This document summarizes a bioinformatic analysis of synthetic lethal genetic interactions in breast cancer. It describes how the researchers used gene expression data from breast cancer samples to predict potential synthetic lethal gene pairs through statistical testing. Many statistically significant interactions were found, including known synthetic lethal partners. The researchers validated some predictions and discuss applications for targeted cancer therapies and chemoprevention. High performance computing resources were crucial for analyzing large genome-scale datasets.

1. Bioinformatic analysis of
synthetic lethal genetic
interactions in breast cancer
Tom Kelly, Parry Guilford and Mik Black
Center for Translational Cancer Research and
Department of Biochemistry, University of Otago

2. Synthetic lethal (SL) interactions
• The reduced viability of a double mutant from the respective single mutants
(Boone et al., 2007)
• Organism or cellular lethality (or reduced growth rate)
Figure adapted from Li et al. (2014) BioMed research international 196034.
Boone, Bussey and Andrews (2007) Genetics 8: 437-449.

3. Synthetic lethal (SL) interactions
• The reduced viability of a double mutant from the respective single mutants
(Boone et al., 2007)
• Organism or cellular lethality (or reduced growth rate)
• SL occurs without mutation
• epigenetic silencing, RNA interference or drug activity.
Figure adapted from Li et al. (2014) BioMed research international 196034.
Boone, Bussey and Andrews (2007) Genetics 8: 437-449.

4. Synthetic Lethality in Cancer
• To identify candidate genes for targeted cancer therapies
• Develop drugs with fewer adverse effects
• incl. chemopreventative for high risk patients
• Strategy against tumour suppressor genes (Ashworth et al., 2011; Kaelin, 2005)
Figure adapted from Li et al. (2014) BioMed research international 196034.
Ashworth, Lord and Reis-Filho (2011) Cell 145: 30-38.
Kaelin, W.G., Jr. (2005) Nature Reviews Cancer 5: 689-698.

5. Genetic Screens
• Detecting SL Interactions by traditional genome-wide SL screens:
• Synthetic gene array (SGA) in Saccharomyces cerevisiae (yeast)
• Short interfering RNAs (siRNA) in Caenorhabditis elegans (nematode worm)
• These technologies are not cost-effective in mammalian cells
• Alternatives:
• candidate gene approach
• unbiased prediction

6. Statistical Method
• A method has been developed to predict Synthetic Lethality from gene
expression data
• Test significance with the Chi-Square Test
• Adjust p-values for multiple comparisons (Holm or False Discovery Rate procedure)
• Score Synthetic Lethality as directional changes in expression

7. Gene Expression Data
• The Cancer Genome Atlas (TCGA Research Network, 2012)
• Microarray Expression data: 17811 genes x 600 samples
• Aligent 244K microarray platform
• RNASeq data: 18176 genes x 878 samples
• Illumina Sequencing platforms (Hi-Seq and Genome Analyser)
• BC2116 Meta-Analysis dataset (Soon et al., 2011)
• Microarray Expression data: 12496 genes x 2116 samples
• Affymetrix U133 microarray platforms
Cancer Genome Atlas Research Network (2012) Nature 490: 61-70.
Soon et al. (2011) EMBO molecular medicine 3: 451-464.

8. Implementation
• Run in R (MPI) on NeSI Pan cluster
• The method tests a particular gene against all others for SL partners
• Genome-scale application is not feasible on a single processing core
• Each gene is embarrassingly parallel

9. Performance
• The NeSI Pan cluster reduced computational time by around 50 fold
• One iteration on BC2116 takes 71 secs
• Estimated time for every gene on a single core: 71 secs x 12496 query genes =
10 days, 6 hours
• The same analysis on BC2116 took just over 5 hours on the cluster (64 cores)
• Enabled replication across expression datasets

