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