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How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine.

Gerlinger M, Swanton C - Br. J. Cancer (2010)

Bottom Line: Evolutionary adaptation has also been proposed as a mechanism that promotes drug resistance during systemic cancer therapy.Clinical implications and strategies that may prevent the evolution of resistance or target the origins of genetic heterogeneity are discussed.New technologies to measure intra-tumour heterogeneity and translational research on serial biopsies of cancer lesions during and after therapeutic intervention are identified as key areas to further the understanding of determinants and mechanisms of the evolution of drug resistance.

View Article: PubMed Central - PubMed

Affiliation: Translational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London WC2A 3LY, UK.

ABSTRACT
Carcinogenesis is an evolutionary process that establishes the 'hallmarks of cancer' by natural selection of cell clones that have acquired advantageous heritable characteristics. Evolutionary adaptation has also been proposed as a mechanism that promotes drug resistance during systemic cancer therapy. This review summarises the evidence for the evolution of resistance to cytotoxic and targeted anti-cancer drugs according to Darwinian models and highlights the roles of genomic instability and high intra-tumour genetic heterogeneity as major accelerators of this evolutionary process. Clinical implications and strategies that may prevent the evolution of resistance or target the origins of genetic heterogeneity are discussed. New technologies to measure intra-tumour heterogeneity and translational research on serial biopsies of cancer lesions during and after therapeutic intervention are identified as key areas to further the understanding of determinants and mechanisms of the evolution of drug resistance.

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Schematic view of tumour heterogeneity during tumour progression and treatment. Acquired mutations in daughter cells of a single founder cell (left) promote diversion into subclones (different colours reflect different clones). Some new mutations lead to accelerated growth (for example yellow and orange clones). Fitness reducing mutations lead to negative selection (cells with brown cytoplasm). Drug treatment leads to selective survival of a drug resistant clone (pink) and generates an evolutionary bottleneck that reduces genetic heterogeneity transiently. Heterogeneity is re-established rapidly through acquisition of mutations by daughter cells of the resistant clone.
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fig1: Schematic view of tumour heterogeneity during tumour progression and treatment. Acquired mutations in daughter cells of a single founder cell (left) promote diversion into subclones (different colours reflect different clones). Some new mutations lead to accelerated growth (for example yellow and orange clones). Fitness reducing mutations lead to negative selection (cells with brown cytoplasm). Drug treatment leads to selective survival of a drug resistant clone (pink) and generates an evolutionary bottleneck that reduces genetic heterogeneity transiently. Heterogeneity is re-established rapidly through acquisition of mutations by daughter cells of the resistant clone.

Mentions: Cancer is caused by DNA and epigenetic alterations and usually arises as a clonal growth from a single founder cell (Fialkow, 1979). Insights into the frequency and pattern of somatic mutations across whole cancer genomes became available recently through the advent of next-generation sequencing technology. For example, sequencing of all protein coding genes in several solid tumours, including glioblastomas, colorectal, pancreatic and breast cancers, revealed approximately 40–80 mutations per tumour, which alter protein sequence (reviewed in Fox et al, 2009). Importantly, the sequencing techniques applied in these studies were not optimised to detect mutations that were only present in a small fraction of tumour cells within the sequenced samples. By the time a cancer reaches detection limits, it is composed of billions of malignant cells, all carrying somatic mutations that were present in the founder cell but also additional mutations acquired by generations of daughter cells during tumour progression, which were passed on to their individual clonal progeny. Thus, although most cancers are of monoclonal origin, the expansion of the population size, which occurs after malignant transformation, coupled with the constant acquisition of mutations promotes the diversion into subclones and a dramatic increase in genetic tumour heterogeneity (Figure 1). Somatic mutation analyses of the immunoglobulin heavy gene locus by ultra-deep sequencing in chronic lymphocytic leukaemia detected multiple subclones in most samples, which supports this model (Campbell et al, 2008). Phylogenetic trees constructed from these data furthermore demonstrated ancestral relationships of subclones and provided evidence for Darwinian evolution by positive selection. Genetic heterogeneity has also been detected in many solid tumours (Marusyk and Polyak, 2010) but is probably significantly under-reported in cancer genome sequencing studies because they overlooked rare mutations. Thus, robust data regarding the total number of mutations and subclones in clinically detectable tumours are unavailable but current estimates are as high as several billions (Klein, 2006). As Darwinian evolution is fuelled by this population heterogeneity, the study of the origin and measurements of the extent of genetic heterogeneity are key steps to understand how cancer drug resistance develops. A further obstacle for the interpretation of large-scale somatic mutation analyses is that fitness effects of the vast majority of mutations are unknown. The RNA interference-based functional genomic screening approaches can experimentally test the phenotypic effect of silencing large numbers of genes individually and may support the interpretation of mutation data sets by identifying genes that influence cellular fitness or drug sensitivity.


How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine.

