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Social factors influence the ability of coastal communities and their populations to anticipate, respond, resist, and recover from disasters. Galveston, TX, offers aunique opportunity to test the efficacy of social vulnerability mapping to identify inequalities in the ways that different parts of the community may react to a disaster. We describe spatial patterns of social vulnerability prior to 2008's Hurricane Ike and compare them to outcomes related to response, impact, recovery resources, and early stages of the rebuilding. Households and neighborhoods identified using vulnerability mapping experienced negative outcomes: later evacuation, a greater degree of damage sustained, fewer private and public resources for recovery, and slower and lower volumes of repair and rebuilding activity. Findings support using community vulnerability mapping as a tool for emergency management, hazard mitigation, and disaster recovery planning, helping communities to reduce losses and enhance response and recovery, ther...

Introduction:Lung cancer remains the leading cause of cancer-related death with poor survival due to the late stage at which lung cancer is typically diagnosed. Given the clinical burden from lung cancer and the relatively favorable survival associated with early-stage lung cancer, biomarkers for early detection of lung cancer are of important potential clinical benefit.Methods:We performed a global lung cancer serum biomarker discovery study using liquid chromatography-tandem mass spectrometry in a set of pooled non-small cell lung cancer case sera and matched controls. Immunoaffinity subtraction was used to deplete the top most abundant serum proteins; the remaining serum proteins were subjected to trypsin digestion and analyzed in triplicate by liquid chromatography-tandem mass spectrometry. The tandem mass spectrum data were searched against the human proteome database, and the resultant spectral counting data were used to estimate the relative abundance of proteins across the case/control serum pools. The spectral counting-derived abundances of some candidate biomarker proteins were confirmed with multiple reaction monitoring mass spectrometry assays.Results:A list of 49 differentially abundant candidate proteins was compiled by applying a negative binomial regression model to the spectral counting data (p < 0.01). Functional analysis with Ingenuity Pathway Analysis tools showed significant enrichment of inflammatory response proteins, key molecules in cell-cell signaling and interaction network, and differential physiological responses for the two common non-small cell lung cancer subtypes.Conclusions:We identified a set of candidate serum biomarkers with statistically significant differential abundance across the lung cancer case/control pools, which, when validated, could improve lung cancer early detection.

Climate change has become a more salient issue on the US policy agenda at all levels of government. Increasing empirical evidence and identification of its potential risks to human populations have increased media, public, and policy-maker interest. There is a gap, however, in our knowledge of sub-national decision-making which suggests several questions: Are community leaders deciding to take action in response to climate change action, and, if so, what is the solution focus – mitigation or adaptation? Our study addresses this gap in the literature by reporting the results of a national survey of local, regional, and state decision-makers whose organisations will be addressing community responses to the threat of climate change. We find that, in general, these agencies are not engaged in climate change policy, nor is the issue on their agendas. Among organisations considering policy responses, there is variation between types of agency and type of solution, mitigation versus adaptation.

This paper offers a potential measurement solution for assessing disaster impacts and subsequent recovery at the household level by using a modified domestic assets index (MDAI) approach. Assessment of the utility of the domestic assets index first proposed by Bates, Killian and Peacock (1984) has been confined to earthquake areas in the Americas and southern Europe. This paper modifies and extends the approach to the Indian sub-continent and to coastal surge hazards utilizing data collected from 1,000 households impacted by the Indian Ocean tsunami (2004) in the Nagapattinam district of south-eastern India. The analyses suggest that the MDAI scale is a reliable and valid measure of household living conditions and is useful in assessing disaster impacts and tracking recovery efforts over time. It can facilitate longitudinal studies, encourage cross-cultural, cross-national comparisons of disaster impacts and inform national and international donors of the itemized monetary losses from disasters at the household level.

