mixomics: a software for researchers to analyse large data sets

Transcription

1 mixomics: a software for researchers to analyse large data sets The University of Queensland Diamantina Institute Brisbane, Australia

2 Systems biology Systems biology is the study of complex interactions in biological systems Holism vs. reductionism Systems biology [...] requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different [...].It means changing our philosophy, in the full sense of the term. Denis Noble (2006) an inter-disciplinary field enabling a better understanding of the entirety of processes that happen in a biological system

3 Challenges A close interaction between statisticians, bioinformaticians and molecular biologists is essential Unlimited quantity of data from multiple and heterogeneous sources Computational issues to foresee Biological interpretation for validation Keep pace with new technologies

4 Statistics Statistics play a crucial role to make sense of biological big data = from PMID: What is the key information I can extract from my data?

5 An R software is a software for researchers to analyse large biological data sets Novel tools for biomarker discovery Integration of multiple biological omics datasets Data visualisation to give greater insight into experimental results mixomics is a freely available software implemented in the R statistical programming language

6 Who are we? The mixomics adventure started with two PhD projects at the Université de Toulouse: Ignacio González (2007) and (2008). In 2008 began the mixomics migration to Australia... Postdoc in Brisbane (IMB, UQ) Core team: (FR, AU), Ignacio González (FR), Sébastien Déjean (FR) First public release of mixomics in followed by more French statisticians 2013: postdoc statistician Florian Rohart (AIBN, UQ) 2014: research assistant programmer Benoît Gautier... as well as 5 Master s by coursework French students since 2010.

7 Where are we? today My research program (NHMRC Career Development Fellowship, ) focuses on the development of methodologies for mixomics. 4,000 downloads/year/version Training workshops Website: Two web-interfaces (beta versions) 15 novel statistical methodologies Team: 3 core, 4 key contributors, 5 postgrad students 13 published articles from the team since 2008.

8 Our vision The core idea of mixomics Model a biological system in a holistic fashion, as molecular entities act together in biological pathways. How? Novel multivariate statistical methods Identification of biomarkers, which in combination act as better prognosticators than conventional biomarkers Integration of multiple sources of biological data Reduce the size of the data for a better understanding of big data

9 Diffuse large B cell lymphoma cancer Diffuse large B cell lymphoma cancer study (UQDI) Gene expression assay using Nanostring TM digital platform on 158 de novo DLBCL Australian patients treated with Rituximab-CHOP chemotherapy. Aim: establish a prognosticator that predicts response to R-CHOP. DLBCL is has 30% mortality despite R-CHOP chemotherapy Existing prognosticators not enough to accurately predict response to initial therapy Classical statistical methods fail C Keane, K-A. Lê Cao, et al. and MK Gandhi (2015). Ratios of T-cell immune-effectors with tumour associated macrophages and PD-1/PD-L1 axis immune-checkpoint molecules, add to the predictive power of conventional prognosticators in diffuse large B cell lymphoma. Lancet Haematology D R2.

10 Diffuse large B cell lymphoma cancer Diffuse large B cell lymphoma cancer study: results Use of an innovative tree-based survival statistical model A combined ratio of immune effectors and checkpoints genes able to stratify overall survival Poor survival can now be predicted (40% of our patient cohort) Validation in two independent cohorts Patent submitted. 50 Overall Survival (%)100 immune-score High (n=94) P < Low (n=64) Years Optimal cut-off on immune ratio CD4*CD8/M2*PD-L1 R-IPI - Poor + immune-score to stratify poor and good risk patients A) 50 all Survival (%)100

11 Breast cancer study Breast cancer multi- omics from The Cancer Genome Atlas Multi omics integration study on four subtypes of 377 breast cancer samples with 4 types of omics features ( 2,000 features each). Aim: identify multi- omics biomarkers to predict subtypes of breast cancer mrna% mirna% CpGs% Proteins% P h e n o t y p e

12 Motivation mixomics Case studies Conclusion Breast cancer study Breast cancer multi- omics TCGA study: results Concatenation Method 752 associations Ensemble Method 458 associations mixomics method 1,671 associations! mixomics integrative method identifies a multi omics set of biomarkers with 14% error rate on 573 external samples, A more highly connected signature extracting common information from different types of omics molecules. Acknowledgements: Amrit Singh, UBC

13 In a nutshell Key messages Statistics has an increasing role in the biological big data era and is now integral to the identification of meaningful results. There is a need for novel integrative statistical methodologies for big data, which mixomics fulfills. Our mixomics user base is steadily growing with training workshops fully subscribed. Future of mixomics: collaboration between France and Australia continues to strengthen and interest worldwide, additional functionalities (e.g. interactive plots, methods), currently seeking partners to support advanced development.

14 In a nutshell mixomics Sébastien Déjean Univ. Toulouse Ignacio González Univ. Toulouse Francois Bartolo Univ. Toulouse Xin Yi Chua QFAB Bioinformatics Benoît Gautier UQDI Florian Rohart AIBN, UQ Methods Amrit Singh UBC, Vancouver Olivier Günther UBC, Vancouver Jasmin Straube QFAB Bioinformatics Chao Liu UQ Kathy Ruggiero Univ. Auckland Christèle Robert INRA Toulouse Case studies Colm Keane, Maher Gandhi DLBCL UQDI The Cancer Genome Atlas Breast Cancer NCI Contact: k.lecao@uq.edu.au Website: