Cancer is a serious disease expected to be the world-leading cause of death in the 21st century. The use of harsh chemotherapies is motivated and accepted but, unfortunately, is often accompanied by severe toxicity and adverse drug reactions (ADRs). These occur because the classical chemotherapies’ common modes of action effectively kill and/or reduce the growth rate not only of tumour cells, but also of many other rapidly dividing healthy cells in the body. There are also considerable interindividual differences in ADRs, even between patients with similar cancers and disease stage treated with equal doses; some experience severe to life-threatening ADRs after one dose, leading to treatment delays, adjustments, or even discontinuation resulting in suboptimal treatment, while others remain unaffected through all treatment cycles. Being able to predict which patients are at high or low risk of ADRs, and to adjust doses accordingly before treatment, would probably decrease toxicity and patient suffering while also increasing treatment tolerability and effects. In this thesis, we have used next-generation sequencing (NGS) and bioinformatics for the prediction of myelosuppressive ADRs in lung and ovarian cancer patients treated with gemcitabine/carboplatin and paclitaxel/carboplatin.

Paper I shows that ABCB1 and CYP2C8 genotypes have small effects inadequate for stratification of paclitaxel/carboplatin toxicity. This supports the transition to whole-exome sequencing (WES) and whole-genome sequencing (WGS). Papers II and IV, respectively, use WES and WGS, and demonstrate that genetic variation in or around genes involved in blood cell regulation and proliferation, or genes differentially expressed at chemotherapy exposure, can be used in polygenic prediction models for stratification of gemcitabine/carboplatininduced myelosuppression. Paper III reassuringly shows that WES and WGS are concordant and mostly yield comparable genotypes across the exome. Paper V proves that single-cell RNA sequencing of hematopoietic stem cells is a feasible method for elucidating differential transcriptional effects induced as a response to in vitro chemotherapy treatment.

In conclusion, our results supports the transition to genome-wide approaches using WES, WGS, and RNA sequencing to establish polygenic models that combine effects of multiple pharmacogenetic biomarkers for predicting chemotherapy-induced ADRs. This approach could be applied to improve risk stratification and our understanding of toxicity and ADRs related to other drugs and diseases. We hope that our myelosuppression prediction models can be refined and validated to facilitate personalized treatments, leading to increased patient wellbeing and quality of life.

Biological systems encompass various molecular entities such as genes, proteins, and other biological molecules, including interactions among those components. Understanding a given phenotype, the functioning of a cell or tissue, aetiology of disease, or cellular organization, requires accurate measurements of the abundance profiles of these molecular entities in the form of biomedical data. The analysis of the interplay between these different entities at various levels represented in the form of biological network provides a mechanistic understanding of the observed phenotype. In order to study this interplay, there is a requirement of a conceptual and intuitive framework which can model multiple omics such as genome, transcriptome, or a proteome. This can be addressed by application of network-based strategies.

Translational bioinformatics deals with the development of analytic and interpretive methods to optimize the transformation of different omics and clinical data to understanding of complex diseases and improving human health. Complex diseases such as multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and non-small cell lung cancer (NSCLC) etc., are hypothesized to be a result of a disturbance in the omic networks rendering the healthy cells to be in a state of malfunction. Even though there are numerous methods to layout the relation of the interactions among omics in complex diseases, the output network modules were not clearly interpreted.

In this PhD thesis, we showed how different omic data such as transcriptome and methylome can be mapped to the network of interactions to extract highly interconnected gene sets relevant to the disease, so called disease modules. First, we selected common module identification methods and assembled them into a unified framework of the methods implemented in an Rpackage MODifieR (Paper I). Secondly, we showed that the concept of the network modules can be applied on the whole genome sequencing data for developing a tested model for predicting myelosuppressive toxicity (Paper II).

Furthermore, we demonstrated that network modules extracted using the methylome data helped identifying several genes that were associated with pregnancy-induced pathways and were enriched for disease-associated methylation changes that were also shared by three auto-immune and inflammatory diseases, namely MS, RA, and SLE (Paper III). Remarkably, those methylation changes correlated with the expected outcome from clinical experience in those diseases. Last, we benchmarked the omic network modules on 19 different complex diseases using both transcriptomic and methylomic data. This led to the identification of a multi-omic MS module that was highly enriched disease-associated genes identified by genome-wide association studies, but also genes associated with the most common environmental risk factors of MS (Paper IV).

