Integrative analysis and visualization of clinical and molecular data for cancer precision medicine...

Enzo Medico

University of Torino

Integrative analysis and visualization

of clinical and molecular data

for cancer precision medicine

Candiolo Cancer Institute

Laboratory of Oncogenomics

[email protected]

Cancer onset and progression

Cancer onset and progression: clonal evolution

Wang et al., Nature 2014

Clonal evolution during cancer treatment

Ding et al, Nature 2012

Towards precision cancer medicine

Targeted

drug Target

Response


Targeted

drug Target

Response

Target

alterations


Targeted

drug Target

Response

Target

alterations

Sensitizing

alterations

De-sensitizing

alterations


Targeted

drug Target

Response

Target

alterations

Tissue/context-

specific modifiers

Sensitizing

alterations

De-sensitizing

alterations

Further elements of complexity

• Intratumoral heterogeneity

De-sensitizing lesions only present in a fraction of the cancer cells

may lead to early recurrence

• Intracellular signaling is governed by networks

Dynamic adaptation to altered signaling.

• Tumor-host interactions

Tumor growth and response also depends on stroma, vasculature,

inflammation and immune response

• Analyzing inter-tumoral heterogeneity requires a

reference background

Focus on one specific tumour type

• Sensitizing/de-sensitizing lesions may be rare

Collect many cases

• Alterations may occur in different ways (mutations, CNA,

rearrangements, etc)

Multi-dimensional genomic exploration of high-quality tumour

material

Facing Challenges

International consortia for cancer genomics

TCGA

The Cancer Genome Atlas:

http://cancergenome.nih.gov

ICGC

International Cancer Genome Consortium:

www.icgc.org

Data available from TCGA (sept 2016)

TCGA data are hosted at the Genomics Data Commons: https://gdc.nci.nih.gov/

https://gdc.nci.nih.gov/

https://gdc.nci.nih.gov/

Data available from ICGC (sept 2016)

The TCGA pipeline

• Tissue samples along with clinical data are collected by Tissue

Source Sites (TSS) and sent to the Biospecimen Core

Resources (BCRs).

• The BCRs submit clinical data and metadata to the Data

Coordinating Center (DCC) and analytes to the Genome

Characterization Centers (GCCs) and Genome Sequencing

Centers (GSCs), where sequences and other molecular profiles are

generated and then submitted to the DCC.

• GSCs submit raw and processed data to the Cancer Genomics

Hub (CGHub) as well.

• Data submitted to the DCC and CGHub are made available to the

research community and Genome Data Analysis Centers

(GDACs).

• Analysis pipelines and data results produced by GDACs are served

to the research community via the DCC.

Multiple types of data

Clinical data

• Clinical diagnosis

• Treatment history

• Histologic diagnosis

• Pathologic status

• Tissue anatomic site

• Others…

Molecular data

• DNA sequence

• DNA copy number

• DNA methylation

• RNA expression

• Protein expression

• Others…

Clinomics:

“the study of -omics data along with its associated clinical data”

…and there is more…

“P0” P1

P2

Biobank Archive

Nucleic Acid Extraction

“Xenotrial” P3

VECTOR

DRUG

More data: patient-derived xenografts (PDX):

"Tumorgrafts", "Xenopatients", "Avatars"

VECTOR

DRUG

(Engraftment) (Expansion)

(Surgery)

Advantages of the PDX approach

• Possibility to conduct population-based studies

• Possibility of treating the same patient/tumor with different drugs, alone and in combination

• Outcome is not confounded by cytotoxic activity of conventional chemotherapeutics

• Treatment versatility: the system is amenable for manipulation of treatment schedules

• Less stringent ethical issues: use of investigational compounds awaiting approval for use in humans

• Virtually unlimited material available for genomic and molecular characterization

Further data: cancer cell lines

The Cancer Cell Line Encyclopedia Consortium & The Genomics of Drug Sensitivity in Cancer Consortium

Nature 1-4 (2015) doi:10.1038/nature15736

Data integration,

analysis and

visualisation

Individual

patient

Patients • Clinical data

• Histology

• Molecular profiles

Patient-derived models

(xenografts, cell cultures) • Histology


• Pharmacology

Public data • Molecular datasets

• Pharmacogenomics

• Biomarker signatures

Bioinformatician

/ Translational

researcher

Data

mining

New biomarker /

stratification

hypotheses

T C G A

I C G C

Ca

ptu

re, S

tora

ge

,

Sta

nd

ard

isatio

n

Integrative

visual reports

Diagnosis,

prognosis and

therapeutic

decision.

