EuroTARGET: European collaborative project on TArgeted therapy in Renal cell cancer: GEnetic and Tumour-related biomarkers for response and toxicity

With an estimated 3.2 million new cases and 1.7 million deaths each year,
cancer remains an important public health problem in Europe for patients
themselves, their family as well as health care systems across Europe. With the
ageing of the European population these numbers are predicted to steadily
increase until the 2040s, even if age-specific rates of cancer remain constant.
In line with the Lisbon objectives of, amongst others, improvement of human
health and quality of life, advancements in the field of medicine are needed to
offer solutions for diseases which are currently untreatable. Also,
advancements in personalised medicine are needed to improve therapeutic
indices, avoid chronicity, prevent relapse, reduce adverse effects and permit
greater cost effectiveness. Definition of new validated risk stratification
criteria to be used in personalized patient management, i.e. prediction of
individual therapy response and resistance leading to optimal treatment outcome
while reducing unnecessary drug use and expense, is therefore urgently needed.

Kidney cancer is the 10th most common cancer in the European Union. Each year
more than 40,000 men and more than 24,000 women are newly diagnosed with the
disease (European Cancer Observatory, 2009; Ferlay et al., 2007). More than
27,000 European Union men and women die from the disease each year. 90% of all
kidney cancers are renal cell carcinomas (RCC). Survival of RCC has remained
more or less stable during the last 20 years and is highly dependent on stage.
Surgery is quite effective for localized disease, leading to 5-year relative
survival rates of more than 70%. However, 20 to 30% of patients with primary
RCC have metastasized disease at diagnosis and approximately 30% of the
remaining patients develop metastases after surgery. For this group of
patients, curative treatment is not possible and, until recently, 5-year
relative survival was extremely poor: 5 to 10%. The poor survival rate
reflected the limited treatment options for patients with metastatic RCC
(mRCC).

Treatment options for mRCC have improved substantially in the past 5 years.
Long mired in therapeutic nihilism because of chemotherapy resistance and
modest effects of immunotherapy, suddenly multiple active agents with robust
clinical effects are available for mRCC. Knowledge of underlying molecular
characteristics identified the vascular endothelial growth factor (VEGF) and
the mammalian target of rapamycin (mTOR) pathways as fundamental to the biology
of RCC. This biologic insight provided a rationale for targeting these growth
factor signaling pathways in RCC. Small molecules inhibitory against the
tyrosine kinase portion of the intracellular receptor for VEGF (VEGFR) have
undergone extensive clinical testing. Two of these drugs, sunitinib and
sorafenib, are now widely used in clinical practice. These agents inhibit not
only VEGFR but also a broad spectrum of related receptor tyrosine kinases (i.e.
are more general tyrosine kinase inhibitors or TKIs). The agents also differ in
the spectrum of inhibitory effects and potency against any single receptor.
These and other targeted agents that are not (or not yet) as widely used as
sunitinib and sorafenib have shown objective response rates up to 45%
(sunitinib), twice as high as immunotherapy treatment. 70 to 75% of all
patients experience some reduction in tumour burden and the median progression
free survival and overall survival has increased with about 6 months to
approximately 1.5 to 2 years. Thus, targeted therapy in mRCC can be considered
a revolution after decades without any progress. Unfortunately, the treatment
is expensive with costs of about ¤4,000 per month per patient (life-long) which
means that the drugs are not available in a large part of the EU, particularly
in the former Eastern-European countries.
Even though the new drugs are *targeted therapy*, aiming at specific pathways,
not all patients show clinical benefit from therapy, and inherent or acquired
resistance to the drugs poses a problem. Sequential therapy is therefore
becoming routine practice. With an increasing number of compounds becoming
available, however, choice of compounds and sequence is becoming extraordinary
challenging. In addition to the highly variable clinical response to the
targeted treatments, toxicity, experienced by a substantial number of patients,
is highly variable and frequently necessitates dose reduction or even cessation
of therapy. Unfortunately, both response and toxicity are not predictable in
the individual patient. Therefore drug choice, dose and sequence are highly
empirical. The fundamental question facing the medical oncologist caring for a
mRCC patient is how to consider the available agents and data to formulate an
evidence-based individualized treatment approach (Rini, 2009). Until now,
treatment choice in mRCC is determined by clinical parameters such as the
patient*s performance status, serum biochemical measurements, and morphological
/ histological features of the tumour. Based on these parameters, patients are
stratified into a good, intermediate or poor risk group. Treatment choice is
partly based on this risk grouping. Although this risk grouping has clear
prognostic value, also among patients on targeted treatment (Heng et al.,
2009), it is very clear that the grouping is far from optimal: the agents lead
to different clinical effects in different patients. Research is therefore
ongoing to find markers with better predictive ability for drug response, both
with regard to efficacy and toxicity in individual patients. On the patient
level, treatment response and toxicity are (partly) derivatives of underlying
interpatient genetic variability. The discipline of pharmacogenetics that
evaluates germline genetic markers in candidate drug metabolizing or drug
target genes (e.g., van Erp et al., 2009) is still in its infancy. In most
cases, studies are small, focus on only a few markers in only a few candidate
genes, and lack an independent replication to validate the results. On the
tumour level, research mainly focuses on gene expression differences (e.g.,
Zhao et al., 2006) or specific gene mutations in tumours that show differences
in response. But here again, studies are usually small and lack independent
replications. In addition, integrated approaches which are envisioned to
identify new and better predictive markers by combining different types of
information from different research platforms and clinical determinants are
lacking. To boost the identification of predictive markers we propose the
application, integration, and validation of high-throughput platforms aimed at
host as well as tumour related markers in an unprecedented scale.

