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Data collected from omics technologies have revealed pervasive heterogeneity and stochasticity of molecular states within and between phenotypes. A prominent example of such heterogeneity occurs between genome-wide mRNA, microRNA, and methylation profiles from one individual tumor to another, even within a cancer subtype. However, current methods in bioinformatics, such as detecting differentially expressed genes or CpG sites, are population-based and therefore do not effectively model intersample diversity. Here we introduce a unified theory to quantify sample-level heterogeneity that is applicable to a single omics profile. Specifically, we simplify an omics profile to a digital representation based on the omics profiles from a set of samples from a reference or baseline population (e.g., normal tissues). The state of any subprofile (e.g., expression vector for a subset of genes) is said to be "divergent" if it lies outside the estimated support of the baseline distribution and is consequently interpreted as "dysregulated" relative to that baseline. We focus on two cases: single features (e.g., individual genes) and distinguished subsets (e.g., regulatory pathways). Notably, since the divergence analysis is at the individual sample level, dysregulation can be analyzed probabilistically; for example, one can estimate the probability that a gene or pathway is divergent in some population. Finally, the reduction in complexity facilitates a more "personalized" and biologically interpretable analysis of variation, as illustrated by experiments involving tissue characterization, disease detection and progression, and disease-pathway associations. [ABSTRACT FROM AUTHOR]

HIV-1 infection is associated with increased risk for B-cell lymphomas. How HIV infection promotes the development of lymphoma is unclear, but it may involve chronic B-cell activation, inflammation, and/or impaired immunity, possibly leading to a loss of control of oncogenic viruses and reduced tumor immunosurveillance. We hypothesized that HIV structural proteins may contribute to lymphomagenesis directly, because they can persist long term in lymph nodes in the absence of viral replication. The HIV-1 transgenic mouse Tg26 carries a noninfectious HIV-1 provirus lacking part of the gag-pol region, thus constituting a model for studying the effects of viral products in pathogenesis. Approximately 15% of Tg26 mice spontaneously develop leukemia/lymphoma. We investigated which viral proteins are associated with the development of leukemia/lymphoma in the Tg26 mouse model, and performed microarray analysis on RNA from spleen and lymph nodes to identify potential mechanisms of lymphomagenesis. Of the viral proteins examined, only expression of HIV-1 matrix protein p17 was associated with leukemia/lymphoma development and was highly expressed in bone marrow before disease. The tumor cells resembled pro-B cells, and were CD19+IgM-IgD-CD93+CD43+CD21-CD23-VpreB+CXCR4+. Consistent with the pro-B-cell stage of B-cell development, microarray analysis revealed enrichment of transcripts, including Rag1, Rag2, CD93, Vpreb1, Vpreb3, and Igll1. We confirmed RAG1 expression in Tg26 tumors, and hypothesized that HIV-1 matrix protein p17 may directly induce RAG1 in B cells. Stimulation of human activated B cells with p17 enhanced RAG1 expression in three of seven donors, suggesting that intracellular signaling by p17 may lead to genomic instability and transformation. [ABSTRACT FROM AUTHOR]

Cancer arises through the sequential accumulation of mutations in oncogenes and tumor suppressor genes. However, how many such mutations are required for a normal human cell to progress to an advanced cancer? The best estimates for this number have been provided by mathematical models based on the relation between age and incidence. For example, the classic studies of Nordling [Nordling CO (1953) Br J Cancer 7(1):68-72] and Armitage and Doll [Armitage P, Doll R (1954) Br J Cancer 8(1):1-12] suggest that six or seven sequential mutations are required. Here, we describe a different approach to derive this estimate that combines conventional epidemiologic studies with genome-wide sequencing data: incidence data for different groups of patients with the same cancer type were compared with respect to their somatic mutation rates. In two well-documented cancer types (lung and colon adenocarcinomas), we find that only three sequential mutations are required to develop cancer. This conclusion deepens our understanding of the process of carcinogenesis and has important implications for the design of future cancer genome-sequencing efforts. [ABSTRACT FROM AUTHOR]

Prostate cancer is the second leading cause of cancer death among United States men. However, disease aggressiveness is varied, with low-grade disease often being indolent and high-grade cancer accounting for the greatest density of deaths. Outcomes are also disparate among men with high-grade prostate cancer, with upwards of 65% having disease recurrence even after primary treatment. Identification of men at risk for recurrence and elucidation of the molecular processes that drive their disease is paramount, as these men are the most likely to benefit from multimodal therapy. We previously showed that androgen-induced expression profiles in prostate development are reactivated in aggressive prostate cancers. Herein, we report the down-regulation of one such gene, Sparcll, a secreted protein, acidic and rich in cysteine (SPARC) family matri-cellular protein, during invasive phases of prostate development and regeneration. We further demonstrate a parallel process in prostate cancer, with decreased expression of SPARCL1 in high-grade/meta-static prostate cancer. Mechanistically, we demonstrate that SPARCL1 loss increases the migratory and invasive properties of prostate cancer cells through Ras homolog gene family, member C (RHOC), a known mediator of metastatic progression. By using models incorporating dinicopathologic parameters to predict prostate cancer recurrence after treatment, we show that SPARCL1 loss is a significant, independent prognostic marker of disease progression. Thus, SPARCL1 is a potent regulator of cell migration/invasion and its loss is independently associated with prostate cancer recurrence. [ABSTRACT FROM AUTHOR]