"Precision Oncology"

Colorectal Cancer: progression and

hallmarks

Uncontrolled proliferation

Resistance to death signals

Invasion and metastasis

Colorectal cancer molecular heterogeneity

85-90%

10-15%

MSS

MSI

MSS

MSI

Normal

epithelium

Hyperproliferative

epithelium

Early Intermediate Late

adenoma Carcinoma

Invasion and

metastasis

Loss of

APC

DNA

hypomethylation

KRAS

activation Loss of 18q PRL3

amplification

TGFβRII, PIK3CA mutations

Loss of p53

Normal

epithelium

Hyperproliferative

epithelium

Early Intermediate Late

adenoma Carcinoma

Invasion and

metastasis

MMR mutation

MLH1 hypermethylation

BRAF

activation

PIK3CA mutations

Loss of p53

TGFβRII, IGF2R, BAX, E2F4,

MRE11A, hRAD50

frameshift mutations

Mutator phenotype

Colorectal cancer molecular heterogeneity

Response to

Cetuximab

(30-40%)

Colorectal cancer transcriptional subtyping:

the class discovery-class prediction strategy

• Class discovery:

Group samples based on their gene expression

profile and find the optimal number of groups

("subtypes")

• Class prediction:

Use subtype-specific genes to classify

independent CRC samples

• Explore correlations between subtypes and molecular,

biological and clinical features

“Sadanandam”

Sybtypes

Good Prognosis

Prognosis

Poor Prognosis

SSM = Stem-

Serrated--

Mesenchymal

Drug response

Responsive to

Cetuximab

Responsive to

Folfiri/Folfox

Resistant to

Folfiri/Folfox

5 subtypes

3 subtypes

CRC

transcriptional

subtypes: how

many?

3 subtypes

5 subtypes

6 subtypes

CRC Consensus Molecular Subtypes

Guinney et al.,

Nature Medicine 2015

INFL

GOBL TA/ENT

SSM

CMS1

INFL

CMS2

TA/ENT

CMS3

GOBL

CMS4

SSM NO CONS

Multidimensional profiling of CRC cell lines

Genetics

STR profiling

Mutational status

(RAS, BRAF, PIK3CA)

Transcriptomics

Illumina HumanHT-12 v4

Pharmacology (cetuximab sensitivity)

CRC Cell Lines (n = 151)

CRC tumors CRC cell lines

MSS

58%

MSI

42%

CRC genetic features: tumors vs cell lines

Nature Communications 2015

CETUXIMAB

CRC cell lines

Response to EGFR blockade in 150 CRC cell lines

Sensitivity Resistance


Inflammatory

(n=27)

Goblet

(n=21)

Enterocyte

(n=19)

Transit

Amplifying

(n=38)

Stem

(n=27)

Marisa et al. C2

(n=41)

C3 (n=19)

C6 (n=15)

C1 (n=15)

C4 (n=27)

C5 (n=26)

Budinska et al.

CCS2 (n=38)

CCS1 (n=50)

CCS3 (n=24)

De Sousa E Melo et al.

A-type (n=31)

B-type (n=44)

C-type (n=31)

Roepman et al.

Sadanandam et al.

C (n=40)

A (n=12)

B (n=33)

D (n=28)

E

(n=6)

Inflammatory / Goblet TA / Enterocyte Stem / Serrated

/ Mesenchymal

Cell lines recapitulate the CRC intrinsic

transcriptional subtypes identified in patients

n = 132

n = 116

n = 119

n = 112

n = 106

CMS1 CMS4 CMS3 CMS2


MSI

BRAFm

CTX

Sensitive

6/9

Cell lines recapitulate the CRC intrinsic

transcriptional subtypes identified in patients


Integrative mRNA-microRNA analysis

microRNA master regulator analysis

microRNA master regulator analysis

microRNAs antagonizing the Stem-Serrated-

Mesenchymal phenotype share mRNA targets

Transcriptional response of CRC cell lines

to microRNA downmodulation

Comments

• The MMRA pipeline combines supervised statistics with

unsupervised network analysis to detect microRNAs

potentially driving CRC subtypes

• This approach allowed detection of four microRNAs

antagonizing the poor-prognosis SSM subtype in tumor

samples and cell lines

• This functional role was validated in vitro, by

downregulating each microRNA in CRC cell lines

Why WT cell lines are resistant to

therapy?

