Description
Date depot: 24 mai 2021
Titre: INFERRING CLONAL EVOLUTION OF TUMORS FROM SPATIAL TRANSCRIPTOMICS AND BULK DNA SEQUENCING DATA
Directrice de thèse:
Alessandra CARBONE (LCQB)
Encadrante :
Ewa SZCZUREK (Institute of informatics)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Sciences de l’information et sciences du vivant
Resumé: Tumors are highly heterogeneous, both genetically and phenotypically. They live in a highly versatile microenvironment composed of clones with different genotypes, and of cell types with different programs of gene expression. Geographically distinct parts of the tumor have not only different genetic and phenotypic composition, but also are surrounded by different amounts of stromal tissue, and differ with the extent of immune infiltration, etc. A thorough characterization of tumor heterogeneity is significant for learning the evolution of cancer, which leads to resistance to the treatments. For this aim, we are interested in developing new statistical models for finding clonal structure within a tumor using integration of different types of data such as bulk sequencing of tumor DNA, spatially resolved tumor transcriptomics, single-cell RNA-seq and BCR-seq.
In the first year of Ph.D. study, we started a project in which we integrated clonal architecture with genomic clustering and transcriptome profiling of single tumor cells in order to assign each cell to a specific clone (CACTUS)[1]. Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. We propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. We implemented and applied CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones.
In the second year of Ph.D. study, we developed a probabilistic graphical model (Tummoroscope) to represent the clonal distribution over the tumor in space. We proposed a probabilistic approach accounting for spatial resolution of tumor heterogeneity. We first infer the clones and their corresponding genotype using existing methods for tumor phylogeny reconstruction from bulk DNA sequencing data. Second, we infer their location from spatial transcriptomics, which consists of mini-bulk RNA-sequencing measurements in multiple spots of the tumor tissue. We investigate the variants of the RNA sequences in each spot on the tumor sample. The model maps variants found in each spot to the variants existing in the genotype of the clones and finds the most likely clonal structure of each spot.
We implemented and tested the Tummoroscope model over simulated data. Now, we are applying Tumoroscope on the real bulk DNA sequencing and spatial transcriptomics data. We plan to apply the model on two different data: prostate and breast cancer tumor. Our primary aim is to achieve comprehensive statistical inference of the clonal structure of cancer and its spatial distribution. In the next step, we would expand both models to include gene expression analysis to get a better understanding of the tumor environment.
References
[1] Shafighi, S.D., Kielbasa, S.M., Sepulveda-Yanez, J. et al. CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells. Genome Med 13, 45 (2021). https://doi.org/10.1186/s13073-021-00842-w
Résumé dans une autre langue: NA
Doctorant.e: Darvish Shafighi Shadi