Machine learning reveals genetic modifiers of the immune microenvironment of cancer.

Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.

PINNED: identifying characteristics of druggable human proteins using an interpretable neural network.

The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between "druggable" and "undruggable" proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein-protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets.

An anti-PD-1-GITR-L bispecific agonist induces GITR clustering-mediated T cell activation for cancer immunotherapy.

Costimulatory receptors such as glucocorticoid-induced tumor necrosis factor receptor-related protein (GITR) play key roles in regulating the effector functions of T cells. In human clinical trials, however, GITR agonist antibodies have shown limited therapeutic effect, which may be due to suboptimal receptor clustering-mediated signaling. To overcome this potential limitation, a rational protein engineering approach is needed to optimize GITR agonist-based immunotherapies. Here we show a bispecific molecule consisting of an anti-PD-1 antibody fused with a multimeric GITR ligand (GITR-L) that induces PD-1-dependent and FcγR-independent GITR clustering, resulting in enhanced activation, proliferation and memory differentiation of primed antigen-specific GITR+PD-1+ T cells. The anti-PD-1-GITR-L bispecific is a PD-1-directed GITR-L construct that demonstrated dose-dependent, immunologically driven tumor growth inhibition in syngeneic, genetically engineered and xenograft humanized mouse tumor models, with a dose-dependent correlation between target saturation and Ki67 and TIGIT upregulation on memory T cells. Anti-PD-1-GITR-L thus represents a bispecific approach to directing GITR agonism for cancer immunotherapy.

VISTA is an activating receptor in human monocytes.

As indicated by its name, V-domain Ig suppressor of T cell activation (VISTA) is thought to serve primarily as an inhibitory protein that limits immune responses. VISTA antibodies can dampen the effects of several concomitantly elicited activation signals, including TCR and TLR activation, but it is currently unclear if VISTA agonism could singly affect immune cell biology. In this study, we discovered two novel VISTA antibodies and characterized their effects on human peripheral blood mononuclear cells by scRNA/CITE-seq. Both antibodies appeared to agonize VISTA in an Fc-functional manner to elicit transcriptional and functional changes in monocytes consistent with activation. We also used pentameric VISTA to identify Syndecan-2 and several heparan sulfate proteoglycan synthesis genes as novel regulators of VISTA interactions with monocytic cells, adding further evidence of bidirectional signaling. Together, our study highlights several novel aspects of VISTA biology that have yet to be uncovered in myeloid cells and serves as a foundation for future research.

Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets.

MOTIVATION: The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples. RESULTS: Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates. AVAILABILITY AND IMPLEMENTATION: The matrix of features for each protein is available at: https://bit.ly/3iLgZTa. Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning prediction of oncology drug targets based on protein and network properties.

BACKGROUND: The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. RESULTS: We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an Area Under the Curve (AUC) of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. CONCLUSIONS: We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

Small molecule schweinfurthins selectively inhibit cancer cell proliferation and mTOR/AKT signaling by interfering with trans-Golgi-network trafficking.

Natural compound schweinfurthins are of considerable interest for novel therapy development because of their selective anti-proliferative activity against human cancer cells. We previously reported the isolation of highly active schweinfurthins E-H, and in the present study, mechanisms of the potent and selective anti-proliferation were investigated. We found that schweinfurthins preferentially inhibited the proliferation of PTEN deficient cancer cells by indirect inhibition of AKT phosphorylation. Mechanistically, schweinfurthins and their analogs arrested trans-Golgi-network trafficking, an intracellular vesicular trafficking system, resulting in the induction of endoplasmic reticulum stress and the suppression of both lipid raft-mediated PI3K activation and mTOR/RheB complex formation, which collectively led to an effective inhibition of mTOR/AKT signaling. The trans-Golgi-network traffic arresting effect of schweinfurthins was associated with their in vitro binding activity to oxysterol-binding proteins that are known to regulate intracellular vesicular trafficking. Moreover, schweinfurthins were found to be highly toxic toward PTEN-deficient B cell lymphoma cells, and displayed 2 orders of magnitude lower activity toward normal human peripheral blood mononuclear cells and primary fibroblasts in vitro. These results revealed a previously unrecognized role of schweinfurthins in regulating trans-Golgi-network trafficking, and linked mechanistically this cellular effect with mTOR/AKT signaling and with cancer cell survival and growth. Our findings suggest the schweinfurthin class of compounds as a novel approach to modulate oncogenic mTOR/AKT signaling for cancer treatment.

