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This paper presents a novel deep learning enabled, video based analysis framework for assessing the Unified Parkinsons Disease Rating Scale (UPDRS) that can be used in the clinic or at home. We report results from comparing the performance of the framework to that of trained clinicians on a population of 32 Parkinsons disease (PD) patients. In-person clinical assessments by trained neurologists are used as the ground truth for training our framework and for comparing the performance. We find that the standard sit-to-stand activity can be used to evaluate the UPDRS sub-scores of bradykinesia (BRADY) and posture instability and gait disorders (PIGD). For BRADY we find F1-scores of 0.75 using our framework compared to 0.50 for the video based rater clinicians, while for PIGD we find 0.78 for the framework and 0.45 for the video based rater clinicians. We believe our proposed framework has potential to provide clinically acceptable end points of PD in greater granularity without imposing burdens on patients and clinicians, which empowers a variety of use cases such as passive tracking of PD progression in spaces such as nursing homes, in-home self-assessment, and enhanced tele-medicine., Comment: Accepted by CVPR Workshops 2021

Next‐generation sequencing (NGS) has emerged as an affordable and reproducible means to query tumors for somatic genetic anomalies. To help interpret somatic NGS data, many institutions have created a molecular tumor board to analyze the results of NGS and make recommendations. This article evaluates the utility of cognitive computing systems to analyze data for clinical decision‐making., Background. Using next‐generation sequencing (NGS) to guide cancer therapy has created challenges in analyzing and reporting large volumes of genomic data to patients and caregivers. Specifically, providing current, accurate information on newly approved therapies and open clinical trials requires considerable manual curation performed mainly by human “molecular tumor boards” (MTBs). The purpose of this study was to determine the utility of cognitive computing as performed by Watson for Genomics (WfG) compared with a human MTB. Materials and Methods. One thousand eighteen patient cases that previously underwent targeted exon sequencing at the University of North Carolina (UNC) and subsequent analysis by the UNCseq informatics pipeline and the UNC MTB between November 7, 2011, and May 12, 2015, were analyzed with WfG, a cognitive computing technology for genomic analysis. Results. Using a WfG‐curated actionable gene list, we identified additional genomic events of potential significance (not discovered by traditional MTB curation) in 323 (32%) patients. The majority of these additional genomic events were considered actionable based upon their ability to qualify patients for biomarker‐selected clinical trials. Indeed, the opening of a relevant clinical trial within 1 month prior to WfG analysis provided the rationale for identification of a new actionable event in nearly a quarter of the 323 patients. This automated analysis took

Accumulating evidence demonstrated that while a small minority of the human genome encodes proteins a large portion of the genome encodes non-protein coding transcripts. Thus it has been suggested that such RNAs, including the recently described family of long non-coding RNAs (lncRNA) are key regulators of cellular homeostasis and transformation leading to tumorigenesis. Here we tested this hypothesis focusing on the role of lncRNAs in T cell leukemia (T-ALL), a pediatric blood cancer, characterized by Notch pathway activation. Using RNA-Sequencing we identified and catalogued a large number of T-ALL-specific lncRNAs targeted by the NOTCH pathway. We further focused on a single such transcript, LUNAR1 (LeUkemia-induced Non-coding Activator RNA-1), and demonstrated that through chromosomal looping it is able to control the expression of the IGF1R gene, IGF1 signaling and growth of T-ALL, providing evidence that suggests that lncRNAs could be used as biomarkers as well as therapy targets in human cancer.

High-grade serous ovarian carcinoma (HGSOC) and basal-like breast cancer (BLBC) share many features including TP53 mutations, genomic instability and poor prognosis. We recently reported that Elafin is overexpressed by HGSOC and is associated with poor overall survival. Here, we confirm that Elafin overexpression is associated with shorter survival in 1000 HGSOC patients. Elafin confers a proliferative advantage to tumor cells through the activation of the MAP kinase pathway. This mitogenic effect can be neutralized by RNA interference, specific antibodies and a MEK inhibitor. Elafin expression in patient-derived samples was also associated with chemoresistance and strongly correlates with bcl-xL expression. We extended these findings into the examination of 1100 primary breast tumors and six breast cancer cell lines. We observed that Elafin is overexpressed and secreted specifically by BLBC tumors and cell lines, leading to a similar mitogenic effect through activation of the MAP kinase pathway. Here too, Elafin overexpression is associated with poor overall survival, suggesting that it may serve as a biomarker and therapeutic target in this setting.

