This document discusses a pathway-guided approach to predicting the impact of mutations by integrating multiple omics data sources to understand how genes function in pathways. It predicts the functional consequences of mutations by quantifying their effects on surrounding pathways. Pathway signatures can implicate mutations in novel genes and identify critical points that distinguish subtypes. The approach uses curated pathway models and patient omics data to infer pathway activities, which are then used as predictive features for outcomes like drug response or subtypes. This reduces the number of features compared to using individual genes.
Mining of electronic health records (EHRs) has recently gained importance. However, most efforts are restricted to analyzing drugs, diseases and their associations. In biomedical research, network analysis has provided the conceptual framework to interpret protein-protein interactions or gene-disease association networks via large-scale network maps.
We analyze associations between drugs, diseases, devices and procedures mined from EHRs using network analysis to extract hidden “modules of care” for hypothesis generation. In particular, we annotated the textual notes of the EHRs of one million patients in the Stanford Clinical Data Warehouse with disease, drug, procedure and device terms using ontologies such as SNOMED-CT or RxNorm. We then used standard co-occurrence statistics to establish associations between these clinical concepts and to construct networks. Hidden modules of care - clusters of diseases, drugs, procedures, devices – useful for hypothesis generation are extracted through network analysis approaches and visualized using Cytoscape.
We present a study for comparative effectiveness of Cilostazol vs. a control group in peripheral artery disease (PAD) patients (see Figure 1) and compare our results derived from the network analysis against standard methods such as regression analysis. We believe that network analysis allows us to uncover hidden (“latent”) modules of care not detected through standard approaches, which do not account for the connectivity of the clinical events and entities.
Mining of electronic health records (EHRs) has recently gained importance. However, most efforts are restricted to analyzing drugs, diseases and their associations. In biomedical research, network analysis has provided the conceptual framework to interpret protein-protein interactions or gene-disease association networks via large-scale network maps.
We analyze associations between drugs, diseases, devices and procedures mined from EHRs using network analysis to extract hidden “modules of care” for hypothesis generation. In particular, we annotated the textual notes of the EHRs of one million patients in the Stanford Clinical Data Warehouse with disease, drug, procedure and device terms using ontologies such as SNOMED-CT or RxNorm. We then used standard co-occurrence statistics to establish associations between these clinical concepts and to construct networks. Hidden modules of care - clusters of diseases, drugs, procedures, devices – useful for hypothesis generation are extracted through network analysis approaches and visualized using Cytoscape.
We present a study for comparative effectiveness of Cilostazol vs. a control group in peripheral artery disease (PAD) patients (see Figure 1) and compare our results derived from the network analysis against standard methods such as regression analysis. We believe that network analysis allows us to uncover hidden (“latent”) modules of care not detected through standard approaches, which do not account for the connectivity of the clinical events and entities.
Presented at the 2014 Bio-IT World Expo in Boston, this slideshow provides info on the use of Lyons-Weiler's entropy-based measures of genotypic signal to improve concordance among alternative variant calling algorithms and to evaluate various steps in the GATK best practices pipeline. The second part of the talk presented data showing a demarcation bias in the widely used measure of fold change in selected differentially expressed genes, transcripts or proteins from microarray and RNASeq data.
http://www.bio-itworldexpo.com/Next-Gen-Sequencing-Informatics/
KDM5 epigenetic modifiers as a focus for drug discoveryChristopher Wynder
A summary presentation of my scientific work.
My laboratory focused on an enzyme KDM5b (aka PLU-1, JARID1b) that was widely expressed during development and played a key role in progression of breast cancer through HER-2.
My lab focused on understanding the key biochemical activity of the enzyme through dissecting the proteomic and genomic interactors.
Our results were confirmed through the use of ES cells, adult stem cells and mouse models.
Much of this work remains unpublished, please contact me for more information and/or access to any reagents that I still have as part of this work.
crwynder@gmail.com
Setting up a qPCR experiment is so simple that it actually becomes dangerous. Without appropriate controls and data normalization, results can be misleading at best. This presentation addresses selection and validation of suitable reference genes as well as the use of the global mean normalization method to obtain accurate data. Also discussed are tools for data generation and analysis.
The number of sequenced genes having unknown function continues to climb with the continuing decrease in the cost of genome sequencing. In Reverse Genetics (RG), functions of known genes are investigated with targeted modulation of gene activity, and hypothesis regarding gene function directly tested in vivo. Several RG approaches like insertional mutagenesis, fast neutron mutagenesis, TILLING and RNA interference have led to the identification of mutations in candidate genes and subsequent phenotypic analysis of these mutants.
