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2011 AACR OncoPanel Poster
1. 2011 AACR 102nd Annual Meeting
Title: Tumor cell line profiling of eighteen cancer
therapeutics to evaluate relationships between
tumor genotypes and cancer cell sensitivities
Authors: Yulia Y. Ovechkina, Christine O'Day, Karen Marcoe, Robert Keyser, Karen
Bernards, Jessica Chesnut-Speelman, Phuong T.B. Nguyen, Jenny Mulligan, Teddy
Lin, Rodney Shively, Jim Hnilo, Brian Nelson. Ricerca Biosciences, LLC, Bothell,
WA
2. Introduction
In vitro cellular response profiling of tumor human cell lines has become a
widely used approach for the targeted cancer therapeutics development. Correlation
of the drug sensitivity and resistance cellular response with genomic data offers a
robust and sensitive system for predicting clinical efficacy and identifying more
efficacious patient populations.
We have developed a high throughput cellular approach to evaluate the
relationship between tumor genotypes and drug sensitivity over 240 human tumor
cell lines. A panel of eighteen cancer therapeutic agents was tested for proliferative,
apoptotic and cell cycle arrest responses using multiplexed high content screening
with automated fluorescence microscopy and image analysis based technology (GE
Healthcare INCell Analyzer 1000). Growth index was measured using nuclear dye.
Activated caspase 3 antibodies were used for the apoptosis induction detection.
Phospho-histone H3 antibodies were used to measure the cell cycle block. We
generated cell line profiles to reveal drug sensitivity and resistance patterns and
identified examples of the genomic markers associated with a specific response. This
approach could provide insight into the mechanisms of enhanced susceptibility or
resistance which in turn could be used for the optimization of the targeted cancer
therapeutics.
3. 240_OncoPanel™ - Human Tumor Cell Line Profiling
240_OncoPanel™ - Cancer Types
• 240 human tumor-derived cell Colon/GI 33
line panel from different origins Haematopoietic 29
with broad genetic Soft Tissue 20
Female GU 20
heterogeneity
CNS 20
• Cell lines are quality controlled Breast 20
(procured from ATCC and Skin 19
Lung 19
other banks)
Pancreas 12
• Sensitive High Content cellular Bladder 11
image analysis Kidney 10
Prostate 7
• High throughput (384 well plates)
Liver 7
• Multiplexed data output
Head and Neck 6
• 10 concentrations (in triplicates) Endocrine 6
Other 1
4. Available genetic information
mRNA expression data 50+ Gene Mutation data
http://www.ebi.ac.uk/microarray-as/ae/files/E-MTAB-37
APC BRAF BRCA1 BRCA2
Gene copy number and mRNA expression data CDH1 CDKN2A CTNNB1 CYLD
https://cabig.nci.nih.gov/caArray_GSKdata/
ERBB2 FBXW7 FGFR3 FLCN
Sanger gene copy number data GNAS HRAS JAK2 KIT
http://www.sanger.ac.uk/genetics/CGP/translation/data/
http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi MAP2K4 MLH1 MSH2 MSH6
NF1 NF2 NOTCH1 NPM1
Sanger gene mutation data
http://www.sanger.ac.uk/genetics/CGP/Celllines NTRK3 PALB2 PDGFRA PIK3C2A
PTCH1 PTEN RB1 RET
The mutation data was obtained from the Sanger Institute Catalogue
Of Somatic Mutations In Cancer web site, SMAD4 SMO SOCS1 STK11
http://www.sanger.ac.uk/cosmic TP53 TSC1 TSC2 VHL
Bamford et al (2004) The COSMIC (Catalogue of Somatic Mutations
in Cancer) database and website. Br J Cancer, 91,355-358.
CBFB EGFR FLT3 KRAS
NRAS PIK3CA RUNX1 MYC
5. 240_OncoPanel™ - Assay Workflow
240 human tumor cell line panel High Content Analysis
O
100
H
C NCH2 CH2 CH2 CH3
Cell proliferation
POC
50
O
N H
N C OCH3
0
-11 -10 -9 -8 -7 -6
N
40 log [Vinblastine], M
Compound Apoptosis
Fold Induction
30
20
10
0
-11 -10 -9 -8 -7 -6
4 log [Vinblastine], M
Cell cycle
Fold Induction
3
2
1
0
-12 -11 -10 -9 -8 -7
log [Vinblastine], M
72 hrs
High Throughput Screening
Sensitive cells Resistant cells
Genotype correlation analysis
6. Criteria for Positive Responses
High Content Multiplexed Output
• Cell proliferation measured by relative cell
counts
» EC50 is a concentration at the curve inflection point
(parameter C)
» IC50 is a concentration at 50% of maximal possible
response
» GI50 is a concentration needed to reduce the
growth of treated cells to half that of untreated cells.
