Elucidating transcriptional rewiring on hematological malignancies via computational methods

Descrizione del progetto

Metodi di calcolo per rilevare la ridefinizione della regolazione genica nel cancro

Così come molte malattie sono interconnesse, molti geni o parti di geni possono interagire per controllare specifiche funzioni cellulari. Essi svolgono un ruolo importante nello sviluppo, nella differenziazione e nella risposta ai segnali ambientali e sono inoltre determinanti nello sviluppo del tumore. Queste reti di regolazione genica (GRN, Gene Regulatory Networks) controllano l’espressione genica spaziale e temporale, controllando la trascrizione genica nell’mRNA. Inoltre, le GRN stesse non sono condizionate ma possono variare in associazione con differenti fenotipi. Il progetto LINKER, finanziato dall’UE, sta applicando metodi di calcolo per identificare le GRN dai dati della sequenza di RNA e studiare la loro ridefinizione nel mieloma multiplo e nella leucemia mieloblastica acuta, due importanti neoplasie ematologiche. Lo studio includerà la valutazione della potenziale utilità prognostica dei nuovi dati delle GRN.

Obiettivo

Genes and their corresponding pathways form networks that regulate various cellular functions that are critical in tumor development. These networks, coined Gene Regulatory Networks (GRNs), define the regulatory relationships among genes and provide a concise representation of the transcriptional regulatory landscape of the cell. Further, different phenotypes can lead to activation of different functional pathways by different global rewiring of the underlying GRNs. To uncover such transcriptional rewiring, in this project I will further advance and optimize a recent efficient computational method developed by myself, coined LINKER, aiming at uncovering GRNs from RNAseq data; and given those networks, develop efficient differential network analysis methods that will shed light into the regulatory rewiring associated with phenotype. As one of the key goals of this proposal is the translation of computational methods to advance clinical cancer knowledge, I will work with the hosting group to apply LINKER to uncover the transcriptional rewiring associated to hematological malignancies. Specifically, I will apply LINKER in a stepwise model first on available RNA-seq data from multiple myeloma and acute myeloblastic leukemia, and second to primary data from patients with these diseases provided by the hosting supervisor. Rewired GRNs between MM and normal BM plasma cells and between leukemic blast and normal hematopoietic progenitor cells, potentially implicated in the pathogenesis of the disease, will be functionally validated by state of the art gain and loss of function technologies (CRISPR/cas9). As data provided by the host institution includes clinical follow up from patients we will also examine the prognostic value of our identified GRN. I envision that LINKER will provide additional novel insights to our understanding of the key rewiring associated with these malignancies, increased by our ability to translate the discovered biomarkers to patient treatment.