Integrative genetic analysis of mouse and human AML identifies cooperating disease alleles.

t(8;21) is one of the most frequent chromosomal abnormalities observed in acute myeloid leukemia (AML). However, expression of AML1-ETO is not sufficient to induce transformation in vivo. Consistent with this observation, patients with this translocation harbor additional genetic abnormalities, suggesting a requirement for cooperating mutations. To better define the genetic landscape in AML and distinguish driver from passenger mutations, we compared the mutational profiles of AML1-ETO-driven mouse models of leukemia with the mutational profiles of human AML patients. We identified TET2 and PTPN11 mutations in both mouse and human AML and then demonstrated the ability of Tet2 loss and PTPN11 D61Y to initiate leukemogenesis in concert with expression of AML1-ETO in vivo. This integrative genetic profiling approach allowed us to accurately predict cooperating events in t(8;21)(+) AML in a robust and unbiased manner, while also revealing functional convergence in mouse and human AML.

A novel Bayesian change-point algorithm for genome-wide analysis of diverse ChIPseq data types.

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein(1). For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment(2). Reliably identifying these regions was the focus of our work. Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics(3-5) to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)(6-8). We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized. Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types. To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs(9), which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor(10,11) and epigenetic data(12) to illustrate its usefulness.