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Transcript of Presentatie maastricht
Discovering novel biomarkers
in breast cancer
Rianne Fijten
Breast cancer subtypes
• ER status
• PR status
• HER2 status
• EGFR status
Breast cancer subtypes
Copy number alterations
• Alterations in allele number
• Can affect gene expression
• In cancer: selection for regions containing oncogenes or tumour suppressors
Normal Deleted Amplified
Public data: in vitro
• NCI60: 59 cell lines for 9 tumour types
• CCLE: 947 cell lines for 36 tumour types
CNmRNA
expressionMutation
statusProteome Metabolome
Drug sensitivity
Public data: in vivo
• Mostly transcriptome data
– Microarray
– SNP array
– RNA sequencing
• Very dependent on availability of datasets and quality of data
Research question
Can we use copy number data as a starting point for exploratory biomarker discovery in breast
cancer?
Identification of amplified genes
• CCLE: Cancer Cell Line Encyclopedia
– 56 breast cancer cell lines
• Amplification CN > 3
Copy numbers in breast cancer
AmplificationCN > 3
DeletionCN < 1
Correlation with mRNA expression
• Significant and valid correlation with mRNA values
Result: 30 genes – 4 amplified regions
In vivo validation – Array Express
• 900+ early stage breast cancer tumour samples
• Copy number data
Gene Average CN Gene Average CN
ERBB2 4.401254 UTP23 2.510294
TRMT12 3.039269 DSCC1 2.45963
NSMCE2 2.833896 VAPB 2.451618
RAD21 2.805559 AURKA 2.408208
KIAA0196 2.75552 RAB22A 2.399712
SQLE 2.70128 STX16 2.371037
RNF139 2.667317 RAE1 2.281577
TAF2 2.650698 CSTF1 2.258596
EIF3H 2.64878 GRB7 -
WDR67 2.625884 MRPL13 -
DERL1 2.625829 PGAP3 -
ATAD2 2.608554 RBM38 -
NDUFB9 2.587144 STARD3 -
C20orf43 2.550113 TCAP -
TMEM65 2.52641 WDYHV1 -
In vivo validation – The Cancer Genome Atlas (TCGA)
• 900+ samples of patient invasive breast carcinomas
• Copy number + mRNA data
In vivo validation – The Cancer Genome Atlas (TCGA)
Gene Ratio CN Ratio mRNA Correlation Gene Ratio CN Ratio mRNA Correlation
ATAD2 0.43 0.162 0.690 * RAD21 0.419 0.157 0.850 *
AURKA 0.215 0.168 0.580 * RAE1 0.215 0.092 0.920 *
C20ORF43 0.237 0.124 0.840 * RBM38 0.215 0.157 0.520 *
CSTF1 0.215 0.108 0.900 * RNF139 0.441 0.135 0.760 *
DERL1 0.419 0.162 0.840 * SQLE 0.43 0.157 0.740 *
DSCC1 0.419 0.173 0.750 * STARD3 0.194 0.119 0.940 *
EIF3H 0.43 0.135 0.600 * STX16 0.226 0.119 0.690 *
ERBB2 0.194 0.135 0.840 * TAF2 0.419 0.168 0.830 *
GRB7 0.183 0.135 0.880 * TCAP 0.194 0.141 0.720 *
KIAA0196 0.441 0.173 0.720 * TMEM65 - - -
MRPL13 0.419 0.168 0.870 * TRMT12 0.441 0.157 0.780 *
NDUFB9 0.43 0.157 0.870 * UTP23 0.419 0.157 0.790 *
NSMCE2 - - - VAPB 0.226 0.119 0.850 *
PGAP3 0.194 0.135 0.870 * WDR67 0.419 0.189 0.660 *
RAB22A 0.226 0.097 0.860 * WDYHV1 0.43 0.135 0.730 *
Survival analysis - KMPlot
• 3000 breast cancer patient samples incl. survival data
• Compare survival between patients with high and low gene expression
Survival analysis - KMPlot
• Significant differences in 11 of 26 genes
RNF139 DERL1 STARD3
Conclusions
Results obtained in vitro were validated using in vivo datasets
Some show differences in breast cancer patient survival
Of interest: SQLE
Cholesterol
Steroid degradation
Steroid hormone biosynthesis
SQLE
• CCLE– Allele copies: 3.084107
– Significant mRNA correlation
• TCGA– CN affected: 43%
– mRNA affected: 18.5%
– Significant correlation
• AE– Allele copies: 2.70128
• Survival
SQLEExtended survival analysis
ER positive Luminal A (ER+ and low grade)
Other receptor/molecular subtypes showed no significant difference
Cholesterol and cancer
• Patients with cancer have abnormal levels of HDL– and LDL-cholesterol
• Transformed cells and tumors exhibit abnormal regulation of LDL-R and HMG-CoA Reductase.
• Transformed cells may require or utilize more cholesterol than normal cells, and this may be associated with their increased rate of proliferation.
Llaverias, G.; Danilo, C.; Mercier, I.; Daumer, K.; Capozza, F.; Williams, T. M.; Sotgia, F.; Lisanti, M. P.; Frank, P. G. Role of cholesterol in the development and progression of breast cancer. The American journal of pathology 2011, 178, 402–12.
Cholesterol and cancer
• Inhibition of squalene synthase decrease proliferation in prostate cell line 1
• inhibition of most enzymes involved in cholesterol biosynthesis from lanosterolresults in cell proliferation inhibition 2
1. Fukuma, Y.; Matsui, H.; Koike, H.; Sekine, Y.; Shechter, I.; Ohtake, N.; Nakata, S.; Ito, K.; Suzuki, K. Role of squalene synthase in prostate cancer risk and the biological aggressiveness of human prostate cancer. Prostate cancer and prostatic diseases 2012, 15, 339–45.2. Lasunción, M. A.; Martín-Sánchez, C.; Canfrán-Duque, A.; Busto, R. Post-lanosterol biosynthesis of cholesterol and cancer. Current opinion in pharmacology 2012, 12, 717–23.
Conclusion
SQLE or the entire cholesterol pathway may play a crucial role in ER+ breast cancer
Systems Biology approach
• Survival analysis for cholesterol pathway genes (alone and groups)
• Create biomarker profile containing SQLE and other genes
• Survival analysis for all genes in amplified regions
• Amplified miRNAs