Tumor-derived exosomes are emerging mediators of tumorigenesis. We explored the function of melanoma-derived exosomes in the formation of primary tumors and metastases in mice and human subjects. Exosomes from highly metastatic melanomas increased the metastatic behavior of primary tumors by permanently 'educating' bone marrow progenitors through the receptor tyrosine kinase MET. Melanoma-derived exosomes also induced vascular leakiness at pre-metastatic sites and reprogrammed bone marrow progenitors toward a pro-vasculogenic phenotype that was positive for c-Kit, the receptor tyrosine kinase Tie2 and Met. Reducing Met expression in exosomes diminished the pro-metastatic behavior of bone marrow cells. Notably, MET expression was elevated in circulating CD45(-)C-KIT(low/+)TIE2(+) bone marrow progenitors from individuals with metastatic melanoma. RAB1A, RAB5B, RAB7 and RAB27A, regulators of membrane trafficking and exosome formation, were highly expressed in melanoma cells. Rab27A RNA interference decreased exosome production, preventing bone marrow education and reducing, tumor growth and metastasis. In addition, we identified an exosome-specific melanoma signature with prognostic and therapeutic potential comprised of TYRP2, VLA-4, HSP70, an HSP90 isoform and the MET oncoprotein. Our data show that exosome production, transfer and education of bone marrow cells supports tumor growth and metastasis, has prognostic value and offers promise for new therapeutic directions in the metastatic process.

/pdf/melanoma-exosomes-educate-bone-marrow-progenitor-cells-e1lwduo16o.pdf

Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET

We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.

/pdf/gistic2-0-facilitates-sensitive-and-confident-localization-5clseou2er.pdf

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers.

https://stacks.cdc.gov/view/cdc/34113/cdc_34113_DS1.pdf

Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

Traditional problem determination techniques rely on static dependency models that are difficult to generate accurately in today's large, distributed, and dynamic application environments such as e-commerce systems. We present a dynamic analysis methodology that automates problem determination in these environments by 1) coarse-grained tagging of numerous real client requests as they travel through the system and 2) using data mining techniques to correlate the believed failures and successes of these requests to determine which components are most likely to be at fault. To validate our methodology, we have implemented Pinpoint, a framework for root cause analysis on the J2EE platform that requires no knowledge of the application components. Pinpoint consists of three parts: a communications layer that traces client requests, a failure detector that uses traffic-sniffing and middleware instrumentation, and a data analysis engine. We evaluate Pinpoint by injecting faults into various application components and show that Pinpoint identifies the faulty components with high accuracy and produces few false-positives.

/pdf/pinpoint-problem-determination-in-large-dynamic-internet-1zglcww7fn.pdf

Pinpoint: problem determination in large, dynamic Internet services

Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration - including meta-dimensional and multi-staged analyses - which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.

/pdf/methods-of-integrating-data-to-uncover-genotype-phenotype-5894exgr6j.pdf

Methods of integrating data to uncover genotype–phenotype interactions

http://www.cs.columbia.edu/~julia/papers/akaviaetal12.pdf

An integrated approach to uncover drivers of cancer.

The authors describe a network management system and illustrate its application to managing a distributed database application on a complex enterprise network.

High speed and robust event correlation

Background
Cancer is caused through a multistep process, in which a succession of genetic changes, each conferring a competitive advantage for growth and proliferation, leads to the progressive conversion of normal human cells into malignant cancer cells. Interrogation of cancer genomes holds the promise of understanding this process, thus revolutionizing cancer research and treatment. As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect.

/pdf/jistic-identification-of-significant-targets-in-cancer-3k9xiv9iyt.pdf

JISTIC: Identification of Significant Targets in Cancer

A deductive database based on aristotelian logic

Motivation: The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes. Results: Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease. Availability: Contact:pinat@rostlab.org; ofran@cubic.bioc.columbia.edu

Eyal Mozes

Papers

An integrated approach to uncover drivers of cancer.

High speed and robust event correlation

JISTIC: Identification of Significant Targets in Cancer

A deductive database based on aristotelian logic

Create and assess protein networks through molecular characteristics of individual proteins