Lenvatinib, an angiogenesis inhibitor targeting VEGFR/FGFR, shows broad antitumor activity in human tumor xenograft models associated with microvessel density and pericyte coverage.

BACKGROUND: Lenvatinib is an oral inhibitor of multiple receptor tyrosine kinases (RTKs) targeting vascular endothelial growth factor receptor (VEGFR1-3), fibroblast growth factor receptor (FGFR1-4), platelet growth factor receptor α (PDGFR α), RET and KIT. Antiangiogenesis activity of lenvatinib in VEGF- and FGF-driven angiogenesis models in both in vitro and in vivo was determined. Roles of tumor vasculature (microvessel density (MVD) and pericyte coverage) as biomarkers for lenvatinib were also examined in this study. METHOD: We evaluated antiangiogenesis activity of lenvatinib against VEGF- and FGF-driven proliferation and tube formation of HUVECs in vitro. Effects of lenvatinib on in vivo angiogenesis, which was enhanced by overexpressed VEGF or FGF in human pancreatic cancer KP-1 cells, were examined in the mouse dorsal air sac assay. We determined antitumor activity of lenvatinib in a broad panel of human tumor xenograft models to test if vascular score, which consisted of high MVD and low pericyte coverage, was associated with sensitivity to lenvatinib treatment. Vascular score was also analyzed using human tumor specimens with 18 different types of human primary tumors. RESULT: Lenvatinib inhibited VEGF- and FGF-driven proliferation and tube formation of HUVECs in vitro. In vivo angiogenesis induced by overexpressed VEGF (KP-1/VEGF transfectants) or FGF (KP-1/FGF transfectants) was significantly suppressed with oral treatments of lenvatinib. Lenvatinib showed significant antitumor activity in KP-1/VEGF and five 5 of 7 different types of human tumor xenograft models at between 1 to 100 mg/kg. We divided 19 human tumor xenograft models into lenvatinib-sensitive (tumor-shrinkage) and relatively resistant (slow-growth) subgroups based on sensitivity to lenvatinib treatments at 100 mg/kg. IHC analysis showed that vascular score was significantly higher in sensitive subgroup than relatively resistant subgroup (p < 0.0004). Among 18 types of human primary tumors, kidney cancer had the highest MVD, while liver cancer had the lowest pericyte coverage, and cancers in Kidney and Stomach had highest vascular score. CONCLUSION: These results indicated that Lenvatinib inhibited VEGF- and FGF-driven angiogenesis and showed a broad spectrum of antitumor activity with a wide therapeutic window. MVD and pericyte-coverage of tumor vasculature might be biomarkers and suggest cases that would respond for lenvatinib therapy.

Gene expression profiling reveals epithelial mesenchymal transition (EMT) genes can selectively differentiate eribulin sensitive breast cancer cells.

OBJECTIVES: Eribulin mesylate is a synthetic macrocyclic ketone analog of the marine sponge natural product halichondrin B. Eribulin is a mechanistically unique inhibitor of microtubule dynamics. In this study, we investigated whether selective signal pathways were associated with eribulin activity compared to paclitaxel, which stabilizes microtubules, based on gene expression profiling of cell line panels of breast, endometrial, and ovarian cancer in vitro. RESULTS: We determined the sets of genes that were differentially altered between eribulin and paclitaxel treatment in breast, endometrial, and ovarian cancer cell line panels. Our unsupervised clustering analyses revealed that expression profiles of gene sets altered with treatments were correlated with the in vitro antiproliferative activities of the drugs. Several tubulin isotypes had significantly lower expression in cell lines treated with eribulin compared to paclitaxel. Pathway enrichment analyses of gene sets revealed that the common pathways altered between treatments in the 3 cancer panels were related to cytoskeleton remodeling and cell cycle regulation. The epithelial-mesenchymal transition (EMT) pathway was enriched in genes with significantly altered expression between the two drugs for breast and endometrial cancers, but not for ovarian cancer. Expression of genes from the EMT pathway correlated with eribulin sensitivity in breast cancer and with paclitaxel sensitivity in endometrial cancer. Alteration of expression profiles of EMT genes between sensitive and resistant cell lines allowed us to predict drug sensitivity for breast and endometrial cancers. CONCLUSION: Gene expression analysis showed that gene sets that were altered between eribulin and paclitaxel correlated with drug in vitro antiproliferative activities in breast and endometrial cancer cell line panels. Among the panels, breast cancer provided the strongest differentiation between eribulin and paclitaxel sensitivities based on gene expression. In addition, EMT genes were predictive of eribulin sensitivity in the breast and endometrial cancer panels.

