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Identification of the metabolic reprogramming underlying metastasis: use and development of PheNoMenal e-infraestructures for

analyzing metabolic phenotype data

Marta CascanteUniversity of Barcelona

EASYM, October 2016

Sustainingproliferative

signaling

Evading GrowthSupressors

Avoiding Inmune Destruction

EnablingReplicativeInmortality

Tumor-promoting

Inflammation

Invasion and metastasis

InducingAngiogenesis

Genomeinstability

Resisting Cell Death

Metabolicreprogramming

CANCER HALLMARKS: METABOLISM

Metabolic reprogramming underlyingmetastatic potential ?

Metastasis: the

leading cause of

death by cancer.

Cancer cells are perfect systems to invade and parasite other tissues

Robust metabolic profile

FRAGILITY

Exploitable Target against tumor progression

and metastasis?

unexpected perturbations

CANCER AS A METABOLIC ALTERATION

Tumor metabolism robustness counteracts single hits

Multiple hit strategies can avoid bypass of single inhibitions

Tumor metabolism response to multiple hits is unpredictable

Rational design of new therapeuticalcombinations is necessary

Highest capacity to predictphenotype:

Understanding metabolic reprogramming in metastatic cancer will permit to discover new drug targets

Metabolites are not only the “end point” also the “driving force”

Fluxomics (analyze the metabolic flux distribution in the cell metabolic network)

EXPERIMENTAL APPROACHES

Integration of data in computer models

Cytomics

Genomics

Proteomics

Metabolomics

“IN SILICO” SIMULATOR

• Analyze metabolic adaptations supporting metastatic potential

• Design metabolic Interventions in drug development

METABOLIC FLUX REPROGRAMMING FINGERPRINT:

Tracer based metabolic flux models and GSMM: two versatile, potent and informative tools in the study of the metabolic

fingerprint of cancer cells

13C-basedMETABOLIC FLUX

MODELS

GENOME-SCALE METABOLIC MODELS

(GSMM)

Fluxomics in a systems medicine approach

13C TRACER-BASED FLUXOMICS WORKFLOW

Incubation withtracer substrates

-[1,2-13C2]-glucose-[3-13C]-glutamine- [U-13C]-glutamine-etc…

Study of isotopomericdistribution

-Glucose-Lactate-Ribose-Fatty Acids-Krebs Cycleintermediates-Aminoacids…

Introduction of additional constraints

-Enzymatic activities- Metabolites uptake/intake

- Glucose- Glutamine- Lactate- Glutamate

- Targeted metabolomics-(e.g. BIOCRATES kits)

Generation of fluxomic model

Cell culture LC/MS, GC/MSLC/MS,

SpectrophotometryComputational

Metabolization of 13C-labelled substrates inside the cells

Incorporation of 13C in intracellular and excreted metabolites

Fluxome: “Total set of fluxes in the metabolicnetwork of a cell.”

…Qualitative: from MIDA (Mass Isotopomer

Distribution Analyisis)

…Quantitative: from software packages (i.e. ISODYN in-house)

13C-basedMETABOLIC FLUX

MODELS

GENOME-SCALE METABOLIC MODELS

(GSMM)

Tracer based metabolic flux models and GSMM: two versatile, potent and informative tools in the study of the metabolic

fingerprint of cancer cells

METABOLIC FLUX REPROGRAMMING FINGERPRINT:

Fluxomics in a systems medicine approach

Feasible solution space(non-condition specific)

Condition specific solution space

Unconstrained solution space

• Stoichiometric constraints• Thermodynamics constraints

GSMMs AND FLUX BALANCE ANALYSIS (FBA)

• Transcriptomic profile• Proteomic and Metabolic profile

Optimal Solution

• Calculate optimal steady-state flux

distributions in metastatic and non-

metastatic cancer cell

subpopulations

GSMMs of human metabolism flux map distribution: constraint based approach:

Challenge: Different tumor subpopulations will have different

vulnerabilities?

• Mathematical representation of the metabolic reaction encoded by an organism’sgenome.

WE KNOW THAT tumor metastatic potential:

● Resides in a minority of malignant cells (tumor heterogeneity), known as

tumor initiating cells (TICs) or cancer stem cells (CSCs).

● It uses at several critical steps the epithelial-mesenchymal transition (EMT).

WE AIM TO:

● Characterize TICs/CSCs and EMT metabolic patterns

● To unveil metabolic vulnerabilities of tumor invasion and metastasis.

IDENTIFICATION OF THE METABOLIC REPROGRAMMING UNDERLYING PROSTATE CANCER METASTASIS:

A dual cell model to study prostate cancer metastasis:

Two related cell subpopulations isolated from the PC-3 prostate

cancer cell line according to their metastatic and invasive potential:

PC-3 cell line

cells isolated from nude mice liver metastasesafter intrasplenic injection of PC-3 cells.

