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Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

Li X, Blount PL, Vaughan TL, Reid BJ - PLoS Comput. Biol. (2011)

Bottom Line: Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers.Unfortunately, this approach has achieved limited successes in reducing cancer mortality.Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution.

View Article: PubMed Central - PubMed

Affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, University of Washington, Seattle, Washington, United States of America. xili@fhcrc.org

ABSTRACT
Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

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Neoplastic evolution, cancer risk prediction, and early cancer detection.(A), (B), and (C) represent evolutionary stages in dynamic clonal progression to cancer. (A) Early stages of clonal evolution have fewer selected genomic alterations, and most individuals do not progress to cancer. (B) A minority of individuals will evolve additional genomic alterations, but the majority of these will not progress to cancer. (C) A small subset of patients will accelerate development of genomic alterations leading to selection of increasing abnormal clones and progression to cancer. These events are stochastic and there are no biomarkers that perfectly distinguish (A), (B), and (C). In this evolutionary process, most clones may evolve in directions that do not lead to cancer (dark gray circles), whereas some others retain great potential for future progression to cancer or development of resistance to interventions to prevent or treat cancer (white circles), depending on selective pressures. Only a minority of the evolving clones will eventually acquire the capacity to become cancerous (red circles), and progression to cancer can occur by multiple possible pathways, also illustrated in the figure. The initial cancer cells may continue to divide locally and produce future metastases (purple circles in red block). The gray bar surrounded by dashed lines at the bottom of the figure illustrates the use of biomarkers in a clonal evolutionary system. Biomarkers with increasing specificity and sensitivity would shrink the gray block from either side toward the small center red bar, at the conceptual transition between non-cancer and cancer. A biomarker with perfect sensitivity and specificity would exactly correspond to the position of the red bar with perfect separation of cancer and non-cancer. Thus far, no biomarkers satisfy the necessary and sufficient conditions for precise cancer risk prediction or early cancer detection (as in Figure 1).
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pcbi-1001087-g003: Neoplastic evolution, cancer risk prediction, and early cancer detection.(A), (B), and (C) represent evolutionary stages in dynamic clonal progression to cancer. (A) Early stages of clonal evolution have fewer selected genomic alterations, and most individuals do not progress to cancer. (B) A minority of individuals will evolve additional genomic alterations, but the majority of these will not progress to cancer. (C) A small subset of patients will accelerate development of genomic alterations leading to selection of increasing abnormal clones and progression to cancer. These events are stochastic and there are no biomarkers that perfectly distinguish (A), (B), and (C). In this evolutionary process, most clones may evolve in directions that do not lead to cancer (dark gray circles), whereas some others retain great potential for future progression to cancer or development of resistance to interventions to prevent or treat cancer (white circles), depending on selective pressures. Only a minority of the evolving clones will eventually acquire the capacity to become cancerous (red circles), and progression to cancer can occur by multiple possible pathways, also illustrated in the figure. The initial cancer cells may continue to divide locally and produce future metastases (purple circles in red block). The gray bar surrounded by dashed lines at the bottom of the figure illustrates the use of biomarkers in a clonal evolutionary system. Biomarkers with increasing specificity and sensitivity would shrink the gray block from either side toward the small center red bar, at the conceptual transition between non-cancer and cancer. A biomarker with perfect sensitivity and specificity would exactly correspond to the position of the red bar with perfect separation of cancer and non-cancer. Thus far, no biomarkers satisfy the necessary and sufficient conditions for precise cancer risk prediction or early cancer detection (as in Figure 1).

Mentions: Many studies have shown that advanced epithelial malignancies have typically accumulated large numbers of genomic abnormalities not found in normal cells, including whole or segmental chromosome copy number amplifications, deletions, loss of heterozygosity, translocations, and point mutations [29]–[35]. Jones et al. [10] used spatial data from individual patients with colorectal cancers and reported that times between benign, invasive, and metastatic colon tumors can be estimated by analysis of the mutations they have in common and knowledge of the time it takes for cell division. Although these biological and genetic models provide significant insights for understanding evolution of a normal cell to cancer, all of them have stochastic characteristics (similar to Figure 2), and none of them meet the conditions required for perfect cancer risk prediction. This is because the exact steps and precise time elapsed between each step cannot be predicted with 100% accuracy in contrast to Figure 1. As a consequence, in clinical practice we will likely have imperfect biomarkers to identify high risk persons for targeted prevention strategies (cancer risk prediction) and those with early stage curable cancer for treatment (combination of cancer risk prediction and early detection). We next propose a schematic model that could be used for modeling cancer risk management with consideration of stochastic characteristics in the evolution of cancer (Figure 3) and more accurate risk stratification using current molecular measurements, as illustrated in Figure 4.


Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

Li X, Blount PL, Vaughan TL, Reid BJ - PLoS Comput. Biol. (2011)

Neoplastic evolution, cancer risk prediction, and early cancer detection.(A), (B), and (C) represent evolutionary stages in dynamic clonal progression to cancer. (A) Early stages of clonal evolution have fewer selected genomic alterations, and most individuals do not progress to cancer. (B) A minority of individuals will evolve additional genomic alterations, but the majority of these will not progress to cancer. (C) A small subset of patients will accelerate development of genomic alterations leading to selection of increasing abnormal clones and progression to cancer. These events are stochastic and there are no biomarkers that perfectly distinguish (A), (B), and (C). In this evolutionary process, most clones may evolve in directions that do not lead to cancer (dark gray circles), whereas some others retain great potential for future progression to cancer or development of resistance to interventions to prevent or treat cancer (white circles), depending on selective pressures. Only a minority of the evolving clones will eventually acquire the capacity to become cancerous (red circles), and progression to cancer can occur by multiple possible pathways, also illustrated in the figure. The initial cancer cells may continue to divide locally and produce future metastases (purple circles in red block). The gray bar surrounded by dashed lines at the bottom of the figure illustrates the use of biomarkers in a clonal evolutionary system. Biomarkers with increasing specificity and sensitivity would shrink the gray block from either side toward the small center red bar, at the conceptual transition between non-cancer and cancer. A biomarker with perfect sensitivity and specificity would exactly correspond to the position of the red bar with perfect separation of cancer and non-cancer. Thus far, no biomarkers satisfy the necessary and sufficient conditions for precise cancer risk prediction or early cancer detection (as in Figure 1).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3044766&req=5

pcbi-1001087-g003: Neoplastic evolution, cancer risk prediction, and early cancer detection.(A), (B), and (C) represent evolutionary stages in dynamic clonal progression to cancer. (A) Early stages of clonal evolution have fewer selected genomic alterations, and most individuals do not progress to cancer. (B) A minority of individuals will evolve additional genomic alterations, but the majority of these will not progress to cancer. (C) A small subset of patients will accelerate development of genomic alterations leading to selection of increasing abnormal clones and progression to cancer. These events are stochastic and there are no biomarkers that perfectly distinguish (A), (B), and (C). In this evolutionary process, most clones may evolve in directions that do not lead to cancer (dark gray circles), whereas some others retain great potential for future progression to cancer or development of resistance to interventions to prevent or treat cancer (white circles), depending on selective pressures. Only a minority of the evolving clones will eventually acquire the capacity to become cancerous (red circles), and progression to cancer can occur by multiple possible pathways, also illustrated in the figure. The initial cancer cells may continue to divide locally and produce future metastases (purple circles in red block). The gray bar surrounded by dashed lines at the bottom of the figure illustrates the use of biomarkers in a clonal evolutionary system. Biomarkers with increasing specificity and sensitivity would shrink the gray block from either side toward the small center red bar, at the conceptual transition between non-cancer and cancer. A biomarker with perfect sensitivity and specificity would exactly correspond to the position of the red bar with perfect separation of cancer and non-cancer. Thus far, no biomarkers satisfy the necessary and sufficient conditions for precise cancer risk prediction or early cancer detection (as in Figure 1).
Mentions: Many studies have shown that advanced epithelial malignancies have typically accumulated large numbers of genomic abnormalities not found in normal cells, including whole or segmental chromosome copy number amplifications, deletions, loss of heterozygosity, translocations, and point mutations [29]–[35]. Jones et al. [10] used spatial data from individual patients with colorectal cancers and reported that times between benign, invasive, and metastatic colon tumors can be estimated by analysis of the mutations they have in common and knowledge of the time it takes for cell division. Although these biological and genetic models provide significant insights for understanding evolution of a normal cell to cancer, all of them have stochastic characteristics (similar to Figure 2), and none of them meet the conditions required for perfect cancer risk prediction. This is because the exact steps and precise time elapsed between each step cannot be predicted with 100% accuracy in contrast to Figure 1. As a consequence, in clinical practice we will likely have imperfect biomarkers to identify high risk persons for targeted prevention strategies (cancer risk prediction) and those with early stage curable cancer for treatment (combination of cancer risk prediction and early detection). We next propose a schematic model that could be used for modeling cancer risk management with consideration of stochastic characteristics in the evolution of cancer (Figure 3) and more accurate risk stratification using current molecular measurements, as illustrated in Figure 4.

Bottom Line: Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers.Unfortunately, this approach has achieved limited successes in reducing cancer mortality.Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution.

View Article: PubMed Central - PubMed

Affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, University of Washington, Seattle, Washington, United States of America. xili@fhcrc.org

ABSTRACT
Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

Show MeSH
Related in: MedlinePlus