Ovarian Cancer
- In the UK, ovarian cancer is the fifth most common cancer in women
- In 2008, there were 6,500 new cases in the UK, and 225,000 world-wide
- Clinicians lack tools for its early detection and effective treatment
- Despite some progress, the benchmark 5-year survival rate for ovarian cancer remains low, at around 30%
The Pacific Yew is a source of
therapeutic organic compounds
such as paclictaxel
The Odds of Survival:
Targeting treatment for ovarian cancer
(Antonis Koussounadis 13/6/2012)
Treatment for ovarian cancer has improved over the last 20 years. This is mainly due to improved surgery and to the introduction of new chemotherapy drugs based on platinum and subsequently the addition of the organic compounds called taxanes. Still, compared to improvements in survival rates of breast and colon cancer patients, progress in ovarian cancer has been modest. Why is this?
The main reason is that early diagnosis of ovarian cancer is difficult. As there are few symptoms until tumours are already advanced, patients usually become aware at a later stage, when prognosis is less favourable.
To make matters worse, current chemotherapy has serious side effects and benefits only about 70% of patients. Selecting patients who would not benefit from chemotherapy would spare them of unnecessary side effects. Also, unresponsive patients could be guided to alternative therapies that may be more successful.
Personalising treatment
There is an acute need to identify biological traits or biomarkers, which indicate whether a patient is responsive to therapy, before submitting them to such a severe therapeutic regime. Recently, researchers have focused on the identification of genes and proteins that play a role in drug resistance. If such genes are discovered, they can be used to test tissue samples in the clinic directly.
In this heatmap gene expression is shown as low (green) to high (green).
A heatmap of the limma top 100 differentially expressed genes for carboplatin-sensitive vs. carboplatin-resistant cell lines. The rightmost samples are the OV1002 (sensitive) and in the left are the HOX424 (resistant). Carboplatin/Taxane treated samples. Red corresponds to high and green to low gene expression.
Novel genomic technologies, including microarrays and next generation sequencing, give researchers the opportunity to query the whole genome simultaneously. These techniques are particularly suitable for the identification of biomarkers that can be used to classify tumours based on their gene and protein pathway activation profiles and the mutated genes they carry. Using this information, each patient can be guided to a tailored treatment that has the best chance of success.
A lot of research is required for the development of biomarkers that can indicate response to chemotherapeutic drugs. Although many gene sets have been identified in the last few years, their ability to predicting reaction to therapy is still moderate and inadequate for clinical use. Tumour genomes vary greatly and change quickly, and the molecular mechanisms behind drug resistance have not been fully explored yet. Consequently, the task of predicting likely therapeutic effects is particularly challenging.
At the School of Biology at St Andrews, in collaboration with the Breakthrough Cancer Unit (Western General Hospital, Edinburgh) scientists and specialists are working together to discover biomarkers for chemotherapy resistance and sensitivity in ovarian cancer. Clinical, genomic and proteomic data from cancer samples are analysed using cutting edge technologies and computational techniques to identify candidate genes that are involved in drug resistance.
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Scientific Background
In our research, we use microarrays to compare expression levels between samples taken from ovarian cancer cell lines. For instance, a gene expression comparison of drug-sensitive and drug-resistant cell lines may yield a large number of differentially expressed genes. These significant genes are studied further to identify the most potent, specific and sensitive predictors.
Dealing with very large number of probes and genes requires specially developed computational tools and data analysis. Further gene characterization and statistical analysis result in filtering the list of significant genes. The more promising leads are then used for experimental validation and more testing in the lab.
Gene expression data can be combined with patient clinical information, tumour grading, histology and biochemistry when developing such new predictive tools. Ultimately, for a set of biomarkers to be useful, it has to be sensitive and specific and perform in independent cases that were not used for initial biomarker identification and characterization.
Microarrays
A microarray is a set of a large number of DNA spots attached to a chip. Microarrays are used to measure gene expression of thousands of genes or whole genomes, simultaneously. In each spot there are tiny amounts of a unique DNA sequence, known as probes. These can be short sequences, each capable to hybridize to a gene. Hybridization of the microarray probes with a sample from, for instance a tumour tissue, is detected and quantified by detection of light signals.
Cell lines
Cell lines of ovarian cancer origin are practical models to study drug resistance. These immortal cells originate from ovarian cancer cells and can be cultured in the lab.