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Previous studies have shown that among populations with a high rate of consanguinity, there is a significant increase in the prevalence of cancer. Single nucleotide polymorphism (SNP) array data (Affymetrix, 50K XbaI) analysis revealed long regions of homozygosity in genomic DNAs taken from tumor and matched normal tissues of colorectal cancer (CRC) patients. The presence of these regions in the genome may indicate levels of consanguinity in the individual's family lineage. We refer to these autozygous regions as identity-by-descent (IBD) segments. In this study, we compared IBD segments in 74 mostly Caucasian CRC patients (mean age of 66 years) to two control data sets: (a) 146 Caucasian individuals (mean age of 80 years) who participated in an age-related macular degeneration (AMD) study and (b) 118 cancer-free Caucasian individuals from the Framingham Heart Study (mean age of 67 years). Our results show that the percentage of CRC patients with IBD segments (≥4 Mb length and 50 SNPs probed) in the genome is at least twice as high as the AMD or Framingham control groups. Also, the average length of these IBD regions in the CRC patients is more than twice the length of the two control data sets. Compared with control groups, IBD segments are found to be more common among individuals of Jewish background. We believe that these IBD segments within CRC patients are likely to harbor important CRC-related genes with low-penetrance SNPs and/or mutations, and, indeed, two recently identified CRC predisposition SNPs in the 8q24 region were confirmed to be homozygous in one particular patient carrying an IBD segment covering the region. [Cancer Res 2008;68(8):2610–21]

Introduction: Cancers are heterogeneous and often genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type occurring within different individuals. However, it does not yet address heterogeneity at the single cell level within individual cancers or the dynamic nature of cancer, due to heritable genetic and epigenetic change, as well as transient functional changes. We established methods for evaluating personalized medicine strategies, and compared the current personalized medicine strategy to alternatives. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression. This strategy focuses on the average, static, and current properties of the sample. Next-generation strategies also consider minor sub-clones, dynamics, and predicted future tumor states. Methods: We developed a mathematical model of targeted cancer therapy incorporating genetic evolutionary dynamics and single cell heterogeneity, and examined simulated clinical outcomes (cell numbers of clones and sub-clones, projected survival). We compared the current personalized medicine strategy to 5 alternative personalized strategies. The latter strategies explicitly considered sub-clones, evolutionary dynamics, and likely future sub-clones in addition to the current predominant clone. Particular emphasis was given to the prevention of incurable, multiply resistant sub-clones. Results: We carried out a computerized virtual clinical trial of over 3 million evaluable cancer “patients,” comparing current personalized medicine and 5 alternative strategies. While the current personalized medicine strategy was equally effective to the alternatives in 2/3 of the cases, in 1/3 of the cases alternative strategies led to improved outcomes. All alternatives tested resulted in an approximate doubling in mean and median survival compared to current personalized medicine and an increase in the apparent cure rate from 0.7% for current personalized medicine to 17-20% for alternatives. In no case was the current personalized medicine strategy superior. Conclusions: These findings may lead to improved patient outcomes. Further, they suggest global enhancements to translational oncology research paradigms: for example, molecular characterization of incurable, multiply resistant “end states” from autopsy may be equally or more important than characterizing initial diagnostic states. We have developed methods to evaluate alternative personalized medicine strategies. Next generation strategies may consider sub-clones, evolutionary dynamics, and predicted future states. Application of knowledge from growing molecular and empirical oncology databases may allow more informative therapeutic simulations than previously possible. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-448. doi:1538-7445.AM2012-LB-448