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Enzo Medico
University of Torino
Integrative analysis and visualization
of clinical and molecular data
for cancer precisio...
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
...
Towards precision cancer medicine
Targeted
drug
Target
Response
Target
alterations
Tissue/context-
specific modifiers
Sens...
Further elements of complexity
• Intratumoral heterogeneity
De-sensitizing lesions only present in a fraction of the cance...
• Analyzing inter-tumoral heterogeneity requires a
reference background
Focus on one specific tumour type
• Sensitizing/de...
International consortia for cancer genomics
TCGA
The Cancer Genome Atlas:
http://cancergenome.nih.gov
ICGC
International C...
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 Bio...
Multiple types of data
Clinical data
• Clinical diagnosis
• Treatment history
• Histologic diagnosis
• Pathologic status
•...
“P0”
P1
P2
Biobank Archive
Nucleic Acid Extraction
“Xenotrial”P3
VECTOR
DRUG
More data: patient-derived xenografts (PDX):
...
Advantages of the PDX approach
• Possibility to conduct population-based studies
• Possibility of treating the same patien...
Further data: cancer cell lines
The Cancer Cell Line Encyclopedia Consortium & The Genomics of Drug Sensitivity in Cancer ...
Data integration,
analysis and
visualisation
Individual
patient
Patients
• Clinical data
• Histology
• Molecular profiles
...
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
epitheliu...
Colorectal cancer molecular heterogeneity
 Response to
Cetuximab
(30-40%)
Colorectal cancer transcriptional subtyping:
the class discovery-class prediction strategy
• Class discovery:
Group sample...
“Sadanandam”
Sybtypes
Good Prognosis
Prognosis
Poor Prognosis
SSM = Stem-
Serrated--
Mesenchymal
Drug response
Responsive ...
 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...
Multidimensional profiling of CRC cell lines
Genetics
STR profiling
Mutational status
(RAS, BRAF, PIK3CA)
Transcriptomics
...
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...
MSI
BRAFm
CTX
Sensitive
6/9
Cell lines recapitulate the CRC intrinsic
transcriptional subtypes identified in patients
Natu...
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
potenti...
Why WT cell lines are resistant to
therapy?
Back to CRC treatment: possible alternative options
to treat WT tumors resista...
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...
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 tumor...
Gene outlier Cell line
CTX
sensitivity
Drugs available
ALK CRC-01 RES Crizotinib
NTRK1 CRC-71 RES
Imatinib, Nilotinib,
CEP...
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 s...
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:...
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
...
Comments
• Dataset size matters
• Once a targetable genetic lesion is identified, not any
drug targeting that lesion will ...
CRC transcriptional subtypes and PDXs
Key questions:
• How reliably can the transcriptional subtypes, and their
correlates...
Tumor vs PDX transcriptome
Total
RNA
Total
RNA
Expression in TumorExpressioninPDX
Human-specific Array
Isella et al., Natu...
Tumor vs PDX transcriptome
Total
RNA
Total
RNA
Expression in TumorExpressioninPDX
Human-specific Array
Genes "Lost in PDX"...
Infl – CMS1 Goblet – CMS3 Ent – CMS2
TA – CMS2 Stem – CMS4
Expression in Tumor
ExpressioninPDX
Expression of subtype genes...
Classification "reshuffling" in PDX
Inflammatory
Goblet
Enterocyte
TA
Stem
CMS1
MSI IMMUNE
CMS3
METABOLIC
CMS2
CANONICAL
C...
Hunting for "lost" genes by
RNAseq analysis of PDX samples
RNAseq
Reads mapped
only on Hs
Genome
Reads mapped
only on Mm
G...
CMS4/SSM genes are expressed
as mouse transcripts in PDXs
Mousetranscriptlevel
(RPM)
Human transcript level (RPM)
INFL-GOB...
–
Definition of stromal cell-specific signatures
Differential
Gene
Expression
PDX human arrays
Genes
never expressed
by canc...
Definition of stromal cell-specific signatures
Leucocyte
Signature
genes
FAP+
Cells
CD45+
Cells
CD31+
Cells
CAF Signature
...
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
Isel...
