Stephen F. Madden

Bio: Stephen F. Madden is an academic researcher from Royal College of Surgeons in Ireland. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 26, co-authored 85 publications receiving 3447 citations. Previous affiliations of Stephen F. Madden include Ludwig Maximilian University of Munich & German Cancer Research Center.

Leukocyte Complexity Predicts Breast Cancer Survival and Functionally Regulates Response to Chemotherapy

Abstract: Immune-regulated pathways influence multiple aspects of cancer development. In this article we demonstrate that both macrophage abundance and T-cell abundance in breast cancer represent prognostic indicators for recurrence-free and overall survival. We provide evidence that response to chemotherapy is in part regulated by these leukocytes; cytotoxic therapies induce mammary epithelial cells to produce monocyte/macrophage recruitment factors, including colony stimulating factor 1 (CSF1) and interleukin-34, which together enhance CSF1 receptor (CSF1R)–dependent macrophage infiltration. Blockade of macrophage recruitment with CSF1R-signaling antagonists, in combination with paclitaxel, improved survival of mammary tumor–bearing mice by slowing primary tumor development and reducing pulmonary metastasis. These improved aspects of mammary carcinogenesis were accompanied by decreased vessel density and appearance of antitumor immune programs fostering tumor suppression in a CD8 + T-cell–dependent manner. These data provide a rationale for targeting macrophage recruitment/response pathways, notably CSF1R, in combination with cytotoxic therapy, and identification of a breast cancer population likely to benefit from this novel therapeutic approach. Significance: These findings reveal that response to chemotherapy is in part regulated by the tumor immune microenvironment and that common cytotoxic drugs induce neoplastic cells to produce monocyte/macrophage recruitment factors, which in turn enhance macrophage infiltration into mammary adenocarcinomas. Blockade of pathways mediating macrophage recruitment, in combination with chemotherapy, significantly decreases primary tumor progression, reduces metastasis, and improves survival by CD8 + T-cell–dependent mechanisms, thus indicating that the immune microenvironment of tumors can be reprogrammed to instead foster antitumor immunity and improve response to cytotoxic therapy. Cancer Discovery; 1(1); 54–67. ©2011 AACR . This article is highlighted in the In This Issue feature, p. 4

Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis

Abstract: Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

Proteomic portrait of human breast cancer progression identifies novel prognostic markers.

Abstract: Breast cancer is the second leading cause of cancer death for women in the United States. Of the different subtypes, estrogen receptor–negative (ER−) tumors, which are ErbB2+ or triple-negative, carry a relatively poor prognosis. In this study, we used system-wide analysis of breast cancer proteomes to identify proteins that are associated with the progression of ER− tumors. Our two-step approach included an initial deep analysis of cultured cells that were obtained from tumors of defined breast cancer stages, followed by a validation set using human breast tumors. Using high-resolution mass spectrometry and quantification by Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC), we identified 8,750 proteins and quantified 7,800 of them. A stage-specific signature was extracted and validated by mass spectrometry and immunohistochemistry on tissue microarrays. Overall, the proteomics signature reflected both a global loss of tissue architecture and a number of metabolic changes in the transformed cells. Proteomic analysis also identified high levels of IDH2 and CRABP2 and low levels of SEC14L2 to be prognostic markers for overall breast cancer survival. Together, our findings suggest that global proteomic analysis provides information about the protein changes specific to ER− breast tumor progression as well as important prognostic information. Cancer Res; 72(9); 2428–39. ©2012 AACR.

BreastMark : An Integrated Approach to Mining Publicly Available Transcriptomic Datasets Relating to Breast Cancer Outcome