This study examines the factors motivating local jurisdictions in the United States (U.S.) to voluntarily adopt policies that mitigate the anthropogenic sources of climate change when there are powerful political and economic incentives to do otherwise. Specifically, we explain adoption of the Cities for Climate Protection (CCP) program at the county level with indicators of climate change risk, climate stress, and opportunity for climate policy action. Statistical and spatial results indicate that counties with high risk, low stress, and high opportunity characteristics associated with climate change are significantly more likely to join the CCP campaign. Results also show that the odds of a locality joining the CCP are predictable by the landscape characteristics of spatial neighbors. Identifying a profile for likely adoption of climate change mitigation strategies can help decision makers effectively target local jurisdictions for recruitment into the CCP and similar programs in the future.

We examine the reasons why a US locality would voluntarily commit to the Cities for Climate Protection (CCP) campaign. Using geographic information systems analytic techniques, we map and measure a locality's vulnerability to climate-change impacts at the county level of spatial precision. We analyze multiple measures of climate-change vulnerability, including expected temperature change, extreme weather events, and coastal proximity, as well as economic variables, demographic variables, and civic-participation variables that constitute a locality's socioeconomic capacity to commit to costly climate-change policy initiatives. Bivariate and logistic regression results indicate that CCP-committed localities are quantitatively different to noncommitted localities on both climate-change risk and socioeconomic-capacity dimensions. On vulnerability measures, the odds of CCP-campaign participation increase significantly with the number of people killed and injured by extreme weather events, projected temperature change, and coastal proximity. On socioeconomic-capacity measures, the odds of CCP-campaign involvement increase with the percentage of citizens that vote Democrat and recycle, as well as the number of nonprofit organizations with an environment focus. The odds decrease in a county area as the percentage of the labor force employed in carbon-intensive industries increases.

Climate change and mitigation policies adopted by a locality indelibly impact urban form, landscape, and economy. The Cities for Climate Protection (CCP) has become a dominant movement organizing the localities to proactively address climate change. This study examines metropolitan area commitment to the CCP. Geographic information systems (GIS) and statistical techniques are used to analyze metros on dimensions of climate change risk, stress, and civic capacity. “Climate change risk” measures a metro area's coastal proximity, ecosystem sensitivity, and susceptibility to extreme weather events. “Climate change stress” summarizes transportation, energy, and production practices that adversely affect climate systems. “Civic capacity” estimates human capital and environmental concern variables that constitute a metro area's ability to commit to policy initiatives. Statistical results indicate that high stressor areas are significantly less likely to participate in the CCP campaign, and metros high in civic capacity are significantly more likely to commit to the CCP campaign.

The rising economic cost of floods in the United States cannot be explained solely by monetary inflation or growth in coastal populations. Damaging flood events are also influenced by the way society plans for and physically develops its communities, influencing where structures and impervious surfaces are concentrated and how hydrological systems are altered. We analyze 383 nonhurricane flood events in Florida counties between 1997 and 2001 to isolate how planning decisions and their effects on the built environment affect property damage caused by floods. Our results suggest that alteration of naturally occurring wetlands significantly increases the property damage caused by floods, all else equal. Also, nonstructural methods such as the Federal Emergency Management Agency's Community Rating System, while providing inexpensive means of reducing property damage directly, may also indirectly encourage more development in hazardous areas.

Although there is a growing body of research examining public perceptions of global climate change, little work has focused on the role of place and proximity in shaping these perceptions. This study extends previous conceptual models explaining risk perception associated with global climate change by adding a spatial dimension. Specifically, Geographic Information Systems and spatial analytical techniques are used to map and measure survey respondents' physical risk associated with expected climate change. Using existing spatial data, multiple measures of climate change vulnerability are analyzed along with demographic, attitudinal, and social contextual variables derived from a representative national survey to predict variation in risk perception. Bivariate correlation and multivariate regression analyses are used to identify and explain the most important indicators shaping individual risk perception. Analysis of the data suggests that the relationship between actual and perceived risk is driven by specific types of physical conditions and experiences.

Climate scientists note that the effects of climate change vary regionally. Citizen willingness to absorb the costs of adaptation and mitigation policies may correspond with these place-specific effects. Geographic information systems (GIS) analytic techniques are used to map and measure survey respondents' climate change risk at various levels of spatial resolution and precision. Spatial data are used to analyze multiple measures of climate change vulnerability along with demographic, attitudinal, and perception-based variables derived from a representative national survey of U.S. residents to predict variation in support for interventionist climate change policies. Ordinary Least Squares (OLS) regression results show that objective risk measures explain a modest amount of variation in our dependent variable. The effect of risk perception on climate policy support is far more robust. Of all variables examined, the extent to which citizens regard climate change as threatening to their material well-being ...

