Deciphering mechanisms underlying tumor heterogeneity using Multi-Omics approaches

Cancer is a complex disease and presents one of the greatest challenges in modern medicine. Despite remarkable advances in treatment of several cancer types, relapse and resistance to therapy remain recurring outcomes in patients, which underscores a need for personalized treatment approaches. These complications have been related to the high genetic diversity observed within tumors, termed intratumor heterogeneity (ITH). While specific mutational profiles have been associated with the development of heterogeneous tumors, the relationship between ITH and phenotype could unveil features that undergo selection and convey fitness. Features presented in the transcriptome, as markers of heterogeneity, might therefore be valuable biomarkers. In this project, these features are explored by assuming a linear relationship between genetic ITH measures and gene expression data from The Cancer Genome Atlas samples. By first reducing the number of variables among the transcriptome to the differentially expressed genes between low and high ITH samples, the association between specific gene expression profiles and ITH is sought with a linear model. By using two different methods for estimating ITH, called Expands and PhyloWGS, the association was modeled with each method. Interestingly, the model based on Expands captured the elevated expression of a chaperone gene DNAJC18 as being consistently associated with lower ITH in four cancer types. On the other hand, models based on PhyloWGS presented lower predictive power. These results demonstrate that the transcriptome can be used to predict genetic ITH, although this depends on the method used for characterizing ITH.Cancer är en komplex sjukdom och en av de största utmaningarna i dagens medicin. Trots stora framsteg i behandlingen av flera cancerformer är återfall och terapiresistens återkommande problem vilket talar starkt för behov av individualiserad behandling. Dessa komplikationer har relaterats till den höga genetiska variabiliteten som observeras inom tumörer, även kallad intratumoral heterogenitet (ITH). Undersökning av relationen mellan ITH och fenotypisk data kan ta fram markörer som är involverade i cancerutvecklingen som bidragare till heterogenitet. Genom att modellera associationen mellan transcriptomen och ITH kan man även hitta kliniskt relevanta biomarkörer. I detta projekt undersöks relationen mellan genutryck och ITH genom att applicera linjär regression. Genom att först reducera antalet variabler i transkriptomen till de diferentiellt utryckta gener, används linjära modellen för att ta fram specifika gener vars utryck kan relateras till ändringar i ITH uppskattad för The Cancer Genome Atlas prover. ITH uppskattas med två algoritmiska metoder, kallade Expands och PhyloWGS. Resultaten visade att förhöjd uttryck an genen DNAJC18 är associerad med lägre ITH uppskattad med Expands bland fyra cancer typer. Trots detta visade inte genutryck och ITH uppskattat med PhyloWGS lika starkt linjärt samband