Abstract
Personalized cancer medicine aims to develop individualized treatment options adapted to factors relevant for the prognosis of each patient. Molecular biomarkers are required to predict the likelihood of an individual tumor's responsiveness or of toxicity in normal organs and to advise optimized treatments with improved efficacy at reduced side effects for each cancer patient. In the present review, we present a concept, which takes advantage of methods of molecular diagnostics to identify predictive markers at the DNA, mRNA, and protein levels. Markers with prognostic value concerning treatment response and patient survival can then be used as targets to develop optimized drugs. We focus on three examples to illustrate this strategy: (i) chemoselective treatment of tumors with 9p21 deletion by L-alanosine, (ii) treatment of multidrug-resistant P-glycoprotein-expressing tumor cells by non-cross-resistant natural products or by inhibitors of P-glycoprotein to overcome multidrug resistance, and (iii) natural products that inhibit the epidermal growth factor receptor (EGFR) in EGFR-overexpressing tumor cells.
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