Recent interest in expanding offshore oil production within waters of the United States has been met with opposition by groups concerned with recreational, environmental, and aesthetic values associated with the coastal zone. Although the proposition of new oil platforms off the coast has generated conflict over how coastal resources should be utilized, little research has been conducted on where these user conflicts might be most intense and which sites might be most suitable for locating oil production facilities in light of the multiple, and often times, competing interests. In this article, we develop a multiple-criteria spatial decision support tool that identifies the potential degree of conflict associated with oil and gas production activities for existing lease tracts in the coastal margin of Texas. We use geographic information systems to measure and map a range of potentially competing representative values impacted by establishing energy extraction infrastructure and then spatially identify which leased tracts are the least contentious sites for oil and gas production in Texas state waters. Visual and statistical results indicate that oil and gas lease blocks within the study area vary in their potential to generate conflict among multiple stakeholders.

A field study team interviewed local government officials, community-based organizations, and nongovernmental organizations to assess community capacity and emergency response to the tsunami. In addition, 1,000 households on the Nagapattinam coast were randomly surveyed to assess household capacity and disaster response. The results presented here show that the district government did have an emergency plan in place, but it was modeled on a neighboring district's plan and focused almost exclusively on floods and drought relief. The local governmental response, therefore, was dominated by skilled improvisation, assisted by state, union, and private sector resources. The nongovernmental and community-based organizations' response was massive, and an attempt was made by the local government to organize and structure the voluntary response.

Mass-spectrometry (MS) based proteomics has become a key enabling technology for the systems approach to biology, providing insights into the protein complement of an organism. Bioinformatics analyses play a critical role in interpretation of large, and often replicated, MS datasets generated across laboratories and institutions. A significant amount of computational effort in the workflow is spent on the identification of protein and peptide components of complex biological samples, and consists of a series of steps relying on large database searches and intricate scoring algorithms. In this work, we share our efforts and experience in efficient handling of these large MS datasets through database indexing and parallelization based on multiprocessor architectures. We also identify important challenges and opportunities that are relevant specifically to the task of peptide and protein identification, and more generally to similar multi-step problems that are inherently parallelizable.

In recent years, we have seen the terrifying impacts of natural disasters, including Hurricane Katrina, the Wenchuan and Kobe earthquakes, the Fukushima tsunami and nuclear disaster, and, most recently, 2012’s Hurricane Sandy. Globally, the average annual number of natural disasters reported has more than doubled since 1980., These catastrophes are increasing in the number of meteorological events (tropical storms, severe weather, winter storms, hail, tornadoes, and local storms), hydrological events (flash floods, river floods, storm surge, and landslides), and climatological events (heatwaves, freezes, wildfires, and drought). Although geophysical events, such as earthquakes and volcanic eruptions, have remained more stable, there has been catastrophic damage to structures and lives, most notably seen in the Kobe earthquake, Wenchuan earthquake, and, more recently, earthquakes in Haiti in 2010 and Japan in 2011. We are experiencing not only an increased number of events but also an increase in their magnitude or severity. The number of “devastating” catastrophes (those with more than 500 fatalities or more than US$650 million in overall losses) and “great” catastrophes (those with more than 2,000 fatalities, 200,000 homeless, severe hits to the gross domestic product (GDP), or the country being dependent on international support) continues to climb globally (figure 1.1).

This chapter introduces and discusses hazard mitigation planning and plans as a critical step in mobilizing a community to increase disaster resiliency. Hazard mitigation plans are in many respects a recent policy tool. Introduced by the federal government, hazard mitigation plans are an approach to help communities better understand their disaster vulnerability, identify strategies and actions the community can use to help lessen their vulnerabilities, and develop priorities for funding these mitigation actions and projects. The Disaster Mitigation Act of 2000 began the process of encouraging local jurisdictions to develop local hazard mitigation plans if the local jurisdiction wanted to gain federal funding to help implement mitigation projects and actions. By mid-2007 approximately 14,000 of these plans had been approved by the Federal Emergency Management Agency (FEMA) throughout the United States. As of 2011, there were more than 26,000 approved plans.

