Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to cluster this dataset and important subsets of it in an effort to characterize the data and validate existing standards for tumour classification. It is apparent from optimal clustering that existing tumour classification is largely unrelated to immunological factors within a patient and that there may be scope for re-evaluating treatment options and survival estimates based on a combination of tumour physiology and patient histochemistry.Comment: Federated Conference on Computer Science and Information Systems (FedCSIS), pp 187-191, 201

Cancer subtype identification pipeline: a classifusion approach

Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes - leading to improved treatment options. However, the current literature on the use of CI to achieve this is fragmented, making further advances challenging. This paper captures developments in this area so far, with the goal to establish a clear, step-by-step pipeline for cancer subtype identification. Based on establishing the pipeline, the paper identifies key potential advances in CI at the individual steps, thus establishing a roadmap for future research. As such, it is the aim of the paper to engage the CI community to address the research challenges and leverage the strong potential of CI in this important area. Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast cancer groups, and introduce Classifusion: a combination of results of multiple classifiers

Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP

Our cluster analysis of the Cancer Genome Atlas for co-expression of HSP27 and CRYAB in breast cancer patients identified three patient groups based on their expression level combination (high HSP27 + low CRYAB; low HSP27 + high CRYAB; similar HSP27 + CRYAB). Our analyses also suggest that there is a statistically significant inverse relationship between HSP27 and CRYAB and known clinicopathological markers in breast cancer. Screening an unbiased 248 breast cancer patient tissue microarray (TMA) for the protein expression of HSP27 and phosphorylated HSP27 (HSP27-82pS) with CRYAB also identified three patient groups based on HSP27 and CRYAB expression levels. TMA24 also had recorded clinical-pathological parameters, such as ER and PR receptor status, patient survival, and TP53 mutation status. High HSP27 protein levels were significant with ER and PR expression. HSP27-82pS associated with the best patient survival (Log Rank test). High CRYAB expression in combination with wild-type TP53 was significant for patient survival, but a different patient outcome was observed when mutant TP53 was combined with high CRYAB expression. Our data suggest that HSP27 and CRYAB have different epichaperome influences in breast cancer, but more importantly evidence the value of a cluster analysis that considers their coexpression. Our approach can deliver convergence for archival datasets as well as those from recent treatment and patient cohorts and can align HSP27 and CRYAB expression to important clinical-pathological features of breast cancer

Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer

Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesized that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables, can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). NPI+ was then used to predict outcome in the different molecular classes with.Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological breast cancer class provides improved patient outcome stratification superior to the traditional NPI. This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making

MYC regulation of glutamine--proline regulatory axis is key in luminal B breast cancer

Background: Altered cellular metabolism is a hallmark of cancer and some are reliant on glutamine for sustained proliferation and survival. We hypothesise that the glutamine–proline regulatory axis has a key role in breast cancer (BC) in the highly proliferative classes. Methods: Glutaminase (GLS), pyrroline-5-carboxylate synthetase (ALDH18A1), and pyrroline-5-carboxylate reductase 1 (PYCR1) were assessed at DNA/mRNA/protein levels in large, well-characterised cohorts. Results: Gain of PYCR1 copy number and high PYCR1 mRNA was associated with Luminal B tumours. High ALDH18A1 and high GLS protein expression was observed in the oestrogen receptor (ER)+/human epidermal growth factor receptor (HER2)– high proliferation class (Luminal B) compared with ER+/HER2– low proliferation class (Luminal A) (P=0.030 and P=0.022 respectively), however this was not observed with mRNA. Cluster analysis of the glutamine–proline regulatory axis genes revealed significant associations with molecular subtypes of BC and patient outcome independent of standard clinicopathological parameters (P=0.012). High protein expression of the glutamine–proline enzymes were all associated with high MYC protein in Luminal B tumours only (P<0.001). Conclusions: We provide comprehensive clinical data indicating that the glutamine–proline regulatory axis plays an important role in the aggressive subclass of luminal BC and is therefore a potential therapeutic target

