 1·18-1·92, p=0·00010; METABRIC cohort [transcript]: 1·68, 1·40-2·01, p<0·0001; and Nottingham-HES-breast cancer cohort [protein]: 1·68, 1·32-2·12, p<0·0001). In multivariable analysis, high SPAG5 transcript and SPAG5 protein expression were associated with reduced breast cancer-specific survival at 10 years compared with lower concentrations (Uppsala: HR 1·62, 95% CI 1·03-2·53, p=0·036; METABRIC: 1·27, 1·02-1·58, p=0·034; untreated lymph node-negative cohort: 2·34, 1·24-4·42, p=0·0090; and Nottingham-HES: 1·73, 1·23-2·46, p=0·0020). In patients with oestrogen receptor-negative breast cancer with high SPAG5 protein expression, anthracycline-based adjuvant chemotherapy increased breast cancer-specific survival overall compared with that for patients who did not receive chemotherapy (Nottingham-oestrogen receptor-negative-ACT cohort: HR 0·37, 95% CI 0·20-0·60, p=0·0010). Multivariable analysis showed high SPAG5 transcript concentrations to be independently associated with longer distant relapse-free survival after receiving taxane plus anthracycline neoadjuvant chemotherapy (MD Anderson-NeoACT: HR 0·68, 95% CI 0·48-0·97, p=0·031). In multivariable analysis, both high SPAG5 transcript and high SPAG5 protein concentrations were independent predictors for a higher proportion of patients achieving a pathological complete response after combination cytotoxic chemotherapy (MD Anderson-NeoACT: OR 1·71, 95% CI, 1·07-2·74, p=0·024; Nottingham-ACT: 8·75, 2·42-31·62, p=0·0010). INTERPRETATION: SPAG5 is a novel amplified gene on Ch17q11.2 in breast cancer. The transcript and protein products of SPAG5 are independent prognostic and predictive biomarkers that might have clinical utility as biomarkers for combination cytotoxic chemotherapy sensitivity, especially in oestrogen receptor-negative breast cancer. FUNDING: Nottingham Hospitals Charity and the John and Lucille van Geest Foundation.

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.

Accuracy of GE digital breast tomosynthesis vs supplementary mammographic views for diagnosis of screen-detected soft-tissue breast lesions.

OBJECTIVE: To compare the accuracy of standard supplementary views and GE digital breast tomosynthesis (DBT) for assessment of soft-tissue mammographic abnormalities. METHODS: Women recalled for further assessment of soft-tissue abnormalities were recruited and received standard supplementary views (typically spot compression views) and two-view GE DBT. The added value of DBT in the assessment process was determined by analysing data collected prospectively by radiologists working up the cases. Following anonymization of cases, there was also a retrospective multireader review. The readers first read bilateral standard two-view digital mammography (DM) together with the supplementary mammographic views and gave a combined score for suspicion of malignancy on a five-point scale. The same readers then read bilateral standard two-view DM together with two-view DBT. Pathology data were obtained. Differences were assessed using receiver operating characteristic analysis. RESULTS: The study population was 342 lesions in 322 patients. The final diagnosis was malignant in 113 cases (33%) and benign/normal in 229 cases (67%). In the prospective analysis, the performance of two-view DM plus DBT was at least equivalent to the performance of two-view DM and standard mammographic supplementary views-the area under the curve (AUC) was 0.946 and 0.922, respectively, which did not reach statistical significance. Similar results were obtained for the retrospective review-AUC was 0.900 (DBT) and 0.873 (supplementary views), which did not reach statistical significance. CONCLUSION: The accuracy of GE DBT in the assessment of screen detected soft-tissue abnormalities is equivalent to the use of standard supplementary mammographic views. ADVANCES IN KNOWLEDGE: The vast majority of evidence relating to the use of DBT has been gathered from research using Hologic equipment. This study provides evidence for the use of the commercially available GE DBT system demonstrating that it is at least equivalent to supplementary mammographic views in the assessment of soft-tissue screen-detected abnormalities.

HAGE in Triple-Negative Breast Cancer Is a Novel Prognostic, Predictive, and Actionable Biomarker: A Transcriptomic and Protein Expression Analysis.

PURPOSE: The expression of HAGE as a novel prognostic and predictive tool was assessed in 1,079 triple-negative breast cancers (TNBC). EXPERIMENTAL DESIGN: HAGE protein expression was investigated in an early primary TNBC (EP-TNBC; n = 520) cohort who received adjuvant chemotherapy (ACT) and in a locally advanced primary TNBC cohort who received anthracycline combination Neo-ACT (n = 110; AC-Neo-ACT). HAGE-mRNA expression was evaluated in the METABRIC-TNBC cohort (n = 311) who received ACT and in a cohort of patients with TNBC who received doxorubicin/cyclophosphamide Neo-ACT, followed by 1:1 randomization to ixabepilone (n = 68) or paclitaxel (n = 64) as part of a phase II clinical trial. Furthermore, a cohort of 128 tumors with integrated HAGE gene copy number changes, mRNA, and protein levels were analyzed. RESULTS: In patients with EP-TNBC, who were chemotherapy-naïve, high HAGE protein expression (HAGE(+)) was associated with a higher risk of death [HR, 1.3; 95% confidence interval (CI), 1.2-1.5; P = 0.000005] when compared with HAGE(-) cases. Patients who received ACT and expressed mRNA-HAGE(+) were at a lower risk of death than those who were mRNA-HAGE(-) (P = 0.004). The expression of HAGE was linked to the presence of tumor-infiltrating lymphocytes (TIL), and both features were found to be independent predictors for pathologic complete response (pCR, P < 0.001) and associated with prolonged survival (P < 0.01), following AC-Neo-ACT. In patients with residual disease, HAGE(+) had a 2-fold death risk increase (P = 0.018) compared with HAGE(-). CONCLUSIONS: HAGE expression is a potential prognostic marker and a predictor of response to anthracycline treatment in TNBC. A prospective clinical trial to examine the therapeutic value of HAGE for TNBC cases is warranted.