A compound stromal score predicts response of
rectal cancer to preoperative radiotherapy
Isella et al., Nature Genetics 47...
Comments
• Transcriptional subtypes hold reasonably well in PDXs,
with the exception of SSM
• SSM genes are expressed by s...
Class discovery in CRC PDXs
• Expression dataset (Illumina human arrays) on 515
PDXs from 250 tumors
• Class discovery by ...
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
Mul...
Matched
PDXs
CRC cell lines
(n=122)
In vitro
response
CRC liver MTS
(n=87)
Molecular
profiles
A two-arm preclinical platfo...
Data integration,
analysis and
visualisation
Individual
patient
Patients
• Clinical data
• Histology
• Molecular profiles
...
Pathology
XENOPATIENTS
PRECLINICAL
STUDIES
DATA
INTEGRATION
MODULE 3:
MOLECULAR DATA
MODULE 1:
CLINICAL DATA
Laboratory Im...
MULTI-DIMENSIONAL MOLECULAR PROFILING
(primary samples, xenopatients, cells)
microRNA
profiling
Sequencing
Genotyping &
Ar...
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
90...
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...
a tri-dimensional environment in which different
types of information, such as gene expression,
dosage, methylation and cl...
Navigating colorectal cancer genomes
…GO!!!
http://genomecruzer.com/
Summary
• Multiple levels of molecular alteration are functionally
involved in cancer initiation, progression, and respons...
Oncogenomics
Claudio Isella
Gabriele Picco
Consalvo Petti
Sara Bellomo
Andrea Terrasi
Daniela Cantarella
Roberta Porporato...
Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico
Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico
Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico
Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico
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Integrative analysis and visualization of clinical and molecular data for cancer precision medicine - Enzo Medico

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SYSTEMS BIOLOGY AND SYSTEMS MEDICINE: TOWARDS A PRECISION MEDICINE
September 26-30, 2016

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

  1. 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. 2. Cancer onset and progression
  3. 3. Cancer onset and progression: clonal evolution Wang et al., Nature 2014
  4. 4. Clonal evolution during cancer treatment Ding et al, Nature 2012
  5. 5. Towards precision cancer medicine Targeted drug Target Response
  6. 6. Towards precision cancer medicine Targeted drug Target Response Target alterations
  7. 7. Towards precision cancer medicine Targeted drug Target Response Target alterations Sensitizing alterations De-sensitizing alterations
  8. 8. Towards precision cancer medicine Targeted drug Target Response Target alterations Tissue/context- specific modifiers Sensitizing alterations De-sensitizing alterations
  9. 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. 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. 11. International consortia for cancer genomics TCGA The Cancer Genome Atlas: http://cancergenome.nih.gov ICGC International Cancer Genome Consortium: www.icgc.org
  12. 12. Data available from TCGA (sept 2016) TCGA data are hosted at the Genomics Data Commons: https://gdc.nci.nih.gov/
  13. 13. Data available from ICGC (sept 2016)
  14. 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. 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. 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. 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. 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. 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. 20. Colorectal Cancer: progression and hallmarks Uncontrolled proliferation Resistance to death signals Invasion and metastasis
  21. 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. 22. Colorectal cancer molecular heterogeneity  Response to Cetuximab (30-40%)
  23. 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. 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. 25.  5 subtypes  3 subtypes CRC transcriptional subtypes: how many?  3 subtypes  5 subtypes  6 subtypes
  26. 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. 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. 28. CRC tumors CRC cell lines MSS 58% MSI 42% CRC genetic features: tumors vs cell lines Nature Communications 2015
  29. 29. CETUXIMAB CRC cell lines Response to EGFR blockade in 150 CRC cell lines SensitivityResistance Nature Communications 2015
  30. 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. 31. MSI BRAFm CTX Sensitive 6/9 Cell lines recapitulate the CRC intrinsic transcriptional subtypes identified in patients Nature Communications 2015
  32. 32. Integrative mRNA-microRNA analysis
  33. 33. microRNA master regulator analysis
  34. 34. microRNA master regulator analysis
  35. 35. microRNAs antagonizing the Stem-Serrated- Mesenchymal phenotype share mRNA targets
  36. 36. Transcriptional response of CRC cell lines to microRNA downmodulation
  37. 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. 38. Why WT cell lines are resistant to therapy? Back to CRC treatment: possible alternative options to treat WT tumors resistant to cetuximab?
