Systems biology of breast cancer

Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach

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

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

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression

Altered chromatin landscape in circulating T follicular helper and regulatory cells following grass pollen subcutaneous and sublingual immunotherapy

BACKGROUND: Allergen-specific immunotherapy (AIT) is a disease-modifying treatment that induces long-term T cell tolerance. OBJECTIVE: To evaluate the role of circulating CXCR5+PD-1+T follicular helper (cTFH) and T follicular regulatory (TFR) cells following grass pollen subcutaneous (SCIT) and sublingual (SLIT) immunotherapy and the accompanying changes in their chromatin landscape. METHODS: Phenotype and function of cTFH cells were initially evaluated in grass pollen-allergics (GPA, n= 28) and non-atopic controls (NAC, n=13) by mathematical algorithms developed to manage high-dimensional data and cell culture, respectively. cTFH and TFR cells were further enumerated in NAC (n=12), GPA (n=14), SCIT (n=10) and SLIT (n=8)-treated groups. Chromatin accessibility in cTFH and TFR cells was assessed by ATAC-seq to investigate epigenetic mechanisms underlying the differences between NAC, GPA, SCIT and SLIT. RESULTS: cTFH cells were shown to be distinct from TH2 and TH2A cell subsets, capable of secreting IL-4 and IL-21. Both cytokines synergistically promoted B cell class switching to IgE and plasma cell differentiation. Grass pollen allergen induced cTFH cell proliferation in GPA but not in NAC (P<.05). cTFH cells were higher in GPA compared to NAC and were lower in SCIT and SLIT (P<.01). Time-dependent induction of IL-4, IL-21 and IL-6 were observed in nasal mucosa following intranasal allergen challenge in GPA but not in SCIT and SLIT groups. TFR and IL-10+ cTFH cells were induced in SCIT and SLIT (all, P<.01). ATAC-seq analyses revealed differentially accessible chromatin regions in all groups. CONCLUSION: For the first time, we showed dysregulation of cTFH cells in GPA compared to NAC, SCIT and SLIT and induction of TFR and IL-10+ cTFH cells following SCIT and SLIT. Changes in the chromatin landscape were observed following AIT in cTFH and TFR cells