D Log GI50 is the log difference from the average
GI50 value
• Apoptosis:
» >5-fold increase in activated caspase-3 signal
indicates apoptosis induction
• Cell cycle block: Cell Nuclei – blue; Apoptotic cells-
» >2-fold increase in phospho-histone-3 indicates green ; Mitotic cells - red
G2/M cell cycle block
» <2-fold decrease in phospho-histone-3 indicates
G1/S cell cycle block
7. Cancer therapeutic agents
Sensitive Resistant Ratio of resistant
Concentration Log GI50
Compound Synonym(s) Target(s) Source CAS No. response response mean GI50 mean to
range, microM range
mean GI50, GI50, microM sensitive GI50 mean
BMS-387032 SNS-032 CDK2, 7 and 9 Selleck 345627-80-7 10 - 0.0003 0.15 1.45 10 2.1
Cl 1040 PD184352 Mek1/2 Selleck 212631-79-3 10 - 0.0003 0.18 4.43 25 3.1
PD0325901 N/A Mek1/2 Calbiochem 391210-10-9 30 - 0.001 0.06 15.53 259 4.5
PD173074 N/A FGFR1, FGFR3 Calbiochem 219580-11-7 5 - 0.0002 0.24 4.63 19 2.4
API-2 Triciribine AKT Tocris 35943-35-2 20 - 0.0006 0.38 12.79 34 2.8
EGFR, ErbB2, Erk-1/2 Sequoia
Lapatinib Tykerb 388082-78-8 20 - 0.0006 0.28 9.26 33 3.4
and AKT Research
BMS-536924 N/A IGF-1R Selleck 468740-43-4 10 - 0.0003 0.19 3.41 18 3
Sequoia
Erlotinib Tarceva EGFR, ErbB2 183321-74-6 14 - 0.0004 0.15 4.48 30 3.2
Research
Sequoia
Geldanamycin N/A HSP-90 30562-34-6 5 - 0.0002 0.03 0.12 4 2.4
Research
BRAF, PDGF, C-Kit, Sequoia
Sorafenib Nexavar 284461-73-0 20 - 0.0006 0.29 4.33 15 2.6
VEGF Research
BCR-ABL, SRC, Sequoia
Dasatinib Sprycel 863127-77-9 5 - 0.0002 0.008 0.87 109 4.5
Ephrins, EGFR Research
Aurora-A,-B,-C Sequoia
VX-680 Tozasertib 639089-54-6 10 - 0.0003 0.06 2.23 37 3.3
kinases Research
FLT3, PDGFRs, Sequoia
Sunitinib Sutent 557795-19-4 20 - 0.0006 0.21 4.37 21 3.2
VEGFRs, Kit Research
Sequoia
Everolimus N/A mTOR 159351-69-6 10 - 0.0003 0.009 4.83 537 4.5
Research
Sequoia
Tandutinib N/A FLT3 ,PDGFR, KIT 387867-13-2 10 - 0.0003 0.22 4.58 21 2.8
Research
Doxorubicin N/A topoisomerase II Calbiochem 25316-40-9 5 - 0.0002 0.005 0.04 8 2.8
Paclitaxel Taxol tubulin Calbiochem 33069-62-4 0.3 - 9.55E-06 0.001 0.009 9 3.5
PKC, PKA,PKG, p60v-
Staurosporine N/A Calbiochem 62996-74-1 1 - 3.18E-05 0.003 0.025 8 2.4
src, CaM kinase II
Midpoint of the GI50 range was used to distinguish sensitive and resistant populations. The GI50 of the sensitive and resistant populations
was then averaged to produce the “sensitive and resistant response mean GI50”.
