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MRes Student publishes on Current Biology
- MRes student Nils Gustafsson has contributed to a microtubule publication in prestigious journal Current Biology
CoMPLEX PhD student publishes on Science
Integrative Genomics and RNA Interference Approaches to Biomarker Discovery in Cancer Medicine
Dr. Charles Swanton FRCP, Translational Cancer Therapeutics, London Research Institute, London, UK
Friday 10th June
Venue: JZ Young LT
Drug resistance contributes to early treatment failure and deteriorating quality of life in patients with cancer. Deriving gene expression signature based predictors of chemotherapy response from microarray data is an associative learning process and is inherently vulnerable to over-fitting of data. To address this problem, Charles and his team are using whole genome RNA interference drug resistance screen datasets to identify functionally relevant gene modules predictive of therapeutic response in vivo.
Using RNAi functional genomic techniques, they have identified genes that induce taxane resistance and aneuploidy independent of drug treatment, suggesting that pathways that prevent chromosomal instability (CIN) are functionally related to taxane response [1-3]. Consistent with these observations, CIN ovarian cancers, from the OV01 clinical trial, demonstrate primary resistance to taxane monotherapy, indicating that CIN may predict drug response in vivo and patients with taxane-resistant disease can be identified in advance of drug exposure . Supporting the power of RNA interference screening datasets to identify therapeutically relevant gene modules in advance of drug exposure, they have identified a “functional metagene” composed of 6 genes implicated in taxane sensitivity. The expression of the functional metagene significantly predicts for breast cancer pathological complete response to paclitaxel-based therapy in two clinical trial datasets .
These data support a new strategy for phase II clinical trial design in cancer medicine through the parallel analysis of whole genome functional RNA interference screening data combined with high-resolution tumour genomics datasets. Such an approach will be presented in pre-operative cancer clinical trials (EU Framework 7 PREDICT Consortium) to optimise patient outcome, identify novel therapeutically relevant targets and limit the health economic burden associated with drug resistant disease .
Charles completed his PhD in 1998 at the Imperial Cancer Research Fund Laboratories on the UCL MBPhD programme before completing his medical oncology and CR-UK funded post-doctoral training in 2008. Charles is a consultant medical oncologist in the Breast and Drug Development Units at the Royal Marsden Hospital with an interest in early phase drug development for the treatment of specific subtypes of metastatic breast cancer. Charles is also a Medical Research Council Group Leader at the CR-UK London Research Institute in the Translational Cancer Therapeutics laboratory focussing on understanding mechanism of drug resistance using high throughput RNA interference functional genomics approaches.
Charles's Webpage: http://london-research-institute.co.uk/research/loc/london/lifch/swantonc/?view=LRI&source=research_portfolio
1. Swanton, C., et al., Regulators of mitotic arrest and ceramide metabolism are determinants of sensitivity to paclitaxel and other chemotherapeutic drugs. Cancer Cell, 2007. 11(6): p. 498-512.
2. Swanton, C., et al., Initiation of high frequency multi-drug resistance following kinase targeting by siRNAs. Cell Cycle, 2007. 6(16): p. 2001-4.
3. Swanton, C., et al., Chromosomal instability determines taxane response. Proc Natl Acad Sci U S A, 2009. 106(21): p. 8671-6.
4. Juul, N., et al., Assessment of an RNA interference screen-derived mitotic and ceramide pathway metagene as a predictor of response to neoadjuvant paclitaxel for primary triple-negative breast cancer: a retrospective analysis of five clinical trials. Lancet Oncol, 2010. 11(4): p. 358-65.
5. Swanton, C., et al., Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets. Genome Med, 2010. 2(8): p. 53.
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