Infrastructure for Developing Gastrointestinal Cancer Prognostic and Predictive Markers
Infrastructure for Developing Gastrointestinal Cancer Prognostic and Predictive Markers
The proposal seeks to establish:
One of the current difficulties in the management of GIC is the decision to treat and the type of treatment to select. Tumour staging and histopathological assessment provides some indication of the likely aggressiveness of a cancer and hence the need to treat; however even within specific disease stages and histopathological types there is much variability in disease outcomes and further sub-categorization is desirable. For the type of treatment to select, there are currently no established criteria, despite the fact there is much inter-individual variability in response rates and the occurrence of drug toxicity. Currently this has also become an increasingly important issue as the numbers of available regimens for GIC chemotherapy such as 5-FU, capecitabine, irinotecan, oxaliplatin, gefitinib, erlotinib, cetuximab, and bevacuzimab either alone or in combination has recently increased, making the treatment selection even more difficult. There is clearly a need for additional prognostic (predictive of disease aggressiveness) and predictive (predictive of likely response to treatment) indicators for GIC.
In over 10 years devoted to developing prognostic and predictive markers in different laboratories and clinics in Australia, Singapore, Europe and USA, the PI has gained deep experience in what it takes to run a successful program for the development of prognostic and predictive markers (Soong et al. 1996, Soong et al. 2000, Mattison et al. 2002).
Firstly needed is a comprehensive database linking clinical, histopathological, treatment and outcome characteristics of each case. This provides multiple functional endpoints to understand the significance of the candidate marker and a complete overview of likely influencing factors.
Secondly needed is a collection of samples linked to the database that are suitable for the testing of candidate markers. In this regard, the types of samples analyzed are also critical:
In the last few years, the distinction between prognostic and predictive markers has been found to be increasingly important (Elsaleh et al. 2000). Some cancers may be aggressive (poor prognosis) but respond well to treatment giving the appearance of good outcome, while others may be relatively benign (good prognosis) but resistant to treatment, giving a poor outcome. Without taking this into consideration, some markers considered to be markers of poor prognosis may be actual markers of poor treatment response but good prognosis, and all the other possible misleading permutations. The methods to clearly delineate prognostic and predictive significance have now been defined: Prognostic significance for a marker is determined by examining its associations with survival in patients without treatment. Predictive significance is determined by comparing the survival of patients with treatment against those without in patient subgroups with and without the candidate marker. The significance of this is that to clearly develop prognostic and predictive markers, investigators need a group of cases that has not received treatment. Since the mid-1990s, chemotherapy became a mainstay for advanced GIC cancer and it is now considered unethical not to treat, implying the only way to accurately assess a marker on its prognostic or predictive significance is to analyze samples pre-mid 1990s with a recording of their treatment status, and this can be obtained from archived fixed tissue collections.
The other sample consideration is that to integrate well into clinical practice, prognostic and predictive markers preferably are analyzable on non-invasive samples and are able to be proven in prospective analysis. The collection of minimally-inconveniencing blood samples would serve this purpose, and to have sufficient sample size available for the validation of future prognostic and predictive markers, it would be provident to begin collection as early as possible.
Thirdly, to have sufficient statistical power and understand the complexity of the disease from various angles of expertise in the management of GIC, cross-department and -institutional collaboration is necessary.
These factors have gone into the proposal of the specific objectives of this current protocol given above.
Inclusion Criteria:
GENDER CRITERIA: None
RACIAL CRITERIA: None
Exclusion Criteria: