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Abstract Background Production of contextually appropriate social behaviors involves integrated activity across many brain regions. Many songbird species produce complex vocalizations called ‘songs’ that serve to attract potential mates, defend territories, and/or maintain flock cohesion. There are a series of discrete interconnect brain regions that are essential for the successful production of song. The probability and intensity of singing behavior is influenced by the reproductive state. The objectives of this study were to examine the broad changes in gene expression in brain regions that control song production with a brain region that governs the reproductive state. Results We show using microarray cDNA analysis that two discrete brain systems that are both involved in governing singing behavior show markedly different gene expression profiles. We found that cortical and basal ganglia-like brain regions that control the socio-motor production of song in birds exhibit a categorical switch in gene expression that was dependent on their reproductive state. This pattern is in stark contrast to the pattern of expression observed in a hypothalamic brain region that governs the neuroendocrine control of reproduction. Subsequent gene ontology analysis revealed marked variation in the functional categories of active genes dependent on reproductive state and anatomical localization. HVC, one cortical-like structure, displayed significant gene expression changes associated with microtubule and neurofilament cytoskeleton organization, MAP kinase activity, and steroid hormone receptor complex activity. The transitions observed in the preoptic area, a nucleus that governs the motivation to engage in singing, exhibited variation in functional categories that included thyroid hormone receptor activity, epigenetic and angiogenetic processes. Conclusions These findings highlight the importance of considering the temporal patterns of gene expression across several brain regions when engaging in social behaviors.

Abstract Introduction Raw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma) are used in our study. Method Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification. Results Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively. Conclusion The results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.

Abstract Background Songbirds hold great promise for biomedical, environmental and evolutionary research. A complete draft sequence of the zebra finch genome is imminent, yet a need remains for application of genomic resources within a research community traditionally focused on ethology and neurobiological methods. In response, we developed a core set of genomic tools and a novel collaborative strategy to probe gene expression in diverse songbird species and natural contexts. Results We end-sequenced cDNAs from zebra finch brain and incorporated additional sequences from community sources into a database of 86,784 high quality reads. These assembled into 31,658 non-redundant contigs and singletons, which we annotated via BLAST search of chicken and human databases. The results are publicly available in the ESTIMA:Songbird database. We produced a spotted cDNA microarray with 20,160 addresses representing 17,214 non-redundant products of an estimated 11,500–15,000 genes, validating it by analysis of immediate-early gene (zenk) gene activation following song exposure and by demonstrating effective cross hybridization to genomic DNAs of other songbird species in the Passerida Parvorder. Our assembly was also used in the design of the "Lund-zfa" Affymetrix array representing ~22,000 non-redundant sequences. When the two arrays were hybridized to cDNAs from the same set of male and female zebra finch brain samples, both arrays detected a common set of regulated transcripts with a Pearson correlation coefficient of 0.895. To stimulate use of these resources by the songbird research community and to maintain consistent technical standards, we devised a "Community Collaboration" mechanism whereby individual birdsong researchers develop experiments and provide tissues, but a single individual in the community is responsible for all RNA extractions, labelling and microarray hybridizations. Conclusion Immediately, these results set the foundation for a coordinated set of 25 planned experiments by 16 research groups probing fundamental links between genome, brain, evolution and behavior in songbirds. Energetic application of genomic resources to research using songbirds should help illuminate how complex neural and behavioral traits emerge and evolve.

Abstract Background Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process. Results Artificial Neural Networks were applied to biochemical and 16S rDNA data derived from 282 strains of Enterobacteriaceae, including 189 E. sakazakii isolates, in order to identify key characteristics which could improve the identification of E. sakazakii. The models developed resulted in a predictive performance for blind (validation) data of 99.3 % correct discrimination between E. sakazakii and closely related species for both phenotypic and genotypic data. Three main regions of the partial rDNA sequence were found to be key in discriminating the species. Comparison between E. sakazakii and other strains also constitutively positive for expression of the enzyme α-glucosidase resulted in a predictive performance of 98.7 % for 16S rDNA sequence data and 100% for phenotypic data. Conclusion The computationally based methods developed here show a remarkable ability in reducing data dimensionality and complexity, in order to eliminate noise from the system in order to facilitate the speed and reliability of a potential strain identification system. Furthermore, the approaches described are also able to provide valuable information regarding the population structure and distribution of individual species thus providing the foundations for novel assays and diagnostic tests for rapid identification of pathogens.

