Deciphering mechanisms underlying tumor heterogeneity using Multi-Omics approaches

July 8, 2020

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.

Authors in the community:

• 

  J

  Joonas Avik

  ist195098

Supervisors of this institution:

• 

  A

  Arsénio do Carmo Sales Mendes Fialho

  ist12833

industrial-biotechnology - Industrial Biotechnology

eng - English

Instituto Superior Técnico