Gerlinger M, Swanton C - Br. J. Cancer (2010)

Schematic view of tumour heterogeneity during tumour progression and treatment. Acquired mutations in daughter cells of a single founder cell (left) promote diversion into subclones (different colours reflect different clones). Some new mutations lead to accelerated growth (for example yellow and orange clones). Fitness reducing mutations lead to negative selection (cells with brown cytoplasm). Drug treatment leads to selective survival of a drug resistant clone (pink) and generates an evolutionary bottleneck that reduces genetic heterogeneity transiently. Heterogeneity is re-established rapidly through acquisition of mutations by daughter cells of the resistant clone.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2967073&req=5

fig1: Schematic view of tumour heterogeneity during tumour progression and treatment. Acquired mutations in daughter cells of a single founder cell (left) promote diversion into subclones (different colours reflect different clones). Some new mutations lead to accelerated growth (for example yellow and orange clones). Fitness reducing mutations lead to negative selection (cells with brown cytoplasm). Drug treatment leads to selective survival of a drug resistant clone (pink) and generates an evolutionary bottleneck that reduces genetic heterogeneity transiently. Heterogeneity is re-established rapidly through acquisition of mutations by daughter cells of the resistant clone.
Mentions: Cancer is caused by DNA and epigenetic alterations and usually arises as a clonal growth from a single founder cell (Fialkow, 1979). Insights into the frequency and pattern of somatic mutations across whole cancer genomes became available recently through the advent of next-generation sequencing technology. For example, sequencing of all protein coding genes in several solid tumours, including glioblastomas, colorectal, pancreatic and breast cancers, revealed approximately 40–80 mutations per tumour, which alter protein sequence (reviewed in Fox et al, 2009). Importantly, the sequencing techniques applied in these studies were not optimised to detect mutations that were only present in a small fraction of tumour cells within the sequenced samples. By the time a cancer reaches detection limits, it is composed of billions of malignant cells, all carrying somatic mutations that were present in the founder cell but also additional mutations acquired by generations of daughter cells during tumour progression, which were passed on to their individual clonal progeny. Thus, although most cancers are of monoclonal origin, the expansion of the population size, which occurs after malignant transformation, coupled with the constant acquisition of mutations promotes the diversion into subclones and a dramatic increase in genetic tumour heterogeneity (Figure 1). Somatic mutation analyses of the immunoglobulin heavy gene locus by ultra-deep sequencing in chronic lymphocytic leukaemia detected multiple subclones in most samples, which supports this model (Campbell et al, 2008). Phylogenetic trees constructed from these data furthermore demonstrated ancestral relationships of subclones and provided evidence for Darwinian evolution by positive selection. Genetic heterogeneity has also been detected in many solid tumours (Marusyk and Polyak, 2010) but is probably significantly under-reported in cancer genome sequencing studies because they overlooked rare mutations. Thus, robust data regarding the total number of mutations and subclones in clinically detectable tumours are unavailable but current estimates are as high as several billions (Klein, 2006). As Darwinian evolution is fuelled by this population heterogeneity, the study of the origin and measurements of the extent of genetic heterogeneity are key steps to understand how cancer drug resistance develops. A further obstacle for the interpretation of large-scale somatic mutation analyses is that fitness effects of the vast majority of mutations are unknown. The RNA interference-based functional genomic screening approaches can experimentally test the phenotypic effect of silencing large numbers of genes individually and may support the interpretation of mutation data sets by identifying genes that influence cellular fitness or drug sensitivity.

Bottom Line: Evolutionary adaptation has also been proposed as a mechanism that promotes drug resistance during systemic cancer therapy.Clinical implications and strategies that may prevent the evolution of resistance or target the origins of genetic heterogeneity are discussed.New technologies to measure intra-tumour heterogeneity and translational research on serial biopsies of cancer lesions during and after therapeutic intervention are identified as key areas to further the understanding of determinants and mechanisms of the evolution of drug resistance.

View Article: PubMed Central - PubMed

Affiliation: Translational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London WC2A 3LY, UK.

ABSTRACT
Carcinogenesis is an evolutionary process that establishes the 'hallmarks of cancer' by natural selection of cell clones that have acquired advantageous heritable characteristics. Evolutionary adaptation has also been proposed as a mechanism that promotes drug resistance during systemic cancer therapy. This review summarises the evidence for the evolution of resistance to cytotoxic and targeted anti-cancer drugs according to Darwinian models and highlights the roles of genomic instability and high intra-tumour genetic heterogeneity as major accelerators of this evolutionary process. Clinical implications and strategies that may prevent the evolution of resistance or target the origins of genetic heterogeneity are discussed. New technologies to measure intra-tumour heterogeneity and translational research on serial biopsies of cancer lesions during and after therapeutic intervention are identified as key areas to further the understanding of determinants and mechanisms of the evolution of drug resistance.

Show MeSH
Related in: MedlinePlus