The Nottingham Prognostic Index (NPI) is a well established and widely used method of predicting survival of operable primary breast cancer. AIMS: Primary: To present the updated survival figures for each NPI Group. Secondary: From the observations to suggest reasons for the reported fall in mortality from breast cancer. METHODS: The NPI is compiled from grade, size and lymph node status of the primary tumour. Consecutive cases diagnosed and treated at Nottingham City Hospital in 1980-1986 (n=892) and 1990-1999 (n=2,238) are compared. Changes in protocols towards earlier diagnosis and better case management were made in the late 1980s between the two data sets. RESULTS: Case survival (Breast Cancer Specific) at 10 years has improved overall from 55% to 77%. Within all Prognostic groups there are high relative and absolute risk reductions. The distribution of cases to Prognostic groups shows only a small increase in the numbers in better groups. CONCLUSION: The updated survival figures overall and for each Prognostic group for the NPI are presented. [ABSTRACT FROM AUTHOR]

Cancer is highly diverse and heterogeneous. Accurate and rapid analysis of the characteristics of individual cancer cells, using a complex array of big data that includes various clinicopathological features and molecular mechanisms, is crucial for advancing precision medicine. In recent years, experts in biomedical sciences and data sciences have explored the potential of artificial intelligence (AI) to analyze such extensive data sets. The next phase of AI-based medical research on cancer should focus on the practical applications of AI tools and how they can be effectively used in actual medical research settings. Recently, translational research that leverages AI and comprehensive genetic analysis data has emerged as a significant research focus. This field represents an opportunity for groundbreaking discoveries to be shared globally. To further precision medicine in clinical practice, it is vital to develop sophisticated AI tools for cancer research. These tools should not only identify potential therapeutic targets through comprehensive genetic analysis but also predict therapeutic outcomes in clinical settings., (© 2024. The Author(s) under exclusive licence to Japan Society of Clinical Oncology.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Szakmany, Fitzgerald, Garlant, Whitehouse, Molnar, Shah, Tong, Hall, Ball and Kempsell.)

Meningioma, a primary brain tumor, is commonly encountered and accounts for 39% of overall CNS tumors. Despite significant progress in clinical research, conventional surgical and clinical interventions remain the primary treatment options for meningioma. Several proteomics and transcriptomics studies have identified potential markers and altered biological pathways; however, comprehensive exploration and data integration can help to achieve an in-depth understanding of the altered pathobiology. This study applied integrated meta-analysis strategies to proteomic and transcriptomic datasets comprising 48 tissue samples, identifying around 1832 common genes/proteins to explore the underlying mechanism in high-grade meningioma tumorigenesis. The in silico pathway analysis indicated the roles of extracellular matrix organization (EMO) and integrin binding cascades in regulating the apoptosis, angiogenesis, and proliferation responsible for the pathobiology. Subsequently, the expression of pathway components was validated in an independent cohort of 32 fresh frozen tissue samples using multiple reaction monitoring (MRM), confirming their expression in high-grade meningioma. Furthermore, proteome-level changes in EMO and integrin cell surface interactions were investigated in a high-grade meningioma (IOMM-Lee) cell line by inhibiting integrin-linked kinase (ILK). Inhibition of ILK by administrating Cpd22 demonstrated an anti-proliferative effect, inducing apoptosis and downregulating proteins associated with proliferation and metastasis, which provides mechanistic insight into the disease pathophysiology.

Background: Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective., Methods: A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins., Results: A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades., Conclusions: The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets., (© 2023. BioMed Central Ltd., part of Springer Nature.)

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., Competing Interests: None., (AJCR Copyright © 2022.)

This study, which performs an extensive mass spectrometry-based analysis of 19 brain regions from both left and right hemispheres, presents the first draft of the human brain interhemispheric proteome. This high-resolution proteomics data provides comprehensive coverage of 3300 experimentally measured (nonhypothetical) proteins across multiple regions, allowing the characterization of protein-centric interhemispheric differences and synapse biology, and portrays the regional mapping of specific regions for brain disorder biomarkers. In the context of the Human Proteome Project (HPP), the interhemispheric proteome data reveal specific markers like chimerin 2 (CHN2) in the cerebellar vermis, olfactory marker protein (OMP) in the olfactory bulb, and ankyrin repeat domain 63 (ANKRD63) in basal ganglia, in line with regional brain transcriptomes mapped in the Human Protein Atlas (HPA). In addition, an in silico analysis pipeline was used to predict the structure and function of the uncharacterized uPE1 protein ANKRD63, and parallel reaction monitoring (PRM) was applied to validate its region-specific expression. Finally, we have built the Interhemispheric Brain Proteome Map (IBPM) Portal (www.brainprot.org) to stimulate the scientific community's interest in the brain molecular landscape and accelerate and support research in neuroproteomics. Data are available via ProteomeXchange with identifier PXD019936.

Meningiomas are brain tumors that originate from the meninges and has been primarily classified into three grades by the current WHO guidelines. Although widely prevalent and can be managed by surgery there are instances when the tumors are located in difficult regions. This results in considerable challenges for complete surgical resection and further clinical management. While the genetic signature of the skull base tumors is now known to be different from the non-skull base tumors, there is a lack of information at the functional aspects of these tumors at the proteomic level. Thus, the current study thereby aims to obtain mechanistic insights between the two radiologically distinct groups of meningiomas, namely the skull base & supratentorial (non-skull base-NSB) regions. We have employed a comprehensive mass spectrometry-based label-free quantitative proteomic analysis in Skull base and supratentorial meningiomas. Further, we have used an Artificial Neural Networking employing a sparse Multilayer perceptron (MLP) architecture to predict protein concordance. A patient-derived spectral library has been employed for a novel peptide-level validation of proteins that are specific to the radiological regions using the SRM assay based targeted proteomics approach. The comprehensive proteomics enabled the identification of nearly 4000 proteins with high confidence (1%FDR ≥ 2 unique peptides) among which 170 proteins were differentially abundant in Skull base vs Supratentorial tumors (p-value ≤0.05). In silico analysis enabled mapping of the major alterations and hinted towards an overall perturbation of extracellular matrix and collagen biosynthesis components in the non-skull base meningiomas and a prominent perturbation of molecular trafficking in the skull base meningiomas. Therefore, this study has yielded novel insights into the functional association of the proteins that are differentially abundant in the two radiological subgroups. SIGNIFICANCE: In the current study, we have performed label-free proteomic analysis on fresh frozen tissue of 14 Supratentorial (NSB) and 7 Skull base meningiomas to assess perturbations in the global proteome, we have further employed an in-depth in silico analysis to map the pathways that have enabled functional mapping of the differentially abundant proteins in the Skull base and Supratentorial tumors. The findings from the above were also subjected to a machine learning-based neural networking to find out the proteins that have the most concordance of occurrence to determine the most influential proteins of the network. We further validated the differential abundance of identified protein markers in a larger patient cohort of Skull base and Supratentorial employing targeted proteomics approach to validate key protein candidates emerging from ours and other recent studies. The previous studies that have explored the skull base and convexity meningiomas have been able to reveal alterations in the genetic mutations in these tumor types. However, there are not many studies that have explored the functional aspects of these tumors, especially at the proteome level. We have attempted for the first time to map the functional modules associated with altered proteins in these tumors and have been able to identify that there is a possibility that the Skull base meningiomas to be considerably different from the Non-skull base (NSB) tumors in terms of the perturbed pathways. Our study employed global as well as targeted proteomics to examine the proteomic alterations in these two tumor groups. The study indicates that proteins that were more abundant in Skull base tumors were part of molecular transport components, non-skull base proteins majorly mapped to the components of extracellular matrix remodeling pathways. In conclusion, this study substantiates the distinction in the proteomic signatures in the skull base and supratentorial meningiomas paving way for further investigation of the identified markers for determining if some of these proteins can be used for therapeutic interventions for cases that pose considerable challenges for complete resection., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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.

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.

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 β-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., (© 2019 by The American Society of Hematology.)

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., (Copyright © 2018. Published by Elsevier Ltd.)

Cell-cell adhesions constitute the structural "glue" that retains cells together and contributes to tissue organisation and physiological function. The integrity of these structures is regulated by extracellular and intracellular signals and pathways that act on the functional units of cell adhesion such as the cell adhesion molecules/adhesion receptors, the extracellular matrix (ECM) proteins and the cytoplasmic plaque/peripheral membrane proteins. In advanced cancer, these regulatory pathways are dysregulated and lead to cell-cell adhesion disassembly, increased invasion and metastasis. The Metastasis suppressor protein 1 (MTSS1) plays a key role in the maintenance of cell-cell adhesions and its loss correlates with tumour progression in a variety of cancers. However, the mechanisms that regulate its function are not well-known. Using a system biology approach, we unravelled potential interacting partners of MTSS1. We found that the secretory carrier-associated membrane protein 1 (SCAMP1), a molecule involved in post-Golgi recycling pathways and in endosome cell membrane recycling, enhances Mtss1 anti-invasive function in HER2+/ER-/PR- breast cancer, by promoting its protein trafficking leading to elevated levels of RAC1-GTP and increased cell-cell adhesions. This was clinically tested in HER2 breast cancer tissue and shown that loss of MTSS1 and SCAMP1 correlates with reduced disease-specific survival. In summary, we provide evidence of the cooperative roles of MTSS1 and SCAMP1 in preventing HER2+/ER-/PR- breast cancer invasion and we show that the loss of Mtss1 and Scamp1 results in a more aggressive cancer cell phenotype.

The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.

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., (Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.)

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., Competing Interests: CONFLICTS OF INTEREST The authors declare they have no competing interests to disclose.

Introduction: Hematological malignancies originate and progress in primary and secondary lymphoid organs, where they establish a uniquely immune-suppressive tumour microenvironment. Although high-throughput transcriptomic and proteomic approaches are being employed to interrogate immune surveillance and escape mechanisms in patients with solid tumours, and to identify actionable targets for immunotherapy, our knowledge of the immunological landscape of hematological malignancies, as well as our understanding of the molecular circuits that underpin the establishment of immune tolerance, is not comprehensive. Areas covered: This article will discuss how multiplexed immunohistochemistry, flow cytometry/mass cytometry, proteomic and genomic techniques can be used to dynamically capture the complexity of tumour-immune interactions. Moreover, the analysis of multi-dimensional, clinically annotated data sets obtained from public repositories such as Array Express, TCGA and GEO is crucial to identify immune biomarkers, to inform the rational design of immune therapies and to predict clinical benefit in individual patients. We will also highlight how artificial neural network models and alternative methodologies integrating other algorithms can support the identification of key molecular drivers of immune dysfunction. Expert commentary: High-dimensional technologies have the potential to enhance our understanding of immune-cancer interactions and will support clinical decision making and the prediction of therapeutic benefit from immune-based interventions.

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., (©2016 American Association for Cancer Research.)

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 β-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 β-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 β-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.

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., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The Nottingham prognostic index plus (NPI+) is based on the assessment of biological class combined with established clinicopathologic prognostic variables providing improved patient outcome stratification for breast cancer superior to the traditional NPI. This study aimed to determine prognostic capability of the NPI+ in predicting risk of development of distant disease. A well-characterised series of 1073 primary early-stage BC cases treated in Nottingham and 251 cases from Budapest were immunohistochemically assessed for cytokeratin (Ck)5/6, Ck18, EGFR, oestrogen receptor (ER), progesterone receptor, HER2, HER3, HER4, Mucin 1 and p53 expression. NPI+ biological class and prognostic scores were assigned using individual algorithms for each biological class incorporating clinicopathologic parameters and investigated in terms of prediction of distant metastases-free survival (MFS). The NPI+ identified distinct prognostic groups (PG) within each molecular class which were predictive of MFS providing improved patient outcome stratification superior to the traditional NPI. NPI+ PGs, between series, were comparable in predicting patient outcome between series in luminal A, basal p53 altered and HER2+/ER+ (p > 0.01) tumours. The low-risk groups were similarly validated in luminal B, luminal N, basal p53 normal tumours (p > 0.01). Due to small patient numbers the remaining PGs could not be validated. NPI+ was additionally able to predict a higher risk of metastases at certain distant sites. This study may indicate the NPI+ as a useful tool in predicting the risk of metastases. The NPI+ provides accurate risk stratification allowing improved individualised clinical decision making for breast cancer.

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.

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., (©2015 American Association for Cancer Research.)

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.

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.

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.

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., (© 2015 International Society for Advancement of Cytometry.)

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.

Background: HAGE protein is a known immunogenic cancer-specific antigen., Methods: The biological, prognostic and predictive values of HAGE expression was studied using immunohistochemistry in three cohorts of patients with BC (n=2147): early primary (EP-BC; n=1676); primary oestrogen receptor-negative (PER-BC; n=275) treated with adjuvant anthracycline-combination therapies (Adjuvant-ACT); and primary locally advanced disease (PLA-BC) who received neo-adjuvant anthracycline-combination therapies (Neo-adjuvant-ACT; n=196). The relationship between HAGE expression and the tumour-infiltrating lymphocytes (TILs) in matched prechemotherapy and postchemotherapy samples were investigated., Results: Eight percent of patients with EP-BC exhibited high HAGE expression (HAGE+) and was associated with aggressive clinico-pathological features (Ps<0.01). Furthermore, HAGE+expression was associated with poor prognosis in both univariate and multivariate analysis (Ps<0.001). Patients with HAGE+did not benefit from hormonal therapy in high-risk ER-positive disease. HAGE+and TILs were found to be independent predictors for pathological complete response to neoadjuvant-ACT; P<0.001. A statistically significant loss of HAGE expression following neoadjuvant-ACT was found (P=0.000001), and progression-free survival was worse in those patients who had HAGE+residual disease (P=0.0003)., Conclusions: This is the first report to show HAGE to be a potential prognostic marker and a predictor of response to ACT in patients with BC.

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.

Background: Breast cancer is a heterogeneous disease characterised by complex molecular alterations underlying the varied behaviour and response to therapy. However, translation of cancer genetic profiling for use in routine clinical practice remains elusive or prohibitively expensive. As an alternative, immunohistochemical analysis applied to routinely processed tissue samples could be used to identify distinct biological classes of breast cancer., Methods: In this study, 1073 archival breast tumours previously assessed for 25 key breast cancer biomarkers using immunohistochemistry and classified using clustering algorithms were further refined using naïve Bayes classification performance. Criteria for class membership were defined using the expression of a reduced panel of 10 proteins able to identify key molecular classes. We examined the association between these breast cancer classes with clinicopathological factors and patient outcome., Results: We confirm patient classification similar to established genotypic biological classes of breast cancer in addition to novel sub-divisions of luminal and basal tumours. Correlations between classes and clinicopathological parameters were in line with expectations and showed highly significant association with patient outcome. Furthermore, our novel biological class stratification provides additional prognostic information to the Nottingham Prognostic Index., Conclusion: This study confirms that distinct molecular phenotypes of breast cancer can be identified using robust and routinely available techniques and both the luminal and basal breast cancer phenotypes are heterogeneous and contain distinct subgroups.

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., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Recent preclinical studies have associated beta-adrenergic receptor (β-AR) signaling with breast cancer pathways such as progression and metastasis. These findings have been supported by clinical and epidemiological studies which examined the effect of beta-blocker therapy on breast cancer metastasis, recurrence and mortality. Results from these studies have provided initial evidence for the inhibition of cell migration in breast cancer by beta-blockers and have introduced the beta-adrenergic receptor pathways as a target for therapy. This paper analyzes gene expression profiles in breast cancer patients, utilising Artificial Neural Networks (ANNs) to identify molecular signatures corresponding to possible disease management pathways and biomarker treatment strategies associated with beta-2-adrenergic receptor (ADRB2) cell signaling. The adrenergic receptor relationship to cancer is investigated in order to validate the results of recent studies that suggest the use of beta-blockers for breast cancer therapy. A panel of genes is identified which has previously been reported to play an important role in cancer and also to be involved in the beta-adrenergic receptor signaling.

Breast cancer is a heterogeneous and complex disease. Although the use of tumor biomarkers has improved individualized breast cancer care, i.e., assessment of risk, diagnosis, prognosis, and prediction of treatment outcome, new markers are required to further improve patient clinical management. In the present study, a search for novel breast cancer-associated genes was performed by mining the UniGene database for expressed sequence tags (ESTs) originating from human normal breast, breast cancer tissue, or breast cancer cell lines. Two hundred and twenty-eight distinct breast-associated UniGene Clusters (BUC1-228) matched the search criteria. Four BUC ESTs (BUC6, BUC9, BUC10, and BUC11) were subsequently selected for extensive in silico database searches, and in vitro analyses through sequencing and RT-PCR based assays on well-characterized cell lines and tissues of normal and cancerous origin. BUC6, BUC9, BUC10, and BUC11 are clustered on 10p11.21-12.1 and showed no homology to any known RNAs. Overall, expression of the four BUC transcripts was high in normal breast and testis tissue, and in some breast cancers; in contrast, BUC was low in other normal tissues, peripheral blood mononuclear cells (PBMCs), and other cancer cell lines. Results to-date suggest that BUC11 and BUC9 translate to protein and BUC11 cytoplasmic and nuclear protein expression was detected in a large cohort of breast cancer samples using immunohistochemistry. This study demonstrates the discovery and expression analysis of a tissue-restricted novel transcript set which is strongly expressed in breast tissue and their application as clinical cancer biomarkers clearly warrants further investigation., (Copyright © 2012 Wiley Periodicals, Inc.)

Unlabelled: Basal-like tumours (BP) are a poor prognostic class of breast cancer but remain a biologically and clinically heterogeneous group. We have previously identified two novel genes PPARα (positive) and GMPR2 (negative) whose expression was significantly associated with BP at the transcriptome level. In this study, using a large and well-characterised series of operable invasive breast carcinomas (1,043 cases) prepared as TMAs, we assessed these targets at the protein level using immunohistochemistry and investigated associations with clinicopathological variables and patient outcome., Results: Lack of PPARα and GMPR2 protein expression was associated with BP, as defined by the expression of cytokeratin (CK) 5/6 and/or CK14, (p = 0.023, p = 0.001, respectively) or as triple-negative (ER-, PR-, HER2-) phenotype (p < 0.001 for both proteins). Positive expression of both markers was associated ER and PR positive status (p < 0.05) and with the good Nottingham Prognostic Index group (p = 0.012, p < 0.001, respectively). Univariate survival analysis showed an association between lack of expression of PPARα and GMPR2 and poor outcome in terms of shorter disease-free survival and shorter breast cancer-specific survival, respectively. However, multivariate analysis showed that these associations were not independent of other prognostic variables, namely tumour size, grade, and nodal stage. In conclusion, this study demonstrates that loss of expression of GMPR2 and PPARα is associated with BP at the protein level; indicating that they may play a role in carcinogenesis of this molecularly complex and clinically important subtype. Further studies into their relevance in further classification of BP are warranted.

Appropriate mitochondrial functioning in normal cells depends on proper functioning of mitochondrial translocation machinery, of which translocase of the outer membrane of mitochondria (TOMM) plays important role. The aim of this study was to explore the expression of TOMM34 in invasive breast cancer (BC) with relevance to BC molecular subtypes and patients' outcome. Gene expression data of 128 BC were analysed using artificial neuronal network (ANN) analysis to identify differentially expressed genes between BC with distant metastases and that without distant metastases. TOMM34 expression was assessed in a large series of BC (n = 1,061) with long-term follow-up using tissue microarray and immunohistochemistry. TOMM34 protein expression was quantitatively measured using the novel reverse phase protein microarray (RPPA) technique. ANN analysis revealed TOMM34 gene transcript as one of the top differentially expressed gene correlated with BC distant metastasis. Protein expression of TOMM34 was associated with features of aggressive behaviour including higher tumour grade, advanced nodal stage, larger tumour size and lymphovascular invasion. TOMM34 over-expression was significantly associated with shorter BC-specific survival and metastasis-free survival independent of standard prognostic parameters. TOMM34 protein expression was quantified by RPPA which showed that the mean expression values of TOMM34 were higher in samples demonstrating features of poor outcome. This study demonstrates at translational protein expression level that TOMM34 is a marker of poor prognosis in BC. Our findings underscore the role played by mitochondrial machinery in BC progression and warrant their validation on a prospective basis.

Male breast cancer remains understudied despite evidence of rising incidence. Using a co-ordinated multi-centre approach, we present the first large scale biomarker study to define and compare hormone receptor profiles and survival between male and female invasive breast cancer. We defined and compared hormone receptor profiles and survival between 251 male and 263 female breast cancers matched for grade, age, and lymph node status. Tissue microarrays were immunostained for ERα, ERβ1, -2, -5, PR, PRA, PRB and AR, augmented by HER2, CK5/6, 14, 18 and 19 to assist typing. Hierarchical clustering determined differential nature of influences between genders. Luminal A was the most common phenotype in both sexes. Luminal B and HER2 were not seen in males. Basal phenotype was infrequent in both. No differences in overall survival at 5 or 10 years were observed between genders. Notably, AR-positive luminal A male breast cancer had improved overall survival over female breast cancer at 5 (P = 0.01, HR = 0.39, 95% CI = 0.26-0.87) but not 10 years (P = 0.29, HR = 0.75, 95% CI = 0.46-1.26) and both 5 (P = 0.04, HR = 0.37, 95% CI = 0.07-0.97) and 10 years (P = 0.04, HR = 0.43, 95% CI = 0.12-0.97) in the unselected group. Hierarchical clustering revealed common clusters between genders including total PR-PRA-PRB and ERβ1/2 clusters. A striking feature was the occurrence of ERα on distinct clusters between genders. In female breast cancer, ERα clustered with PR and its isoforms; in male breast cancer, ERα clustered with ERβ isoforms and AR. Our data supports the hypothesis that breast cancer is biologically different in males and females suggesting implications for clinical management. With the incidence of male breast cancer increasing this provides impetus for further study.

Purpose: Colorectal cancer (CRC) is a clinically diverse disease whose molecular etiology remains poorly understood. The purpose of this study was to identify miRNA expression patterns predictive of CRC tumor status and to investigate associations between microRNA (miRNA) expression and clinicopathological parameters., Methods: Expression profiling of 380 miRNAs was performed on 20 paired stage II tumor and normal tissues. Artificial neural network (ANN) analysis was applied to identify miRNAs predictive of tumor status. The validation of specific miRNAs was performed on 102 tissue specimens of varying stages., Results: Thirty-three miRNAs were identified as differentially expressed in tumor versus normal tissues. ANN analysis identified three miRNAs (miR-139-5p, miR-31, and miR-17-92 cluster) predictive of tumor status in stage II disease. Elevated expression of miR-31 (p = 0.004) and miR-139-5p (p < 0.001) and reduced expression of miR-143 (p = 0.016) were associated with aggressive mucinous phenotype. Increased expression of miR-10b was also associated with mucinous tumors (p = 0.004). Furthermore, progressively increasing levels of miR-10b expression were observed from T1 to T4 lesions and from stage I to IV disease., Conclusion: Association of specific miRNAs with clinicopathological features indicates their biological relevance and highlights the power of ANN to reliably predict clinically relevant miRNA biomarkers, which it is hoped will better stratify patients to guide adjuvant therapy.

Metastasis of breast cancer is a major contributor to mortality. Histological assessment of vascular invasion (VI) provides important prognostic information and demonstrates that VI occurs predominantly via lymphatics in breast cancer. We sought to examine genes and proteins involved in lymphovascular invasion (LVI) to understand the mechanisms of this key disease process. A gene expression array of 91 breast cancer patients was analysed by an Artificial Neural Network (ANN) approach using LVI to supervise the analysis. 89 transcripts were significantly associated (p<0.001) with the presence of LVI. Calpastatin, a specific calpain inhibitor, had the second lowest selection error and was investigated in breast cancer specimens using real-time PCR (n=56) and immunohistochemistry (n=53). Both calpastatin mRNA and protein levels were significantly associated with the presence of LVI (p=0.014 and p=0.025 respectively). The data supports the hypothesis that calpastatin may play a role in regulating the initial metastatic dissemination of breast cancer., (Copyright © 2011 Elsevier Ltd. All rights reserved.)