10. Global SL Data

11. Results
• Predicted SL interactions were common showing high connectivity consistent
with model organism experiments
• Genes were predicted with high average numbers of SL partners
Global Interactions TCGA Microarray TCGA RNA-Seq BC2116 Microarray
FDR adjusted p-values 28,694,615 35,838,861 19,273,827
(percentage of gene pairs) (9.05%) (10.85%) (12.34%)
Holm-adjusted p-values 14,855,272 13,232,981 9,157,579
(percentage of gene pairs) (4.68%) (4.01%) (5.86%)
Number of Gene Partners TCGA Microarray TCGA RNA-Seq BC2116 Microarray
FDR adjusted p-values Mean 1611 (9.04%) 1972 (10.85%) 1542 (12.34%)
(percentage of genes) Std Dev 1059 (5.95%) 548 (3.01%) 412 (3.30%)
Holm-adjusted p-values Mean 834 (4.68%) 728 (4.01%) 733 (5.86%)
(percentage of genes) Std Dev 561 (3.15%) 351 (1.63%) 215 (1.72%)
Number of Gene Partners TCGA Microarray TCGA RNA-Seq BC2116 Microarray
FDR adjusted p-values Mean 1611 (9.04%) 1972 (10.85%) 1542 (12.34%)
(percentage of genes) 95% < 4043 (22.7%) 2896 (15.9%) 2341 (18.7%)
Holm-adjusted p-values Mean 834 (4.68%) 728 (4.01%) 733 (5.86%)
(percentage of genes) 95% < 2041 (11.5%) 1287 (7.1%) 1155 (9.2%)

12. Results
• Highly connected hub genes were involved in:
• Cell signalling
• Metabolism
• Immune system
• Functions with known role in cancer progression and metastasis
• Functions with known hereditary risk genes (early-onset cancer)

13. Results
• Detected known SL interactions:
• SL candidates for CDH1 identified from experimental screens
• The published BRCA1 and BRCA2 interactions with PARP1
Figure adapted from Polyak and Garber (2011) Nature medicine 17: 283-284

14. Applications
• Triage drug targets in experimental screens
• Targeted treatment and chemoprevention
• E.g. BRCA1/2 mutations in breast and ovarian cancer (Bryant et al., 2005;
Farmer et al., 2005)
• E.g. CDH1 mutations in stomach and breast cancer (Guilford et al., 1998)
Bryant et al. (2005) Nature 434: 913-917.
Farmer et al. (2005) Nature 434: 917-921.
Guilford et al. (1998) Nature 392: 402-405.

15. Validation
• Expression is a cost-effective predictor of SL but is not conclusive
• Any predictions need experimental validation for application
• siRNA screens in cancer cell lines and mouse xenograft models
• Drug testing (if possible)
• Drug target development
• Repurposing existing treatments
• Develop novel drugs

16. Future Directions
• Replicating findings in other datasets
• same tissue, different tissue, different species
• Pathway analysis
• gene function
• Replication and comparative analysis
would not be possible without access to
High Performance Computing resources

17. Network Analysis
• Network-based analysis for synthetic lethality
• Integrate other data types: e.g., mutation and protein data
• Develop a more powerful predictor: cross-validation possible in yeast
• Investigation Tissue-specificity and Pan-Cancer effects
• Identify genetic factors and drug targets unique to tissue of origin
• Complements the Pan-cancer initiative

18. Conclusions
• We have developed a bioinformatics tool which detects known and
potentially novel SL interactions
• SL interactions occur frequently in the human genome
• SL interactions are detectable in a heterogeneous tumour, testing a
limitation of experimental models
• SL interactions could be exploited for anti-cancer therapy

19. Acknowledgements
• Bioinformatics Group
• Mik Black
• James Boocock
• Tom Brew
• Cancer Genetics Laboratory
• PIs: Parry Guilford, Anita Dunbier
• SL group: Augustine Chen, Bryony Telford, Henry Beetham, Andrew Single, James Frick
• NeSI Support Team
• Ben Roberts
• Marcus Gustafsson
• Funding Sources
• Otago School of Medical Sciences
• University of Otago Postgraduate Tassell Scholarship in Cancer Research
• Google (eResearch 2014 Student Sponsor)

20. Image created by Erik Johansson for Google Stockholm
http://erikjohanssonphoto.com/work/google-stockholm-office-print/