Disasters occur when physical and social systems interact with natural or technological hazards. The characteristics of the hazard itself are typically the most significant determinants of damage and loss. For example, in our work in Galveston, the exposure to storm surge (proximity to the bay side of the island) was the most important predictor of damage. Hazard exposure depends, to some degree, on the geographic location of the community.

Consistency, in a person, is defined as “the quality or fact of staying the same at different times.” This implies that various dimensions of a person such as thought, behavior, feelings, reason, and will are consistent with one another over time. This does not necessarily guarantee consistent outcomes, but it is compatible with opinions and values of the person. When consistency is applied to human groups, communities, organizations, and companies, researchers identify two important aspects: one that stands together as a whole and a reasonable or logical harmony between parts.

The fact basis for both hazard mitigation and comprehensive planning has long been based on hazard exposure and physical or structural vulnerability. As discussed in chapter 4, hazard exposure is a function of the nature of the hazard agent and its potential to affect the geography of urban areas captured in risk maps. Physical or structural vulnerability, on the other hand, is a function of the location of the population and the built environment relative to the hazard. In other words, hazards become disasters when they interact with populated areas. When they strike communities, hazards interact with physical systems that include elements of the built and natural environment that are often taken for granted (figure 5.1). How often do we think about the pipes that carry our water or electricity? Do we ever consider the investment and value of wastewater or sewage facilities and the strength and integrity of our schools or fire stations? Thus, physical vulnerability is the susceptibility to damage and loss based on the interaction between exposure and physical characteristics. These include the following

With the previous chapter’s definitions of resilience and community capital assets in hand, we now turn to a broad conceptualization of the disaster management phases. Actions taken to build resilience in a community can occur at any of the four phases of disaster management: mitigation, preparedness, response, and recovery.

Our findings with respect to hazard mitigation planning were somewhat discouraging but not completely unexpected given the literature. The basic patterns we saw in Texas were similar to those found in the variety of states examined by Lyles, Berke, and Smith. Hazard mitigation plans are complying with Federal Emergency Management Agency (FEMA) guidelines but are often quite weak when it comes to actually assessing community vulnerabilities and risks, and the proposed policies and actions tend to be limited in scope, focusing on structural mitigation solutions and more traditional emergency management approaches. These findings led us to consider whether the narrow scope of mitigation actions, particularly the nonstructural policies and strategies, proffered by these plans was perhaps a function of an already narrow repertoire of planning polices and strategies actually being used by jurisdictions in the first place.

To begin tackling the problem of increased vulnerability to natural disasters, we must understand what we are trying to achieve. In recent years, the term resilience has gained popularity, but it is used in widely varying ways. All communities should strive for resilience, but what does it mean? Resilience has different definitions arising from a range of disciplines that use the concept, including natural hazard management, ecology, psychology, sociology, geography, psychiatry, and public health. These different perspectives mean that resilience is a widely used term that can take on different meanings in different contexts. The following is an in-depth look at the ecological and social aspects of resilience as defined in various fields of research.

A critical piece, and often the most neglected piece, of resilience to disaster is the identification and mapping of a community’s social vulnerabilities. When disaster strikes, its impact is not just a function of its magnitude and where it strikes. Development patterns characterized by sprawl, concentrated poverty, and segregation shape urban environments in ways that isolate vulnerable populations so that poor and rich, white and black, owners and renters, primary residents and vacationers, are separated from one another in clusters and pockets across the community. In many communities, if not most, the social geography interacts with the physical geography to expose vulnerable populations to greater risk. Vulnerable populations are less likely to have access to both information and resources that would allow them to anticipate and respond to a real or perceived threat, yet they are more often than not the groups who most need to attend to warnings to evacuate or seek shelter.