The sbv IMPROVER (systems biology verification—Industrial Methodology for Process Verification in Research) process aims to help companies verify component steps or tasks in larger research workflows for industrial applications. IMPROVER is built on challenges posed to the community that draws on the wisdom of crowds to assess the most suitable methods for a given research task. The Diagnostic Signature Challenge, open to the public from Mar. 5 to Jun. 21, 2012, was the first instantiation of the IMPROVER methodology and evaluated a fundamental biological question, specifically, if there is sufficient information in gene expression data to diagnose diseases. Fifty-four teams used publically available data to develop prediction models in four disease areas: multiple sclerosis, lung cancer, psoriasis, and chronic obstructive pulmonary disease. The predictions were scored against unpublished, blinded data provided by the organizers, and the results, including methods of the top performers, presented at a con...

Evaluating the performance of computational methods to analyze high throughput data are an integral component of model development and critical to progress in computational biology. In collaborative-competitions, model performance evaluation is crucial to determine the best performing submission. Here we present the scoring methodology used to assess 54 submissions to the IMPROVER Diagnostic Signature Challenge. Participants were tasked with classifying patients’ disease phenotype based on gene expression data in four disease areas: Psoriasis, Chronic Obstructive Pulmonary Disease, Lung Cancer, and Multiple Sclerosis. We discuss the criteria underlying the choice of the three scoring metrics we chose to assess the performance of the submitted models. The statistical significance of the difference in performance between individual submissions and classification tasks varied according to these different metrics. Accordingly, we consider an aggregation of these three assessment methods and present the approa...

Objective:To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.Methods:Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs.Results:More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts.Conclusions:The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible.ClinicalTrials.gov identifier:NCT02725684.

Introduction Objective monitoring of movement in Parkinson’s Disease (PD) is important to assess response to treatment. Currently, PD treatment decisions are made based on expert assessment and conduct of clinical rating scales and patient diaries. Each of these is insensitive, episodic and subjective with inherent rater bias. Wearable inertial sensor technology using accelerometers and gyroscopes, which can quantify gait metrics, have been studied for their ability to monitor PD signs. We sought to evaluate the performance of wearable inertial sensor technology in quantifying movement related to PD signs using the Mobility Lab system ( http://www.apdm.com ). Methods We compared endpoints of the Mobility Lab 2-min walk test in 41 healthy volunteers (HV) and in 25 PD patients. HV had two test sessions at greater than a one hour interval. Each PD patient also performed the task twice: once in ON state ( ∼ 1 h after administering the patient’s scheduled levodopa dose) and again in the OFF state (immediately prior to administering the patient’s next scheduled levodopa dose). PD patients were randomized with half performing on state evaluations prior to off state evaluations, and the other half reversing this order. Each subject walked 10 meter linear laps with a 180 degree turn at the apex for two minutes while wearing sensors on each limb, and the sternum and lumbar regions. Results We found the highest reliability of the following endpoints in HV: cadence (Pearson’s r = 0.9860), stride length (r = 0.963), and arm range of motion (r = 0.962). In PD patients, Mobility Lab endpoints including turn duration ( p = 0.003), gait speed (p = 0.009), stride length (p = 0.006) and arm range of motion (p = 0.02) significantly differed before and after levodopa intake, and correlated with the Unified Parkinson Disease Rating Scale Motor Score (e.g. turn duration r = 0.4). Conclusion The Mobility Lab System 2-min walk test generated endpoints with good reliability and detected changes in PD patients before and after levodopa. Wearable inertial sensors have the potential to enhance our ability to detect signals of efficacy in therapeutic development.

