The Temporal Order of Genetic and Pathway Alterations in Tumorigenesis

Cancer evolves through the accumulation of mutations, but the order in which mutations occur is poorly understood. Inference of a temporal ordering on the level of genes is challenging because clinically and histologically identical tumors often have few mutated genes in common. This heterogeneity may at least in part be due to mutations in different genes having similar phenotypic effects by acting in the same functional pathway. We estimate the constraints on the order in which alterations accumulate during cancer progression from cross-sectional mutation data using a probabilistic graphical model termed Hidden Conjunctive Bayesian Network (H-CBN). The possible orders are analyzed on the level of genes and, after mapping genes to functional pathways, also on the pathway level. We find stronger evidence for pathway order constraints than for gene order constraints, indicating that temporal ordering results from selective pressure acting at the pathway level. The accumulation of changes in core pathways differs among cancer types, yet a common feature is that progression appears to begin with mutations in genes that regulate apoptosis pathways and to conclude with mutations in genes involved in invasion pathways. H-CBN models provide a quantitative and intuitive model of tumorigenesis showing that the genetic events can be linked to the phenotypic progression on the level of pathways

Patient-oriented gene set analysis for cancer mutation data

Recent research has revealed complex heterogeneous genomic landscapes in human cancers. However, mutations tend to occur within a core group of pathways and biological processes that can be grouped into gene sets. To better understand the significance of these pathways, we have developed an approach that initially scores each gene set at the patient rather than the gene level. In mutation analysis, these patient-oriented methods are more transparent, interpretable, and statistically powerful than traditional gene-oriented methods

Wanted: cancer boss

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62915/1/440978a.pd

IST Austria Technical Report

A comprehensive understanding of the clonal evolution of cancer is critical for understanding neoplasia. Genome-wide sequencing data enables evolutionary studies at unprecedented depth. However, classical phylogenetic methods often struggle with noisy sequencing data of impure DNA samples and fail to detect subclones that have different evolutionary trajectories. We have developed a tool, called Treeomics, that allows us to reconstruct the phylogeny of a cancer with commonly available sequencing technologies. Using Bayesian inference and Integer Linear Programming, robust phylogenies consistent with the biological processes underlying cancer evolution were obtained for pancreatic, ovarian, and prostate cancers. Furthermore, Treeomics correctly identified sequencing artifacts such as those resulting from low statistical power; nearly 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Importantly, we show that the evolutionary trees generated with Treeomics are mathematically optimal

Genetic progression and the waiting time to cancer

Cancer results from genetic alterations that disturb the normal cooperative behavior of cells. Recent high-throughput genomic studies of cancer cells have shown that the mutational landscape of cancer is complex and that individual cancers may evolve through mutations in as many as 20 different cancer-associated genes. We use data published by Sjoblom et al. (2006) to develop a new mathematical model for the somatic evolution of colorectal cancers. We employ the Wright-Fisher process for exploring the basic parameters of this evolutionary process and derive an analytical approximation for the expected waiting time to the cancer phenotype. Our results highlight the relative importance of selection over both the size of the cell population at risk and the mutation rate. The model predicts that the observed genetic diversity of cancer genomes can arise under a normal mutation rate if the average selective advantage per mutation is on the order of 1%. Increased mutation rates due to genetic instability would allow even smaller selective advantages during tumorigenesis. The complexity of cancer progression thus can be understood as the result of multiple sequential mutations, each of which has a relatively small but positive effect on net cell growth.Comment: Details available as supplementary material at http://www.people.fas.harvard.edu/~antal/publications.htm

IgH gene rearrangements as plasma biomarkers in Non-Hodgkin's Lymphoma patients

New biomarkers with improved accuracy could be helpful for monitoring disease in patients with Non-Hodgkin's lymphomas (NHL). Towards this end, we have explored the feasibility of identifying the sequence of rearranged IgH genes using next-generation sequencing, then using PCR to detect specific rearranged DNA fragments in patients' plasma. By capturing and sequencing the IgH genomic regions (IgCap), we were able to detect and precisely determine the sequence of rearranged IgH loci in the tumors of three NHL patients. Moreover, circulating rearranged DNA fragments could be identified in the plasma of all three patients. Even in cases wherein tumor biopsies were unavailable, we were able to use the IgH capture approach to identify rearranged DNA loci in the plasma of 8 of 14 patients. IgCap may enable a more informed management of selected patients with NHL and other B-cell malignancies in the future

Mutant PIK3CA promotes cell growth and invasion of human cancer cells

SummaryPIK3CA is mutated in diverse human cancers, but the functional effects of these mutations have not been defined. To evaluate the consequences of PIK3CA alterations, the two most common mutations were inactivated by gene targeting in colorectal cancer (CRC) cells. Biochemical analyses of these cells showed that mutant PIK3CA selectively regulated the phosphorylation of AKT and the forkhead transcription factors FKHR and FKHRL1. PIK3CA mutations had little effect on growth under standard conditions, but reduced cellular dependence on growth factors. PIK3CA mutations resulted in attenuation of apoptosis and facilitated tumor invasion. Treatment with the PI3K inhibitor LY294002 abrogated PIK3CA signaling and preferentially inhibited growth of PIK3CA mutant cells. These data have important implications for therapy of cancers harboring PIK3CA alterations

Integrated genomic analyses identify ARID1A and ARID1B alterations in the childhood cancer neuroblastoma

Neuroblastomas are tumors of peripheral sympathetic neurons and are the most common solid tumor in children. To determine the genetic basis for neuroblastoma we performed whole-genome sequencing (6 cases), exome sequencing (16 cases), genome-wide rearrangement analyses (32 cases), and targeted analyses of specific genomic loci (40 cases) using massively parallel sequencing. On average each tumor had 19 somatic alterations in coding genes (range, 3–70). Among genes not previously known to be involved in neuroblastoma, chromosomal deletions and sequence alterations of chromatin remodeling genes, ARID1A and ARID1B, were identified in 8 of 71 tumors (11%) and were associated with early treatment failure and decreased survival. Using tumor-specific structural alterations, we developed an approach to identify rearranged DNA fragments in sera, providing personalized biomarkers for minimal residual disease detection and monitoring. These results highlight dysregulation of chromatin remodeling in pediatric tumorigenesis and provide new approaches for the management of neuroblastoma patients