gEM/GANN: A multivariate computational strategy for auto-characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high-dimensional flow cytometry data.

The dramatic increase in the complexity of flow cytometric datasets requires new computational approaches that can maximize the amount of information derived and overcome the limitations of traditional gating strategies. Herein, we present a multivariate computational analysis of the HIV-infected flow cytometry datasets that were provided as part of the FlowCAP-IV Challenge using unsupervised and supervised learning techniques. Out of 383 samples (stimulated and unstimulated), 191 samples were used as a training set (34 individuals whose disease did not progress, and 157 individuals whose disease did progress). Using the results from the training set, the participants in the Challenge were then asked to predict the condition and progression time of the remaining individuals (45 "nonprogressors" and 147 "progressors"). To achieve this, we first scaled down data resolution and then excluded doublet cells from the analysis using Expectation Maximization approaches. We then standardized all samples into histograms and used Genetic Algorithm-Neural Network to extract feature sets from the datasets, the reliability of which were examined using WEKA-implemented classifiers. The selected feature set resulted in a high sensitivity and specificity for the discrimination of progressors and nonprogressors in the training set (average True Positive Rate = 1.00 and average False Positive Rate = 0.033). The capacity of the feature set to predict real-time survival time was better when using data from the "unstimulated" training set (r = 0.825). The P-values and 95% confidence interval log-rank ratios between actual and predicted survival time in the test set were 0.682 and 0.9542 ± 0.24 for the unstimulated dataset, and 0.4451 and 0.9173 ± 0.23 for the stimulated dataset. Our analytic strategy has demonstrated a promising capacity to extract useful information from complex flow cytometry datasets, despite a significance imbalance and variation between the training and test sets.

Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer.

The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two carefully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10(-11), the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient's response to treatment or be used as a novel target for therapy.

Artificial neural network inference (ANNI): a study on gene-gene interaction for biomarkers in childhood sarcomas.

OBJECTIVE: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). METHOD: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. RESULTS: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing's sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. CONCLUSIONS: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.

DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.

BACKGROUND: Oestrogen receptor (ER) positive (luminal) tumours account for the largest proportion of females with breast cancer. Theirs is a heterogeneous disease presenting clinical challenges in managing their treatment. Three main biological luminal groups have been identified but clinically these can be distilled into two prognostic groups in which Luminal A are accorded good prognosis and Luminal B correlate with poor prognosis. Further biomarkers are needed to attain classification consensus. Machine learning approaches like Artificial Neural Networks (ANNs) have been used for classification and identification of biomarkers in breast cancer using high throughput data. In this study, we have used an artificial neural network (ANN) approach to identify DACH1 as a candidate luminal marker and its role in predicting clinical outcome in breast cancer is assessed. MATERIALS AND METHODS: A reiterative ANN approach incorporating a network inferencing algorithm was used to identify ER-associated biomarkers in a publically available cDNA microarray dataset. DACH1 was identified in having a strong influence on ER associated markers and a positive association with ER. Its clinical relevance in predicting breast cancer specific survival was investigated by statistically assessing protein expression levels after immunohistochemistry in a series of unselected breast cancers, formatted as a tissue microarray. RESULTS: Strong nuclear DACH1 staining is more prevalent in tubular and lobular breast cancer. Its expression correlated with ER-alpha positive tumours expressing PgR, epithelial cytokeratins (CK)18/19 and 'luminal-like' markers of good prognosis including FOXA1 and RERG (p<0.05). DACH1 is increased in patients showing longer cancer specific survival and disease free interval and reduced metastasis formation (p<0.001). Nuclear DACH1 showed a negative association with markers of aggressive growth and poor prognosis. CONCLUSION: Nuclear DACH1 expression appears to be a Luminal A biomarker predictive of good prognosis, but is not independent of clinical stage, tumour size, NPI status or systemic therapy.

A quantifier-based fuzzy classification system for breast cancer patients.

OBJECTIVES: Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups. MATERIALS AND METHODS: In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called 'fuzzy quantification subsethood-based algorithm') and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches was used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients. RESULTS: A rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1073 remain unclassified, representing a more clinically usable class assignment algorithm. CONCLUSION: The fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups.