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Supplementary Figure Legends 1-9 from Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome

Supplementary Figures 1-9 from Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome

Supplementary Tables 1- 9 from Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome

Post-translational histone modifications are known to be altered in cancer cells, and loss of selected histone acetylation and methylation marks has recently been shown to predict patient outcome in human carcinoma. Immunohistochemistry was used to detect a series of histone lysine acetylation (H3K9ac, H3K18ac, H4K12ac, and H4K16ac), lysine methylation (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) marks in a well-characterized series of human breast carcinomas (n = 880). Tissue staining intensities were assessed using blinded semiquantitative scoring. Validation studies were done using immunofluorescence staining and Western blotting. Our analyses revealed low or absent H4K16ac in the majority of breast cancer cases (78.9%), suggesting that this alteration may represent an early sign of breast cancer. There was a highly significant correlation between histone modifications status, tumor biomarker phenotype, and clinical outcome, where high relative levels of global histone acetylation and methylation were associated with a favorable prognosis and detected almost exclusively in luminal-like breast tumors (93%). Moderate to low levels of lysine acetylation (H3K9ac, H3K18ac, and H4K12ac), lysine (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) were observed in carcinomas of poorer prognostic subtypes, including basal carcinomas and HER-2-positive tumors. Clustering analysis identified three groups of histone displaying distinct pattern in breast cancer, which have distinct relationships to known prognostic factors and clinical outcome. This study identifies the presence of variations in global levels of histone marks in different grades, morphologic types, and phenotype classes of invasive breast cancer and shows that these differences have clinical significance. [Cancer Res 2009;69(9):3802–9]

Supplementary Methods and Materials from Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome

Colorectal cancer (CRC) is one of the most common cancers worldwide. This study was designed to evaluate biological patterns, explore molecular classification and correlate with survival outcome in treatment naïve CRC patients.\ud Methods\ud Over 11 years consecutive series of 435 CRC patients were operated on as primary surgical therapy. A total of 201 CRC patients were included, whose complete set of clinical information was available, and their good quality tumour blocks were retrieved. Immunohistochemistry was used for tumour analysis, and partitional clustering was performed using R software for cluster analysis.\ud Results\ud The median age was 43 (range 10-85) years; adenocarcinoma was the most commonly seen histological type. The great majority had positive CK20, CEA, E-Cadherin, Ki67, CDX2, and p53 expression. There were four distinct molecular classes found, whereas Ki67, CDX2, and p53 play 3 the main role in partitioning. Younger age negatively impacted survival; overall and diseasespecific survival was 26 months only with 50 months’ longest survival.\ud Conclusion\ud Colorectal cancer is a biologically heterogeneous disease with at least four distinct molecular patterns, where cell proliferation and gene repair mechanisms appear to play the key role.

Classifying subjects into risk categories is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., healthy/at risk). Similar to statistical inference modelling, ML modelling is subject to the problem of class imbalance and is affected by the majority class, increasing the false-negative rate. In this study, we built and evaluated thirty-six ML models to classify approximately 4300 female and 4100 male participants from the UK Biobank into three categorical risk statuses based on discretised visceral adipose tissue (VAT) measurements from magnetic resonance imaging. We also examined the effect of sampling techniques on the models when dealing with class imbalance. The sampling techniques used had a significant impact on the classification and resulted in an improvement in risk status prediction by facilitating an increase in the information contained within each variable. Based on domain expert criteria the best three classification models for the female and male cohort visceral fat prediction were identified. The Area Under Receiver Operator Characteristic curve of the models tested (with external data) was 0.78 to 0.89 for females and 0.75 to 0.86 for males. These encouraging results will be used to guide further development of models to enable prediction of VAT value. This will be useful to identify individuals with excess VAT volume who are at risk of developing metabolic disease ensuring relevant lifestyle interventions can be appropriately targeted.

Background Prostate cancer (CaP) has a rising number of patients requiring routine follow up. In this study, we aimed to test a computer led follow up service for prostate cancer in two UK hospitals. The testing aimed to validate the computer Expert system in making clinical decisions according to the individual patient’s clinical need. The valid model should accurately identify patients with disease recurrence or treatment failure based on their blood test and clinical picture. Methods A clinical decision support system (CDSS) was developed from European (EAU) and national (NICE) guidelines along with knowledge acquired from Urologists. This model was then applied in two UK hospitals to review patients post CaP treatment. These patients’ data (n= 200) were then reviewed by two independent Urology consultants (blinded from the CDSS and other consultant’s rating) and the agreement was calculated by kappa statistics for validation. The second objective aimed to verify the system by estimating the system reliability. Results The two individual urology consultants identified 12 % & 15% of the patients to have potential disease progression and recommended their referral to the Urology care. The kappa coefficient for the agreement between the CDSS and the 2 consultants was 0.81 (p < 0.001) and 0.84 (p < 0.001). The agreement among both specialist was also high with k = 0.83 (p < 0.001). The system reliability was estimated on all cases and this demonstrated 100% repeatability of the decisions. Conclusion The computer led follow up is a valid model for providing safe follow up for prostate cancer.

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as “How can we measure their interpretability?” “How can we make an informed assessment of how HFSs should be designed to enhance interpretability?” The challenges consist of measuring the interpretability of HFSs include issues such as their topological structure, the number of layers, the meaning of intermediate variables, and so on. In this article, an initial framework to measure the interpretability of HFSs is proposed, combined with a participatory user design process to create a specific instance of the framework for an application context. This approach enables the subjective views of a range of practitioners, experts in the design and creation of FLSs, to be taken into account in shaping the design of a generic framework for measuring interpretability in HFSs. This design process and framework are demonstrated through two classification application examples, showing the ability of the resulting index to appropriately capture interpretability as perceived by system design experts.

Although driving behaviour has been largely studied amongst private motor vehicles drivers, the literature addressing heavy goods vehicle (HGV) drivers is scarce. Identifying the existing groups of driving stereotypes and their proportions enables researchers, companies and policy makers to establish group-specific strategies to improve safety and economy. In addition, insights into driving styles can assist predicting drivers' reactions and therefore enable the modelling of interactions between vehicles and the possible obstacles encountered on a journey. Consequently, there are also contributions to the research and development of autonomous vehicles and smart roads. In this study our interest lies in investigating driving behaviour within the HGV community in the United Kingdom (UK). We conduct the analysis of a telematics dataset containing incident information on 21,193 HGV drivers across the UK. We are interested in answering two research questions: (i) What groups of behaviour are we able to uncover? (ii) How do these groups complement current findings in the literature? To answer these questions we apply a two-stage data analysis methodology involving consensus clustering and ensemble classification to the dataset. Through the analysis, eight patterns of behaviour are uncovered. It is also observed that although our findings have similarities to those from previous work on driving behaviour, further knowledge is obtained, such as extra patterns and driving traits arising from vehicle and road characteristics.