Complex-based analysis of dysregulated cellular processes in cancer

Background: Differential expression analysis of (individual) genes is often used to study their roles in diseases. However, diseases such as cancer are a result of the combined effect of multiple genes. Gene products such as proteins seldom act in isolation, but instead constitute stable multi-protein complexes performing dedicated functions. Therefore, complexes aggregate the effect of individual genes (proteins) and can be used to gain a better understanding of cancer mechanisms. Here, we observe that complexes show considerable changes in their expression, in turn directed by the concerted action of transcription factors (TFs), across cancer conditions. We seek to gain novel insights into cancer mechanisms through a systematic analysis of complexes and their transcriptional regulation. Results: We integrated large-scale protein-interaction (PPI) and gene-expression datasets to identify complexes that exhibit significant changes in their expression across different conditions in cancer. We devised a log-linear model to relate these changes to the differential regulation of complexes by TFs. The application of our model on two case studies involving pancreatic and familial breast tumour conditions revealed: (i) complexes in core cellular processes, especially those responsible for maintaining genome stability and cell proliferation (e.g. DNA damage repair and cell cycle) show considerable changes in expression; (ii) these changes include decrease and countering increase for different sets of complexes indicative of compensatory mechanisms coming into play in tumours; and (iii) TFs work in cooperative and counteractive ways to regulate these mechanisms. Such aberrant complexes and their regulating TFs play vital roles in the initiation and progression of cancer.Comment: 22 pages, BMC Systems Biolog

The RNA polymerase I transcription inhibitor CX-5461 cooperates with topoisomerase 1 inhibition by enhancing the DNA damage response in homologous recombination-proficient high-grade serious ovarian cancer

Background: Intrinsic and acquired drug resistance represent fundamental barriers to the cure of high-grade serous ovarian carcinoma (HGSC), the most common histological subtype accounting for the majority of ovarian cancer deaths. Defects in homologous recombination (HR) DNA repair are key determinants of sensitivity to chemotherapy and poly-ADP ribose polymerase inhibitors. Restoration of HR is a common mechanism of acquired resistance that results in patient mortality, highlighting the need to identify new therapies targeting HR-proficient disease. We have shown promise for CX-5461, a cancer therapeutic in early phase clinical trials, in treating HR-deficient HGSC. Methods: Herein, we screen the whole protein-coding genome to identify potential targets whose depletion cooperates with CX-5461 in HR-proficient HGSC. Results: We demonstrate robust proliferation inhibition in cells depleted of DNA topoisomerase 1 (TOP1). Combining the clinically used TOP1 inhibitor topotecan with CX-5461 potentiates a G2/M cell cycle checkpoint arrest in multiple HR-proficient HGSC cell lines. The combination enhances a nucleolar DNA damage response and global replication stress without increasing DNA strand breakage, significantly reducing clonogenic survival and tumour growth in vivo. Conclusions: Our findings highlight the possibility of exploiting TOP1 inhibition to be combined with CX-5461 as a non-genotoxic approach in targeting HR-proficient HGSC.The China Scholarship Council University of Melbourne Ph.D. Scholarship supported S.Y. A National Health and Medical Research Council (NHMRC) Grant and NHMRC Senior Research Fellowship to R.B.P. supported this work. The Victorian Centre for Functional Genomics (K.J.S.) is funded by the Australian Cancer Research Foundation (ACRF), the Australian Phenomics Network (APN) through funding from the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS) programme, the Peter MacCallum Cancer Centre Foundation and the University of Melbourne Research Collaborative Infrastructure Programm

RMaNI: Regulatory module network inference framework

Background: Cell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.Results: We present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmani. Conclusion: RMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions

Voci e suoni dall'aldilà. L'utopia musicale dell'Elisio in Luciano di Samosata (VH II 5-16)

The aim of this essay is to identify the distinctive elements and motives in the Elysium described by Lucian of Samosata in his True Histories, an extraordinary and paradoxical tale of 'Lucian's Travels'. The music, produced by the Nature and by the voices and the instruments of the Blessed in their hereafter symposium, becomes the main sign of Blissfulness. Lucian builds his Elysium through a literary technique that he calls mixis, consisting in a programmatic contamination of different sources in the poetic tradition. In this research the origins of Lucian's brilliant and complex image of musical Bliss are traced back in earlier Greek literature, passing by ancient Paradises, the Golden Age, Utopia and other fanciful worlds of Happiness

Gene regulatory network inference: Evaluation and application to ovarian cancer allows the prioritization of drug targets

Background: Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality

Towards intellectual freedom in an AI Ethics Global Community

The recent incidents involving Dr. Timnit Gebru, Dr. Margaret Mitchell, and Google have triggered an important discussion emblematic of issues arising from the practice of AI Ethics research. We ofer this paper and its bibliography as a resource to the global community of AI Ethics Researchers who argue for the protection and freedom of this research community. Corpo�rate, as well as academic research settings, involve responsibility, duties, dissent, and conficts of interest. This article is meant to provide a reference point at the beginning of this decade regarding matters of consensus and disagreement on how to enact AI Ethics for the good of our institutions, society, and individuals. We have herein identifed issues that arise at the intersection of information technology, socially encoded behaviors, and biases, and individual researchers’ work and responsibilities. We revisit some of the most pressing problems with AI decision-making and examine the difcult relationships between corporate interests and the early years of AI Ethics research. We propose several possible actions we can take collectively to support researchers throughout the feld of AI Ethics, especially those from marginalized groups who may experience even more bar�riers in speaking out and having their research amplifed. We promote the global community of AI Ethics researchers and the evolution of standards accepted in our profession guiding a technological future that makes life better for all