Fission products from the damaged Fukushima reactor observed in Hungary.

Fission products, especially (131)I, (134)Cs and (137)Cs, from the damaged Fukushima Dai-ichi nuclear power plant (NPP) were detected in many places worldwide shortly after the accident caused by natural disaster. To observe the spatial and temporal variation of these isotopes in Hungary, aerosol samples were collected at five locations from late March to early May 2011: Institute of Nuclear Research, Hungarian Academy of Sciences (ATOMKI, Debrecen, East Hungary), Paks NPP (Paks, South-Central Hungary) as well as at the vicinity of Aggtelek (Northeast Hungary), Tapolca (West Hungary) and Bátaapáti (Southwest Hungary) settlements. In addition to the aerosol samples, dry/wet fallout samples were collected at ATOMKI, and airborne elemental iodine and organic iodide samples were collected at Paks NPP. The peak in the activity concentration of airborne (131)I was observed around 30 March (1-3 mBq m(-3) both in aerosol samples and gaseous iodine traps) with a slow decline afterwards. Aerosol samples of several hundred cubic metres of air showed (134)Cs and (137)Cs in detectable amounts along with (131)I. The decay-corrected inventory of (131)I fallout at ATOMKI was 2.1±0.1 Bq m(-2) at maximum in the observation period. Dose-rate contribution calculations show that the radiological impact of this event at Hungarian locations was of no considerable concern.

Circulating miRNA biomarkers for Alzheimer's disease.

A minimally invasive diagnostic assay for early detection of Alzheimer's disease (AD) is required to select optimal patient groups in clinical trials, monitor disease progression and response to treatment, and to better plan patient clinical care. Blood is an attractive source for biomarkers due to minimal discomfort to the patient, encouraging greater compliance in clinical trials and frequent testing. MiRNAs belong to the class of non-coding regulatory RNA molecules of ∼22 nt length and are now recognized to regulate ∼60% of all known genes through post-transcriptional gene silencing (RNAi). They have potential as useful biomarkers for clinical use because of their stability and ease of detection in many tissues, especially blood. Circulating profiles of miRNAs have been shown to discriminate different tumor types, indicate staging and progression of the disease and to be useful as prognostic markers. Recently their role in neurodegenerative diseases, both as diagnostic biomarkers as well as explaining basic disease etiology has come into focus. Here we report the discovery and validation of a unique circulating 7-miRNA signature (hsa-let-7d-5p, hsa-let-7g-5p, hsa-miR-15b-5p, hsa-miR-142-3p, hsa-miR-191-5p, hsa-miR-301a-3p and hsa-miR-545-3p) in plasma, which could distinguish AD patients from normal controls (NC) with >95% accuracy (AUC of 0.953). There was a >2 fold difference for all signature miRNAs between the AD and NC samples, with p-values<0.05. Pathway analysis, taking into account enriched target mRNAs for these signature miRNAs was also carried out, suggesting that the disturbance of multiple enzymatic pathways including lipid metabolism could play a role in AD etiology.

Gene expression analyses support fallopian tube epithelium as the cell of origin of epithelial ovarian cancer.

Folate receptor alpha (FOLR1/FRA) is reported to be overexpressed in epithelial ovarian cancers (EOC), especially the serous histotype. Further, while dysregulation of the folate-dependent 1-carbon cycle has been implicated in tumorogenesis, little is known relative to the potential mechanism of action of FOLR1 expression in these processes. We therefore investigated the expression of FOLR1, other folate receptors, and genes within the 1-carbon cycle in samples of EOC, normal ovary and fallopian tube on a custom TaqMan Low Density Array. Also included on this array were known markers of EOC such as MSLN, MUC16 and HE4. While few differences were observed in the expression profiles of genes in the 1-carbon cycle, genes previously considered to be overexpressed in EOC (e.g., FOLR1, MSLN, MUC16 and HE4) showed significantly increased expression when comparing EOC to normal ovary. However, when the comparator was changed to normal fallopian tube, these differences were abolished, supporting the hypothesis that EOC derives from fallopian fimbriae and, further, that markers previously considered to be upregulated or overexpressed in EOC are most likely not of ovarian origin, but fallopian in derivation. Our findings therefore support the hypothesis that the cell of origin of EOC is tubal epithelium.

"Topological significance" analysis of gene expression and proteomic profiles from prostate cancer cells reveals key mechanisms of androgen response.