Okabe et al. (2011) employed TILLING technique to screen six ethylene receptor genes in tomato (SlETR1–SlETR6) and two allelic mutants of SlETR1 (Sletr1-1 and Sletr1-2) with reduced ethylene response were identified. Using fast neutron mutagenesis, Li et al. (2001) obtained arabidopsis deletion mutants for bZIP transcription factor viz. AHBP 1b and OBF 5, a key regulator for systemic acquired resistance but their role were compensated by other regulatory factors in mutants. Terada et al. (2007) successfully blocked the expression of the Adh 2 gene through homologous recombination followed by transgenesis in rice however phenotype could not be determined since no differences were observed between wild and transgenic plants. RNA interference (RNAi) works as sequence-specific gene regulation and has been used in determination of function of many genes. Saurabh et al. (2014) reviewed the impact of RNAi in crop improvement and found its application in improvement of nutritional aspects, biotic and abiotic stresses, morphol¬ogy, crafting male sterility, enhanced secondary metabolite synthesis.
In addition, new advances in technology and reduction in sequencing cost may soon make it practical to use whole genome sequencing or gene targeting like ZFN technology and TAL effectors technology on a routine basis to identify or generate mutations in specific genes. Scholze and Boch (2011) mentioned that TAL effectors technology is more specific and predictable than ZFN. RG techniques have their own advantages and disadvantages depending on the species being targeted and the questions being addressed. Finally, with the continuous development of new technologies, the most efficient RG technique in the future may involve high throughput direct sequencing of part or complete genomes of individual plants followed by efficient novel tools to determine the function for utilization in crop improvement.
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
Random Artificial Incorporation of Noise in a Learning Classifier System Envi...Daniele Loiacono
Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, Jason H. Moore. "Random Artificial Incorporation of Noise in a Learning Classifier System Environment", IWLCS, 2011
Deciphering the regulatory code in the genomeDenis C. Bauer
There are messages hidden within our genome, regulating when and how long a gene is switched on. The presentation describes a method, STREAM, targeted at deciphering this regulatory code.
Presented at the 2014 Bio-IT World Expo in Boston, this slideshow provides info on the use of Lyons-Weiler's entropy-based measures of genotypic signal to improve concordance among alternative variant calling algorithms and to evaluate various steps in the GATK best practices pipeline. The second part of the talk presented data showing a demarcation bias in the widely used measure of fold change in selected differentially expressed genes, transcripts or proteins from microarray and RNASeq data.
http://www.bio-itworldexpo.com/Next-Gen-Sequencing-Informatics/
KDM5 epigenetic modifiers as a focus for drug discoveryChristopher Wynder
A summary presentation of my scientific work.
My laboratory focused on an enzyme KDM5b (aka PLU-1, JARID1b) that was widely expressed during development and played a key role in progression of breast cancer through HER-2.
My lab focused on understanding the key biochemical activity of the enzyme through dissecting the proteomic and genomic interactors.
Our results were confirmed through the use of ES cells, adult stem cells and mouse models.
Much of this work remains unpublished, please contact me for more information and/or access to any reagents that I still have as part of this work.
crwynder@gmail.com
Setting up a qPCR experiment is so simple that it actually becomes dangerous. Without appropriate controls and data normalization, results can be misleading at best. This presentation addresses selection and validation of suitable reference genes as well as the use of the global mean normalization method to obtain accurate data. Also discussed are tools for data generation and analysis.
The number of sequenced genes having unknown function continues to climb with the continuing decrease in the cost of genome sequencing. In Reverse Genetics (RG), functions of known genes are investigated with targeted modulation of gene activity, and hypothesis regarding gene function directly tested in vivo. Several RG approaches like insertional mutagenesis, fast neutron mutagenesis, TILLING and RNA interference have led to the identification of mutations in candidate genes and subsequent phenotypic analysis of these mutants.
Okabe et al. (2011) employed TILLING technique to screen six ethylene receptor genes in tomato (SlETR1–SlETR6) and two allelic mutants of SlETR1 (Sletr1-1 and Sletr1-2) with reduced ethylene response were identified. Using fast neutron mutagenesis, Li et al. (2001) obtained arabidopsis deletion mutants for bZIP transcription factor viz. AHBP 1b and OBF 5, a key regulator for systemic acquired resistance but their role were compensated by other regulatory factors in mutants. Terada et al. (2007) successfully blocked the expression of the Adh 2 gene through homologous recombination followed by transgenesis in rice however phenotype could not be determined since no differences were observed between wild and transgenic plants. RNA interference (RNAi) works as sequence-specific gene regulation and has been used in determination of function of many genes. Saurabh et al. (2014) reviewed the impact of RNAi in crop improvement and found its application in improvement of nutritional aspects, biotic and abiotic stresses, morphol¬ogy, crafting male sterility, enhanced secondary metabolite synthesis.
