Selecting the best targeted agent in first-line treatment of unresectable liver metastases from colorectal cancer:does the bench have the answers?

For physicians facing patients with organ-limited metastases from colorectal cancer, tumor shrinkage and sterilization of micrometastatic disease is the main goal, giving the opportunity for secondary surgical resection. At the same time, for the majority of patients who will not achieve a sufficient tumor response, disease control remains the predominant objective. Since FOLFOX or FOLFIRI have similar efficacies, the challenge is to define which could be the most effective targeted agent (anti-EGFR or anti-VEGF) to reach these goals. Therefore, a priori molecular identification of patients that could benefit from anti-EGFR or anti-VEGF monoclonal antibodies (i.e. the currently approved targeted therapies for metastatic colorectal cancer) is of critical importance. In this setting, the KRAS mutation status was the first identified predictive marker of response to anti-EGFR therapy. Since it has been demonstrated that tumors with KRAS mutation do not respond to anti-EGFR therapy, KRAS status must be determined prior to treatment. Thus, for KRAS wild-type patients, the choices that remain are either anti-VEGF or anti-EGFR. In this review, we present the most updated data from translational research programs dealing with the identification of biomarkers for response to targeted therapies.

Automating fault tolerance in high-performance computational biological jobs using multi-agent approaches

Background: Large-scale biological jobs on high-performance computing systems require manual intervention if one or more computing cores on which they execute fail. This places not only a cost on the maintenance of the job, but also a cost on the time taken for reinstating the job and the risk of losing data and execution accomplished by the job before it failed. Approaches which can proactively detect computing core failures and take action to relocate the computing core's job onto reliable cores can make a significant step towards automating fault tolerance. Method: This paper describes an experimental investigation into the use of multi-agent approaches for fault tolerance. Two approaches are studied, the first at the job level and the second at the core level. The approaches are investigated for single core failure scenarios that can occur in the execution of parallel reduction algorithms on computer clusters. A third approach is proposed that incorporates multi-agent technology both at the job and core level. Experiments are pursued in the context of genome searching, a popular computational biology application. Result: The key conclusion is that the approaches proposed are feasible for automating fault tolerance in high-performance computing systems with minimal human intervention. In a typical experiment in which the fault tolerance is studied, centralised and decentralised checkpointing approaches on an average add 90% to the actual time for executing the job. On the other hand, in the same experiment the multi-agent approaches add only 10% to the overall execution time