PC-3/M cells

Selection by limiting dilution: usingthe Matrigel invasiveness assay

PC-3/S cells

EMT

Celia-Terrassa T, et al. (2012) . J Clin Invest 122(5):1849-1868 .

-Can be transformed into each other (factors, ↓ or ↑ genes, etc.)

PC-3/M

-Cooperation to establish metastasis

Celia-Terrassa T, et al.(2012) . J Clin Invest122(5):1849-1868 .

Metabolic characterization to find metabolic targets in order to:

• Kill these cells• Promote the transformation of these distinct phenotypes

Introduction to the PC-3/M and PC-3/S cell model

1

5

9

13

0 24 48 72

Re

lati

ve c

ell

nu

mb

er (

Ntf

/ N

t=0

h)

Hours

Growth curve

PC-3/S

PC-3/M ↑ metastatic potential↓ invasive

TICs/CSCs (stem cellmarkers)

PC-3/S ↓ metastatic potential

↑ invasiveEMT

Here we propose an strategy to characterize the metabolism

of these cells in order to find specific targets, combining and

integrating experimental and computational methods:

• Detailed metabolic characterization of both PC-3/M and PC-3/S cell lines

• Novel genome scale metabolic network reconstructionanalysis that integrates the transcriptomic and metabolicanalyses on the two clonal cell lines.

Combining Experimental and Computational approaches

Here we propose an strategy to characterize the

metabolism of these cells in order to find specific targets,

combining and integrating experimental and

computational methods:

● Detailed metabolic characterization of both PC-3/M and

PC-3/S cell lines.

● Novel genome scale metabolic network reconstruction

analysis that integrates transcriptomic and metabolic

analyses on the two clonal cell lines.


Aguilar, E Marín de Mas, I, Zodda E, Marin S., Morrish F; Selivanov V; Meca-Cortés O; Delowar H; Pons M; Izquierdo I., Celia-Terrassa T; de Atauri P; Centelles JJ; Hockenbey D; Thomson TM; Cascante M, Stem Cells, 2016 May;34(5):1163-76. doi: 10.1002/stem.2286.

PC-3M have enhanced glycolytic metabolism and fuel TCA cycle using glutamine

***

0,0

0,2

0,4

0,6

0,8

1,0

PC-3M PC-3S

mm

ol/h

· 1

06

cells

Glucose consumption

***

0,0

1,0

2,0

3,0

PC-3M PC-3S

mm

ol/h

· 1

06

cells

Lactate production

**

0,00

0,05

0,10

0,15

PC-3M PC-3S

mm

ol/h

· 1

06

cells

Glutamine consumption

***

*****

*** **

0,0

0,1

0,2

0,3

0,4

0,5

SIG

m

Label enrichment in TCA intermediates

**

***

0,0

0,1

0,2

0,3

0,4

0,5

0,6

Pyruvate Lactate Alanine

SIG

m

Label enrichment

PC-3/M

PC-3/S

100% 1,2-13C2-glucose

***

***

* ** **

0,0

0,2

0,3

0,5

0,6

0,8

0,9

SIG

m

Label enrichment in TCA intermediates

100% U-13C5-glutamine

- Glucose contributes to a greater extent to the labellingof several final glycolytic products in PC-3M

-Glutamine contributes to a greater extent tothe labeling of the TCA intermediates in PC-3M

Aguilar, E Marín de Mas, I, Zodda E, Marin S., Morrish F; Selivanov V; Meca-Cortés O; DelowarH; Pons M; Izquierdo I., Celia-Terrassa T; de Atauri P; Centelles JJ; Hockenbey D; Thomson TM; Cascante M, Stem Cells, 2016 May;34(5):1163-76. doi: 10.1002/stem.2286.

ISODYN MODEL PREDICTS:

• PC3-M (e-CSC) cells display enhanced glycolysis and fuel TCA cycle using glutamine

• increased TCA cycle relative to glucose uptake and mitochondrial respiration fluxes in PC-3S cells

• Increased one-carbon metabolism in PC3-M cells

PC-3S (stable EMT and non-eCSC) have enhanced TCA cycle and mitochondrial respiration

Integration of 13C-based metabolomics data in a flux model generated usingISODYN software package to estimate quantitative flux maps in PC-3S and PC-3M cells:


Metastatic metabolic gene signature

• Metastatic metabolic gene signature (MMGS) : (genes over-expressed in PC-3/M compared with PC-3/S)

• MMGS is consistent with metabolic differences observed between PC-3/M and PC-3/S:

• Correlation between MMGS and prostate cancer progression by applying GSEA:

We found a significant correlation between MMGS and tumor progression in prostate cancerand in other 11 cancer types.