Public perceptions of risk from climate change are an important determinant of the willingness of citizens to support climate change mitigation and adaptation policies. Although there is a growing body of research focusing on a variety of individual, cultural, and organisational factors that affect an individual's perception of risk, only a few studies have adopted a multivariate analytical approach to understand public perceptions of climate change risks. This study extends earlier interdisciplinary research initiatives and proposes a more comprehensive, integrated model for understanding climate change risk perception. Using measures of objective risk, individual climate stress, and individual capacity, we explain public perceptions of climate change risks. The analysis is based on a national representative survey of US citizens. Geographic information systems and spatial analytical techniques are used to supplement the survey data with measures of objective risk associated with the location of each respondent. Analysis of the data using multivariate regression suggests that increased objective risk and individual capacity result in significantly higher perception of risk from climate change, whereas higher individual climate stress results in lower risk perceptions.

Background: Lower risk non-del5q MDS patients (pts) have limited treatment options and majority pts are transfusion dependent. Erythropoiesis stimulating agents (ESA) are first line of therapy for LR-MDS pts but almost half are resistant to ESA or become resistant after responding. Hence, LR-MDS represents an area of unmet medical need for novel agents to improve hematopoiesis and reduce transfusion dependency. Rigosertib (RIG) blocks RAS-mediated activation of proteins containing a common RAS binding domain, or RBD (Athuluri-Divaker, et al; Cell 165; 643'16). Since many pathways include proteins that employ RBD (i.e. ras, raf PI3-Kinase), interference with RBD provides a novel approach to block proliferation. While RIG showed encouraging response in LR-MDS pts (Tycko, et al; Blood 2013; 122:2745), there is a need for a biomarker that can differentiate responders from non-responders. To correlate genomics with response to RIG, we employed computational tools to integrate molecular data and observed responses. Aim: To determine predictive value of a computational biology derived genomic signature in LR-MDS pts who are treated with RIG. Methods: Materials were derived from Phase I and II studies of RIG in LR-MDS pts. Efficacy was reported as transfusion independence. Bone marrow samples were collected and processed using standard methods. DNA was extracted from bone marrow mononuclear cells or T-cells (germline control) purified from peripheral blood samples. Exome sequencing was performed using Agilent SureSelect Human All Exon v4 - 51Mb kit and HiSeq 2000. Raw sequences were aligned to the human genome build hg19 using BWA software; SNPs and In/Dels analysis was performed using PICARD, SAMTOOLS and GATK. These results, entered into predictive computational biology software (Cellworks Group), generated disease-specific protein network maps using PubMed and other online resources. Digital drug simulations are conducted by measuring drug effect on a score: a composite of cell proliferation, viability and apoptosis. Each patient-specific network map was screened for RIG reduced progression in a dose-respondent manner. Computer predictions were blindly correlated with clinical outcomes. Results: Predicted response was blindly correlated with the clinical outcome for 19 pts: Results showed 16 matches and 3 mismatches; and the following predictive test statistics: PPV - 81.25%, NPV - 100%, Sensitivity - 100%: accuracy of the predictive test at 84.21%. Modeling of LR-MDS pts predicted amplification of genes MYC, FNTA on chromosome 8; amplification of genes LGALS3, AJUBA, MAX on chromosome 14; TIAM1 on chromosome 21; HMGCR, on chromosome 5; PDPK1, MAPK3, PLK1 on chromosome 16 and PIK3CA, RAF1 on chromosome 3 correlated with increased response to RIG. Aberrations that enhanced the downstream of RAS signaling via the RAF-ERK or PI3K-AKT-MTOR pathway increased the signal flow through the drug response paths. MYC amplification on chromosome 8 via increasing the Purine synthesis pathway and production of GTP increased the RAS-ERK and RHEB-MTOR signaling. MYC also enhanced CCND1 production/proliferation. Prenylation pathway genes FNTA and HMGCR amplification also affect RAS and RHEB; enhance the drug response pathways. Another pathway that was found to be significant in the drug response, reducing proliferation, was inhibition of PLK1 and AURKA. Amplification of AJUBA, PLK1 made this pathway more significant and improved drug response via inhibition of proliferation via TIAM1-RAC1-PAK1-AURKA-PLK1 and RAF-PLK1 pathway. Deletion of TP53 and NF1 on chromosome 17 did not correlate with response. MYC amplification was seen in LR and HR MDS pts and correlated with response to RIG. However, the computational biology method showed that MYC amplification alone is not a predictor for response since there were non-responder profiles with MYC amplification and responder profiles with MYC deletion that had other aberrations in genes on chromosomes 3, 14, 16 or 21. Conclusions: A predictive method that models multiple genomic abnormalities simultaneously showed greater than 80% correlation between RIG mediated protein network perturbations and clinical outcomes in LR-MDS. The method also explained lack of response and highlighted resistance pathways that could be targeted to recover sensitivity. We also established eligibility criteria for greater precision enrollment in future trials. Disclosures Fruchtman: Onconova: Employment. Petrone:Onconova Therapeutics, Inc.: Employment. Zbyszewski:Onconova Therapeutics, Inc.: Employment. Hoffman:Onconova Therapeutics, Inc.: Employment. Vali:Cellworks Group: Employment. Singh:Cellworks Group: Employment. Usmani:Cellworks: Employment. Grover:Cellworks Group: Employment. Abbasi:Cellworks: Employment.

Background: MDS is a marrow stem cell disorder with limited treatments. Due to outcome heterogeneity of MDS, it is imperative to identify prognostic tools for patients in clinical trials. Rigosertib (RIG) is a RAS-mimetic that inhibits cellular signaling pathways by binding to the RAS-binding Domain found in RAS effector proteins. No approved treatment options are available for Higher-risk myelodysplastic syndromes (HR-MDS) pts after Hypomethylating Agent (HMA) failure. The "ONTIME" trial was a Phase III, randomized study of rigosertib in a heterogeneous population of HR-MDS pts who failed to respond to or progressed on HMAs. Aim: To determine the predictive value of a genomics-informed computational biology method and to design a patient selection signature in HR-MDS patients who are treated with RIG. Methods: ONTIME trial included HR-MDS pts who had progressed on (37% of total enrollment), failed to respond to (25%), or relapsed after (38%) HMA treatment. Patients were randomized 2:1 to receive RIG (199 pts) or best supportive care (BSC) (100 pts) (Garcia-Manero et al, Lancet Oncology, 2016).The primary endpoint was overall survival, analyzed on an intention-to-treat basis using the Kaplan-Meier method. Genomic DNA was isolated from single microscopic slides from 153 pts and subjected to sequence analysis of a "myeloid panel" comprising of 24 selected loci frequently mutated in MDS and AML. Standardized cytogenetic investigations were performed. The chromosome aberrations and clone definition followed the International System for Cytogenetic Nomenclature. Depending on the aberrations detected during karyotyping, further probes were applied. A complex karyotype was defined as ≥3 independent aberrations within 1 clone. These genomic results were input into predictive computational biology software (Cellworks Group, Fig. 1), which generates disease-specific dysregulated protein network maps using PubMed and other resources. Digital drug simulations are conducted by quantitatively measuring drug effect on a cell growth score, which is a composite of cell proliferation, viability and apoptosis. Each patient-specific protein network map was digitally screened for the extent by which RIG reduced disease growth in a dose-respondent manner. Clinical outcomes were prospectively recorded. Results: Based on input data from cell-culture studies the predicted response was blindly correlated with the clinical outcome for 54 patients with the following predictive test statistics: positive predictive value (PPV) - 85%, negative predictive value (NPV) - 94.12%, Sensitivity - 89.47%, Specificity - 91.43% and an accuracy of the predictive test at 90.74%. The accuracy of the genomics-informed computer method was significantly greater than empiric drug administration. New genomic signature rules were discovered to correlate with clinical response after RIG treatment. The predictive computational analysis identified a novel signature for selecting HR-MDS patient's responding to RIG. We identified patients with either trisomy 8 or trisomy 21 that also do not have any of the following aberrations including: Del 5q or Del 7/7q or Del 3 or Del 14 or Del 16 or Del 20, would be responders to the drug (Trisomy 8 OR Trisomy 21) AND NOT (Del 5 q OR (Del 7/7q OR Del 3 OR Del 14 or Del 16 or Del 20). We validated this patient selection rule prospectively via statistical correlation methods and through a computer based simulation of a clinical trial generated predicted Kaplan-Meir curve with a significant p-value of 0.003 (Fig. 2). Conclusions: A predictive computational method that models multiple high-risk MDS genomic abnormalities simultaneously showed greater than 90% correlation between protein network perturbations and clinical outcomes. The computational method also helps explain reasons for lack of response to RIG and highlights resistance pathways that could be targeted to recover chemo-sensitivity. This technology also established eligibility criteria for precision enrollment in drug development trials. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures Petrone: Onconova Therapeutics, Inc.: Employment. Fruchtman:Onconova: Employment. Brownstein:Onconova Therapeutics, Inc.: Consultancy. Loferer:Onconova Therapeutics, Inc.: Employment. Azarnia:Onconova Therapeutics, Inc.: Employment. Vali:Cellworks Group: Employment. Singh:Cellworks Group: Employment. Usmani:Cellworks: Employment. Grover:Cellworks Group: Employment. Abbasi:Cellworks: Employment. Silverman:Onconova Therapeutics, Inc.: Patents & Royalties: Co-Patent holder for the combination of azacitidine and rigosertib, Research Funding.

