
In recent years, cancer drug discovery has faced the challenging task of integrating the huge amount of information coming from the genomic studies with the need of developing highly selective target-based strategies within the context of tumour cells that experience massive genome instability. The combination between genetic and genomic technologies has been extremely useful and has contributed to efficiently transfer certain approaches typical of basic science to drug discover projects. An example comes from the synthetic lethal approaches, very powerful procedures that employ the rational used by geneticists working on model organisms. Applying the synthetic lethality (SL) screenings to anticancer therapy allows exploiting the typical features of tumour cells, such as genome instability, without changing them, as opposed to the conventional anticancer strategies that aim at counteracting the oncogenic signalling pathways. Recent and very encouraging clinical studies clearly show that certain promising anticancer compounds work through a synthetic lethal mechanism by targeting pathways that are specifically essential for the viability of cancer cells but not of normal cells. Herein we describe the rationale of the synthetic lethality approaches and the potential applications for anticancer therapy.
Drug Discovery; Drug Screening Assays, Antitumor; Neoplasms; Drug Interactions; Genome, Human; Antineoplastic Agents; Humans; Mutation; Genes, Lethal, Genome, Human, Antineoplastic Agents, Neoplasms, Drug Discovery, Mutation, Humans, Drug Interactions, Genes, Lethal, Drug Screening Assays, Antitumor
Drug Discovery; Drug Screening Assays, Antitumor; Neoplasms; Drug Interactions; Genome, Human; Antineoplastic Agents; Humans; Mutation; Genes, Lethal, Genome, Human, Antineoplastic Agents, Neoplasms, Drug Discovery, Mutation, Humans, Drug Interactions, Genes, Lethal, Drug Screening Assays, Antitumor
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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