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Reading between the lines; understanding drug response in the post genomic era.

ABSTRACT: Following the fanfare of initial, often dramatic, success with small molecule inhibitors in the treatment of defined genomic subgroups, it can be argued that the extension of targeted therapeutics to the majority of patients with solid cancers has stalled. Despite encouraging FDA approval rates, the attrition rates of these compounds remains high in early stage clinical studies, with single agent studies repeatedly showing poor efficacy In striking contrast, our understanding of the complexity of solid neoplasms has increased in huge increments, following the publication of large-scale genomic and transcriptomic datasets from large collaborations such as the International Cancer Genome Consortium (ICGC http://www.icgc.org/) and The Cancer Genome Atlas (TCGA http://cancergenome.nih.gov/). However, there remains a clear disconnect between these rich datasets describing the genomic complexity of cancer, including both intra- and inter-tumour heterogeneity, and what a treating oncologist can consider to be a clinically "actionable" mutation profile. Our understanding of these data is in its infancy and we still find difficulties ascribing characteristics to tumours that consistently predict therapeutic response for the majority of small molecule inhibitors. This article will seek to explore the recent studies of the patterns and impact of mutations in drug resistance, and demonstrate how we may use this data to reshape our thinking about biological pathways, critical dependencies and their therapeutic interruption.

SUBMITTER: Alifrangis CC

PROVIDER: S-EPMC5528621 | biostudies-other | 2014 Sep

REPOSITORIES: biostudies-other

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Project description:The insertion of mobile elements into the genome represents a new class of genetic markers for the study of human evolution. Long interspersed elements (LINEs) have amplified to a copy number of about 100,000 over the last 100 million years of mammalian evolution and comprise approximately 15% of the human genome. The majority of LINE-1 (L1) elements within the human genome are 5' truncated copies of a few active L1 elements that are capable of retrotransposition. Some of the young L1 elements have inserted into the human genome so recently that populations are polymorphic for the presence of an L1 element at a particular chromosomal location. L1 insertion polymorphisms offer several advantages over other types of polymorphisms for human evolution studies. First, they are typed by rapid, simple, polymerase chain reaction (PCR)-based assays. Second, they are stable polymorphisms that rarely undergo deletion. Third, the presence of an L1 element represents identity by descent, because the probability is negligible that two different young L1 repeats would integrate independently between the exact same two nucleotides. Fourth, the ancestral state of L1 insertion polymorphisms is known to be the absence of the L1 element, which can be used to root plots/trees of population relationships. Here we report the development of a PCR-based display for the direct identification of dimorphic L1 elements from the human genome. We have also developed PCR-based assays for the characterization of six polymorphic L1 elements within the human genome. PCR analysis of human/rodent hybrid cell line DNA samples showed that the polymorphic L1 elements were located on several different chromosomes. Phylogenetic analysis of nonhuman primate DNA samples showed that all of the recently integrated "young" L1 elements were restricted to the human genome and absent from the genomes of nonhuman primates. Analysis of a diverse array of human populations showed that the allele frequencies and level of heterozygosity for each of the L1 elements was variable. Polymorphic L1 elements represent a new source of identical-by-descent variation for the study of human evolution. [The sequence data described in this paper have been submitted to the GenBank data library under accession nos. AF242435-AF242451.]

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Reading between the lines; understanding drug response in the post genomic era.

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