The Empirical Proteomic Ontology Knowledge Base (EPO-KB) is an online database that represents current knowledge of biomarkers and contains associations between mass-to-charge (m/z) ratios of mass-spectrometry peaks to proteins. Such a database is a useful tool for identifying putative proteins associated with a m/z ratio. At present, EPO-KB contains data that have been extracted from 120 published research papers. It has been used in successful identification of a protein associated with a biomarker.

Floods continue to pose the greatest threat to the property and safety of human communities among all natural hazards in the United States. This study examines the relationship between the built environment and flood impacts in Texas, which consistently sustains the most damage from flooding of any other state in the country. Specifically, we calculate property damage resulting from 423 flood events between 1997 and 2001 at the county level. We identify the effect of several built environment measures, including wetland alteration, impervious surface, and dams on reported property damage while controlling for biophysical and socio-economic characteristics. Statistical results suggest that naturally occurring wetlands play a particularly important role in mitigating flood damage. These findings provide guidance to planners and flood managers on how to alleviate most effectively the costly impacts of foods at the community level.

Background: The observation that B-cell receptor (BCR) signaling play a critical role in the survival of malignant Waldenstrom macroglobulinemia (WM) cells is highlighted by clinical efficacy of the BTK-inhibitor, ibrutinib in WM patients. BCR signaling leads to downstream activation of several intermediary components whose activity results in increased cell proliferation. Although disruption of this axis at the proximal end (BCR) has proved successful in WM, the exact role and impact of targeting the distal intermediaries remains unknown, particularly in aggressive forms of the disease. This is important as induction of resistance to ibrutinib is clearly documented which can either be mediated through mutational changes in BTK or other members within the BCR signalosome. Using a novel simulation-based approach, we reverse-engineered the WM cell molecular architecture to uncover the role of oncogenic intermediaries distal to the BCR complex in advanced-stage WM and conducted a virtual drug-screen targeting these pathways with in vitro validation. Methods: The human WM cell line, RPCI-WM1, was used in simulation and validation experiments. RPCI-WM1 was established from a highly drug-refractory advanced disease stage patient refractory to both rituximab and bortezomib. Publically available as well as proprietary genomic and cytogenetic data was utilized for the creation of the RPCI-WM1 patient avatar, which through simulation identified the salient and prominently dysregulated cellular pathways. Importantly, illustrating these pathways highlights common convergence points on increased proliferation and viability. These convergence points were then directly and indirectly targeted by simulated testing of a library of FDA approved drugs and those impacting these dysregulated pathways were shortlisted. A standardized library of equations models all the biological reactions such as enzymatic reactions, allosteric binding and protein modulation by phosphorylation, de-phosphorylation, ubiquitination, acetylation, prenylation and others. A library of FDA approved drug agents (n~150) and those in clinical study has been developed and was simulated individually and in combination on the RPCI-WM1 (advanced stage WM patient) avatar. Results: MYD88L265P mutation, absence of CXCR4 mutations and additional chromosomal aberrations (derivatives, translocations, deletions and amplifications of chromosomes 3, 6, 9, 13, 18 and 19), which are notable features of RPCI-WM1 cells were configured and modeled in silico. The