The combined expression of solute carriers is associated with a poor prognosis in highly proliferative ER+ breast cancer

Purpose: Breast cancer (BC) is a heterogeneous disease characterised by variant biology, metabolic activity, and patient outcome. Glutamine availability for growth and progression of BC is important in several BC subtypes. This study aimed to evaluate the biological and prognostic role of the combined expression of key glutamine transporters, SLC1A5, SLC7A5 and SLC3A2 in BC with emphasis on the intrinsic molecular subtypes. Methods: SLC1A5, SLC7A5 and SLC3A2 were assessed at the protein level, using immunohistochemistry on tissue microarrays constructed from a large well characterised BC cohort (n=2,248). Patients were stratified into accredited clusters based on protein expression and correlated with clinicopathological parameters, molecular subtypes, and patient outcome. Results: Clustering analysis of SLC1A5, SLC7A5 and SLC3A2 identified three clusters Low SLCs (SLC1A5-/SLC7A5-/SLC3A2-), High SLC1A5 (SLC1A5+/SLC7A5-/SLC3A2-) and High SLCs (SLC1A5+/SLC7A5+/SLC3A2+) which had distinct correlations to known prognostic factors and patient outcome (p<0.001). The key regulator of tumour cell metabolism, c-MYC, was significantly expressed in tumours in the High SLCs cluster (p<0.001). When different BC subtypes were considered, the association with the poor outcome was observed in the ER+ high proliferation/luminal B class only (p= 0.003). In multivariate analysis, SLC clusters were independent risk factor for shorter breast cancer specific survival (p= 0.001). Conclusion: The co-operative expression of SLC1A5, SLC7A5 and SLC3A2 appears to play a role in the aggressive subclass of ER+ high proliferation/ luminal BC, driven by c-MYC, and therefore have the potential to act as therapeutic targets, particularly in synergism

Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data

<p>Abstract</p> <p>Background</p> <p>Molecular studies of breast cancer revealed biological heterogeneity of the disease and opened new perspectives for personalized therapy. While multiple gene expression-based systems have been developed, current clinical practice is largely based upon conventional clinical and pathologic criteria. This gap may be filled by development of combined multi-IHC indices to characterize biological and clinical behaviour of the tumours. Digital image analysis (DA) with multivariate statistics of the data opens new opportunities in this field.</p> <p>Methods</p> <p>Tissue microarrays of 109 patients with breast ductal carcinoma were stained for a set of 10 IHC markers (ER, PR, HER2, Ki67, AR, BCL2, HIF-1α, SATB1, p53, and p16). Aperio imaging platform with the Genie, Nuclear and Membrane algorithms were used for the DA. Factor analysis of the DA data was performed in the whole group and hormone receptor (HR) positive subgroup of the patients (n = 85).</p> <p>Results</p> <p>Major factor potentially reflecting aggressive disease behaviour (i-Grade) was extracted, characterized by opposite loadings of ER/PR/AR/BCL2 and Ki67/HIF-1α. The i-Grade factor scores revealed bimodal distribution and were strongly associated with higher Nottingham histological grade (G) and more aggressive intrinsic subtypes. In HR-positive tumours, the aggressiveness of the tumour was best defined by positive Ki67 and negative ER loadings. High Ki67/ER factor scores were strongly associated with the higher G and Luminal B types, but also were detected in a set of G1 and Luminal A cases, potentially indicating high risk patients in these categories. Inverse relation between HER2 and PR expression was found in the HR-positive tumours pointing at differential information conveyed by the ER and PR expression. SATB1 along with HIF-1α reflected the second major factor of variation in our patients; in the HR-positive group they were inversely associated with the HR and BCL2 expression and represented the major factor of variation. Finally, we confirmed high expression levels of p16 in Triple-negative tumours.</p> <p>Conclusion</p> <p>Factor analysis of multiple IHC biomarkers measured by automated DA is an efficient exploratory tool clarifying complex interdependencies in the breast ductal carcinoma IHC profiles and informative value of single IHC markers. Integrated IHC indices may provide additional risk stratifications for the currently used grading systems and prove to be useful in clinical outcome studies.</p> <p>Virtual Slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/1512077125668949</url></p