The application of the network modules concept on different omics is the centrepiece of the research presented in this PhD thesis. The thesis represents the application of omic network modules in complex diseases and how these modules should be integrated and interpreted. In particular, it aimed to show the importance of networks owing to the incomplete knowledge of the genes dysregulated in complex diseases and the contribution of this thesis that provides tools and benchmarks for the methods as well as insights into how a network module can be extracted and interpreted from the omic data in complex diseases.

Biologiska system består av gener, proteiner och andra biologiska molekyler, liksom interaktioner mellan dessa komponenter. Förståelse av en given fenotyp, funktion av en cell eller vävnad, etiologi av sjukdomar eller cellulär organisation kräver exakta mätningar av uttrycksprofilerna för dessa molekyler, vilket ger upphov till enorma mängder av biomedicinska data. Analys av biomedicinska data tillåter oss att förklara viktiga funktioner i interaktionerna som leder till en mekanistisk förståelse av den observerade fenotypen. Samspelet mellan olika komponenter på olika nivåer kan representeras i form av biologiska nätverk, till exempel protein-protein interaktioner (PPI). Nätverk ger en konceptuell och intuitiv ram för att modellera olika komponenter i flera omik-data, såsom transkriptom. De topologiska egenskaperna hos sjukdomsassocierade gener varierar signifikant från sjukdom till sjukdom.

Translationell bioinformatik handlar om utveckling av analytiska och tolkningsmetoder för att omvandla omik-data till förståelsen av komplexa sjukdomar. Komplexa sjukdomar som multipel skleros, reumatoid artrit och lungcancer är några av de sjukdomar som antas vara resultat av underliggande störningar i omik nätverken. Även om det finns många metoder för att modellera interaktioner mellan omik-data vid komplexa sjukdomar saknas det fortfarande tydlighet i hur de resulterande nätverksmodulerna ska tolkas.

I denna doktorsavhandling visade vi hur olika omik-data som transkriptom och metylom kan användas överlagrat på nätverket av proteininteraktioner och att extrahera tätt sammankopplade nätverksstrukturer av relevans för sjukdom, så kallade sjukdomsmoduler. I den första artikeln gjorde vi ett urval av de mest förekommande metoder för identifiering av sjukdomsmoduler och implementerade dessa i ett R-paket MODifieR, som erbjuder en lättanvänd gemensam struktur för olika metoder, samt möjlighet att kombinera moduler från olika metoder. I den andra artikeln visade vi hur nätverksmodulskoncept kan tillämpas på data från helgenomsekvensering för att utveckla en modell för prediktion av myelosuppressiv toxicitet i icke-småcellig lungcancer.

I tredje artikeln demonstrerades ytterligare en framgångsrik tillämning av nätverksmoduler som användes för att identifiera gener som är associerade med biologiska "pathways" samt sjukdomsassocierade metyleringsförändringar relaterade till multipel skleros, reumatoid artrit och systemisk lupus erythematosus, där sjukdomskopplingar till graviditet undersöktes.

Sedan utvärderades de omiska nätverksmodulerna på 19 olika komplexa sjukdomar genom att använda både transkriptom och metylom data. Vidare identifierade vi också en multi-omik modul i multipel skleros, med signifikant koppling till sjukdomsriskfaktorer genom att utnyttja genomisk överensstämmelse, dvs att flera omik ska ge höga genöverlapp.

Tillämpningen av nätverksmodulerna som ett koncept för att koppla omikdata till sjukdomsmekanismer är kärnan i forskningen som presenteras i denna doktorsavhandling. I synnerhet syftade den till att visa betydelse av hur nätverksomik-koncept kan bidra till kunskap om gener som är dysreglerade vid komplexa sjukdomar för att förstå sjukdomsmekanismer. Denna avhandling ger också verktyg och riktmärken för metoder och insikter i hur en nätverksmodul kan extraheras och tolkas från omik-data vid komplexa sjukdomar.