8. Multiplexed high content approach provides insight into the drug
mechanisms of action
PD0325901 treated Colo829 cell line PD173074 treated KatoIII cell line
Cell proliferation
Cell proliferation
Apoptosis
Apoptosis
Cell cycle
Cell cycle
9. Multiplexed high content approach provides insight into the drug
mechanisms of action
Lapatinib treated CHL-1 cell line BMS-536924 treated HT-29 cell line
Cell proliferation
Cell proliferation
Apoptosis
Apoptosis
Cell cycle
Cell cycle
10. Multiplexed high content approach provides insight into the drug
mechanisms of action
BMS-387032 treated MV-4-11 cell line API-2 treated T47D cell line
Cell proliferation
Cell proliferation
Apoptosis
Apoptosis
Cell cycle
Cell cycle
17. Chemosensitivity profiles ranked by the D GI50 value across 240 cell lines
PD0325901 Lapatinib BMS-387032
-3.0 -3.00 -1.00
-2.5
-2.50
D Log GI50, microM
-2.0 -0.50
-2.00
D Log GI50, microM
D Log GI50, microM
D Log GI50, microM
-1.5
0.00
-1.0 -1.50
-0.5
-1.00 0.50
0.0
0.5 -0.50
1.00
1.0 0.00
1.5 1.50
0.50
2.0
2.5 1.00 2.00
PD173074 BMS-536924 API-2 (Triciribine)
-2.50 -2.50 -2.50
-2.00 -2.00 -2.00
D Log GI50, microM
D Log GI50, microM
-1.50
D Log GI50, microM
-1.50
-1.50
-1.00
-1.00
-1.00 -0.50
-0.50
0.00
-0.50
0.00
0.50
0.00 0.50 1.00
0.50 1.00 1.50
Sensitive 240 cell lines Resistant
18. Chemosensitivity profiles ranked by the D GI50 value across 240 cell lines
Erlotinib CL 1040 Sorafenib
-3.00 -2.50 -2.50
-2.50 -2.00 -2.00
D Log GI50, microM
D Log GI50, microM
D Log GI50, microM
-2.00
D Log GI50, microM
-1.50 -1.50
-1.50
-1.00 -1.00
-1.00
-0.50 -0.50
-0.50
0.00 0.00
0.00
0.50 0.50 0.50
1.00 1.00 1.00
Dasatinib VX-680 Everolimus
-3.00 -2.50 -3.00
-2.50 -2.50
-2.00
-2.00 -2.00
D Log GI50, microM
D Log GI50, microM
D Log GI50, microM
-1.50 -1.50 -1.50
-1.00 -1.00
-0.50
-1.00 -0.50
0.00
-0.50 0.00
0.50 0.50
1.00 0.00 1.00
1.50 1.50
0.50
2.00 2.00
2.50 1.00 2.50
Sensitive 240 cell lines Resistant
19. Chemosensitivity profiles ranked by the D GI50 value across 240 cell lines
Sunitinib Tandutinib Doxorubicin
-2.50 -2.50 -2.00
-2.00 -2.00 -1.50
D Log GI50, microM
-1.50
D Log GI50, microM
D Log GI50, microM
D Log GI50, microM
-1.50 -1.00
-1.00
-1.00 -0.50
-0.50
-0.50 0.00
0.00
0.00 0.50
0.50
1.00 0.50 1.00
1.50 1.00 1.50
Paclitaxel Staurosporine Geldanamycin
-2.50 -1.50 -1.50
-2.00
-1.00
-1.50 -1.00
D Log GI50, microM
D Log GI50, microM
D Log GI50, microM
-1.00 -0.50
-0.50
-0.50
0.00
0.00
0.00
0.50 0.50
1.00 0.50
1.00
1.50
2.00 1.00 1.50
Sensitive 240 cell lines Resistant
20. Raf/Ras mutations are associated with sensitivity to Mek inhibitor, PD0325901, while
PIK3CA and RB mutations correlate with resistance
-3.0 PD0325901
-2.5
-2.0
D Log GI50, microM
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
Sensitive Resistant
Raf/Ras mutations PIK3CA mutations RB1 mutations
The log difference from the average GI50 value is plotted across the 240 cell lines
21. Raf/Ras mutations are associated with sensitivity to Mek inhibitor, CL 1040, while PIK3CA
and RB mutations correlate with resistance
CL 1040
-2.50
-2.00
D Log GI50, microM
-1.50
-1.00
-0.50
0.00
0.50
1.00
Sensitive Resistant
Raf/Ras mutations PIK3CA mutations RB1 mutations
The log difference from the average GI50 value is plotted across the 240 cell lines
22. PIK3CA mutations are associated with sensitivity to Everolimus, while KRAS
mutations confer resistance
Everolimus
-4.00
-3.00
D Log GI50, microM
-2.00
-1.00
0.00
1.00
2.00
3.00
Sensitive Resistant
PIK3CA mutations PIK3CA and KRAS KRAS mutations
mutations
The log difference from the average GI50 value is plotted across the 240 cell lines
23. Ephrin type-A7 & A3 receptor mRNA overexpression and BCR-ABL translocation
are associated with sensitivity to Dasatinib
Dasatinib
-3.00
-2.50
-2.00
D Log GI50, microM
-1.50
-1.00
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
Sensitive Resistant
EphA7 & EphA3 mRNA BCR-ABL translocation
overexpression