RPCI-WM1 patient avatar was predicted to have increased IRAK1/4 engagement due to MYD88 mutation and high copy number (CN) of IL18. Downregulation of DUSP1 through MYD88-mediated signaling was noted to result in high ERK activity. Increase gene copy number of both FOS and ETS1, which are downstream of ERK, were noted. Notably, FOS is a key regulator of the AP1 complex, whose activity is regulated upstream by ERK. The transcription factor, ETS1 was also predicted as over expressed and ERK-mediated phosphorylation of ETS1 regulates the activity of ETS1. The RPCI-WM1 patient avatar model also indicated high AKT due to indirect convergence of multiple aberrations. Drug screening revealed sensitivity to the MEK inhibitor, binimetinib. Although no copy number variation in MEK-ERK pathway genes were detected, per simulation, MYD88 mutation through inactivation of DUSP1 activated ERK and downstream associated survival pathways. The simulation predictions were experimentally validated. As predicted, binimetinib significantly inhibited proliferation and viability of RPCI-WM1 cells (IC50 Conclusions: Our study demonstrates the functional role and impact of MEK1/2 - an oncogenic component distal to the BCR, and whose activity can be targeted with binimetinib to elicit a lethal effect in advanced-stage WM cells. We also show the utility of a novel technology, which is capable of integrating genome-wide molecular data points to emulate the salient genomic drivers of a given tumor cell and test its sensitivity to numerous drugs in a high-throughput manner for truly personalized therapy. Disclosures Vali: Cellworks Group, Inc.: Employment, Equity Ownership. Kumar:Cellworks Group, Inc.: Employment. Singh:Cellworks Group, Inc.: Employment. Usmani:3Cellworks Research India Limited: Employment. Grover:3Cellworks Research India Limited: Employment. Abbasi:Cellworks Group, Inc.: Employment, Equity Ownership.

2519 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of GBM patients remains relatively poor. Therapy selection is often based on information considering only a single aberration and ignoring other patient-specific omics data which could potentially enable more effective treatment selection. The Cellworks Singula™ report predicts response for physician prescribed therapies (PPT) using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. We test the hypothesis that Singula is a superior predictor of progression-free survival (PFS) and overall survival (OS) compared to PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 109 GBM patients aged 17 to 83 years treated with PPT. Patient omics data was available from TCGA. Singula uses PubMed to generate protein interaction network activated and inactivated disease pathways. We simulated PPT for each patient and calculated the quantitative drug effect on a composite GBM disease inhibition score based on specific phenotypes while blinded to clinical response. Univariate and multivariate proportional hazards (PH) regression analyses were performed to determine if Singula provides predictive information for PFS and OS, respectively, above and beyond age and PPT. Results: In univariate analyses, Singula was a significant predictor of both PFS (HR = 4.130, p < 0.000) and OS (HR = 2.418, p < 0.0001). In multivariate PH regression analyses, Singula (HR = 4.033, p < 0.0001) remained an independent predictor of PFS after adjustment for PPT (p = 0.1453) and patient age (p = 0.4273). Singula (HR = 1.852, p = 0.0070) was also a significant independent predictor of OS after adjustment for PPT (p = 0.4127) and patient age (p = 0.0003). Results indicate that Singula is a superior predictor of both PFS and OS compared to PPT. Singula provided alternative therapy selections for 29 of 52 disease progressors detected by Cellworks. Conclusions: Singula is a superior predictor of PFS and OS in GBM patients compared to PPT. Singula can identify non-responders to PPT and provide alternative therapy selections.