Publications by authors named "Farid R Mehrabadi"

Article Synopsis
  • Cancer progression is an evolutionary process where certain cells adapt to grow better than others, leading to diverse subclones.
  • The study used advanced modeling to analyze how gene expression changes during this subclonal evolution, using data from mouse melanoma cells.
  • Findings showed that different sublines exhibited unique gene expression patterns; resistant sublines adapted genes related to invasion, while responsive sublines focused on proliferation, highlighting non-genetic aspects of cancer evolution.
View Article and Find Full Text PDF

Primary liver cancer (PLC) consists of two main histological subtypes; hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). The role of transcription factors (TFs) in malignant hepatobiliary lineage commitment between HCC and iCCA remains underexplored. Here, we present genome-wide profiling of transcription regulatory elements of 16 PLC patients using single-cell assay for transposase accessible chromatin sequencing.

View Article and Find Full Text PDF

Intratumoral heterogeneity (ITH) can promote cancer progression and treatment failure, but the complexity of the regulatory programs and contextual factors involved complicates its study. To understand the specific contribution of ITH to immune checkpoint blockade (ICB) response, we generated single cell-derived clonal sublines from an ICB-sensitive and genetically and phenotypically heterogeneous mouse melanoma model, M4. Genomic and single cell transcriptomic analyses uncovered the diversity of the sublines and evidenced their plasticity.

View Article and Find Full Text PDF

Single-cell sequencing (SCS) data have great potential in reconstructing the evolutionary history of tumors. Rapid advances in SCS technology in the past decade were followed by the design of various computational methods for inferring trees of tumor evolution. Some of the earliest methods were based on the direct search in the space of trees with the goal of finding the maximum likelihood tree.

View Article and Find Full Text PDF

Available computational methods for tumor phylogeny inference via single-cell sequencing (SCS) data typically aim to identify the most likely satisfying the (ISA). However, the limitations of SCS technologies including frequent allele dropout and variable sequence coverage may prohibit a perfect phylogeny. In addition, ISA violations are commonly observed in tumor phylogenies due to the loss of heterozygosity, deletions, and convergent evolution.

View Article and Find Full Text PDF