RESUMEN
BACKGROUND: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction model that is used to compute probabilities of carrying mutations in the high-risk breast and ovarian cancer susceptibility genes BRCA1 and BRCA2, and to estimate the future risks of developing breast or ovarian cancer. In this paper, we describe updates to the BOADICEA model that extend its capabilities, make it easier to use in a clinical setting and yield more accurate predictions. METHODS: We describe: (1) updates to the statistical model to include cancer incidences from multiple populations; (2) updates to the distributions of tumour pathology characteristics using new data on BRCA1 and BRCA2 mutation carriers and women with breast cancer from the general population; (3) improvements to the computational efficiency of the algorithm so that risk calculations now run substantially faster; and (4) updates to the model's web interface to accommodate these new features and to make it easier to use in a clinical setting. RESULTS: We present results derived using the updated model, and demonstrate that the changes have a significant impact on risk predictions. CONCLUSION: All updates have been implemented in a new version of the BOADICEA web interface that is now available for general use: http://ccge.medschl.cam.ac.uk/boadicea/.
Asunto(s)
Neoplasias de la Mama Masculina/epidemiología , Neoplasias de la Mama/epidemiología , Internet , Modelos Estadísticos , Adulto , Anciano , Anciano de 80 o más Años , Proteína BRCA1/genética , Proteína BRCA2/genética , Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , Neoplasias de la Mama Masculina/genética , Neoplasias de la Mama Masculina/patología , Femenino , Predisposición Genética a la Enfermedad/epidemiología , Humanos , Incidencia , Masculino , Persona de Mediana Edad , Mutación , Riesgo , Adulto JovenRESUMEN
Multiple genetic loci confer susceptibility to breast and ovarian cancers. We have previously developed a model (BOADICEA) under which susceptibility to breast cancer is explained by mutations in BRCA1 and BRCA2, as well as by the joint multiplicative effects of many genes (polygenic component). We have now updated BOADICEA using additional family data from two UK population-based studies of breast cancer and family data from BRCA1 and BRCA2 carriers identified by 22 population-based studies of breast or ovarian cancer. The combined data set includes 2785 families (301 BRCA1 positive and 236 BRCA2 positive). Incidences were smoothed using locally weighted regression techniques to avoid large variations between adjacent intervals. A birth cohort effect on the cancer risks was implemented, whereby each individual was assumed to develop cancer according to calendar period-specific incidences. The fitted model predicts that the average breast cancer risks in carriers increase in more recent birth cohorts. For example, the average cumulative breast cancer risk to age 70 years among BRCA1 carriers is 50% for women born in 1920-1929 and 58% among women born after 1950. The model was further extended to take into account the risks of male breast, prostate and pancreatic cancer, and to allow for the risk of multiple cancers. BOADICEA can be used to predict carrier probabilities and cancer risks to individuals with any family history, and has been implemented in a user-friendly Web-based program (http://www.srl.cam.ac.uk/genepi/boadicea/boadicea_home.html).
Asunto(s)
Neoplasias de la Mama/genética , Genes BRCA1 , Genes BRCA2 , Predisposición Genética a la Enfermedad , Mutación , Neoplasias Ováricas/genética , Adulto , Factores de Edad , Anciano , Neoplasias de la Mama/etiología , Femenino , Tamización de Portadores Genéticos , Humanos , Persona de Mediana Edad , Modelos Genéticos , Neoplasias Primarias Secundarias/etiología , Neoplasias Primarias Secundarias/genética , Neoplasias Ováricas/etiologíaRESUMEN
Current standard cancer therapies (chemotherapy and radiation) often cause serious adverse off-target effects. Drug design strategies are therefore being developed that will more precisely target cancer cells for destruction while leaving surrounding normal cells relatively unaffected. Telomerase, widely expressed in most human cancers but almost undetectable in normal somatic cells, provides an exciting drug target. This review focuses on recent pharmacogenomic approaches to telomerase inhibition. Antisense oligonucleotides, RNA interference, ribozymes, mutant expression, and the exploitation of differential telomerase expression as a strategy for targeted oncolysis are discussed here in the context of cancer therapeutics. Reports of synergism between telomerase inhibitors and traditional cancer therapeutic agents are also analyzed.