Klement Giannoula Lakka, Arkun Knarik, Valik Dalibor, Roffidal Tina, Hashemi Ali, Klement Christos, Carmassi Paolo, Rietman Edward, Slaby Ondrej, Mazanek Pavel, Mudry Peter, Kovacs Gabor, Kiss Csongor, Norga Koen, Konstantinov Dobrin, André Nicolas, Slavc Irene, van Den Berg Henk, Kolenova Alexandra, Kren Leos, Tuma Jiri, Skotakova Jarmila, Sterba Jaroslav
Department of Pediatric Hematology/Oncology, Floating Hospital for Children at Tufts Medical Center, Boston, MA, USA.
Department of Cell, Molecular and Developmental Biology, Sackler School of Graduate Biomedical Sciences, Tufts University, Boston, MA, USA.
Oncotarget. 2016 Jul 19;7(29):46813-46831. doi: 10.18632/oncotarget.9488.
Research has exposed cancer to be a heterogeneous disease with a high degree of inter-tumoral and intra-tumoral variability. Individual tumors have unique profiles, and these molecular signatures make the use of traditional histology-based treatments problematic. The conventional diagnostic categories, while necessary for care, thwart the use of molecular information for treatment as molecular characteristics cross tissue types.This is compounded by the struggle to keep abreast the scientific advances made in all fields of science, and by the enormous challenge to organize, cross-reference, and apply molecular data for patient benefit. In order to supplement the site-specific, histology-driven diagnosis with genomic, proteomic and metabolomics information, a paradigm shift in diagnosis and treatment of patients is required.While most physicians are open and keen to use the emerging data for therapy, even those versed in molecular therapeutics are overwhelmed with the amount of available data. It is not surprising that even though The Human Genome Project was completed thirteen years ago, our patients have not benefited from the information. Physicians cannot, and should not be asked to process the gigabytes of genomic and proteomic information on their own in order to provide patients with safe therapies. The following consensus summary identifies the needed for practice changes, proposes potential solutions to the present crisis of informational overload, suggests ways of providing physicians with the tools necessary for interpreting patient specific molecular profiles, and facilitates the implementation of quantitative precision medicine. It also provides two case studies where this approach has been used.
研究表明癌症是一种异质性疾病，肿瘤间和肿瘤内存在高度变异性。单个肿瘤具有独特的特征，而这些分子特征使得基于传统组织学的治疗方法存在问题。传统的诊断分类虽然对治疗有必要，但由于分子特征跨越组织类型，阻碍了将分子信息用于治疗。紧跟所有科学领域取得的科学进展存在困难，以及为了患者利益整理、交叉引用和应用分子数据面临巨大挑战，使问题更加复杂。为了用基因组学、蛋白质组学和代谢组学信息补充特定部位、组织学驱动的诊断，需要在患者的诊断和治疗方面进行范式转变。虽然大多数医生愿意并渴望将新出现的数据用于治疗，但即使是精通分子治疗学的医生也被大量可用数据淹没。即使人类基因组计划在十三年前就已完成，我们的患者却并未从这些信息中受益，这并不奇怪。医生无法也不应被要求自行处理千兆字节的基因组和蛋白质组信息，以便为患者提供安全的治疗方法。以下共识总结确定了实践变革的必要性，提出了应对当前信息过载危机的潜在解决方案，建议了为医生提供解读患者特定分子特征所需工具的方法，并促进了定量精准医学的实施。它还提供了两个使用这种方法的案例研究。