Back to CRC treatment: possible alternative options

to treat WT tumors resistant to cetuximab?

Hunting for exceptions:

the "outlier" approach

What is an outlier?

"…rara avis in terris nigroque simillima cygno"

Juvenale, Saturae, VI, 165.

“…a rare bird in the lands and very much like a black swan"

When the phrase was coined,

black swans were presumed not to exist.

Indeed, they do exist.

A graphical definition

Outlier kinase genes are aberrantly expressed

in cell lines

CRC cell lines

(n=151)

Outlier kinase genes identified in cell lines are

aberrantly expressed also in CRC tumors

CRC cell lines

(n=151)

CRC tumors

(TCGA; n=352)

Gene outlier Cell line CTX

sensitivity Drugs available

ALK CRC-01 RES Crizotinib

NTRK1 CRC-71 RES Imatinib, Nilotinib,

CEP107, AR523

NTRK2 CRC-122 RES Imatinib, Nilotinib,

CEP107, AR523

FGFR2 CRC-97 RES AZD4547

KIT CRC-43 RES Imatinib, Nilotinib

PDGFRA CRC-12 RES Sorafenib, Sunitinib

Imatinib, Nilotinib

RET CRC-97 RES Sunitinib

Outliers: 7/8 are druggable kinase

FGFR2 genetic amplification induces oncogenic

addiction in CRC cell lines

Characterization of oncogenic EML4-ALK gene fusion

in the CRC cells CRC-01

Characterization of oncogenic TPM3-NTRK1

gene fusion

Identification of NTRK1 and ALK fusions in

CRC samples

TPM3-NTRK1

EML4-ALK

Overexpressed kinase genes are therapeutic

targets in CRC

Overexpression

Pharmacological addiction

Comments

• The compendium of 151 CRC cell lines properly

recapitulates:

– genetic heterogeneity of CRC

– transcriptional subtypes and mRNA/microRNA interactions

– Genotype- and subtype-drug correlations

• Transcriptional outlier analysis identified a subset of

KRAS/BRAF wild type cells, intrinsically resistant to

EGFR inhibition, which are functionally and

pharmacologically addicted to kinase genes

ALK <1%

RET <1%

KIT <1%

FGFR2 <1% NTRK1 <1%

NTRK2 <1%

CRC PDXs

@IRCC

n = 180 n = 110 n = 515

Bertotti et al, Cancer Discovery 2011

Response of colorectal cancer PDXs to cetuximab Genetic status significantly affects CRC response rate

Cancer Discovery 1:508-523

KRAS cod 12

KRAS cod 13

Genetic selection increases the response rate Other genetic biomarkers of resistance?


Analysis of gene expression outliers


Analysis of gene expression outliers HER2 amplification, in cetuximab-resistant CRC


Efficacy of combinatorial anti-EGFR/HER2

treatment in HER2-amplified CRC PDXs

Pertuzumab

Vehicle

Cetuximab+Pmab

Lapatinib

Cmab+Lapatinib

Pmab+Lapatinib


Comments

• Dataset size matters

• Once a targetable genetic lesion is identified, not any

drug targeting that lesion will be effective

• Rational combinations are more likely to be effective,

and preclinical testing may help choose the most

promising one

HERACLES trial:

Targeting HER2 & EGFR

in liver-metastatic CRC

with amplified HER2

CRC transcriptional subtypes and PDXs

Key questions:

• How reliably can the transcriptional subtypes, and their

correlates, be explored in CRC PDXs?

– Are the subtypes maintained in PDXs?

– What is the role of the tumor stroma?

• How reliably could information obtained in PDXs be

applied to CRC patients?

Tumor vs PDX transcriptome

Total

RNA

Total

RNA Expression in Tumor

Exp

res

sio

n i

n P

DX

Human-specific Array

Isella et al., Nature Genetics 47:312, 2015

PDX Sample

Cancer Cells

(human)

Stromal Cells

(human)

+

Human Tumor

Cancer Cells

(human)

Stromal Cells

(mouse)

+

?