Here we propose an strategy to characterize the metabolism ofthese cells in order to find specific targets, combining andintegrating experimental and computational methods

● Detailed metabolic characterization of both PC-3/M and PC-

3/S cell lines

● Novel genome scale metabolic network reconstruction

analysis that integrates the transcriptomic and metabolic

analyses on the two clonal cell lines. (work in progress)


Computational approach based on CBM:

● Uses transcriptomic data of PC-3/M and PC-3/S to infer the activity

state of their metabolic networks

Cancer-specificMetabolic model

Infer the activity stateof metabolic networks

Non-specific genome-scale

metabolic network reconstruction.

transcriptomicdata of PC-3/M & PC-3/S

Constraint-based method on genome-scale models of human metabolism (GSMMs)

This approach uses an objective function maximizes the similarity

between gene expression and reaction activity state:

● Maximizes the number

of active reactions

associated to

highly expressed genes

● Minimizes the number

of active reactions

associated to

lowly expressed genes

This analysis was performed by using the software fasimu

Shlomi et al; Nat Biotechnol. 2008 Sep;26(9):1003-10

Constraint-based method on genome-scale models of human metabolism (GSMMs)

• Use and development of e-infraestructures for analyzing metabolic phenotype data:

Making existing Metabolomics and Fluxomics toolsavailable to serve Systems Medicine:

• PhenoMeNal will support the data processing and analysis pipelines for molecular phenotype data generated by metabolomics applications.

• PhenoMeNal will provide services enabling computation and analysis to improve the understanding of the causes and mechanisms underlying health and diseases.

(Phenome and Metabolome aNalysis)

This project has received funding from theEuropean Union’s Horizon 2020 research andInnovation programme under grant agreementNo 65424121Consortium Coordinator: Christoph Steinbeck

GC/MS(raw) Isotopologue

distributions

Additional Inputs:-SBML model-Flux

boundaries

Etc..

Patient 4

Midcor

Iso2FluxPatient specific

flux distribution


distributions


boundaries

Etc..

Patient 3

Midcor


flux distribution


distributions


boundaries

Etc..

Patient 2

Midcor


flux distribution

ISODYN

GC/MSLC/MS

(raw)Metabolights

Isotopologuedistributions

Additional Inputs:

-SBML model-Flux

boundariesEtc..

Patient 1

Midcor

Iso2Flux

Patient specific

flux distribution

Automatic workflows for 13C- fluxomics in the framework of PhenoMeNal in development

Making existing Metabolomics and Fluxomics toolsavailable to serve Systems Medicine:

EC- H2020

Automatic Data Curation

CONCLUSIONS

• Metabolic programs underlying Cancer stem cell (CSC) and epithelial-mesenchymal transition (EMT) programs are mutually uncoupled and associated with preferred metabolic dependencies required for optimal cell proliferation and sustaining specific cell phenotypes.

• The complementary 13C-based fluxomics and genome scale metabolic models applied here have identified tumor subpopulation specific metabolic targets with potential impact on the design of new treatments for metastatic cancer.

• 13C-based fluxomics tools and GSMMs in conjunction with simulation techniques such as FBA need to be integrated.

• Automatic workflows for 13C-fluxomics can be developed to serve systems medicine (PhenoMeNal e-infrastructure in development ).

Spanish Systems Medicine Network

Objectives

Update the pathophysiology of the massive

data provided by omics and quantitative

biology and processing using

computational tools

Increase Spanish science in the medicine

field by enhancing cooperation and

synergy in biomedical research and clinical

field industry at national and international

level

Groups

University of Barcelona

PR: Marta Cascante

Institute of Biomedical Research

(IIBM-CSIC)

PR: Lisardo Boscá Gomar

Pompeu Fabra University

PR: Pura Muñoz Canoves

Institute of Biomedicine

of Valencia (IBV-CSIC)

PR: Marta Casado Pinna

Institute of Biology and

Molecular Genetics (CSIC)

PR: Mariano Sanchez

Crespo

Institute of Parasitology and

Biomedicine López Crespo (CSIC)

PR: Dolores González Pacanowsca

Institute of Biomedical

Research (IIBM-CSIC)

PR: Paloma Martin-Sanz

University of Navarra

PR: Fernando J. Corrales

Institute Consortium of

Biomedical Research August Pi

and Sunyer (IDIBAPS)

PR: Josep Roca Torrent

Contact:Network Coordinator: Marta CascateEmail: [email protected] Assistant: Pablo M. BlancoEmail: [email protected]

mailto:[email protected]


Acknowledgements

THANK YOU FOR YOUR ATTENTION

Fondos Feder “Una manera de hacer Europa”

Group of Integrative Systems Biology, Metabolomics and Cancer

Department of Biochemistry and Molecular Biomedicine, University of Barcelona

Contact: Marta Cascante, [email protected]

Collaborators:

• Team of Dr Timothy Thomson, IBMB-CSIC, Barcelona

• Team of Dr Balazs Papp BiologicalResearch Center Szeged, Hungary

• Team of Dr David Hockenbery and DrFionnuala Morrish, Fred Hutchinson Cancer Research Center, Seattle

EC- H2020

EC-H2020

Pedro de Atauri

Vitaly Selivanov

Carles Foguet

Esther Aguilar

Erika Zodda

Josep Centelles

Silvia Marín

Igor Marín de Mas


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