This project aims to identify and characterize host and tumour related
biomarkers and to predict responders and/or adverse responders from
non-responders for targeted therapy in mRCC. Our overall concept is to focus on
germline genome and tumour transcriptome, methylome and kinome-related
biomarkers using an hypothesis-free and integrative approach and to evaluate
promising findings via replication as well as functional assays.

The specific objectives are:

• To create a standardized European clinical databank and bio-repository
(germline DNA of all patients and serum and frozen tumour tissue of a subgroup)
of a large series of patients with mRCC treated with different agents;
• To identify genetic markers for treatment response and toxicity by performing
a high-resolution germline whole-genome profiling in patients treated with
sunitinib or sorafenib, the most commonly used drugs at this moment;
• To identify exon and microRNA expression markers for treatment response and
toxicity by gene expression profiling of tumours from patients with and without
a good response to these drugs;
• To identify kinase activity profiles related to TKI response;
• To identify promoter hypermethylation markers TKI response;
• To identify the resulting protein profiles corresponding to genomic,
epigenetic and expression alterations related to TKI response;
• To replicate all identified markers in independent patient series;
• To study the functional relevance of replicated markers/networks in vitro by
knock-out and knock-in transfection experiments;
• To identify differentially expressed proteins before and after knock-down/
upregulation of genes of interest;
• To identify plasma drug and metabolite levels as a phenotype of response to
sunitinib;
• To explore the possibility of individualizing dosage regimens by integrating
plasma biomarker level-time profiles into pharmacokinetics / pharmacodynamics
models for sunitinib as a model drug;
• To conduct integrated bioinformatical analyses of the results obtained by all
different approaches in order to maximize the probability to find new markers
and to understand the interrelatedness between them;
• To construct new risk stratification criteria to be used for personalized
mRCC patient management;
• To disseminate the new knowledge to medical oncologists, urologists and the
scientific community.

The project will be based on a design that makes it possible to:
1. Identify new markers for response (efficacy and toxicity) to TKI treatment
in a discovery phase.
2. Use the same blood samples and tumour tissue on different platforms so that
an optimal integration of different types of data can take place, leading to a
maximal yield.
3. Replicate the identified markers in a validation phase.
4. Build on the possibility of future extensions of the project with a focus on
other agents.