Tumor vs PDX transcriptome

Total

RNA

Total

RNA Expression in Tumor

Exp

res

sio

n i

n P

DX

Human-specific Array

Genes "Lost in PDX"


PDX Sample

Cancer Cells

(human)

Stromal Cells

(human)

+

Human Tumor

Cancer Cells

(human)

Stromal Cells

(mouse)

+

Infl – CMS1 Goblet – CMS3 Ent – CMS2

TA – CMS2 Stem – CMS4

Expression in Tumor

Ex

pre

ss

ion

in

PD

X

Expression of subtype genes in tumor vs

PDX

Classification "reshuffling" in PDX

Inflammatory

Goblet

Enterocyte

TA

Stem

CMS1

MSI IMMUNE

CMS3

METABOLIC

CMS2

CANONICAL

CMS4

MESENCHYMAL

Hunting for "lost" genes by

RNAseq analysis of PDX samples

RNAseq

Reads mapped

only on Hs

Genome

Reads mapped

only on Mm

Genome

Cancer Cell

Transcriptome

Stromal Cell

Transcriptome

PDX Sample

Cancer Cells

(human)

Stromal Cells

(mouse)

+ Total

RNA


CMS4/SSM genes are expressed

as mouse transcripts in PDXs

Mo

use

tra

nscrip

t le

ve

l

(RP

M)

Human transcript level (RPM)

INFL-GOBL

(CMS 1-3)

TA-ENT

(CMS2)

SSM

(CMS4)

–

Definition of stromal cell-specific signatures

Differential

Gene

Expression

PDX human arrays

Genes

never expressed

by cancer cells

Stromal cell-

specific

signatures


Definition of stromal cell-specific signatures

Leucocyte

Signature

genes

FAP+

Cells

CD45+

Cells

CD31+

Cells

CAF Signature

genes

Endothelial

Signature

genes

EPCAM+

Cells


Stromal scores reflect tumor biology

Triple low score

TCGA Digital Slide Archive


CAF++ score



Endo++ score



Leuco++ score



Triple high score


A CAF-specific score predicts CRC prognosis

and treatment response

All cases No Adjuvant

Treatment

Adjuvant

Treatment


A compound stromal score predicts response of

rectal cancer to preoperative radiotherapy


Comments

• Transcriptional subtypes hold reasonably well in PDXs,

with the exception of SSM

• SSM genes are expressed by stromal rather than

epithelial cancer cells

• Most SSM genes are readily detected in PDX samples

as mouse rather than human transcripts, confirming their

stromal origin

• Stromal transcriptomes reflect the composition and

functional state of stromal cells, with prognostic and

therapeutic implications.

Class discovery in CRC PDXs

• Expression dataset (Illumina human arrays) on 515

PDXs from 250 tumors

• Class discovery by NMF-consensus

• Construction of a classifier excluding genes also

expressed by the stroma

• Assessment of classification performance on

independent human CRC datasets and analysis of

molecular, biological and clinical correlates

Integrating cell lines and PDXs

to test new actionable pathways in CRC

TARGET

Pevonedistat blocks the NEDD8

conjugation pathway

• Shah et al., CCR 2016: Phase I

Study on Relapsed/Refractory

Multiple Myeloma or Lymphoma.

• Sarantopoulos et al., CCR 2015:

Phase I Study on Advanced Solid

Tumors.

N8

Cul

N8

E1 E2

N8

E3 NEDD8-Activating

Enzyme

Pevonedistat (MLN4924)

N8

Matched

PDXs

CRC cell lines

(n=122)

In vitro

response

CRC liver MTS

(n=87)

Molecular

profiles

A two-arm preclinical platform to study CRC

response to NEDD8 pathway inhibition by

pevonedistat.

Molecular

predictor

Predicted

sensitive

Predicted

resistant

In vivo

response

In vivo

response

Data integration,

analysis and

visualisation

Individual

patient

Patients • Clinical data

• Histology


Patient-derived models

(xenografts, cell cultures) • Histology


• Pharmacology

Public data • Molecular datasets

• Pharmacogenomics

• Biomarker signatures

Bioinformatician

/ Translational

researcher

Data

mining

New biomarker /

stratification

hypotheses

T C G A

I C G C

Ca

ptu

re, S

tora

ge

,

Sta

nd

ard

isatio

n

Integrative

visual reports

Diagnosis,

prognosis and

therapeutic

decision.

