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Mechanistic Models of Cancer Progression in the Space of Pathways Elena Edelman [email protected] Computational Biology and Bioinformatics Program Institute of Genome Policy and Science Duke University
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Mechanistic Models of Cancer Progression in the Space of Pathways

Jan 20, 2016

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Mechanistic Models of Cancer Progression in the Space of Pathways. Elena Edelman [email protected] Computational Biology and Bioinformatics Program Institute of Genome Policy and Science Duke University. Outline. I.Biological Background Problems with single gene analysis - PowerPoint PPT Presentation
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Page 1: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression in the Space of Pathways

Elena [email protected]

Computational Biology and Bioinformatics ProgramInstitute of Genome Policy and Science

Duke University

Page 2: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Outline

I. Biological Background– Problems with single gene analysis– Advantages of pathway analysis

II. Gene Sets– How they are derived– Importance of understanding context

III. Modeling Cancer Progression– Overview of multitask model– Prostate cancer example– Melanoma example Mechanistic Models of Cancer Progression, Elena

Edelman presenting

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Page 3: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Disadvantages of single gene based methods

• Hundreds of differentially expressed genes• Subtle signals• Lack of consensus

Page 4: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Solutions

• Hundreds of differentially expressed genes – group together in a small number of pathways

• Subtle signals – brought to attention when seen as a group

• Lack of consensus – consensus in processes/pathways, not single genes

Page 5: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Disadvantage of single gene methods

13,023 genes↓

1,149 mutated genes

189 candidate cancer genes

Each sample of a given tumor type had no more than six mutated CAN genes in common

Sjoblom 2006

Page 6: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Importance of pathway analysis

• Deregulation of specific processes are necessary for tumor formation. Each process has many potential member genes.

• Alteration of a number of different genes will provide the same phenotypic result.

Page 7: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Rb pathway

• Several cancer genes control transitions from resting state (G0 or G1) to replicating phase (S) of cell cycle.

• Diverse protein products:– cdk4 (kinase), oncogene– cyclin D1 (activates cdk4),

oncogene– Rb (transcription factor),

TSG – p16 (inhibits cdk4), TSG

Page 8: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

P53 TSG

• P53 is a transcription factor that inhibits cell growth and stimulates cell death

• Point mutation inactivates its capacity to bind specifically to its recognition sequence.

• Other ways to achieve the same effect– Amplification of MDM2– Infection with DNA

tumor viruses whose products bind to p53 and functionally inactivate it.

Page 9: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Pathway Analysis

• Identify gene sets whose expression patterns characterize specific genetic or molecular perturbations.

• Early pathway analysis: Apply methods such as t-tests to determine differentially expressed genes between two classes. Use database such as Gene Ontology to relate individual genes in terms of general cellular function.

Page 10: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Pathway Analysis

• Next step in pathway analysis: Gene Set Enrichment Analysis (GSEA) & Analysis of Sample Set Enrichment Score (ASSESS)– Start with biological information: Gene sets– Score enrichment of gene sets in an expression profile with

samples from two classes– GSEA outputs enrichment scores for each gene set in each

phenotype– ASSESS outputs enrichment scores for each gene set in each

individual

Page 11: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

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Given a ranked gene list and a gene set of interest, find genes in the set that are “enriched” at the top or bottom of the list.

How could we conclude that G1 is enriched but G2 and G3 are not?

Enrichment Analysis

Page 12: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Outline

I. Biological Background– Problems with single gene analysis– Advantages of pathway analysis

II. Gene Sets– How they are derived– Importance of understanding context

III. Modeling Cancer Progression– Overview of multitask model– Prostate cancer example– Melanoma example

Page 13: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Gene Sets

• Defined functionally or structurally

• Defined by experimental methods or through literature.– Experimental: Knockouts, infections– Literature: Biochemical experiments, reported in databases

such as BioCarta and GenMapp

Page 14: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

GSEA of male vs. female in lymphoblastoid cells

GENE SETGENE SET SOURCESOURCE ESES NESNES NOM p-vNOM p-v FDR q-vFDR q-v

Enriched in Males 

s1:chrY Genome 0.778 2.465 < 0.001 < 0.001

s1:chrYp11 Genome 0.759 2.181 < 0.001 < 0.001

s1:chrYq11 Genome 0.886 2.175 < 0.001 < 0.001

s1:Testis expressed genes Experimental GNF 0.656 2.018 < 0.001 0.009

Enriched in Females  

s2:Genes that escape XinactivationDisteche et al, Willard et al -0.800 -2.295 < 0.001 < 0.001

s2:Female reproductive tissue expressed genes Experimental GNF -0.485 -1.892 0.013 0.045

Page 15: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

ASSESS of male vs. female in lymphoblastoid cells

SAMPLES

GE

NE

SE

TS

Page 16: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

• Analyses will depend on accuracy of gene sets. We ask:– What is the accuracy of gene sets annotated according to

known perturbations?– How do gene sets defined by experimental studies vs.

expert knowledge compare?

Gene Set Accuracy

Page 17: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Hypoxia Gene Set

• Hypoxia: The cellular response to low oxygen conditions. Includes new blood vessel formation

• Seven hypoxia gene sets describing the cellular response to hypoxia

Gene Set Source

Hypoxia Down Manalo et al

Hypoxia Up Manalo et al

Hypoxia Fibro Up Kim et al

Hypoxia Reg Up Leonard et al

Hypoxia Review Harris

VEGF Pathway BioCarta

HIF Pathway BioCarta

Page 18: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Hypoxia gene set accuracy

• Expression data set with 6 hypoxic and 6 normal cells (Mense 2006)

• GSEA applied with database of 508 gene sets.

Rank Gene Set NES P-val

Enriched in Hypoxic Cells

3 Hypoxia Up -1.96 0.008

4 Hypoxia Review -1.95 0

6 Hypoxia Fibro Up

-1.84 0.004

9 Hypoxia Reg Up -1.73 0.02

10 HIF Pathway -1.73 0.02

53 VEGF Pathway -1.39 0.055

Enriched in Normal Cells

17 Hypoxia Down 1.48 0.167

Page 19: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

RAS

• 3 Ras gene sets: K-Ras, H-Ras, and the Ras pathway from Biocarta.

• K-RAS and H-RAS are experimentally defined and context specific.

• Biocarta's Ras gene set in the most general, consisting of genes thought to biochemically interact with RAS and proteins associated with RAS.

Page 20: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

RAS gene set accuracy

• Gene expression profile of 31 cells with tumors caused by K-RAS mutation and 19 normal cells.

• H-RAS does not capture K-RAS specificity.

• BioCarta's RAS gene set is appropriate to use regardless of the specific RAS mutation.

Gene Set NES Pval

Enriched in Tumor

RAS Up BioCarta 1.51 0

SRC Down 1.41 0.09

MYC Up 1.25 0.15

SRC Up 1.25 0.15

HRAS Up 1.12 0.26

E2F3 Up 1.12 0.25

BCAT Up 0.81 0.74

Enriched in Normal

RAS Down BioCarta

-1.51 0.12

E2F3 Down -1.29 0.10

HRAS Down -1.18 0.19

BCAT Down -1.14 0.29

MYC Down -0.99 0.55

Page 21: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

RAS gene set accuracy

• Gene expression profile of 45 adenocarcinomas and 48 squamous lung cancer samples.

• Data set indirectly involves RAS perturbations.

• Enrichment scores from ASSESS were used to predict phenotype. Class prediction accuracy for the three sets:– 69.9% for the H-RAS pathway gene set– 75.3% for the K-RAS pathway gene set– 79.6% for the BioCarta RAS pathway gene set

Page 22: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Outline

I. Biological Background– Problems with single gene analysis– Advantages of pathway analysis

II. Gene Sets– How they are derived– Importance of understanding context

III. Modeling Cancer Progression– Overview of multitask model– Prostate cancer example– Melanoma example

Page 23: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Dynamics of Cancer Progression

• Long lists of genes implicated in various stages of cancer exist for many different cancer types. Want to learn about the interaction of these genes via signaling pathways and functional relationships.

• Next step is for a mechanistic understanding of cancer progression on the pathway level.

• There are only a few types of cancers where we know which pathways acquire mutations that initiate tumorigenesis. – Eye: RB1

• Are other types of cancer initiated by one or several pathways becoming altered?

• The alteration of one gene hardly ever suffices to give rise to full blown cancer.– Oncogenes, tumor suppressor genes (TSGs), and stability genes drive

tumor progression.– Mammalian cells have multiple safeguards . Several genes must be

defective for invasive cancer to develop.

Page 24: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Objectives

• Identify pathways most relevant throughout progression and pathways most relevant to individual transitions.

• Build pathway networks: Estimate the interdependence of pathways relevant to each step of tumor progression.

• Refine relevant pathways and infer a gene network for those relevant genes sets.

Page 25: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Hierarchical Modeling

• Tumor progression– FIXED EFFECTS: Stage in cancer progression. Individuals

will show similar pathway deregulation as cancer progresses depending on whether they have benign, primary or metastatic lesions.

– RANDOM EFFECTS: Within a stage, individuals will have differences based on how they specifically developed the disease.

