Prediction of drug sensitivity and drug resistance in cancer by transcriptional and proteomic profiling.

The oncologist's challenges, particularly with advanced cancers, are (a) how to predict tumor response to a given drug or regimen; (b) how to predict which tumors of identical histology will remain indolent and which will be likely to progress; and (c) how to determine the appropriate timing of the emergence of drug-resistant cancer cells and hence switch to appropriate therapy. These issues are still unresolved; current clinical practice is hampered by the complexity and heterogeneity of anti-tumor drug resistance where multiple cellular, tumor microenvironment and host factors operate simultaneously. The rapid accumulation of genomic and proteomic databases for complex biological systems, such as cancer, together with advances in technology platforms, have paved the way to an increased molecular understanding and prediction of antitumor drug response. The complex phenotype of drug resistance can now be dissected and specific, clinically relevant markers pinpointed. Several microarray studies of genetic patterns from untreated and pre-treated cancers have provided "fingerprints" that can predict response to therapeutics. Nevertheless, such approaches require further validation in experimental models and in large clinical trials before their routine clinical use. Moreover, comparative transcriptional profiling alone is unlikely to predict drug sensitivity/resistance, a dynamic process where protein phosphorylation, protein trafficking, and protein-protein interactions with secondary effectors play key roles in the fate of cancer cells following therapeutic stress. Functional proteomics is potentially more predictive, but still faces technical challenges with regards to sampling, tumor heterogeneity, and lack of standardized methodologies. These obstacles are surmountable with current concerted research efforts and availability of powerful high-throughput genomic and proteomic instrumentations, and thus approaches to predict and overcome drug resistance could be rationalized.