"Precision Oncology"

Data integration,

analysis and

visualisation

Pathology

XENOPATIENTS

PRECLINICAL

STUDIES

DATA

INTEGRATION

MODULE 3:

MOLECULAR DATA

MODULE 1:

CLINICAL DATA

Laboratory Imaging Medical

Records

Interface

Interfaces Interfaces Interfaces Interfaces

BIOREPOSITORY

DNA profiling

Interfaces

RNA profiling

Interfaces

Microscopy

Interfaces

Protein profiling

Interfaces

Interface

MODULE 2:

BANK/XENO DATA

Interface

MODULE 4:

in vitro DATA

Interface

TISSUE

SAMPLES

MULTI-DIMENSIONAL MOLECULAR PROFILING

(primary samples, xenopatients, cells)

microRNA

profiling Sequencing

Genotyping &

Array-CGH Epigenomics Proteomics

mRNA

profiling

Sequence/expression

databases

Gene sets (MSigDB)

Functional databases miRNA targets

Promoters

protein interaction Published signatures

Genome and

transcriptome

DATA INTEGRATION STANDARDIZATION – STORAGE

PROCESSING – ANNOTATION

ANALYSIS – VISUALIZATION

CLINICAL AND

PATHOLOGICAL

DATA

PRECISION MEDICINE

Predictions of individual treatment

response/resistance, risk stratification,

definition of clinical decision trees

Treatments and

responses in

Xenopatients

CANDIDATE PRIORITIZATION Coding/non-coding sequences whose

gain/loss-of-function is likely to affect

response to treatments

DATA MINING

Follow-up

Anamnestic data

Clinical history

Imaging

Pathology

Treatment(s)

EXPERIMENTAL

DATA

Treatments and

responses in cells

Functional/drug

screenings in

cells

The Genomic Data Flood

Typical reactions - I

Refuse

Despair

Succumb

Typical reactions - II

Ignore Adapt

…but what if…

…but what if…

Enjoy!!

• Choose the best data analysis tool on earth

• Process and organize data for the tool

• Keep in mind the end-user(s)

The most efficient pattern-

finding tool available on earth

• Choose the best data analysis tool on earth

• Process and organize data for the tool

• Keep in mind the end-user(s)

DA

TA

MA

TR

IX

12’0

00 g

en

es

300

samples

5 samples

9 g

en

es

The visualization problem:

reading numbers does not work

50

samples

90 g

en

es

Basic

Object

The concept of "visual metaphors"

Height

Color

Basic

Object

Width, depth Continuous

Variables


Group Member

Height

Color

Basic

Object

Size

Highlight Blink

Continuous

Variables

Discrete

Variables


a tri-dimensional environment in which different

types of information, such as gene expression,

dosage, methylation and clinical data can be

concomitantly visualized and analyzed.

:

http://genomecruzer.com/

Navigating colorectal cancer genomes

…GO!!!

http://genomecruzer.com/

Summary

• Multiple levels of molecular alteration are functionally

involved in cancer initiation, progression, and response to

treatment.

• Tumor cells interact with stromal and inflammatory cells,

which influence cancer progression and therapy response.

• Pathological, radiological, clinical and preclinical data

contribute important prognostic and predictive information

that should be further incorporated

• Reliable prediction of tumor aggressiveness and therapy

response requires integrative analysis of all data.

• Particular attention should be dedicated to interactive visual

environments, where end-users could easily navigate the

integrated information, at the genome, gene or patient level.

Oncogenomics

Claudio Isella

Gabriele Picco

Consalvo Petti

Sara Bellomo

Andrea Terrasi

Daniela Cantarella

Roberta Porporato

Molecular Oncology & Cancer Epigenetics Carlotta Cancelliere

Mariangela Russo

Michela Buscarino

Federica Di Nicolantonio

Alberto Bardelli

Surgery &

Gastroenterology

Alfredo Mellano

Michele De Simone

Andrea Muratore

Giovanni Galatola

Pathology , Torino University Paola Cassoni

Translational Cancer Medicine Giorgia Migliardi

Davide Torti

Francesco Galimi

Francesco Sassi

Eugenia Zanella

Stefania Gastaldi

Andrea Bertotti

Livio Trusolino

Candiolo Cancer Institute

UZ Brussel Mark De Ridder

Guy Storme

Acknowledgments

Millennium Pharmaceuticals

Allison Berger

[email protected]

Luca Vezzadini

Riccardo Corsi

www.kairos3d.it

Date post:	16-Apr-2017
Category:	Education
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Integrative analysis and visualization of clinical and molecular data for cancer precision medicine...

Education