Navigating Disease Maps to Understand Disease Mechanisms and Develop Actionable Models
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BioNetVisA 2018 ECCB workshop From biological network reconstruction to data visualization and analysis in molecular biology and medicine. http://eccb18.org/workshop-2/ https://bionetvisa.github.io/
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Navigating Disease Maps to Understand Disease Mechanisms and Develop Actionable Models
- 1. Joaquín Dopazo Clinical Bioinformatics Area, Fundación Progreso y Salud, Functional Genomics Node, (INB-ELIXIR-es), Bioinformatics in Rare Diseases (BiER-CIBERER), Sevilla, Spain. Navigating through disease maps http://www.clinbioinfosspa.es http://www. babelomics.org @xdopazo, @ClinicalBioinfo BioNetVisA, Athens, 9 September, 2018
- 2. Why disease maps? • Conventional single-gene biomarkers have a demonstrated clinical utility. However, their success is purely probabilistic, often modest and frequently lack any mechanistic anchoring to the fundamental cellular processes responsible for the disease or therapeutic response. • Despite most biomarkers used are single gene variants, most human genetic diseases (and almost all traits) have a modular nature: Causative genes for the same or phenotypically similar diseases may generally reside in the same biological module, either a protein complex (Lage et al, 2007), a sub- network of protein interactions (Lim et al, 2006) , or a pathway (Wood et al, 2007) Goh 2007 PNAS Same disease in different populations is caused by different genes affecting the same functionsFernandez, 2013, Orphanet J Rare Dis. Disease genes are close in the interactome
- 3. Pathways: maps of cell activity (in sickness and in health) disease-maps.org/ www.genome.jp/kegg reactome.org www.wikipathways.org/ Oncogénesis Alzheimer More disease maps… Parkinson https://www.pathwaycommons.org/
- 4. Navigating maps A map describes connections between elements An influence map describes origin and destinations that define processes
- 5. Defining pathway activity We first need a map: pathways are defined in different repositories (KEGG, Reactome, Biocarta, disease maps, etc.) What pathway level makes a real biological meaning? Gene sub-pathway pathway Enrichment methods (pathway-level): Different and often opposite cell behaviors are triggered by the same pathway. E.g.: death and survival Gene level: The same gene can trigger different (and often opposite) responses, depending on the stimulus Survival Death Sub-pathway (elementary circuit) connects stimulus to response
- 6. Decomposition of a pathway into their elementary circuits
- 7. How realistic are models of pathway activity? Beyond static biomarkers—The activity of signalling networks as an alternate biomarker? Fey et al., Sci. Signal. 8, ra130 (2015). Inability of JNK activation (that mediates apoptosis) is associated to bad prognostic, irrespective of MYCN amplification status Problem: ODE can efficiently solve only small systems Construct, activity inferred The system is more than the sum of its parts!
- 8. 𝑆 𝑛 = 𝜐 𝑛 ∙ 1 − 1 − 𝑠 𝑎 𝑠 𝑎 ∈𝐴 ⋅ 1 − 𝑠𝑖 𝑠𝑖∈𝐼 From individual gene expression profiles… …to profiles of circuit activity (and functional activity) Two types of activities Signal propagation models of signaling pathways Are scalable
- 9. Gene expression data are transformed into signal activity intensities / cell functions Cases / controls Normalizedgenes Ps1 344 344 4556 667 88 Ps2 543 67 88 90 12 36 Ps3 36 833 78 38 99 00 Ps4 59 73 336 677 00 31 Ps1 344 344 4556 667 88 Ps2 543 67 88 90 12 36 Ps3 36 833 78 38 99 00 Ps4 59 73 336 677 00 31 ……. ……. ……. Cases / controls Circuits Ps1 344 344 4556 667 88 Ps2 543 67 88 90 12 36 Ps3 36 833 78 38 99 00 Ps4 59 73 336 677 00 31 Ps1 344 344 4556 667 88 Ps2 543 67 88 90 12 36 ……. ……. ……. • Gene expression data are cheap and easy to obtain. • Gene expression intensities are often difficult to interpret • Gene expression can be transformed in accurate estimations of circuit activities. • Circuits trigger cell functions with a clear biological meaning Circuits within pathwaysCases / controls Rawdata Ps1 344 344 4556 667 88 Ps2 543 67 88 90 12 36 Ps3 36 833 78 38 99 00 Ps4 59 73 336 677 00 31 Ps1 344 344 4556 667 88 Ps2 543 67 88 90 12 36 Ps3 36 833 78 38 99 00 Ps4 59 73 336 677 00 31 ……. ……. …….
- 10. Signaling activity trigger cell functions directly related to cancer progression DNA replication function is a construct: the activity is inferred not measured DNA replication= f(gene1, gene2, … genen) Hidalgo et al., 2017 Oncotarget
- 11. Actually, signal activity triggers cancer hallmarks Hanahan, Weinberg, 2011 Hallmarks of cancer: the next generation. Cell 144, 646 Negative regulation of release of cytochrome c from mitochondria (inhibition of apoptosis)
- 12. The inferred function activity (mechanistic biomarker) is more correlated to survival than the activity of any gene (conventional biomarker) in the circuit p-val=5.9x10-8 The system is more than the sum of its parts!