Introduction Reliably detecting ON/OFF states is important for monitoring PD treatment and progression. Currently, subjective patient diaries capture this information. We investigate if detection of motor signs of ON/OFF states can be achieved by using kinematic measurements from wearable sensor technology combined with a machine learning (ML) pipeline. Methods Twenty-five PD subjects (19 males, 69 ± 7 years) taking levodopa performed 10-m Instrumented Stand and Walk (ISAW) tests in their ON and OFF states while wearing Ambulatory Parkinson Disease Monitoring (APDM) sensors on their sternum, wrists, lumbar and lower extremities. A neurologist scored each ISAW according to the MDS-UPDRS-III. We analyzed 98 kinematic features for significance to neurologist total motor score and ON/OFF using both statistical (repeated-measures ANOVA, step-wise mixed-model regression, likelihood-ratio test, ridge regression) and ML methods. Results Twenty-two features significantly differed between patient reported ON/OFF states, with the most significant being trunk transverse range-of-motion (RofM), arm RofM, mid-swing elevation, stride length, turn velocity, steps in turn and toe out angles. Estimates from regression model showed average difference of 14 points between OFF/ON states in total UPDRS score and 9 points when adjusted for 5 significant features for individual baseline (mean trunk transverse RofM, right arm RofM, and toe out angle having highest effect; coeff. −8.67, −5.25, −3.36 respectively). Several approaches were employed for predicting ON/OFF states based on these features: direct binary classification (acc = 0.56), regression to total UPDRS score (acc = 0.76), regression to PIGD sub-score (acc = 0.64), and classification of ON–OFF/OFF–ON transitions using feature differences (Naive Bayes: acc = 0.74, AUC = 0.78; Random Forest: acc = 0.76, AUC = 0.90). Conclusion Wearable inertial sensors hold promise for detecting ON/OFF states in PD patients using an augmented ML approach. This could be particularly useful for monitoring response to therapy in an outpatient setting.

DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org.Availability: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools.

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. Contact: ebilal@us.ibm.com or gustavo@us.ibm.com. Supplementary information: Supplementary Data are available at Bioinformatics online.

Recent advances in technology present an important opportunity in medicine to augment episodic, expert-based observations of patients’ disease signs, obtained in the clinic, with continuous and sensitive measures using wearable and ambient sensors. In Parkinson's disease (PD), such technology-based objective measures have shown exciting potential for passively monitoring disease signs, their fluctuation, and their progression. We are developing a system to passively and continuously capture data from people with PD in their daily lives, and provide a real-time estimate of their motor functions, that is analogous to scores obtained during Part III of the human-administered Movement Disorder Society's Unified Parkinson's Disease assessment (MDS-UPDRS3). Our hypothesis is that complex human movements can be decomposed into movement primitives related to the performance of the MDS-UPDRS3 motor assessment. Toward this hypothesis, we developed a system for integrating and analyzing multiple streams of sensor data collected from volunteers executing the tasks based on the MDS-UPDRS3. In this paper, we show how we can leverage the data collected from MDS-UPDRS3 tasks to develop machine learning models that can identify movement primitives in activities of daily living.

Motivation: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development. However, in many cases, a naive ‘extrapolation’ between the two species has not succeeded. As a result, clinical trials of new drugs sometimes fail even after considerable success in the mouse or rat stage of development. In addition to in vitro studies, inter-species translation requires analytical tools that can predict the enriched gene sets in human cells under various stimuli from corresponding measurements in animals. Such tools can improve our understanding of the underlying biology and optimize the allocation of resources for drug development. Results: We developed an algorithm to predict differential gene set enrichment as part of the sbv IMPROVER (systems biology verification in Industrial Methodology for Process Verification in Research) Species Translation Challenge, which focused on phosphoproteomic and transcriptomic measurements of normal human bronchial epithelial (NHBE) primary cells under various stimuli and corresponding measurements in rat (NRBE) primary cells. We find that gene sets exhibit a higher inter-species correlation compared with individual genes, and are potentially more suited for direct prediction. Furthermore, in contrast to a similar cross-species response in protein phosphorylation states 5 and 25 min after exposure to stimuli, gene set enrichment 6 h after exposure is significantly different in NHBE cells compared with NRBE cells. In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods. Availability and implementation: Implementation of all algorithms is available as source code (in Matlab) at http://bhanot.biomaps.rutgers.edu/wiki/codes_SC3_Predicting_GeneSets.zip, along with the relevant data used in the analysis. Gene sets, gene expression and protein phosphorylation data are available on request. Contact: hormoz@kitp.ucsb.edu