Page 26: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Regularized Multitask Learning (RML)

• Current analyses of genomic data evaluate each stage in progression independently, missing relationships between the data.

• Integration of the data over all stages will provide a more complete picture of the processes underlying tumorigenesis.

• RML learns a problem together with other related problems at the same time. Learning the problems in parallel can help each problem be better learned by using a shared representation.

• Problems: Which pathways are relevant to transition 1? Transition 2? Which pathways are relevant throughout progression?

Page 27: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Stratifying Data

• States: normal (n), early (e), metastatic (m).

• Data: Gene expression for g genes in s samples. Stratify data into T datasets, one for each step in progression.

T=2: D1 D2

n e

n e m e m

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Mechanistic Models of Cancer Progression, Elena Edelman presenting

Modeling tumor progression

• Model Summary: Find relevant pathways in the overall progression

{n→e→m}

And the relevant pathways at different stages

{n→e} and {e→m}

The task t corresponds to progression from less serious to more serious states

t=1: {n→e}, t=2: {e→m}

Page 29: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Transformation

• Transformation: Gene expression data is transformed using ASSESS

D: genes x samples S: gene sets x samples

D1 D2 S1 S2 n e

e m

genes

20,000

1

Gene s

ets

n e

e m

Page 30: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Multitask SVM

• Support vector machines (SVMs) - regularization method– Input regression data – Estimate a regression function f - a summary statistic of

Y|X.

• Multitask SVM – builds classification models jointly over all data sets, Y|

S1, S2.– Provides a baseline model for gene sets relevant to

predicting phenotype in both data sets, Y|S1,S2 – Provides gene sets relevant to only one data set, Y|S1

and Y|S2– These regressions provide data set dependent

corrections to the baseline model.

Page 31: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

The Model

• Input: x= S1, S2 • class labels, y={-1,1} where -1=less serious, 1=more serious.

• Build two regression models ft1(x) and ft2(x), for transition 1 data and transition 2 data.

– b(x)=baseline term over all tasks and rt(x)=task specific corrections

• Discriminate functions:

– w0 is a vector of baseline weights for the gene sets

– vt1 is the vector of correction terms for transition 1

– vt2 is the vector of correction terms for transition 2

– b is a scalar offset

f t1 (x)b(x) rt1 (x)

f t2 (x)b(x) rt2 (x)

f t1(x)w0 x wt1 x b,

f t2 (x)w0 x v t2 x b,

v t1

Page 32: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

The Model

• Parameters are estimated by minimization problem:

where v(f(xit), yit) is a loss function. If tasks are thought to be highly related, set λ2/λ1 ratio to be large.

Page 33: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Model Interpretation

• Interpretation: wjo – weight of jth gene set in a baseline model. Gene sets for which |wj0| are largest are relevant in

{n→e→m}

vjt – weight of the jth gene set in state progression t.

Gene sets for which |vj1| is large are relevant in

{n→e}

and gene sets for which |vj2| are large are relevant in

{e→m}.

Page 34: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Prostate Cancer

• Gene expression profile of 22 benign epithelium samples (b), 32 primary prostate cancer samples (p), and 17 metastatic prostate cancer samples (m). Tomlins, 2007

• Progression {b→p→m}

w0

v1

v2

Page 35: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Results

• Categorized results by “Hallmarks of Cancer” – Hanahan, 2000– Self sufficiency of growth signals– Insensitivity to anti-growth signals– Evasion of apoptosis– Defense against limitless replicative potential– Angiogenesis– Invasion and metastasis

Page 36: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Results

• Self sufficiency in growth signals– Cell cycle gene sets– ErbB4, EGF, Sprouty, ERK

Page 37: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Results

• Evidence for insensitivity to anti-growth signals: – PTEN down-regulation– PTDINS up-regulation

• Evasion of apoptosis:– IGF1R up-regulation– ROS down-regulation

• Energy production– Glycolysis gene set up-regulation– ATP synthesis gene set up-regulation– Oxidative phosphorylation up-regulation

Page 38: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Novel Findings

• Took previous analysis a step further by discovering the specific pathways implicated in tumorigenesis.– Previous work identified single genes which were relevant in

progression and grouped them together to form important concepts.

• Currently little known about ErbB4 deregulation in PCA – EGF receptors have been implicated in several tumor type –

stomach, brain, breast.– ErbB2/HER2 has been shown to be overexpressed in prostate

cancer

Tomlins 2007

Page 39: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Objective 2: Pathway dependency structure

• Infer a pathway interaction network for each stage of progression using learning gradients and inverse regression .

• Provide knowledge on how certain pathways relate, interact, and influence one another with respect to phenotype.

