Medical Oncology Department, Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain.
Clin Transl Oncol. 2010 Mar;12(3):174-80. doi: 10.1007/s12094-010-0487-7.
Systematic collection of phenotypes and their correlation with molecular data has been proposed as a useful method to advance in the study of disease. Although some databases for animal species are being developed, progress in humans is slow, probably due to the multifactorial origin of many human diseases and to the intricacy of accurately classifying phenotypes, among other factors. An alternative approach has been to identify and to study individuals or families with very characteristic, clinically relevant phenotypes. This strategy has shown increased efficiency to identify the molecular features underlying such phenotypes. While on most occasions the subjects selected for these studies presented harmful phenotypes, a few studies have been performed in individuals with very favourable phenotypes. The consistent results achieved suggest that it seems logical to further develop this strategy as a methodology to study human disease, including cancer. The identification and the study with high-throughput techniques of individuals showing a markedly decreased risk of developing cancer or of cancer patients presenting either an unusually favourable prognosis or striking responses following a specific treatment, might be promising ways to maximize the yield of this approach and to reveal the molecular causes that explain those phenotypes and thus highlight useful therapeutic targets. This manuscript reviews the current status of selection of extreme phenotypes in cancer research and provides directions for future development of this methodology.
系统地收集表型及其与分子数据的相关性，被提议作为推进疾病研究的一种有用方法。尽管正在为动物物种开发一些数据库，但人类的进展缓慢，这可能是由于许多人类疾病的多因素起源以及准确分类表型的复杂性等因素造成的。另一种方法是识别和研究具有非常特征性、临床相关表型的个体或家族。这种策略已被证明在识别这些表型背后的分子特征方面具有更高的效率。虽然在大多数情况下，这些研究中选择的受试者都表现出有害的表型，但也有一些针对具有非常有利表型的个体进行的研究。一致的研究结果表明，进一步发展这种方法作为研究人类疾病（包括癌症）的方法似乎是合乎逻辑的。通过高通量技术识别和研究表现出显著降低癌症发病风险的个体，或表现出异常有利预后或对特定治疗有明显反应的癌症患者，可能是最大限度地提高这种方法的效果并揭示解释这些表型的分子原因的有前途的方法，从而突出有用的治疗靶点。本文综述了在癌症研究中选择极端表型的现状，并为该方法的未来发展提供了方向。