In addition, new advances in technology and reduction in sequencing cost may soon make it practical to use whole genome sequencing or gene targeting like ZFN technology and TAL effectors technology on a routine basis to identify or generate mutations in specific genes. Scholze and Boch (2011) mentioned that TAL effectors technology is more specific and predictable than ZFN. RG techniques have their own advantages and disadvantages depending on the species being targeted and the questions being addressed. Finally, with the continuous development of new technologies, the most efficient RG technique in the future may involve high throughput direct sequencing of part or complete genomes of individual plants followed by efficient novel tools to determine the function for utilization in crop improvement.
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
Random Artificial Incorporation of Noise in a Learning Classifier System Envi...Daniele Loiacono
Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, Jason H. Moore. "Random Artificial Incorporation of Noise in a Learning Classifier System Environment", IWLCS, 2011
Deciphering the regulatory code in the genomeDenis C. Bauer
There are messages hidden within our genome, regulating when and how long a gene is switched on. The presentation describes a method, STREAM, targeted at deciphering this regulatory code.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
1. Predicting the impact of
mutations using pathway-
guided integrative genomics
Network Biology SIG, ISMB 2012
Josh Stuart, UC Santa Cruz
July 12, 2012
2. Overview of pathway-guided approach
Integrate many data sources to gain accurate
view of how genes are functioning in pathways
Predict the functional consequences of mutations
by quantifying the effect on the surrounding
pathway
Use pathway signatures to implicate mutations in
novel genes to (re-)focus targeting
Identify critical “Achilles Heels” in the pathways
that distinguish a particular sub-type
3. Flood of Data Analysis Challenges
Genomics, Functional Genomics, Metabolomics, Epigenomics =
Exome
Sequences
Multiple, Possibly
Structural Conflicting Signals
Variation Expression
Copy Numberis What it
This
Does to You
Alterations DNA Methylation
4. Analysis of disease samples like automotive repair
(or detective work or other sleuthing)
Patient Sample 1 Patient Sample 2
Sleuths use as much
knowledge as possible.
Patient Sample 3 Patient Sample N
…
5. Much Cell Machinery Known:
Gene circuitry now available.
Curated and/or Collected
Reactome
KEGG
Biocarta
NCI-PID
Pathway Commons
…
6.
Expression of 3 transcription factors:
high TF
high TF low TF
Inference: Inference: Inference:
TF is ON TF is OFF TF is ON
(expression (high expression (low-expression
reflects but inactive) but active )
activity)
7.
BUT, targets are amplified
Expression -> TF ON Copy Number -> TF OFF
TF
Lowers our belief
in active TF because
explained away by
cis evidence.
10. Integration Approach: Detailed models
of expression and interaction
Two Parts:
1. Gene Level Model
(central dogma)
2. Interaction Model
(regulation)
11. 1. Central Dogma-Like
Gene Model of Activity
2. Interactions that
connect to specific points
in gene regulation map
Charlie Vaske
Vaske et al. 2010. Bioinformatics Steve Benz
12. Multimodal Data Pathway Model
Cohort Inferred Activities
of Cancer
CNV
mRNA
meth
…
16. Mutated genes are the focus of many targeted
approaches.
Some patients with “right” mutation don’t respond.
Why?
Many cancers have one of several “novel” mutations.
Can these be targeted with current approaches?
Pathway-motivated approaches:
Identify gain-of-function from loss-of-function.
Sam Ng
Compare novel signatures ISMB
Oral Poster
17. High
Inferred
Activity Inference using Inference using
Inference using all downstream upstream neighbors
neighbors neighbors
mutated
FG gene FG SHIFT FG
Low
Inferred
Activity
Sam Ng, ECCB 2012
28. Focus Gene Key
P-Shift
T-Run
R-Run
Expression
Mutation
Neighbor Gene Key
Activity
Expression
RB1 Mutation
NFE2L2
Sam Ng
29. RB1 TP53 NFE2L2
Signal Score (t-statistic) = -5.78 Signal Score (t-statistic) = -10.94 Signal Score (t-statistic) = 4.985
Observed SS
Background SS
Sam Ng
35. Identify sub-pathways that
distinguish patients sub-types (e.g. Insight from contrast
mutant vs. non-mutant, response
to drug, etc)
Predict mutation impact on
pathway “neighborhood”
Identify master control points for
drug targeting.
Predict outcomes with quantitative
simulations.