Motivation: Using gene expression to infer changes in protein phosphorylation levels induced in cells by various stimuli is an outstanding problem. The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue. Results: Rat lung epithelial cells were treated with 52 stimuli, and gene expression and phosphorylation levels were measured. Competing teams used gene expression data from 26 stimuli to develop protein phosphorylation prediction models and were ranked based on prediction performance for the remaining 26 stimuli. Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation. In spite of the similar performance, the approaches used by these three teams, described in detail in this article, were different, with the average number of predictor genes per phosphoprotein used by the teams ranging from 3 to 124. However, a significant overlap of gene signatures between teams was observed for the majority of the proteins considered, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in the union of the predictor genes of the three teams for multiple proteins. Availability and implementation: Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091). Software implementation of the approach of Teams 49 and 75 are available at http://bioinformaticsprb.med.wayne.edu and http://people.cs.clemson.edu/∼luofeng/sbv.rar, respectively. Contact: gyanbhanot@gmail.com or luofeng@clemson.edu or atarca@med.wayne.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data. Results: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species. Availability and implementation: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip. Contact: meikelbiehl@gmail.com

The biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine and in the field of toxicology, and cannot be easily studied in humans. Thus, biomedical research has continuously relied on animal models for studying the impact of these compounds and attempted to 'translate' the results to humans. In this context, the SBV IMPROVER (Systems Biology Verification for Industrial Methodology for PROcess VErification in Research) collaborative initiative, which uses crowd-sourcing techniques to address fundamental questions in systems biology, invited scientists to deploy their own computational methodologies to make predictions on species translatability. A multi-layer systems biology dataset was generated that was comprised of phosphoproteomics, transcriptomics and cytokine data derived from normal human (NHBE) and rat (NRBE) bronchial epithelial cells exposed in parallel to more than 50 different stimuli under identical conditions. The present manuscript describes in detail the experimental settings, generation, processing and quality control analysis of the multi-layer omics dataset accessible in public repositories for further intra- and inter-species translation studies.

Background: Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results: We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions: A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission., American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship, National Science Foundation (U.S.). Graduate Research Fellowship Program

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models., Author Summary We developed an extensible software framework for sharing molecular prognostic models of breast cancer survival in a transparent collaborative environment and subjecting each model to automated evaluation using objective metrics. The computational framework presented in this study, our detailed post-hoc analysis of hundreds of modeling approaches, and the use of a novel cutting-edge data resource together represents one of the largest-scale systematic studies to date assessing the factors influencing accuracy of molecular-based prognostic models in breast cancer. Our results demonstrate the ability to infer prognostic models with accuracy on par or greater than previously reported studies, with significant performance improvements by using state-of-the-art machine learning approaches trained on clinical covariates. Our results also demonstrate the difficultly in incorporating molecular data to achieve substantial performance improvements over clinical covariates alone. However, improvement was achieved by combining clinical feature data with intelligent selection of important molecular features based on domain-specific prior knowledge. We observe that ensemble models aggregating the information across many diverse models achieve among the highest scores of all models and systematically out-perform individual models within the ensemble, suggesting a general strategy for leveraging the wisdom of crowds to develop robust predictive models.

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.

Several epidemiological studies have suggested a link between melanoma and breast cancer. Metabotropic glutamate receptor 1 (GRM1), which is involved in many cellular processes including proliferation and differentiation, has been implicated in melanomagenesis, with ectopic expression of GRM1 causing malignant transformation of melanocytes. This study was undertaken to evaluate GRM1 expression and polymorphic variants in GRM1 for associations with breast cancer phenotypes. Three single nucleotide polymorphisms (SNPs) in GRM1 were evaluated for associations with breast cancer clinicopathologic variables. GRM1 expression was evaluated in human normal and cancerous breast tissue and for in vitro response to hormonal manipulation. Genotyping was performed on genomic DNA from over 1,000 breast cancer patients. Rs6923492 and rs362962 genotypes associated with age at diagnosis that was highly dependent upon the breast cancer molecular phenotype. The rs362962 TT genotype also associated with risk of estrogen receptor or progesterone receptor positive breast cancer. In vitro analysis showed increased GRM1 expression in breast cancer cells treated with estrogen or the combination of estrogen and progesterone, but reduced GRM1 expression with tamoxifen treatment. Evaluation of GRM1 expression in human breast tumor specimens demonstrated significant correlations between GRM1 staining with tissue type and molecular features. Furthermore, analysis of gene expression data from primary breast tumors showed that high GRM1 expression correlated with a shorter distant metastasis-free survival as compared to low GRM1 expression in tamoxifen-treated patients. Additionally, induced knockdown of GRM1 in an estrogen receptor positive breast cancer cell line correlated with reduced cell proliferation. Taken together, these findings suggest a functional role for GRM1 in breast cancer.

Contains fulltext : 107772.pdf (Publisher’s version ) (Open Access) In non-cancerous cells, phosphorylated proteins exist transiently, becoming de-phosphorylated by specific phosphatases that terminate propagation of signaling pathways. In cancers, compromised phosphatase activity and/or expression occur and contribute to tumor phenotype. The non-receptor phosphatase, PTPN13, has recently been dubbed a putative tumor suppressor. It decreased expression in breast cancer correlates with decreased overall survival. Here we show that PTPN13 regulates a new signaling complex in breast cancer consisting of ErbB2, Src, and EphrinB1. To our knowledge, this signaling complex has not been previously described. Co-immunoprecipitation and localization studies demonstrate that EphrinB1, a PTPN13 substrate, interacts with ErbB2. In addition, the oncogenic V660E ErbB2 mutation enhances this interaction, while Src kinase mediates EphrinB1 phosphorylation and subsequent MAP Kinase signaling. Decreased PTPN13 function further enhances signaling. The association of oncogene kinases (ErbB2, Src), a signaling transmembrane ligand (EphrinB1) and a phosphatase tumor suppressor (PTPN13) suggest that EphrinB1 may be a relevant therapeutic target in breast cancers harboring ErbB2-activating mutations and decreased PTPN13 expression.

Breast cancer progression and metastasis are driven by complex and reciprocal interactions, between epithelial cancer cells and their surrounding stromal microenvironment. We have previously shown that a loss of stromal Cav-1 expression is associated with an increased risk of early tumor recurrence, metastasis and decreased overall survival. To identify and characterize the signaling pathways that are activated in Cav-1 negative tumor stroma, we performed gene expression profiling using laser microdissected breast cancer-associated stroma. Tumor stroma was laser capture microdissected from 4 cases showing high stromal Cav-1 expression and 7 cases with loss of stromal Cav-1. Briefly, we identified 238 gene transcripts that were upregulated and 232 gene transcripts that were downregulated in the stroma of tumors showing a loss of Cav-1 expression (p ≤ 0.01 and fold-change ≥ 1.5). Gene set enrichment analysis (GSEA) revealed "stemness," inflammation, DNA damage, aging, oxidative stress, hypoxia, autophagy and mitochondrial dysfunction in the tumor stroma of patients lacking stromal Cav-1. Our findings are consistent with the recently proposed "Reverse Warburg Effect" and the "Autophagic Tumor Stroma Model of Cancer Metabolism." In these two complementary models, cancer cells induce oxidative stress in adjacent stromal cells, which then forces these stromal fibroblasts to undergo autophagy/mitophagy and aerobic glycolysis. This, in turn, produces recycled nutrients (lactate, ketones and glutamine) to feed anabolic cancer cells, which are undergoing oxidative mitochondrial metabolism. Our results are also consistent with previous biomarker studies showing that the increased expression of known autophagy markers (such as ATG16L and the cathepsins) in the tumor stroma is specifically associated with metastatic tumor progression and/or poor clinical outcome.

