Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico

Lake Como School of Advanced Studies
Lake Como School of Advanced StudiesLake Como School of Advanced Studies
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
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
Towards precision cancer medicine
Targeted
drug
Target
Response
Target
alterations
Towards precision cancer medicine
Targeted
drug
Target
Response
Target
alterations
Sensitizing
alterations
De-sensitizing
alterations
Towards precision cancer medicine
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/
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
• Molecular profiles
• 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
Capture,Storage,
Standardisation
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
SensitivityResistance
Nature Communications 2015
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 CMS4CMS3 CMS2
Nature Communications 2015
MSI
BRAFm
CTX
Sensitive
6/9
Cell lines recapitulate the CRC intrinsic
transcriptional subtypes identified in patients
Nature Communications 2015
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?
Cancer Discovery 1:508-523
Analysis of gene expression outliers
Cancer Discovery 1:508-523
Analysis of gene expression outliers
HER2 amplification, in cetuximab-resistant CRC
Cancer Discovery 1:508-523
Efficacy of combinatorial anti-EGFR/HER2
treatment in HER2-amplified CRC PDXs
Pertuzumab
Vehicle
Cetuximab+Pmab
Lapatinib
Cmab+Lapatinib
Pmab+Lapatinib
Cancer Discovery 1:508-523
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
Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico
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 TumorExpressioninPDX
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 TumorExpressioninPDX
Human-specific Array
Genes "Lost in PDX"
Isella et al., Nature Genetics 47:312, 2015
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
ExpressioninPDX
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
Isella et al., Nature Genetics 47:312, 2015
CMS4/SSM genes are expressed
as mouse transcripts in PDXs
Mousetranscriptlevel
(RPM)
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
Isella et al., Nature Genetics 47:312, 2015
Definition of stromal cell-specific signatures
Leucocyte
Signature
genes
FAP+
Cells
CD45+
Cells
CD31+
Cells
CAF Signature
genes
Endothelial
Signature
genes
EPCAM+
Cells
Isella et al., Nature Genetics 47:312, 2015
Stromal scores reflect tumor biology
Triple low score
TCGA Digital Slide Archive
Stromal scores reflect tumor biology
CAF++ score
TCGA Digital Slide Archive
Stromal scores reflect tumor biology
Endo++ score
TCGA Digital Slide Archive
Stromal scores reflect tumor biology
Leuco++ score
TCGA Digital Slide Archive
Stromal scores reflect tumor biology
Triple high score
TCGA Digital Slide Archive
A CAF-specific score predicts CRC prognosis
and treatment response
All cases
No Adjuvant
Treatment
Adjuvant
Treatment
Isella et al., Nature Genetics 47:312, 2015
A compound stromal score predicts response of
rectal cancer to preoperative radiotherapy
Isella et al., Nature Genetics 47:312, 2015
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
E3NEDD8-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
• 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
T
C
G
A
I
C
G
C
Capture,Storage,
Standardisation
Integrative
visual reports
Diagnosis,
prognosis and
therapeutic
decision.
"Precision Oncology"
Data integration,
analysis and
visualisation
Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico
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
Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico
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 interactionPublished 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)
DATAMATRIX
12’000genes
300
samples
5 samples
9genes The visualization problem:
reading numbers does not work
50
samples
90genes
Basic
Object
The concept of "visual metaphors"
Height
Color
Basic
Object
Width, depth
Continuous
Variables
The concept of "visual metaphors"
Group Member
Height
Color
Basic
Object
Size
Highlight Blink
Continuous
Variables
Discrete
Variables
The concept of "visual metaphors"
Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico
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
enzo.medico@ircc.it
Luca Vezzadini
Riccardo Corsi
www.kairos3d.it
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Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico

  • 1. 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
  • 2. Cancer onset and progression
  • 3. Cancer onset and progression: clonal evolution Wang et al., Nature 2014
  • 4. Clonal evolution during cancer treatment Ding et al, Nature 2012
  • 5. Towards precision cancer medicine Targeted drug Target Response
  • 6. Towards precision cancer medicine Targeted drug Target Response Target alterations
  • 7. Towards precision cancer medicine Targeted drug Target Response Target alterations Sensitizing alterations De-sensitizing alterations
  • 8. Towards precision cancer medicine Targeted drug Target Response Target alterations Tissue/context- specific modifiers Sensitizing alterations De-sensitizing alterations
  • 9. 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
  • 10. • 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
  • 11. International consortia for cancer genomics TCGA The Cancer Genome Atlas: http://cancergenome.nih.gov ICGC International Cancer Genome Consortium: www.icgc.org
  • 12. Data available from TCGA (sept 2016) TCGA data are hosted at the Genomics Data Commons: https://gdc.nci.nih.gov/
  • 13. Data available from ICGC (sept 2016)
  • 14. 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.