- 13. Actionable models The real advantage of models is that, the same way they can be used to transforming omics data into measurements of cell functionalities that help in the interpretation of disease mechanisms and drug MoA, they can be used to test mechanistic hypothesis such as “what if I suppress (or over-express) this (these) gen(es)?” This lead to the concept of actionable models. By simulating changes of gene expression/activity in a given system it is easy to: • Directly study of the consequences of induced gene over-expressions or KOs, inhibitions, drugs, etc., alone or in combinations. • Carry out reverse engineering studies about what genes need to be perturbed to change specific cell functionalities, such as: • Reverting the “normal” functional status of a cell • Selectively kill diseased cells without affecting normal cells • Enhancing or reducing cell functionalities (e.g., apoptosis or proliferation, respectively, to fight cancer) • Etc.
- 14. Model validation (1) The activity of some signaling circuits is correlated with cell survival Increasedsurvival Increased circuit activity Repression of apoptosis Activation of proliferation Functions: Activation of apoptosis Repression of proliferation Survival data from Achilles cell line KOs (Broad Institute) can be compared to the change in circuit activities predicted by the model Essential circuits: once found, other ways of deactivating these circuits can be find, opening the door to knowledge-based target discovery Onco-circuit Tumor suppressor circuit
- 15. Model validation (2) Prediction of other gene targets, whose inhibition (modeled KO) deactivate these circuits and consequently decrease cell viability 1 04 9 8 7 5 2 6 3 1 2 3Achilles I Circuit activity with gene KO Circuit activity Cellsurvival 4 5 9 0 6 7 8 Potential target (inhibition of circuit activity)
- 16. Model validation (3) Deactivation of tumor suppressor modules Deactivation in onco-modules Prostate cell line Skin cell line Validation of the real KO effects with Achilles II (Tsherniak A, et al. 2017, Cell) We obtained 65% correct predictions (Çubuk et al., 2018, Cancer Res)
- 17. Interventions on pathways made easy Freely available software PathAct http://pathact.babelomics.org/
- 18. Actionable pathway models Transcription We can inhibit EGFR (target of Afanatib) by reducing its activity value (0.56 in cancer). Absolute KO value = 0 Estrogen signaling pathway http://pathact.babelomics.org/
- 19. Actionable pathway models The inhibition of the transcription sought has been attained, but six more pathways have been affected in different ways http://pathact.babelomics.org/
- 20. Actionable pathway models Cell cycle inhibited in Proteoglycans in cancer pathway Transcription, angiogenesis and other are inhibited in ErbB signaling pathway Cell cycle is inhibited in Oxytocin signaling pathway http://pathact.babelomics.org/
- 21. Simulating drug inhibition “Ideal” KO of EGFR affects 7 pathways Real inhibition with Afanatib affects 11 pathways Inhibition with broader spectrum Trastuzumab affects 13 pathways
- 22. There are 94 modules that recapitulate the main aspects of metabolism of lipids, carbohydrates, amino acids and nucleotides Metabolic activity: Differential metabolic activity: two conditions are compared by means of a Wilcoxon test (FDR adjusted across modules) Simple propagation models can also be extended to model metabolic pathways
- 23. Metabolic modules capture differential metabolic activity
- 24. Metabolic modules also capture cell functionality associated to cancer prognostic High activity of Guanine ribonucleotide biosynthesis and Pyrimidine ribonucleotide biosynthesis modules is associated to low survival. These modules are target of Mercaptopurine and Gemcitabine. The mechanism of action of these drugs involves inhibition of DNA synthesis and that leads to cell death
- 25. Correspondence between module activity and change in metabolite abundance Module Cancer type First metabolite Intermediate metabolite End metabolite Prediction M00035_1 C00073 C00019 / C00021 / C00155 C02291 BRCA 1,9 NA / 2.7 / NA 7,8 Up Correct KIRC 0,5 0.9 / 1.7 / 0.6 NA Down NA M00100 C00319 C06124 C00346 BRCA 11,1 NA 17,3 Up Correct KIRC 2,2 NA 0,8 Down Correct M00135 C00134 C02714 / C05936 / C02946 C00334 BRCA 12,9 NA / NA / NA 1,2 Down Correct KIRC 3,6 NA / NA / NA 0,3 Down Correct
- 26. The metabolizer web server http://metabolizer.babelomics.org/ Input: gene expression matrix Analyses: • Differential metabolic activity • Case control • Correlation • KnockOut. • Tests the effect of a KO in the metabolic profile • Auto KO: in a case/control search for the KO that makes the metabolic activity profile of cases as close as possible to thoso of controls (phenotype reversion) • Prediction: Builds a class predictor (RF or SVM) based on metabolic profiles
- 27. An example of KO prediction Pyrimidine degradation pathway was predicted to be an onco-module in gastric cancer cell lines. Predicted genes that switch the pathway off are DPYD, DPYS (confirmed in Achilles) and UPB1 (not present in Achilles)
- 28. Prediction of gene essentiality from metabolic pathway essentiality UPB1 encodes an enzyme (β-ureidopropionase) that catalyzes the last step in the pyrimidine degradation pathway, required for epithelial-mesenchymal transition Pyrimidine degradation pathway was predicted to be an onco-module in gastric cancer cell lines. Predicted genes that switch the pathway off are DPYD, DPYS (confirmed in Achilles) and UPB1
- 29. Models of cell functional activity bring the dream of precision personalized (even individualized) treatments closer From: Dopazo, 2014, Genomics and transcriptomics in drug discovery. Drug Discovery Today
- 30. Clinical Bioinformatics Area Fundación Progreso y Salud, Sevilla, Spain, and… ...the INB-ELIXIR-ES, National Institute of Bioinformatics and the BiER (CIBERER Network of Centers for Research in Rare Diseases) @xdopazo @ClinicalBioinfo Follow us on twitter https://www.slideshare.net/xdopazo/