Sam Ng Ted Goldstein
36. “ ”
Pathway Activities
Pathway Activities
Ted Goldstein Sam Ng
37. “ ”
SuperPathway Activities
SuperPathway Activities
Pathway
Signature
Ted Goldstein Sam Ng
38. Traditional methods treat
each gene as a separate
feature
Use features reflecting
overall pathway activity
Smaller number of
features are now fed to
predictors
Predictor
Artem Sokolov
39. Traditional methods treat
each gene as a separate
feature
Use features reflecting
overall pathway activity
Smaller number of
features are now fed to
predictors
Artem Sokolov
Predictor ISMB poster
40. Basal vs.
Luminal
Recursive
feature
elimination:
we train an
SVM, drop the
least
important half
of features
and recurse
The number of
times each
feature
survived the
elimination
across 100
random splits
of data
Artem Sokolov
42. One large highly-connected
component (size and connectivity
significant according to permutation
980 pathway concepts
analysis)
1048 interactions
Characterized by
several “hubs’
IL23/JAK2/TYK2
P53 ER
tetramer
HIF1A/ARNT
FOXA1
Myc/Max
Higher activity in ER-
Lower activity in ER-
Sam Ng, Ted Goldstein
43. Identify master controllers using
SPIA (signaling pathway impact analysis)
Google PageRank for Networks
Determines affect of a given pathway on each node
Calculates perturbation factor for each node in the network
Takes into account regulatory logic of interactions.
n
IF ( g j )
Impact IF ( gi ) = s ( gi ) + å bij ×
factor:
j=1 N up ( g j )
Google’s PageRank-Like
Yulia Newton (NetBio SIG Poster)
44. Slight Trick: Run SPIA in reverse
Reverse edges in Super Pathway
High scoring genes now those at the “top” of the
pathway
PageRank finds Reverse to find
highly referenced Highly referencing
Yulia Newton
46. • DNA damage network is
upregulated in basal
breast cancers
• Basal breast cancers are
sensitive to PLK inhibitors
GSK-PLKi
Basal
Claudin-low
Luminal
Ng, Goldstein Up
Heiser et al. 2011 PNAS Down
47. • HDAC Network is down-
regulated in basal breast
cancer cell lines
• Basal/CL breast cancers are
resistant to HDAC inhibitors
HDAC inhibitor VORINOSTAT
Heiser et al. 2011 PNAS Ng, Goldstein
48. Connect genomic alterations to downstream
expression/activity
?
• What circuitry connects mutations to
transcriptional changes?
– Mutations general (epi-) genomic perturbation
– Expression activity
• Mutation/perturbation and expression/activity
treated as heat diffusing on a network
– HotNet, Vandin F, Upfal E, B.J. Raphael, 2008.
Evan Paull
– HotNet used in ovarian to implicate Notch pathway
• Find subnetworks that link genetic to mRNA and ISMB
protein-level changes. Oral Poster
70. UCSC Integrative Genomics Group
Please See Posters!
Sam Ng Dan Carlin Evan Paull
Marcos Woehrmann
Ted Golstein
James Durbin Artem Sokolov Yulia Newton
Chris Szeto
Chris Wong
71. David Haussler
Buck Institute for Aging Chris Benz,
• Christina Yau
• Sean Mooney
UCSC Cancer Genomics
Jing Zhu • Janita Thusberg
• Kyle Ellrott
Collaborators
• Brian Craft
• Chris Wilks • Joe Gray, LBL
• Amie Radenbaugh • Laura Heiser, LBL
• Mia Grifford • Eric Collisson, UCSF
• Sofie Salama • Nuria Lopez-Bigas, UPF
• Steve Benz • Abel Gonzalez, UPF
Broad Institute
Funding Agencies
UCSC Genome Browser Staff • Gaddy Getz
• Mark Diekins • NCI/NIH
• Mike Noble
• SU2C
• Melissa Cline • Daniel DeCara
• NHGRI
• Jorge Garcia • AACR
• Erich Weiler • UCSF Comprehensive Cancer Center
• QB3
77. “Backbone” of 43 genes, 90 connections
Major PARADIGM hubs included: MYC, FOXM1, FOXA1, HIF1A/ARNT
78. “Backbone” of 43 genes, 90 connections
Signaling through beta-catenin explains MYC activity in basals:
-deletions in CDKN2A de-repress CTNNB1 in basals or
-lower expression of Cyclin D1 de-repress CTNNB1
80. RNAi vs. Master Controller (after recurring
runs)
Basal vs Luminal RNAi Growth
AKT2
RPS6KA3
- p90 S6 kinase
PDPK1
AKT1
High-scoring after
Iterative runs.
RAF1
Basals differentially
RPS6KA3
Sensitive to RNAi
Inhibitors available.
Master Controller Score
Yulia Newton
Editor's Notes
Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
GBM results:PDGFR predicted as GOF. Exon 8/9 deletion shown to be oncogenic.
Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
