KURTIS COLWELL8.27.2012CELL STRESS BIOLOGY

The search of a predictive marker of anti-cancer efficacy of novel anti-cancer drug, Curaxin

Cancer and Treatment ICancer remains the 2nd leading cause of death in the United States closely following Heart Disease.

Cancer and Treatment II

2005 2012

Cancer Treatment III

The standard model for cancer treatment has been early detection through screening followed by surgical resection, radiation, or chemotherapy. Efficacy of these approaches is limited to early

stage diseases However some advanced patients demonstrate

complete response and their cancer can be cured All three of these can prove to be invasive and

harmful to healthy tissue Therefore identification of predictive markers of

anti-cancer efficacy is very important

Predictive Marker Specific proteins or genes in which

expression or presence is associated with sensitivity or resistance to a cancer therapy

New targeted therapies give better chances of Predictive Markers identification since mechanism of activity of new compounds may be known

There are several examples of successful use of predictive markers in the clinic

Predictive Marker Exampleso Gleevac inhibition of

Bcr-Abl encoded constitutively active tyrosine kinase. PM – presence of Bcr-Abl translocation

o Herceptin inhibits binding growth factors at Her2 receptors. PM – overexpression of Her2

o Farnesyltransferase inhibitors supresses activity of constitutively active Ras in tumors with mutant Ras oncogene. PM: mutation in Ras

o Serine/Threonine Kinase inhibition of RAF and MEK

Pohlmann P R et al. Clin Cancer Res 2009;15:7479-7491

Curaxins

Anti-cancer compounds, improved derivatives of quinacrine, an anti-malarial drug

Curaxins were identified in the screening as modulators of p53 and NF-kB activity

Curaxins activate p53 and inhibit NF-kB in cancer cells

p53, NF-kB and Cancerp53 (apoptosis/growth arrest)•sensor and mediator of intrinsic stresses•commonly inactive in cancer

TARGET FOR ACTIVATION

NF-kB (survival, resistance, growth)•sensor and mediator of extrinsic stresses•commonly active in tumors

TARGET FOR SUPPRESSION

Inactive in the majority of tumors

apoptosis inhibitorsanti-oxidants

cytokinesIkB

NF-kBinactive

NF-kBactive

Mdm2apoptosis

growth arrestgenomic stability

DNAdamage

oncogeneactivation

p53inactive

p53active

apoptosis

virusesbacteria

cytokines

survival and resistance

death or growth arrest

p53 sensor and mediator of intrinsic stresses

NF-kB sensor and mediator of extrinsic stresses

Permanently active in the majority of tumors

Curaxins Curaxin demonstrated broad

anti-cancer activity against different mouse models of cancer

Lead Curaxin compounds with optimized pharmacological properties is CBLC137

However some tumors were much more sensitive to Curaxins than others

Antitumor Efficacy in Mouse Tumor Models II

• Effective against breast, prostate, pancreatic, melanoma models• Effective against chemotherapy sensitive and resistant tumors• Weak correlation between efficacy and status of p53

www.ScienceTranslationalMedicine.org 10 August 2011 Vol 3 Issue 95 95ra74

Effect of Treatment on MDA-MB-231 Tumor Growth

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Days from start of treatment

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0.2% HMC30 mg/kg CBL01375 mg/kg CBL010025 mg/kg CBL01751 mg/kg Dox

Different efficacy of CBLC137 against leukemia models in mice

ALL3 ALL8

ALL7

Mechanism of Activity of curaxins I It was shown that p53 activation, NF-

kB inhibition and tumor cells toxicity of Curaxins depend on the presence of Facilitates Chromatin Transcription (FACT) complex

Facilitates Chromatin TranscriptionA heterodimer complex of Structure Specific Recognition Protein 1 (SSRP1) and Suppressor of Ty16 (SPT16) Exhibits both nucleosome disassembly and Nuclear chaperone (non-ATP-dependent chromatin remodeling) activity Changes stability of nucleosomal components (histone H2a/H2B dimers and H3/H4 tetramers)Can bind bended DNA through HMG domain of SSRP1

Rienberg, Danny and Sims, Robert J III, THE JOURNAL OF BIOLOGICAL CHEMISTRY VOL. 281, NO. 33, pp. 23297–23301, August 18, 2006

Mechanism of Activity of curaxins II Curaxins are DNA intercalators that

fit into the DNA minor groove and cause a conformational change of the DNA which causes recruitment of FACT to the site of the change and depletion of FACT in the nucleoplasm

Curaxins target FACT-dependent transcription

20

NFkB

p53

RNA-polNFkB

RNA-pol

NFkB

p53

RNA-polNFkB

RNA-pol+ Curaxin

Trap of FACT on chromatin results in blocking FACT-

dependent transcription and p53 activation

NF-kB-dependent transcription requires FACT

Toxicity of Curaxins depends on the level of FACT in tumor cells

HT1080 cells

Experiment 1

shGFP shFACT

control controlCBLC137 CBLC137

Colony number

a/b SSRP1 fluorescence

Toxicity of Curaxins depends on the level of FACT in tumor cells

HT1080 cells

Experiment 2

shFACT 48hrs

Staining with anti-SSRP1 antibodies

CBLC13772hrs 96hrs

Hypothesis

Can basal level of FACT in in non-syngenic tumor cells be predictive marker of tumor cell

sensitivity to curaxins?