Normal cellular behavior can be described as a complex, regulated network of interaction between genes and proteins. Targeted cancer therapies aim to neutralize specific proteins that are necessary for the cancer cell to remain viable in vivo. Ideally, the proteins targeted should be such that their downregulation has a major impact on the survival/fitness of the tumor cells and, at the same time, has a smaller effect on normal cells. It is difficult to use standard analysis methods on gene or protein expression levels to identify these targets because the level thresholds for tumorigenic behavior are different for different genes/proteins. We have developed a novel methodology to identify therapeutic targets by using a new paradigm called “gene centrality.” The main idea is that, in addition to being overexpressed, good therapeutic targets should have a high degree of connectivity in the tumor network because one expects that suppression of its expression would affect many other genes. We propose a mathematical quantity called “centrality,” which measures the degree of connectivity of genes in a network in which each edge is weighted by the expression level of the target gene. Using our method, we found that several SRC proto-oncogenes LYN, YES1, HCK, FYN, and LCK have high centrality in identifiable subsets of basal-like and HER2+ breast cancers. To experimentally validate the clinical value of this finding, we evaluated the effect of YES1 knockdown in basal-like breast cancer cell lines that overexpress this gene. We found that YES1 downregulation has a significant effect on the survival of these cell lines. Our results identify YES1 as a target for therapeutics in a subset of basal-like breast cancers.

We report results from the analysis of complete mitochondrial DNA (mtDNA) sequences from 112 Japanese semi-supercentenarians (aged above 105 years) combined with previously published data from 96 patients in each of three non-disease phenotypes: centenarians (99-105 years of age), healthy non-obese males, obese young males and four disease phenotypes, diabetics with and without angiopathy, and Alzheimer's and Parkinson's disease patients. We analyze the correlation between mitochondrial polymorphisms and the longevity phenotype using two different methods. We first use an exhaustive algorithm to identify all maximal patterns of polymorphisms shared by at least five individuals and define a significance score for enrichment of the patterns in each phenotype relative to healthy normals. Our study confirms the correlations observed in a previous study showing enrichment of a hierarchy of haplogroups in the D clade for longevity. For the extreme longevity phenotype we see a single statistically significant signal: a progressive enrichment of certain "beneficial" patterns in centenarians and semi-supercentenarians in the D4a haplogroup. We then use Principal Component Spectral Analysis of the SNP-SNP Covariance Matrix to compare the measured eigenvalues to a Null distribution of eigenvalues on Gaussian datasets to determine whether the correlations in the data (due to longevity) arises from some property of the mutations themselves or whether they are due to population structure. The conclusion is that the correlations are entirely due to population structure (phylogenetic tree). We find no signal for a functional mtDNA SNP correlated with longevity. The fact that the correlations are from the population structure suggests that hitch-hiking on autosomal events is a possible explanation for the observed correlations.

We report new results from the re-analysis of 672 complete mitochondrial (mtDNA) genomes of unrelated Japanese individuals stratified into seven equal sized groups by the phenotypes: diabetic patients, diabetic patients with severe angiopathy, healthy non-obese young males, obese young males, patients with Alzheimer's disease, patients with Parkinson's disease and centenarians. Each phenotype had 96 samples over 27 known haplogroups: A, B4a, B4b, B4c, B*, B5, D*, F1, F2, M*, M7a, M7b, M8, M9, D4a, D4b1, D4b2, D4d, D4e, D4g, D4h, D5, G, Z, M*, N9a, and N9b. A t-test comparing the fraction of samples in a haplogroup to healthy young males showed a significant enrichment of haplogroups D4a, D5, and D4b2 in centenarians. The D4b2 enrichment was limited to a subgroup of 40 of 61 samples which had the synonymous mutation 9296CT. We identified this cluster as a distinct haplogroup and labeled it as D4b2b. Using an exhaustive procedure, we constructed the complete list of "mutation patterns" for centenarians and showed that the most significant patterns were in D4a, D5, and D4b2b. We argue that if a selection for longevity appeared only once, it was probably an autosomal event which could be dated to after the appearance of the D mega-group but before the coalescent time of D4a, D5, and D4b2b. Using a simple procedure, we estimated that this event occurred 24.4 +/- 0.9 kYBP.