Progressive plasticity during colorectal cancer metastasis.

Metastasis is the principal cause of cancer death, yet we lack an understanding of metastatic cell states, their relationship to primary tumor states, and the mechanisms by which they transition. In a cohort of biospecimen trios from same-patient normal colon, primary and metastatic colorectal cancer, we show that while primary tumors largely adopt LGR5 + intestinal stem-like states, metastases display progressive plasticity. Loss of intestinal cell states is accompanied by reprogramming into a highly conserved fetal progenitor state, followed by non-canonical differentiation into divergent squamous and neuroendocrine-like states, which is exacerbated by chemotherapy and associated with poor patient survival. Using matched patient-derived organoids, we demonstrate that metastatic cancer cells exhibit greater cell-autonomous multilineage differentiation potential in response to microenvironment cues than their intestinal lineage-restricted primary tumor counterparts. We identify PROX1 as a stabilizer of intestinal lineage in the fetal progenitor state, whose downregulation licenses non-canonical reprogramming.

The onset of p53 loss of heterozygosity is differentially induced in various stem cell types and may involve the loss of either allele.

p53 loss of heterozygosity (p53LOH) is frequently observed in Li-Fraumeni syndrome (LFS) patients who carry a mutant (Mut) p53 germ-line mutation. Here, we focused on elucidating the link between p53LOH and tumor development in stem cells (SCs). Although adult mesenchymal stem cells (MSCs) robustly underwent p53LOH, p53LOH in induced embryonic pluripotent stem cells (iPSCs) was significantly attenuated. Only SCs that underwent p53LOH induced malignant tumors in mice. These results may explain why LFS patients develop normally, yet acquire tumors in adulthood. Surprisingly, an analysis of single-cell sub-clones of iPSCs, MSCs and ex vivo bone marrow (BM) progenitors revealed that p53LOH is a bi-directional process, which may result in either the loss of wild-type (WT) or Mut p53 allele. Interestingly, most BM progenitors underwent Mutp53LOH. Our results suggest that the bi-directional p53LOH process may function as a cell-fate checkpoint. The loss of Mutp53 may be regarded as a DNA repair event leading to genome stability. Indeed, gene expression analysis of the p53LOH process revealed upregulation of a specific chromatin remodeler and a burst of DNA repair genes. However, in the case of loss of WTp53, cells are endowed with uncontrolled growth that promotes cancer.

Inferring subnetworks from perturbed expression profiles.

Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. We extend this framework to correctly handle perturbations, and to identify significant subnetworks of interacting genes. We apply this method to expression data of S. cerevisiae mutants and uncover a variety of structured metabolic, signaling and regulatory pathways.

Using Bayesian networks to analyze expression data.

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes and because they provide a clear methodology for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes. We then describe a method for recovering gene interactions from microarray data using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al. (1998).