ConclusionLevels of FACT in syngenic tumor cell pairs defines sensitivity of these cells to curaxin

Approach

Previous experiments were done on syngenic cell pairs with artificially reduced levels of FACT

Patients tumors are much more heterogeneous, that not only FACT levels are different but many other genetic and epigenetic factors

Approach Step I: Analysis of NCI 60 data NCI60 study contained 54 cell lines from various

cancer types (including breast, colon, lung etc.) Gene expression profiles of all these cells

(including SSRP1 (but not SPT16) mRNA levels are available through GEO database

LC50 of CBLC137 to these cells was determined through NCI Development therapeutic program

The goal of this approach was to calculate the correlation between the LC50 of the cells treated with CBLC137 and their SSRP1 mRNA levels

CBLC137 toxicity from NCI60

CBLC137 toxicity depending on cancer type

SSRP1 mRNA expression data from GEO NCI60 database

Assessment of correlation I

Pearson Correlation CoefficientsProb > |r| under H0: Rho=0Number of Observations

SSRP1_level_log2_ratio_ GI50 TGI LC50SSRP1_level_log2_ratio_SSRP1 level(log2 ratio)

1.00000

57

0.070780.6008

57

0.237490.0838

54

0.332300.0141

54

GI50GI50

0.070780.6008

57

1.00000

58

0.83927<.0001

55

0.413450.0017

55

TGITGI

0.237490.0838

54

0.83927<.0001

55

1.00000

55

0.60920<.0001

52

LC50LC50

0.332300.0141

54

0.413450.0017

55

0.60920<.0001

52

1.00000

55

Assessment of Correlation II

Click icon to add picture

Summary I

Type Pearson correlation coefficient p-value number of samples

parameter with best coefficient

Overall 0.33 0.0141 54 LC50

Leukemia 0.59 0.285 5 LC50

NSCLC -0.025 0.55 8 LC50

Colon Cancer 0.87 0.0241 6 LC50

CNS 0.89 0.0176 6 TGI

Melanoma -0.2 0.61 9 LC50

Ovarian Cancer 0.73 0.064 7 LC50

Renal Cancer 0.63 0.18 6 LC50

Breast Cancer 0.68 0.2 5 LC50

Conclusion

There is some statistically significant correlation between LC50 of CBLC137 and the level of SSRP1 mRNA in some cancer types.

There may be not enough cell samples of other types to see the correlation.

Level of SSRP1 or SPT16 proteins may be better markers than mRNA

Approach II

To expand a panel of cell lines of certain type

To measure protein levels of both FACT subunits, SSRP1 and SPT16

To measure simultaneously toxicity of CBLC137 to the same panel of cells (LC50)

To assess correlation

Melanoma

Arises from transformed melanocytes

Deadly disease if advanced due to its ability to metastasize to distant tissues after penetrating through the layers of the skin

No effective treatment at this stage Based on NCI60 data melanoma was

one of the most sensitivite cancer types to CBLC137

Method

Cytotoxicity assay on melanoma cell lines treated with CBLC137 at a range of 0.8-20µM

Negative control – 0.1% DMSO, positive control – 50uM of 9-aminoacridine (9AA)

Measurement of the LC50 from the linear section of the sigmoid growth curve

Measurement of whole cell extract SSRP1 and SPT16 levels by Western Blot

Assess correlation between FACT subunit protein levels and LC50 using Pearson Correlation (Product –Moment Correlation)

Sample of data analysis

0.1%DMSO

50uM 9AA

Linear range

LC50

Growth of melanoma cells in the presence of CBLC137

Extrapolated LC50 Values

skmel19 skmel28 skmel29 skmel103 skmel147 skmel173 uacc257 g3610

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2 IC50 for Cell Panel

LC50

(µM

)

Assessment of FACT Levels I Western Blot Probed for SPT16 and β-Actin Loading Control

Western Blot Probed For SSRP1, SPT16 and GAPDH Loading Control

Assessment of FACT Levels II -Normalization of Protein Levels Western Blot Images were quantified

by Imagequant software Imagequant quantifies the pixels and

intensity of a given western blot well SPT16 and SSRP1 images where

normalized to β-Actin and GAPDH quantified images respectively

Assessment of correlation II

SKMEL19 SKMEL28 SKMEL29 SKMEL103 SKMEL147 SKMEL173 G361 UACC2570

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Total Protien Levels of FACT and LC50 in Melonoma Cell Panel

LC50 (µM) SPT16

SSRP1

Assessment of Correlation I

LC50 SSRP1 SPT16 Mean SD Total Potein Actin GAPDH Total Potein Actin GAPDH

SKMEL19 0.88 0.3460.96

4.32 0.74 1.35 3.90 0.67

SKMEL28 0.62 0.9510.34

1.39 0.27 0.42 1.09 0.21

SKMEL29 0.7 3.7410.93

3.39 0.58 0.75 1.74 0.30

SKMEL103 0.36 0.5461.18

3.39 0.74 1.19 2.19 0.48

SKMEL147 0.61 0.481.24

3.08 0.76 1.39 2.19 0.54

SKMEL173 0.48 0.780.94

3.60 0.54 0.94 2.31 0.34

G361 0.52 0.4360.92

2.85 0.55 0.64 1.27 0.24

UACC257 0.76 0.3751.49

4.04

1.31 2.27

LC50 Correlation

0.0530 0.3444 0.0648 0.2477 0.4944 0.3734

Conclusions

All tested melanoma cells were sensitive to curaxin 137 at submicromolar concentration. Curaxin-137 treatments induced complete cell death at low micromolar concentration. Therefore melanoma may be considered highly sensitive tumor type to curaxin-137 treatment in vitro.

All melanoma cells tested expressed variable amount of SSRP1 and SPT16.

There was no statistically significant correlation between basal levels of FACT subunits and melanoma cell sensitivity to curaxin-137 in vitro

Acknowledgment

Thank you to the Gurova Lab Group for all of their support during my time in the Lab! I Would especially like to thank Dr. Gurova, Mairead, Peter, Daria, and Alfiya for answering all of my questions and helping me become a better scientist.

Thank you to my wife Shelley for supporting me while pursuing this project.

Questions???