The log difference from the average GI50 value is plotted across the 240 cell lines
24. CDKN2A mutations are associated with sensitivity to CDK inhibitor, BMS-387032,
while CCND2 amplification correlates with resistance
-1.00 BMS-387032
-0.50
D Log GI50, microM
0.00
0.50
1.00
1.50
2.00
Sensitive Resistant
CDKN2A mutation CCND2 (Cyclin D2) copy number
amplification
The log difference from the average GI50 value is plotted across the 240 cell lines
25. AKT2 amplification and PTEN mutations are associated with resistance to AKT
inhibitor, API-2 (Triciribine)
-2.50 API-2 (Triciribine)
-2.00
D Log GI50, microM
-1.50
-1.00
-0.50
0.00
0.50
1.00
1.50
Sensitive Resistant
PTEN mutation AKT2 copy number amplification
The log difference from the average GI50 value is plotted across the 240 cell lines
26. FGFR protein family overexpression is associated with sensitivity to FGFR inhibitor,
PD173074, while EGFR overexpression correlates with resistance
-2.50 PD173074
-2.00
D Log GI50, microM
-1.50
-1.00
-0.50
0.00
0.50
Sensitive Resistant
FGFR protein family mRNA
EGFR mRNA FGFR overexpression in Ras/Raf or
overexpression overexpression PIK3CA mutation background
The log difference from the average GI50 value is plotted across the 240 cell lines
27. Raf/Ras, PIK3CA and PTEN mutations correlate with resistance to FGFR inhibitor, PD173074
-2.50 PD173074
-2.00
D Log GI50, microM
-1.50
-1.00
-0.50
0.00
0.50
Sensitive Resistant
Raf/Ras mutations PIK3CA mutations PTEN mutations
The log difference from the average GI50 value is plotted across the 240 cell lines
28. EGFR overexpression is associated with sensitivity to EGFR inhibitor, Lapatinib, while
Raf/Ras, PIK3CA or PTEN mutations may confer resistance to the EGFR overexpressing cell
lines
-3.00 Lapatinib
-2.50
D Log GI50, microM
-2.00
-1.50
-1.00
-0.50
0.00
0.50
1.00
Sensitive Resistant
EGFR mRNA EGFR overexpression in Raf/Ras, PIK3CA or
overexpression PTEN mutation background
The log difference from the average GI50 value is plotted across the 240 cell lines
29. Raf/Ras, PIK3CA and PTEN mutations may correlate with resistance to EGFR
inhibitor, Lapatinib
-3.00 Lapatinib
-2.50
D Log GI50, microM
-2.00
-1.50
-1.00
-0.50
0.00
0.50
1.00
Sensitive Resistant
Raf/Ras mutations PIK3CA mutations PTEN mutations
The log difference from the average GI50 value is plotted across the 240 cell lines
30. EGFR overexpression is associated with resistance to IGF-1R inhibitor, BMS-
536924
-2.50 BMS-536924
-2.00
D Log GI50, microM
-1.50
-1.00
-0.50
0.00
0.50
1.00
Sensitive Resistant
EGFR overexpression in EGFR mRNA
EGFR mutation background overexpression
The log of the difference from the average GI50 value is plotted across the 240 cell lines
31. K-Ras, PIK3CA and PTEN mutations may correlate with resistance to IGF-1R
inhibitor, BMS-536924
-2.50 BMS-536924
-2.00
D Log GI50, microM
-1.50
-1.00
-0.50
0.00
0.50
1.00
Sensitive Resistant
K-Ras mutations PIK3CA mutations PTEN mutations
The log difference from the average GI50 value is plotted across the 240 cell lines
32. Conclusions
We generated chemosensitivity profiles over 240 cell lines to reveal drug
sensitivity and resistance patterns and identified markers associated with a
specific response. It was found that Raf/Ras mutations confer sensitivity to MEK
inhibitors, PD0325901 and Cl 1040, while PIK3CA and RB mutations were
associated with resistance. Resistance to IGF-1R, FGFR, and EGFR inhibitors
correlated with PI3K/PTEN/Akt or Raf/Ras pathway activation. EGFR mRNA
overexpression was associated with resistance to FGFR and IGF-1R inhibitors.
Resistance to AKT inhibitor, API-2 (Triciribine), was associated with PTEN
mutations and amplification of AKT. PIK3CA mutations were associated with
sensitivity to Everolimus, while KRAS mutations confer resistance. Dasatinib
sensitivity was associated with BCR-ABL translocation and Ephrin type-A7 and
A3 receptor mRNA overexpression.