  • 15. 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…
  • 16. “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)
  • 17. 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
  • 18. 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
  • 19. 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 T C G A I C G C Capture,Storage, Standardisation Integrative visual reports Diagnosis, prognosis and therapeutic decision. "Precision Oncology"
  • 20. Colorectal Cancer: progression and hallmarks Uncontrolled proliferation Resistance to death signals Invasion and metastasis
  • 21. 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
  • 22. Colorectal cancer molecular heterogeneity  Response to Cetuximab (30-40%)
  • 23. 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
  • 24. “Sadanandam” Sybtypes Good Prognosis Prognosis Poor Prognosis SSM = Stem- Serrated-- Mesenchymal Drug response Responsive to Cetuximab Responsive to Folfiri/Folfox Resistant to Folfiri/Folfox
  • 25.  5 subtypes  3 subtypes CRC transcriptional subtypes: how many?  3 subtypes  5 subtypes  6 subtypes
  • 26. 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
  • 27. 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)
  • 28. CRC tumors CRC cell lines MSS 58% MSI 42% CRC genetic features: tumors vs cell lines Nature Communications 2015
  • 29. CETUXIMAB CRC cell lines Response to EGFR blockade in 150 CRC cell lines SensitivityResistance Nature Communications 2015
  • 30. 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 CMS4CMS3 CMS2 Nature Communications 2015
  • 31. MSI BRAFm CTX Sensitive 6/9 Cell lines recapitulate the CRC intrinsic transcriptional subtypes identified in patients Nature Communications 2015
  • 35. microRNAs antagonizing the Stem-Serrated- Mesenchymal phenotype share mRNA targets
  • 36. Transcriptional response of CRC cell lines to microRNA downmodulation
  • 37. 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
  • 38. Why WT cell lines are resistant to therapy? Back to CRC treatment: possible alternative options to treat WT tumors resistant to cetuximab?
  • 39. Hunting for exceptions: the "outlier" approach
  • 40. 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.
  • 42. Outlier kinase genes are aberrantly expressed in cell lines CRC cell lines (n=151)
  • 43. Outlier kinase genes identified in cell lines are aberrantly expressed also in CRC tumors CRC cell lines (n=151) CRC tumors (TCGA; n=352)
  • 44. 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
  • 45. FGFR2 genetic amplification induces oncogenic addiction in CRC cell lines
  • 46. Characterization of oncogenic EML4-ALK gene fusion in the CRC cells CRC-01
  • 47. Characterization of oncogenic TPM3-NTRK1 gene fusion
  • 48. Identification of NTRK1 and ALK fusions in CRC samples TPM3-NTRK1 EML4-ALK
  • 49. Overexpressed kinase genes are therapeutic targets in CRC Overexpression Pharmacological addiction
  • 50. 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%
  • 51. CRC PDXs @IRCC n = 180 n = 110 n = 515 Bertotti et al, Cancer Discovery 2011
  • 52. 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
  • 53. Genetic selection increases the response rate Other genetic biomarkers of resistance? Cancer Discovery 1:508-523
  • 54. Analysis of gene expression outliers Cancer Discovery 1:508-523
  • 55. Analysis of gene expression outliers HER2 amplification, in cetuximab-resistant CRC Cancer Discovery 1:508-523
  • 56. Efficacy of combinatorial anti-EGFR/HER2 treatment in HER2-amplified CRC PDXs Pertuzumab Vehicle Cetuximab+Pmab Lapatinib Cmab+Lapatinib Pmab+Lapatinib Cancer Discovery 1:508-523
  • 57. 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
  • 59. 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?
