the national Lung Screening trial (nLSt) demonstrated that screening with low-dose computed tomography (LDCT) is associated with a 20% reduction in lung cancer mortality. One potential limitation of LDct screening is overdiagnosis of slow growing and indolent cancers. in this study, peritumoral and intratumoral radiomics was used to identify a vulnerable subset of lung patients associated with poor survival outcomes. incident lung cancer patients from the nLSt were split into training and test cohorts and an external cohort of non-screen detected adenocarcinomas was used for further validation. After removing redundant and non-reproducible radiomics features, backward elimination analyses identified a single model which was subjected to Classification and Regression tree to stratify patients into three risk-groups based on two radiomics features (nGtDM Busyness and Statistical Root Mean Square [RMS]). The final model was validated in the test cohort and the cohort of non-screen detected adenocarcinomas. Using a radio-genomics dataset, Statistical RMS was significantly associated with FOXF2 gene by both correlation and two-group analyses. our rigorous approach generated a novel radiomics model that identified a vulnerable high-risk group of early stage patients associated with poor outcomes. these patients may require aggressive follow-up and/or adjuvant therapy to mitigate their poor outcomes. The National Lung Screening Trial (NLST) demonstrated that annual screening with low-dose helical computed tomography (LDCT) compared to chest radiography is associated with a 20% relative reduction in lung cancer mortality among high-risk individuals 1. However, LDCT screening can lead to overdiagnosis and overtreatment of slow growing, indolent cancers that may pose no threat if left untreated 2,3. Prior post-hoc analyses of the NLST have estimated that 18-22.5% of screen-detected cancers would not become symptomatic in a patient's lifetime and would remain as indolent lung cancer 4. At present there is limited data regarding the potential harmful impact of overdiagnosis on lung cancer outcomes; however, studies have suggested overdiagnosis is associated with increased operative mortality, severe disability among survivors, and reduction in longer term disease-free survival attributed to loss of pulmonary reserve 5. Though clinical guidelines provide recommendations for the management of screen-detected nodules, there are limited tools to discriminate between indolent and aggressive lung cancers diagnosed in the lung cancer screening setting 6-9. As such, biomarkers that can classify behavior of screen-detected lung cancers is an unmet clinical need since prior studies have suggested that 10 to 27% of lung cancers are over-diagnosed in lung cancer screening 10-13. Quantitative image features, also known as radiomics 14 , are non-invasive biomarkers that are generated from medical imaging and reflect the underlying tumor pathophysiology and heterogeneity. Radiomics have many advantages over circulating and tissue-based bio...

In 2017, approximately 5 million Americans were living with Alzheimer’s disease (AD), and it is estimated that by 2050 this number could increase to 16 million. In this study, we apply mathematical optimization to approach microarray analysis to detect differentially expressed genes and determine the most correlated structure among their expression changes. The analysis of GSE4757 microarray dataset, which compares expression between AD neurons without neurofibrillary tangles (controls) and with neurofibrillary tangles (cases), was casted as a multiple criteria optimization (MCO) problem. Through the analysis it was possible to determine a series of Pareto efficient frontiers to find the most differentially expressed genes, which are here proposed as potential AD biomarkers. The Traveling Sales Problem (TSP) model was used to find the cyclical path of maximal correlation between the expression changes among the genes deemed important from the previous stage. This leads to a structure capable of guiding biological exploration with enhanced precision and repeatability. Ten genes were selected (FTL, GFAP, HNRNPA3, COX1, ND2, ND3, ND4, NUCKS1, RPL41, and RPS10) and their most correlated cyclic structure was found in our analyses. The biological functions of their products were found to be linked to inflammation and neurodegenerative diseases and some of them had not been reported for AD before. The TSP path connects genes coding for mitochondrial electron transfer proteins. Some of these proteins are closely related to other electron transport proteins already reported as important for AD.

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.

Multiple studies suggest that chronic stress accelerates the growth of existing tumors by activating the sympathetic nervous system. Data suggest that sustained adrenergic signaling can induce tumor growth, secretion of pro-inflammatory cytokines, and macrophage infiltration. Our goal was to study the role of adrenergic-stimulated macrophages in ovarian cancer biology. Cytokine arrays were used to assess the effect of adrenergic stimulation in pro-tumoral cytokine networks. An orthotopic model of ovarian cancer was used to assess the in vivo effect of daily restraint stress on tumor growth and adrenergic-induced macrophages. Cytokine analyses showed that adrenergic stimulation modulated pro-inflammatory cytokine secretion in a SKOV3ip1 ovarian cancer cell/U937 macrophage co-culture system. Among these, platelet-derived growth factor AA (PDGF-AA), epithelial cell-derived neutrophil-activating peptide (ENA-78), Angiogenin, vascular endothelial growth factor (VEGF), granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-5 (IL-5), Lipocalin-2, macrophage migration inhibitory factor (MIF), and transferrin receptor (TfR) were upregulated. Enriched biological processes included cytokine-mediated signaling pathways and positive regulation of cell proliferation. In addition, daily restraint stress increased ovarian cancer growth, infiltration of CD68+ macrophages, and expression of PDGF-AA in orthotopic models of ovarian cancer (SKOV3ip1 and HeyT30), while zoledronic acid, a macrophage-depleting agent, abrogated this effect. Furthermore, in ovarian cancer patients, high PDGFA expression correlated with worse outcomes. Here, it is shown that the adrenergic regulation of macrophages and PDGFA might play a role in ovarian cancer progression.

Cancer is the leading cause of death in Puerto Rico (PR). Hurricane Maria (HM) and its aftermath lead to widespread devastation on the island, including the collapse of the healthcare system. Medically fragile populations, such as cancer survivors, were significantly affected. The goal of this study was to assess the impact of HM on barriers to care, emotional distress, and inflammatory biomarkers among cancer survivors in PR. This exploratory longitudinal study was conducted in health care facilities and community support groups from PR. Cancer survivors (n = 50) and non-cancer participants (n = 50) completed psychosocial questionnaires and provided blood samples that were used to assess inflammatory cytokines levels. Among this cohort, we identified 41 matched cancer survivors/noncancer participants pairs. Data were analyzed through descriptive, frequencies, correlational, and regression analyses. Cancer survivors that were affected by HM reported increased barriers in accessing medical care, which were directly associated with anxiety, perceived stress, and post-traumatic symptomatology. Moreover, being a cancer survivor, predicted more barriers to receiving health care, especially in the first six weeks after the event, after which the effect was attenuated. Several inflammatory cytokines, such as CD31, BDNF, TFF3, Serpin E-1, VCAM-1, Vitamin D BP, and PDGF-AA, were significantly upregulated in cancer survivors while MMP9 and Osteopontin both had significant positive correlations with barriers to care. HM significantly impacted Puerto Ricans psychosocial wellbeing. Cancer survivors had significant barriers to care and showed increased serum inflammatory cytokines but did not show differences in anxiety, stress, and post-traumatic symptoms compared to non-cancer participants. Natural disasters can significantly alter an individual's daily life and lead to psychological distress, particularly in medically fragile populations such as cancer survivors. Hurricane Maria (HM) made landfall in Puerto Rico (PR) on September 20, 2017, as Category 4 storm, killing an estimated 2,975 people and causing an estimated $90 billion in damages 1-3. Widespread devastation included loss of power and potable water infrastructure; destruction of buildings, bridges, and roads; lack of telecommunications; and closing of ports and airports 4. Lack of access to food and clean water was a significant problem for residents of PR 4. Mudslides rendered many roads in rural