Following analysis of an in-house pilot dataset of normal breast tissue single-cell RNA-seq (scRNA-seq), several similar yet larger scale datasets were published. I analysed them and developed a method to use them for classification of breast tumour cells. In addition, I applied my cell atlas experience to a new collaboration aiming to generate a single cell atlas of the healthy fallopian tube.
I used several breast patient derived tumour xenografts (PDTXs) datasets generated in our lab to develop and test a computational framework to infer somatic clones from scRNA-seq data. A genomic clone is defined predominantly by the set of copy number alterations and single nucleotide variants its cells harbour. I spent considerable effort in adjusting existing tools that infer both and devised a novel method to integrate both clonal outputs and improve the accuracy of the clonal inference.
The developments described above enabled me to analyse a large cohort of diverse breast PDTXs. My aim was to classify each cell by three different classifications: genomic clone, cell identity, and transcriptional cell state. Integration of these classifications allowed the assessment of intra-tumour transcriptional plasticity and the transcriptional plasticity within a genomic clone. There are three types of epithelial cells in the healthy breast – basal, luminal progenitor and mature luminal. Surprisingly, we found that tumour cells assume intermediate phenotypes, mixing several normal cell types in the same cell. The observed phenotypes corresponded to the originating tumour types but were not fully determined by them. Although transcriptional plasticity was pervasive, we detected a minority of tumours that maintained extremely homogenous transcriptional profile in all their cells. We are currently focusing on this intriguing group of tumours to understand the mechanism that maintains tumour homogeneity. Integration of the inferred genomic clones with the phenotypic classifications elucidates the dynamics of clonal evolution and transcriptional changes and revealed examples where newly emerged clones manage to span the full transcriptional repertoire of the parent clone, and where new clones introduce new phenotypes to the tumour. A comprehensive literature scan yielded many breast tumour datasets that I analyse aiming to see if and how the above results constitute in larger cohorts of human breast tumours scRNA-seq datasets.
The above work is shaping into two papers I now wrap up and plan to submit in the upcoming months. I also presented this work in the annual conference of the European Association of Cancer Resarch (EACR 2022) in a selected talk, and in the Computational Cancer Genomics (CCG2022) conference in a poster.
