Enzo Medico

University of Torino

Integrative analysis and visualization

of clinical and molecular data

for cancer precision medicine

Candiolo Cancer Institute

Laboratory of Oncogenomics

enzo.medico@ircc.it

Cancer onset and progression

Normal

epithelium

Hyperproliferative

epithelium

Early → Intermediate → Late

adenomaCarcinoma

Invasion and

metastasis

Loss of

APC

DNA

hypomethylation

KRAS

activation

PRL3

amplification

TGFβRII, PIK3CA mutations

Loss of p53

Normal

epithelium

Hyperproliferative

epithelium

Early → Intermediate → Late

adenomaCarcinoma

Invasion and

metastasis

MMR mutation

MLH1 hypermethylation

BRAF

activationPIK3CA mutations, Loss of p53, frameshift mutations of

TGFβRII, IGF2R, BAX, E2F4, MRE11A, hRAD50

MSS

MSS

Towards precision cancer medicine

Targeted

drugTarget

Response

Target

alterations

Tissue/context-

specific modifiers

Sensitizing

alterations

De-sensitizing

alterations

Patient-specific

modifiers

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

Data integration,

analysis and

visualisation

Individual

patient

Patients• Clinical data

• Histology

• Molecular profiles

Patient-derived models

(xenografts, cell cultures)• Histology

• Molecular profiles

• Pharmacology

Public data• Molecular datasets

• Pharmacogenomics

• Biomarker signatures

Bioinformatician

/ Translational

researcher

Data

mining

New biomarker /

stratification

hypotheses

TCGA

ICGC

Cap

ture

, Sto

rag

e,

Sta

nd

ard

isatio

n

Integrative

visual reports

Diagnosis,

prognosis and

therapeutic

decision.

Cancer genomics and "Precision Oncology"

Data integration,

analysis and

visualisation

Towards clinomics

MULTI-DIMENSIONAL MOLECULAR PROFILING

(primary samples, xenopatients, cells)

microRNA

profilingSequencing

Genotyping &

Array-CGHEpigenomics Proteomics

mRNA

profiling

Sequence/expression

databases

Gene sets (MSigDB)

Functional databases miRNA targets

Promoters

protein interactionsPublished signatures

Genome and

transcriptome

DATA INTEGRATIONSTANDARDIZATION – STORAGE

PROCESSING – ANNOTATION

ANALYSIS – VISUALIZATION

CLINICAL AND

PATHOLOGIC

DATA

PRECISION MEDICINEPredictions of individual treatment

response/resistance, risk stratification,

definition of clinical decision trees

Treatments and

responses in

Xenografts

CANDIDATE PRIORITIZATIONCoding/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…

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

Group Member

Height

Color

Basic

Object

Size

Highlight Blink

Continuous

Variables

Discrete

Variables

The concept of "visual metaphors"

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.

:

www.kairos3d.it

Navigating colorectal cancer omics

DEMO

Summary

• Multiple levels of molecular alteration are functionally

involved in cancer initiation, progression, and response to

treatment.

• 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 and

analyze the integrated information, at the genome, gene or

patient level.

GenomeCruzer playlist on Youtube:https://www.youtube.com/watch?v=buKx67CjcwY&list=PLuVag8CXc5J7wGiohs8Satz7TfUQYRaG2

Oncogenomics

Claudio Isella

Gabriele Picco

Consalvo Petti

Sara Bellomo

Andrea Terrasi

Daniela Cantarella

Roberta Porporato

Molecular Oncology & Cancer EpigeneticsCarlotta Cancelliere

Mariangela Russo

Michela Buscarino

Federica Di Nicolantonio

Alberto Bardelli

Surgery &

Gastroenterology

Alfredo Mellano

Michele De Simone

Andrea Muratore

Giovanni Galatola

Translational CancerMedicineGiorgia Migliardi

Davide Torti

Francesco Galimi

Francesco Sassi

Eugenia Zanella

Stefania Gastaldi

Andrea Bertotti

Livio Trusolino

Candiolo Cancer Institute

Acknowledgments

enzo.medico@ircc.it

Luca Vezzadini

Riccardo Corsi

www.kairos3d.it