从“火”箭到卫星——意中空间科技合作回眸与展望

 意大利作为世界重要航天国家和欧洲航天局主要成员国,与中国有着久远的人文与科技交流,为人类探索太空的许多里程碑任务做出了不可忽略的贡献。追溯了意大利悠久的空间科技发展史和诸多亮点,包括中国火药对其“火”箭技术发展的促进作用;归纳了意-中在载人航天、地震空间电磁监测、空间科学卫星等领域开展的重要合作以及系列丰硕的成果;并指出意中科技文化交流源远流长,共同促进了文明的进步,如“西学东渐”等自然和社会科学知识的传播活动等。展望未来,在“一带一路”倡议的推动下,意中携手,可在探索浩瀚宇宙奥秘征程中为人类做出新的贡献。     

The patterns and dynamics of genomic instability in metastatic pancreatic cancer

Pancreatic cancer is an aggressive malignancy with a five-year mortality of 97-98%, usually due to widespread metastatic disease. Previous studies indicate that this disease has a complex genomic landscape, with frequent copy number changes and point mutations, but genomic rearrangements have not been characterized in detail. Despite the clinical importance of metastasis, there remain fundamental questions about the clonal structures of metastatic tumours, including phylogenetic relationships among metastases, the scale of ongoing parallel evolution in metastatic and primary sites, and how the tumour disseminates. Here we harness advances in DNA sequencing to annotate genomic rearrangements in 13 patients with pancreatic cancer and explore clonal relationships among metastases. We find that pancreatic cancer acquires rearrangements indicative of telomere dysfunction and abnormal cell-cycle control, namely dysregulated G1-to-S-phase transition with intact G2-M checkpoint. These initiate amplification of cancer genes and occur predominantly in early cancer development rather than the later stages of the disease. Genomic instability frequently persists after cancer dissemination, resulting in ongoing, parallel and even convergent evolution among different metastases. We find evidence that there is genetic heterogeneity among metastasis-initiating cells, that seeding metastasis may require driver mutations beyond those required for primary tumours, and that phylogenetic trees across metastases show organ-specific branches. These data attest to the richness of genetic variation in cancer, brought about by the tandem forces of genomic instability and evolutionary selection.     

Gene expression profiling predicts clinical outcome of breast cancer

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.