Clinicians need improved tools for selecting treatments and follow-up regimens for individual patients.
Most cancer treatments benefit only a fraction of the patients to whom they are administered.
Being able to predict the treatment response or follow-up with the highest accuracy, and identify which patients are most likely NOT to benefit from therapy, would not only spare patients from unnecessary toxicity and inconvenience, but might also facilitate their receiving drugs that are more likely to help them and reduce medical costs.
A plethora of prognostic factors, including molecular alterations, have seen light in the last 10 years.
However, very few such factors are used in clinical practice and those mainly as target identification.
One reason for this is that the cohorts of patients examined are too heterogeneous with regard to stage and treatment to support therapeutically relevant conclusions.
Another drawback is that most prognostic studies develop new markers but do not test pre-specified models using data from independent patient samples. This is in sharp contrast to those who carry out clinical drug trials, as these are generally prospective, with patient selection criteria, primary end point, hypotheses, and analysis plan specified in advance in a written protocol.
The number of variables a clinician has to consider before planning a treatment and follow-up plan for a given patient is constantly increasing. With more treatment options available, knowledge of the genetic composition of the patient ( e.g. poor or efficient metabolizer), presence or absence of predictive markers, and the classic knowledge of clinico-pathological variables, the considerations are becoming complex, and often have to be carried out under time constraints. In this project we aim at establishing mathematical predictive algorithms or nomograms that can help guide the clinical selection of therapeutic regimens and follow-up plans.
The research groups behind the project have already documented a long standing and highly competitive activity within the fields of molecular predictive markers, multigene genomic classifiers, statistical competence, biobanking, molecular methods, molecular epidemiology, nomogram construction etc. This in addition to a large experience in the clinical research and management of the disease.
The UROMOL project is a FP7 project. It starts on February 1, 2008 and ends in September 2013.
The consortium behind the project comprise a cross-disciplinary research team comprised of researchers from:
Bladder cancer is, in terms of incidence, the fifth most common neoplasm in industrialised countries, accounting for about 5-7% of all new diagnosed malignancies in men, and about 2-2.5% in women. In addition to male gender, acknowledged risk factors today include high age, tobacco smoking and occupational exposure to carcinogens. The prevalence (persons alive with bladder cancer at any given time) is three to eight times higher than the incidence, making bladder cancer one of the most prevalent neoplasms, and hence, a major burden for all health care systems. The overall cause-specific five-year survival rate is about 65%. Bladder cancer outcomes are directly influenced by social deprivation. Thus people with high age and poor socioeconomic status are especially vulnerable regarding this disease.
The advent of genome-wide transcriptome profiling has had a big impact on the discovery rate of new molecular markers or gene expression signatures for classifying and predicting disease outcome in various cancers, including bladder cancer. Prediction of disease progression from superficial to invasive stage would be of great benefit in the clinical management of patients diagnosed with early stage bladder tumors.
The progression signature and signatures for disease stage, recurrence, and CIS reported by the Aarhus group have now been validated in a large retrospective study using bladder tumors from a cohort of 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, France, England and Spain (Dyrskjot 2007).
The participation of local chronic inflammation in the development of several cancers such as those from the liver, pancreas, stomach, oesophagus, colon, and bladder is well established, although the specific pathogenic mechanisms - both direct and indirect - are under study (Thun, Henley et al. 2004).
Regarding bladder carcinogenesis, the evidences supporting the involvement of inflammatory processes are:
1) the causal association of Schistosoma haematobium, a common parasite in the North-Eastern area of Africa, with squamous carcinoma of the bladder (IARC 1994)
2) the association between recurrent cystitis with transitional cell carcinoma (Silverman 2006)
3) the protective effect of anti-inflammatory drugs for bladder cancer (Fortuny, Kogevinas et al. 2006)
4) the over-expression of COX2 protein in tumor tissue (Ristimaki 2001).
Chronic diseases, such as bladder cancer, can be considered as a continuum, from initiation of the disease at the subclinical level to patient cure/death.
This standpoint contrasts with the common view of disease as a two stage process, i.e. before and after diagnosis.
Actually, in diseases with such a protracted clinical course it is conceivable that factors that play a role in determining cancer risk may also play a role later on in the disease process. This is more so taking into consideration that there is substantial evidence for an oligoclonal origin of bladder cancer in some patients.
Several inflammation-related factors have been suggested to predict the course of both nonneoplastic and neoplastic disease, including bladder cancer (Hilmy, Campbell et al. 2006). The importance of elucidating the relationship between inflammatory markers and bladder cancer prognosis is even higher because BCG, the main conservative treatment for high grade non-muscle invasive tumors, is thought to be beneficial thanks to the extensive inflammatory reaction it causes in the bladder.
The long survival of UCC patients and the need to monitor them continuously makes UCC the most costly cancer when calculated per patient (Botteman, Pashos et al. 2003). In the Western world it has been estimated that 1 in 1,450 persons are under surveillance for non-muscle invasive UCC (Mariappan and Smith 2005). When each of these patients has 1-2 cystoscopies per year, the total number of cystoscopies per year in the EU with a population of 450 million will amount to 460,000. The cost of a cystoscopy is at least 1,000 Euros leading to a total cost of 460 Mill Euros annually. If this could be reduced by just 10% using other methods as suggested in this proposal, EU would save 46 Mill Euros annually. The invasive cystoscopy procedure is associated with anxiety and physical complaints, including dysuria and urinary tract infection. It is for this reason that much effort is undertaken to reduce the cystoscopy frequency, both by designing alternative, noninvasive, tests in urine and by the search for prognostic markers that can identify patients with UCC with a low chance on recurrent disease.
Cancer is such a complex process, involving a large number of molecular alterations, that it should be recognized as a systems biology problem.
The aim of systems biology is to integrate the available knowledge at different levels of biomolecular organization, providing a more global picture of the whole process.
This integrated approach offers new prospects for understanding the complexity of cancer.
However, all this genomic knowledge will not have an impact into the effective treatment of cancer unless it is translated into the clinic in a pragmatic and practical way. To that effect, prognostic models for the risk of disease recurrence and progression including the genomic information in addition to the usual clinical and pathological prognostic markers will help clinicians in their risk assessment of bladder cancer patients.
Evidence-based decision support is then possible for different treatment options considering risks, expected benefits and medical costs. An appropriate modeling of the natural history of bladder cancer is essential for obtaining efficient prognostic models of disease. This modeling is usually addressed using traditional survival analytical techniques. Though this approach provides a good insight into the evolution of aggressive cancers such as those of lung or pancreas, the chronic and particular evolution of bladder cancer adds another level of complexity that can only be addressed through more advanced methods.
In particular, two scenarios that should be properly addressed are competing risks and recurrent events. While the former deals with several causes for a unique time to failure, the latter considers a type of event which may occur repeatedly over time.