  • 60. Tumor vs PDX transcriptome Total RNA Total RNA Expression in TumorExpressioninPDX 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) + ?
  • 61. Tumor vs PDX transcriptome Total RNA Total RNA Expression in TumorExpressioninPDX Human-specific Array Genes "Lost in PDX" Isella et al., Nature Genetics 47:312, 2015 PDX Sample Cancer Cells (human) Stromal Cells (human) + Human Tumor Cancer Cells (human) Stromal Cells (mouse) +
  • 62. Infl – CMS1 Goblet – CMS3 Ent – CMS2 TA – CMS2 Stem – CMS4 Expression in Tumor ExpressioninPDX Expression of subtype genes in tumor vs PDX
  • 63. Classification "reshuffling" in PDX Inflammatory Goblet Enterocyte TA Stem CMS1 MSI IMMUNE CMS3 METABOLIC CMS2 CANONICAL CMS4 MESENCHYMAL
  • 64. 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 Isella et al., Nature Genetics 47:312, 2015
  • 65. CMS4/SSM genes are expressed as mouse transcripts in PDXs Mousetranscriptlevel (RPM) Human transcript level (RPM) INFL-GOBL (CMS 1-3) TA-ENT (CMS2) SSM (CMS4)
  • 66.
  • 67. Definition of stromal cell-specific signatures Differential Gene Expression PDX human arrays Genes never expressed by cancer cells Stromal cell- specific signatures Isella et al., Nature Genetics 47:312, 2015
  • 68. Definition of stromal cell-specific signatures Leucocyte Signature genes FAP+ Cells CD45+ Cells CD31+ Cells CAF Signature genes Endothelial Signature genes EPCAM+ Cells Isella et al., Nature Genetics 47:312, 2015
  • 69. Stromal scores reflect tumor biology Triple low score TCGA Digital Slide Archive
  • 70. Stromal scores reflect tumor biology CAF++ score TCGA Digital Slide Archive
  • 71. Stromal scores reflect tumor biology Endo++ score TCGA Digital Slide Archive
  • 72. Stromal scores reflect tumor biology Leuco++ score TCGA Digital Slide Archive
  • 73. Stromal scores reflect tumor biology Triple high score TCGA Digital Slide Archive
  • 74. A CAF-specific score predicts CRC prognosis and treatment response All cases No Adjuvant Treatment Adjuvant Treatment Isella et al., Nature Genetics 47:312, 2015
  • 75. A compound stromal score predicts response of rectal cancer to preoperative radiotherapy Isella et al., Nature Genetics 47:312, 2015
  • 76. 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.
  • 77. 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
  • 78. Integrating cell lines and PDXs to test new actionable pathways in CRC
  • 79. 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 E3NEDD8-Activating Enzyme Pevonedistat (MLN4924) N8
  • 80. 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
  • 81. 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 T C G A I C G C Capture,Storage, Standardisation Integrative visual reports Diagnosis, prognosis and therapeutic decision. "Precision Oncology" Data integration, analysis and visualisation
  • 83. 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
  • 85. 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 interactionPublished 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
  • 87. Typical reactions - I Refuse Despair Succumb
  • 88. Typical reactions - II Ignore Adapt
  • 91. • Choose the best data analysis tool on earth • Process and organize data for the tool • Keep in mind the end-user(s)
  • 92. The most efficient pattern- finding tool available on earth
  • 93. • Choose the best data analysis tool on earth • Process and organize data for the tool • Keep in mind the end-user(s)
  • 94. DATAMATRIX 12’000genes 300 samples 5 samples 9genes The visualization problem: reading numbers does not work 50 samples 90genes
  • 95. Basic Object The concept of "visual metaphors"
  • 99. 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/
  • 100. Navigating colorectal cancer genomes …GO!!! http://genomecruzer.com/
  • 101. 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.
  • 102. 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 enzo.medico@ircc.it Luca Vezzadini Riccardo Corsi www.kairos3d.it