The pivotal part of the project will be WP1 in which patients with mRCC will be
recruited in (parts of) The Netherlands, The United Kingdom, Germany, Austria,
Switzerland, Iceland and Romania. A standardized protocol and web-based Case
Record Forms to be used by all participants will guarantee consistency of data
collection procedures. The clinical data at baseline and follow-up will be
maintained in a secure database under the control of one of the participants.
Blood samples will be collected from all patients and shipped to the central
biorepository in Nijmegen, the Netherlands. Frozen tumour tissue will be
collected from as many patients as possible and sent to the central
biorepository. The central biorepository will distribute samples to the
different participants for biomarker identification in the discovery phase and
biomarker replication and validation in the validation phase. We will use the
following platforms to identify new biomarkers:
1. A whole-genome genotyping of 600 patients (150 with a good response to
sunitinib, 150 with a poor response to sunitinib; 150 with a good response to
sorafenib, 150 with a poor response to sorafenib) in the discovery phase and
again of 500 patients in the validation phase. In both phases, we will use
Illumina HumanOmni1*QuadBeadChips because this allows a combined analysis of
both phases at limited additional costs in comparison with a smaller-scale
replication of only the top hits from the discovery phase. (WP2)
2. We will conduct gene expression profiling on the exon-level (geneChip® human
exon 1.0ST Array, Affymetrix) and on the microRNA level (GeneChip® miRNA Array,
Affymetrix) on RNA isolated from frozen tumour tissue of 120 patients (30 with
a good response to sunitinib, 30 with a poor response to sunitinib; 30 with a
good response to sorafenib, 30 with a poor response to sorafenib). The results
will be replicated on another 120 patients. (WP3)
3. Extracts from the same tumours will be used for kinase activity profiling.
The functional readout will be combined with pharmacological studies in which
other kinase inhibitors will be assessed for their potency of inhibiting kinase
activities extracted from the tumours and reference cell lines or xenograft
tissue. An assay will be developed for on-chip analysis of tumour biopsies by
ex-vivo incubation of extracts with drug for monitoring on-target (efficacy)
and off-target (toxicity) kinase activities. (WP4)
4. Again, the same tumour tissue will be used for the identification and
characterization of DNA methylation biomarkers using the Infinium
HumanMethylation27 BeadChip for a whole-genome interrogation of differentially
methylated regions. In the replication phase, the selected top differentially
methylated genes will be analyzed by ultra deep bisulfite sequencing in order
to test the sensitivity of the proposed DNA methylation biomarkers. (WP5)
5. We will link PK/PD models and integrate the pharmacokinetic, biomarker and
clinical data and thereby develop predictive models for response and toxicity
in mRCC patients ultimately leading to individualized prediction of drug doses
and therapy response. (WP7)
6. All generated data from the profiling studies will be transferred and kept
in DiseaseMiner, a highly scalable, multi-user, rich client platform (RCP)
based on multi-tiered architecture, optimized for large number of clinical
samples as well as the extremely high volume of marker data that are currently
produced by modern chip technology. (WP8)
7. Integrative analyses will take place of all the different data types
generated within this project, with and without external data sources, to
increase insight into molecular pathways and, ultimately, to be able to predict
response to therapy based on a fusion of all the measured high-throughput
datasets: clinical, SNP, mRNA expression, miRNA expression, kinase activity and
methylation status. (WP9)
8. Functional confirmation studies will take place focusing on selected
candidate genes/networks identified in the previous profiling studies and
integrative analyses. (WP6)
9. Finally, based on the results of the project, a web-based treatment
algorithm will be developed to guide treatment selection in clinical practice:
the *Predicted Response and Toxicity Calculator*. (WP9)
The biorepository of mRCC will be unique for Europe and an extremely valuable
resource for future studies on prognostic and predictive markers.

The burden and risk (vena punction) of participation is negligible.