  39. 39. Hunting for exceptions: the "outlier" approach
  40. 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.
  41. 41. A graphical definition
  42. 42. Outlier kinase genes are aberrantly expressed in cell lines CRC cell lines (n=151)
  43. 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. 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. 45. FGFR2 genetic amplification induces oncogenic addiction in CRC cell lines
  46. 46. Characterization of oncogenic EML4-ALK gene fusion in the CRC cells CRC-01
  47. 47. Characterization of oncogenic TPM3-NTRK1 gene fusion
  48. 48. Identification of NTRK1 and ALK fusions in CRC samples TPM3-NTRK1 EML4-ALK
  49. 49. Overexpressed kinase genes are therapeutic targets in CRC Overexpression Pharmacological addiction
  50. 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. 51. CRC PDXs @IRCC n = 180 n = 110 n = 515 Bertotti et al, Cancer Discovery 2011
  52. 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. 53. Genetic selection increases the response rate Other genetic biomarkers of resistance? Cancer Discovery 1:508-523
  54. 54. Analysis of gene expression outliers Cancer Discovery 1:508-523
  55. 55. Analysis of gene expression outliers HER2 amplification, in cetuximab-resistant CRC Cancer Discovery 1:508-523
  56. 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. 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
  58. 58. 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?
  59. 59. 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) + ?
  60. 60. 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) +
  61. 61. Infl – CMS1 Goblet – CMS3 Ent – CMS2 TA – CMS2 Stem – CMS4 Expression in Tumor ExpressioninPDX Expression of subtype genes in tumor vs PDX
  62. 62. Classification "reshuffling" in PDX Inflammatory Goblet Enterocyte TA Stem CMS1 MSI IMMUNE CMS3 METABOLIC CMS2 CANONICAL CMS4 MESENCHYMAL
  63. 63. 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
  64. 64. 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)
  65. 65.
  66. 66. 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
  67. 67. 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
  68. 68. Stromal scores reflect tumor biology Triple low score TCGA Digital Slide Archive
  69. 69. Stromal scores reflect tumor biology CAF++ score TCGA Digital Slide Archive
  70. 70. Stromal scores reflect tumor biology Endo++ score TCGA Digital Slide Archive
  71. 71. Stromal scores reflect tumor biology Leuco++ score TCGA Digital Slide Archive
  72. 72. Stromal scores reflect tumor biology Triple high score TCGA Digital Slide Archive
  73. 73. 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
  74. 74. A compound stromal score predicts response of rectal cancer to preoperative radiotherapy Isella et al., Nature Genetics 47:312, 2015
  75. 75. 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.
  76. 76. 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
  77. 77. Integrating cell lines and PDXs to test new actionable pathways in CRC
  78. 78. 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
  79. 79. 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
  80. 80. 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
  81. 81. 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
  82. 82. 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
  83. 83. The Genomic Data Flood
  84. 84. Typical reactions - I Refuse Despair Succumb
  85. 85. Typical reactions - II Ignore Adapt
  86. 86. …but what if…
  87. 87. …but what if… Enjoy!!
  88. 88. • Choose the best data analysis tool on earth • Process and organize data for the tool • Keep in mind the end-user(s)
  89. 89. The most efficient pattern- finding tool available on earth
  90. 90. • Choose the best data analysis tool on earth • Process and organize data for the tool • Keep in mind the end-user(s)
  91. 91. DATAMATRIX 12’000genes 300 samples 5 samples 9genes The visualization problem: reading numbers does not work 50 samples 90genes
  92. 92. Basic Object The concept of "visual metaphors"
  93. 93. Height Color Basic Object Width, depth Continuous Variables The concept of "visual metaphors"
  94. 94. Group Member Height Color Basic Object Size Highlight Blink Continuous Variables Discrete Variables The concept of "visual metaphors"
  95. 95. 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/
  96. 96. Navigating colorectal cancer genomes …GO!!! http://genomecruzer.com/
  97. 97. 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.
  98. 98. 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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