The potential to rapidly identify predictive biomarkers of drug response in tumour tissue to define sensitive and resistant patient cohorts has recently been accelerated through advances in functional genomics techniques that have been intensively developed by the PREDICT consortium using large scale RNA interference (RNAi) screening approaches (CR-UK) (Downward, 2004; Downward, 2009; Nicke et al., 2005; Swanton et al., 2007a). By using this technology, the consortium has identified genes regulating response and resistance to common cytotoxic agents used in cancer medicine (Juul et al., 2010; Swanton et al., 2007a; Swanton et al., 2007b; Swanton et al., 2009; Swanton et al., 2008). Through the integrative genomics analysis of these functional RNA interference datasets in breast and ovarian cancer we have identified genes with both prognostic and predictive relevance to patient outcome and confirmed their relevance in independent clinical trial cohorts (Juul et al., 2010; Swanton et al., 2007a; Swanton et al., 2007b; Swanton et al., 2009; Swanton et al., 2008). Importantly, this integrative genomics analysis of RNA interference datasets with pre-treatment expression has identified a “functional metagene” that is predictive of pathological complete response to paclitaxel in breast cancer with a high sensitivity and specificity (AUC=0.8 under the Receiver Operator Curve) (Juul et al., 2010) and that outperforms any other clinical or molecular predictor of paclitaxel sensitivity identified to date. These studies highlight the impressive power of comprehensive functional genomics datasets combined with monotherapy clinical trial tumour genomics datasets to illuminate the clinical relevance of specific genes to individual patient drug sensitivity. Furthermore, the studies provide robust and efficient methodological tools to accelerate predictive biomarker development and identify mechanisms of drug resistance that will be applied to biomarker discovery in RCC in this proposal.

Based on the clinical and molecular evidence reviewed above, we hypothesize that resistance of RCCs to sunitinib and everolimus might occur through one or a combination of the mechanisms (table 1). PREDICT consortium’s functional genomics RNA interference approaches will be applied to identify genes contributing to these resistance mechanisms.

Potential mechanisms of sunitinib resistance:Potential mechanisms of everolimus resistance:
- Hypoxia resistance of RCC cells

- Vascular resistance to VEGFR and PDGFR inhibition by sunitinib
- Resistance of tumour cells to direct anti-proliferative everolimus effects

- Resistance of HIF1a target gene expression to repression by everolimus

- Hypoxia resistance of RCC cells

- Vascular resistance to VEGF-pathway inhibition

PREDICT consortium’s functional genomics RNA interference approaches will be applied to identify genes contributing to these resistance mechanisms.

Consistent with PREDICT’s recently published predictive biomarker in breast cancer based on this strategy, genes identified across multiple cell lines or in multiple screens by this in vitro approach are likely to be implicated in everolimus and sunitinib sensitivity in patients. Data from these screens will be integrated with genomics and proteomics datasets from patient tumours and genes that are identified through multiple approaches (e.g. modifying resistance in functional genomic screens and altered expression/copy number/sequence in resistant vs. sensitive tumours, Figure 1) will be prioritised for development of predictive signatures of sunitinib and everolimus response for the use in pre-treatment RCC biopsies.

The PREDICT consortium will analyse tumour samples from patients treated on the E-PREDICT and S-PREDICT trials by established genome wide mRNA expression and DNA copy number profiling methods and by novel techniques including genome-wide exon sequencing and phospho-kinome analysis of tumour lysates to probe pathway activity. Central to this proposal will be the derivation of up to 30 ex-vivo cultured patient-derived RCC cell bulks from which personalised RNA interference (pshRNA) libraries will be generated to identify tumour-individualised autologous mechanisms of drug response. These will be used to yield vital complementary information about the functional role of each gene expressed in tumour samples that may determine resistance or sensitivity to sunitinib or everolimus. Genomic, proteomic and functional RNA interference datasets will be integrated together with clinical response data into a meta-dataset containing expression, mutation, copy number and functional data in a genome-wide manner. Bioinformatics analysis of the meta-dataset for genes altered in resistant vs. sensitive samples that functionally influence resistance in laboratory model systems of RCC will lead to the prioritisation of predictive biomarkers for sunitinib and everolimus in the validation cohorts of the E-PREDICT and S-PREDICT clinical trials (figure 1).

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