Pathological complete response induced by first-line chemotherapy with single agent docetaxel in a patient with advanced non small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Defining the optimal treatment for patients with inoperable non small cell lung cancer (NSCLC), presenting with metastatic mediastinal lymph nodes, is challenging. Nevertheless, preoperative chemotherapy or radiotherapy might offer a chance for these patients for radical surgical resection and, possibly, complete recovery.</p> <p>Case Presentation</p> <p>A 62-year old man with IIIA-N2 inoperable NSCLC was treated with first-line single agent docetaxel. A platinum-based treatment, though considered more active, was ruled out because of renal impairment. The patient tolerated the treatment very well and, although his initial response was not impressive, after 14 cycles he obtained a complete clinical response, which was confirmed pathologically after he underwent surgical lobectomy.</p> <p>Conclusion</p> <p>In non-operable NSCLC patients not eligible for a platinum-based treatment, single-agent docetaxel can provide complete pathologic responses. Failure to obtain a response after the first few cycles should not automatically discourage to continue treatment.</p

A complex network approach reveals pivotal sub-structure of genes linked to Schizophrenia

Research on brain disorders with a strong genetic component and complex heritability, like schizophrenia and autism, has promoted the development of brain transcriptomics. This research field deals with the deep understanding of how gene-gene interactions impact on risk for heritable brain disorders. With this perspective, we developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters, also called community, of strongly interacting genes. The aim is to uncover a pivotal module of genes by gaining biological insight upon them. Our approach combined network topological properties, to highlight the presence of a pivotal community, matchted with information theory, to assess the informativeness of partitions. Shannon entropy of the complex networks based on average betweenness of the nodes is adopted for this purpose. We analyzed the publicly available BrainCloud dataset, containing post-mortem gene expression data and we focused on the Dopamine Receptor D2, encoded by the DRD2 gene. To parse the DRD2 community into sub-structure, we applied and compared four different community detection algorithms. A pivotal DRD2 module emerged for all procedures applied and it represented a considerable reduction, compared with the beginning network size. Dice index 80% for the detected community confirmed the stability of the results, in a wide range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified that the DRD2 was strongly connected to its neighborhood, stronger than any other randomly selected community and more than the Weighted Gene Coexpression Network Analysis (WGCNA) module, commonly considered the standard approach for these studies