Abstract: Breast cancer is a complex heterogeneous disease for which a substantial resource of transcriptomic data is available. Gene expression data have facilitated the division of breast cancer into, at least, five molecular subtypes, namely luminal A, luminal B, HER2, normal-like and basal. Once identified, breast cancer subtypes can inform clinical decisions surrounding patient treatment and prognosis. Indeed, it is important to identify patients at risk of developing aggressive disease so as to tailor the level of clinical intervention. We have developed a user-friendly, web-based system to allow the evaluation of genes/microRNAs (miRNAs) that are significantly associated with survival in breast cancer and its molecular subtypes. The algorithm combines gene expression data from multiple microarray experiments which frequently also contain miRNA expression information, and detailed clinical data to correlate outcome with gene/miRNA expression levels. This algorithm integrates gene expression and survival data from 26 datasets on 12 different microarray platforms corresponding to approximately 17,000 genes in up to 4,738 samples. In addition, the prognostic potential of 341 miRNAs can be analysed. We demonstrated the robustness of our approach in comparison to two commercially available prognostic tests, oncotype DX and MammaPrint. Our algorithm complements these prognostic tests and is consistent with their findings. In addition, BreastMark can act as a powerful reductionist approach to these more complex gene signatures, eliminating superfluous genes, potentially reducing the cost and complexity of these multi-index assays. Known miRNA prognostic markers, mir-205 and mir-93, were used to confirm the prognostic value of this tool in a miRNA setting. We also applied the algorithm to examine expression of 58 receptor tyrosine kinases in the basal-like subtype, identifying six receptor tyrosine kinases associated with poor disease-free survival and/or overall survival (EPHA5, FGFR1, FGFR3, VEGFR1, PDGFRβ, and TIE1). A web application for using this algorithm is currently available. BreastMark is a powerful tool for examining putative gene/miRNA prognostic markers in breast cancer. The value of this tool will be in the preliminary assessment of putative biomarkers in breast cancer. It will be of particular use to research groups with limited bioinformatics facilities.

Overexpression of the microRNA miR-433 promotes resistance to paclitaxel through the induction of cellular senescence in ovarian cancer cells.

Abstract: Annually, ovarian cancer (OC) affects 240,000 women worldwide and is the most lethal gynecological malignancy. High-grade serous OC (HGSOC) is the most common and aggressive OC subtype, characterized by widespread genome changes and chromosomal instability and is consequently poorly responsive to chemotherapy treatment. The objective of this study was to investigate the role of the microRNA miR-433 in the cellular response of OC cells to paclitaxel treatment. We show that stable miR-433 expression in A2780 OC cells results in the induction of cellular senescence demonstrated by morphological changes, downregulation of phosphorylated retinoblastoma (p-Rb), and an increase in β-galactosidase activity. Furthermore, in silico analysis identified four possible miR-433 target genes associated with cellular senescence: cyclin-dependent kinase 6 (CDK6), MAPK14, E2F3, and CDKN2A. Mechanistically, we demonstrate that downregulation of p-Rb is attributable to a miR-433-dependent downregulation of CDK6, establishing it as a novel miR-433 associated gene. Interestingly, we show that high miR-433 expressing cells release miR-433 into the growth media via exosomes which in turn can induce a senescence bystander effect. Furthermore, in relation to a chemotherapeutic response, quantitative real-time polymerase chain reaction (qRT-PCR) analysis revealed that only PEO1 and PEO4 OC cells with the highest miR-433 expression survive paclitaxel treatment. Our data highlight how the aberrant expression of miR-433 can adversely affect intracellular signaling to mediate chemoresistance in OC cells by driving cellular senescence.

Microenvironmental regulation of tumor progression and metastasis.

Abstract: Cancers develop in complex tissue environments, which they depend on for sustained growth, invasion and metastasis. Unlike tumor cells, stromal cell types within the tumor microenvironment (TME) are genetically stable and thus represent an attractive therapeutic target with reduced risk of resistance and tumor recurrence. However, specifically disrupting the pro-tumorigenic TME is a challenging undertaking, as the TME has diverse capacities to induce both beneficial and adverse consequences for tumorigenesis. Furthermore, many studies have shown that the microenvironment is capable of normalizing tumor cells, suggesting that re-education of stromal cells, rather than targeted ablation per se, may be an effective strategy for treating cancer. Here we discuss the paradoxical roles of the TME during specific stages of cancer progression and metastasis, as well as recent therapeutic attempts to re-educate stromal cells within the TME to have anti-tumorigenic effects.

Coordinated regulation of myeloid cells by tumours

Abstract: Here, the authors discuss how the immune activities of myeloid cells, such as macrophages and dendritic cells, are affected by the immunosuppressive tumour environment. They propose that tumours can evade the immune system by promoting aberrant differentiation and function of the entire myeloid system.

Patterns of Somatic Mutation in Human Cancer Genomes

Abstract: AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore PL02-05 All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.