In the PNAS Early Edition this week, a Harvard-led team describes a mathematical model "that begins to address" the challenge of distinguishing driver from passenger mutations during tumorigenesis. "We model tumors as a discrete time branching process that starts with a single driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of clonal expansion," the authors write. With their approach, the team has seen "tremendous variation in the rare of tumor development," which they suggest could add to the understanding of tumor heterogeneity. In addition, the group's model also "provides a simple formula for the number of driver mutations as a function of the total number of mutations in the tumor," and confers a small selective advantage — 0.004 ± 0.0004 — for somatic mutations in human tumors in situ.
Co-authors Sriram Chandrasekarana and Nathan Price at the University of Illinois, Urbana-Champaign, present a method for the "probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis," in PNAS this week. In particular, the team's probabilistic regulation of metabolism — or PROM — approach "enables straightforward, automated, and quantitative integration of high-throughput data into constraint-based modeling," and "introduces probabilities to represent gene states and gene-transcription factor interactions," the authors report. Chandrasekarana and Price tested PROM on extensive data sets from both E. coli and M. tuberculosis and suggest that their method is a "successful integration of a top-down reconstructed, statistically inferred regulatory network with a bottom-up reconstructed, biochemically detailed metabolic network."
Investigators at the University of Connecticut Stem Cell Institute and their colleagues report an induced-pluripotent stem cell model of Angelman and Prader-Willi syndromes, both genomic imprinting disorders. The team developed iPSCs derived from patients with AS and PWS that "show no evidence of DNA methylation imprint erasure at the cis-acting PSW imprinting center," and may be important tools to investigate the "developmental timing and mechanism of UBE3A repression in human neurons," they write.
In another paper published online in advance in PNAS, researchers at the Memorial Sloan-Kettering Cancer Center and their collaborators describe "a mathematical framework to determine the temporal sequence of somatic genetic events in cancer." The group's approach, RESIC — for "retracing the evolutionary steps in cancer" — aims to "deduce the temporal sequence of genetic events during tumorigenesis from cross-sectional genomic data of tumors at their fully transformed stage." When applied to data gleaned from advanced colorectal cancer samples, RESIC correctly predicted the sequence of APC, KRAS, and TP53 mutations, as validated by previous analyses of such tumors at different developmental stages, the team reports.