Page 40: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Objective 2

• Standard regression methods show which gene sets are correlated with class labels but do not provide information on the co-variation of gene sets correlated with class labels.

• Estimate covariance of inverse regression C=cov(X|Y)– Input matrix of enrichment scores (X) and class labels (Y)– Output covariance matrix C=cov(X|Y)

• Diagonal elements measure relevance of i-th gene set with respect to change in label.

• ij-th off diagonal element measures the dependence between gene sets i and j.

• Relationships will be visualized in graphical models.

Page 41: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Objective 2

• Analysis can identify pathways that are closely associated throughout progression:– IGF1R and ERK are linked through their association with RAS.

ERK ranks 9th out of 522 gene sets based on the covariance with the IGF1R pathway.

– PTDINS ranks 15th based on the covariance with the PTEN gene set

– IGF1R ranks 32nd based on the covariance with PTDINS

Page 42: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Objective 2

• A: Dependency structure of the 10 gene sets most relevant in the benign to prostate cancer transition

• B: Extended dependency structure

Page 43: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Objective 3: Refinement

• Gene sets available are not always in the right context for a specific data set.

• The refinement procedure adapts the gene set to the context of the data set. Shows which genes are dependent on each other and if there is substructure in the gene set.

• Cluster genes in gene set based on their covariance: C=cov(X|Y);– X= gene expression value of genes in the gene set– Y= class labels

• A gene network modeling the interdependence of the genes in the refined gene set is inferred.

Page 44: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Gene Set Refinement

• The genes of BioCarta's ERK pathway• Refine the pathway to those genes most relevant for this data set. • A and B differ in threshold values

Page 45: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Melanoma Progression

• Gene expression profile of 4 normal skin samples (n), 4 primary melanoma samples (p), and 4 metastatic melanoma samples (m). Smith, 2005.

• Progression {n→p→m}

w0

v1

v2

Page 46: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Melanoma Results

• Self-sufficiency of growth– AKT up-regulation throughout progression– PTDINS up-regulation throughout progression

• Escape from apoptosis– IGF1R up-regulation in the late transition– p53 down-regulation throughout progression

• Defense against limitless replicative potential– HTERT up-regulation in the early transition

• Angiogenesis– HIF up-regulation throughout progression– Angiogenesis gene set up-regulation in the early transition

• Invasion and Metastasis– CDC42RAC up-regulation throughout progression– MTA3 down-regulation in the early transition

Page 47: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Validation

• Gene expression profile of 9 samples of benign nevis, 6 samples of primary melanoma, and 19 samples of metastatic melanoma (Haqq 2005)

• Both analysis found:– p53 gene set down-regulation– D4-GDI pathway over-expression– HTERT gene set over-expression– CDC42RAC pathway over-expression

w0

v2v1

Page 48: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Pathway Dependencies

• A: Dependency structure of top 10 gene sets most relevant in the normal skin to primary melanoma transition

• B: Extended dependency structure

Page 49: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Sterol Biosynthesis

• Sterol biosynthesis gene set is highly connected

• Tumor cells often have sterol synthesis deficiencies

• One component of the sterol biosynthesis pathway is mevalonate pathway.

• Many tumor cells can not synthesize mevalonate so they obtain is from the host

Page 50: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Pathways Dependencies

• Interdependence with sterol biosynthesis gene sets out of 523 gene sets:– Fatty acid synthesis ranks 14th

– Cyanoamino acid metabolism ranks 19th

– Gamma hexachlorocyclohexane ranks 3rd

• All are closely tied to the inability of a tumor to synthesize certain metabolites and its increasing need for these metabolites as it grows and develops.

Page 51: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Colon Cancer Example

• Multitask learning can be applied to data sets with more than 3 classes (2 tasks).

• Colon cancer gene expression profile: 32 normal, 32 adenoma, 35 stage 1 carcinoma, 82 stage 2 carcinoma, 70 stage 3 carcinoma, and 43 stage 4 carcinoma.

Vogelstein, 1990

Page 52: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Future

• Expand analyses to datasets with more than 3 classes– Prostate cancer: benign, PIN, PCA low, PCA high,

metastatic– Colon cancer: normal, adenoma, carcinomas stage1-4

• Gene set expansion– After refining the gene sets, find genes outside of the set

with strong dependencies to the core genes in the gene set

Page 53: Mechanistic Models of Cancer Progression in the Space of Pathways

Mechanistic Models of Cancer Progression, Elena Edelman presenting

Acknowledgements

• Sayan Mukherjee• Phillip Febbo• Joe Nevins• Ashley Chi• Justin Guinney