A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’ by using a range of techniques to reach consensus across several different clustering algorithms, and to ascertain the key characteristics of these classes. We apply the methodology to immunohistochemical data from breast cancer patients. In doing so, we identify six core classes, of which several may be novel sub-groups not previously emphasised in literature

An exploration of methodologies to improve semi-supervised hierarchical clustering with knowledge-based constraints

Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have been introduced to the field of machine learning as a significant variant to the conventional unsupervised clustering learning algorithms. They have been demonstrated to achieve better performance due to integrating prior knowledge during the clustering process, that enables uncovering relevant useful information from the data being clustered. However, the research conducted within the context of developing semi-supervised hierarchical clustering techniques are still an open and active investigation area. Majority of current semi-supervised clustering algorithms are developed as partitional clustering (PC) methods and only few research efforts have been made on developing semi-supervised hierarchical clustering methods. The aim of this research is to enhance hierarchical clustering (HC) algorithms based on prior knowledge, by adopting novel methodologies. [Continues.

The biological heterogeneity of oestrogen receptor positive breast cancer and its phenotypic characterisation

Although global gene microarray studies have demonstrated the molecular heterogeneity of breast cancer (BC) and provided potential for clinical applications, the molecular subclassification of luminal/ER-positive tumours, which is the largest class of BC, remains unclear. Characterisation of luminal/ER-positive subtypes could have important implications in clinical decision-making and patient management. The patient study cohort is derived from a consecutive series of approximately 1902 cases of primary operable invasive breast carcinoma obtained from the Nottingham Tenovus Primary Breast Carcinoma Series, with patients presenting between 1986 and 1998. This is a well-characterized series of primary breast carcinoma that has been treated in a uniform way and previously used to study a wide range of proteins. Using gene microarray experiments in 128 frozen invasive BC derived from this series , 47,2 93 gene transcripts were analysed using a number of different bio-statistical models to identify a transcript signature for luminal/ER-positive BC, from which candidate genes were selected and that can be used to characterise ER-positive breast cancer. In addition, other biomarkers with strong relevance in ER-positive breast cancer were studied because the evidence strongly suggests an important role in the biology and molecular classification of ER-positive breast cancer. The selection criteria was based on published literature concentrating mainly on ER related pathways including ER coregulators (CARMI, PELPI), cellular proliferation (p27. TK1, cyclin B1), apoptosis (Bc12), Akt/PIK3 pathway (FOX03a), gene expression profiling (FOXA1, XBP1, TFF1) and endocrine resistance (CD71). Immunohistochemistry and high throughput tissue micro array technology were used to study the protein expression of 16 biomarkers with strong relevance to ER pathways in a well characterised consecutive series of invasive BC (n=1902) in addition to anther 9 markers that were available from the database of the breast cancer research group, University of Nottingham. The data were analysed using different clustering methods including K-means and Partitioning around Medoids. Kaplan Meier plots with Log-rank test (LR) were used to model clinical outcome. A transcript signature for ER positive BC was identified including RERG, GATA3 and other genes by a supervised classification analysis using 10-fold external cross-validation of the gene microarray data. Immunohistochemical validation studies confirmed their association with ER positive BC. Through a consensus approach using different clustering techniques applied to protein expression data 25 markers, three biological clusters (patient subclasses) in ER positive breast cancer showing significant difference in clinical outcome (LR= 28.185 & p<0.001) have been identified. Importantly, the poor prognosis cluster was significantly characterised by high tumour grade and frequent development of distant metastasis. In conclusion, our results emphasised the heterogeneity of luminal/ER-positive BC. Molecular profiling of breast cancer using protein biomarkers on TMAs can sub-classify ER-positive tumours into clinically and biologically relevant subgroups