BACKGROUND: The problem of prostate cancer progression to androgen independence has been extensively studied. Several studies systematically analyzed gene expression profiles in the context of biological networks and pathways, uncovering novel aspects of prostate cancer. Despite significant research efforts, the mechanisms underlying tumor progression are poorly understood. We applied a novel approach to reconstruct system-wide molecular events following stimulation of LNCaP prostate cancer cells with synthetic androgen and to identify potential mechanisms of androgen-independent progression of prostate cancer. METHODOLOGY/PRINCIPAL FINDINGS: We have performed concurrent measurements of gene expression and protein levels following the treatment using microarrays and iTRAQ proteomics. Sets of up-regulated genes and proteins were analyzed using our novel concept of "topological significance". This method combines high-throughput molecular data with the global network of protein interactions to identify nodes which occupy significant network positions with respect to differentially expressed genes or proteins. Our analysis identified the network of growth factor regulation of cell cycle as the main response module for androgen treatment in LNCap cells. We show that the majority of signaling nodes in this network occupy significant positions with respect to the observed gene expression and proteomic profiles elicited by androgen stimulus. Our results further indicate that growth factor signaling probably represents a "second phase" response, not directly dependent on the initial androgen stimulus. CONCLUSIONS/SIGNIFICANCE: We conclude that in prostate cancer cells the proliferative signals are likely to be transmitted from multiple growth factor receptors by a multitude of signaling pathways converging on several key regulators of cell proliferation such as c-Myc, Cyclin D and CREB1. Moreover, these pathways are not isolated but constitute an interconnected network module containing many alternative routes from inputs to outputs. If the whole network is involved, a precisely formulated combination therapy may be required to fight the tumor growth effectively.

Network analysis of human glaucomatous optic nerve head astrocytes.

BACKGROUND: Astrocyte activation is a characteristic response to injury in the central nervous system, and can be either neurotoxic or neuroprotective, while the regulation of both roles remains elusive. METHODS: To decipher the regulatory elements controlling astrocyte-mediated neurotoxicity in glaucoma, we conducted a systems-level functional analysis of gene expression, proteomic and genetic data associated with reactive optic nerve head astrocytes (ONHAs). RESULTS: Our reconstruction of the molecular interactions affected by glaucoma revealed multi-domain biological networks controlling activation of ONHAs at the level of intercellular stimuli, intracellular signaling and core effectors. The analysis revealed that synergistic action of the transcription factors AP-1, vitamin D receptor and Nuclear Factor-kappaB in cross-activation of multiple pathways, including inflammatory cytokines, complement, clusterin, ephrins, and multiple metabolic pathways. We found that the products of over two thirds of genes linked to glaucoma by genetic analysis can be functionally interconnected into one epistatic network via experimentally-validated interactions. Finally, we built and analyzed an integrative disease pathology network from a combined set of genes revealed in genetic studies, genes differentially expressed in glaucoma and closely connected genes/proteins in the interactome. CONCLUSION: Our results suggest several key biological network modules that are involved in regulating neurotoxicity of reactive astrocytes in glaucoma, and comprise potential targets for cell-based therapy.

Identifying disease-specific genes based on their topological significance in protein networks.

BACKGROUND: The identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest. RESULTS: In this paper we describe novel computational methodology capable of predicting key regulatory genes and proteins in disease- and condition-specific biological networks. The algorithm builds shortest path network connecting condition-specific genes (e.g. differentially expressed genes) using global database of protein interactions from MetaCore. We evaluate the number of all paths traversing each node in the shortest path network in relation to the total number of paths going via the same node in the global network. Using these numbers and the relative size of the initial data set, we determine the statistical significance of the network connectivity provided through each node. We applied this method to gene expression data from psoriasis patients and identified many confirmed biological targets of psoriasis and suggested several new targets. Using predicted regulatory nodes we were able to reconstruct disease pathways that are in excellent agreement with the current knowledge on the pathogenesis of psoriasis. CONCLUSION: The systematic and automated approach described in this paper is readily applicable to uncovering high-quality therapeutic targets, and holds great promise for developing network-based combinational treatment strategies for a wide range of diseases.

A comprehensive functional analysis of tissue specificity of human gene expression.

BACKGROUND: In recent years, the maturation of microarray technology has allowed the genome-wide analysis of gene expression patterns to identify tissue-specific and ubiquitously expressed ('housekeeping') genes. We have performed a functional and topological analysis of housekeeping and tissue-specific networks to identify universally necessary biological processes, and those unique to or characteristic of particular tissues. RESULTS: We measured whole genome expression in 31 human tissues, identifying 2374 housekeeping genes expressed in all tissues, and genes uniquely expressed in each tissue. Comprehensive functional analysis showed that the housekeeping set is substantially larger than previously thought, and is enriched with vital processes such as oxidative phosphorylation, ubiquitin-dependent proteolysis, translation and energy metabolism. Network topology of the housekeeping network was characterized by higher connectivity and shorter paths between the proteins than the global network. Ontology enrichment scoring and network topology of tissue-specific genes were consistent with each tissue's function and expression patterns clustered together in accordance with tissue origin. Tissue-specific genes were twice as likely as housekeeping genes to be drug targets, allowing the identification of tissue 'signature networks' that will facilitate the discovery of new therapeutic targets and biomarkers of tissue-targeted diseases. CONCLUSION: A comprehensive functional analysis of housekeeping and tissue-specific genes showed that the biological function of housekeeping and tissue-specific genes was consistent with tissue origin. Network analysis revealed that tissue-specific networks have distinct network properties related to each tissue's function. Tissue 'signature networks' promise to be a rich source of targets and biomarkers for disease treatment and diagnosis.

Genome-wide functional synergy between amplified and mutated genes in human breast cancer.

A single cancer cell contains large numbers of genetic alterations that in combination create the malignant phenotype. However, whether amplified and mutated genes form functional and physical interaction networks that could explain the selection for cells with combined alterations is unknown. To investigate this issue, we characterized copy number alterations in 191 breast tumors using dense single nucleotide polymorphism arrays and identified 1,747 genes with copy number gain organized into 30 amplicons. Amplicons were distributed unequally throughout the genome. Each amplicon had distinct enrichment pattern in pathways, networks, and molecular functions, but genes within individual amplicons did not form coherent functional units. Genes in amplicons included all major tumorigenic pathways and were highly enriched in breast cancer-causative genes. In contrast, 1,188 genes with somatic mutations in breast cancer were distributed randomly over the genome, did not represent a functionally cohesive gene set, and were relatively less enriched in breast cancer marker genes. Mutated and gained genes did not show statistically significant overlap but were highly synergistic in populating key tumorigenic pathways including transforming growth factor beta, WNT, fibroblast growth factor, and PIP3 signaling. In general, mutated genes were more frequently upstream of gained genes in transcription regulation signaling than vice versa, suggesting that mutated genes are mainly regulators, whereas gained genes are mostly regulated. ESR1 was the major transcription factor regulating amplified but not mutated genes. Our results support the hypothesis that multiple genetic events, including copy number gains and somatic mutations, are necessary for establishing the malignant cell phenotype.

Examination of the effect of particle size on the radionuclide content of soils.

Information on the distribution of radiocaesium as a function of soil particle size is fundamental for its use as tracer in soil transport. Since the processes involved in soil erosion are known to remove and transport finer particles with larger efficiency, the aim of this work was to obtain data on the particle size versus radionuclide content distribution regarding the reference site of a soil erosion study. The analysis done was based on more than 5kg of source material and the changes happened to the radionuclide content of the different size fractions between the individual separation steps have been carefully monitored. About 10% of the total amount of (137)Cs present was found to be trapped in the serrations of larger stone-fragments while another 10% is transportable only during heavier storm-events. Within the remaining 80%, physical weathering products and clay particles have a (137)Cs-activity concentration, transportability and mass ratio of about 1:10, 1:2 and 1:1, respectively.

The genomic landscapes of human breast and colorectal cancers.

Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.

Modeling bursts and heavy tails in human dynamics.

The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behavior into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. Here we provide direct evidence that for five human activity patterns, such as email and letter based communications, web browsing, library visits and stock trading, the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. We show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times. In contrast, priority blind execution is well approximated by uniform interevent statistics. We discuss two queuing models that capture human activity. The first model assumes that there are no limitations on the number of tasks an individual can handle at any time, predicting that the waiting time of the individual tasks follow a heavy tailed distribution P(tau(w)) approximately tau(w)(-alpha) with alpha=3/2. The second model imposes limitations on the queue length, resulting in a heavy tailed waiting time distribution characterized by alpha=1. We provide empirical evidence supporting the relevance of these two models to human activity patterns, showing that while emails, web browsing and library visitation display alpha=1, the surface mail based communication belongs to the alpha=3/2 universality class. Finally, we discuss possible extension of the proposed queuing models and outline some future challenges in exploring the statistical mechanics of human dynamics.