Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer.

Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy.

Computed tomography-histologic correlations in lung cancer.

The multidisciplinary approach is ideal in the management of patients with lung cancer. Multidisciplinary evaluation strengthens the differential diagnosis of aspecific radiological findings, indeed. Notably, the differential diagnosis of early stage lung cancer is a current challenge of CT imaging because the earlier the detection, the lower the accuracy of radiological features. This is particularly true for the most common subtype of lung cancer, adenocarcinoma, because it shows various radiological features. Such variety is also reflected by the 2011 classification of lung cancer, that likely affected the diagnostic agreement between radiologist and clinician. This review discusses the common issues of lung cancer diagnosis by paired radiological-histologic interpretation of CT findings.

An alkB gene homolog is differentially transcribed during the Caulobacter crescentus cell cycle.

A Caulobacter crescentus alkB gene homolog was identified in a clone previously shown to contain the heat shock genes dnaK and dnaJ; the homolog is located upstream of dnaK and is transcribed in the opposite orientation. An analysis of the alkB gene has shown that the deduced amino acid sequence is that of a 21-kDa protein, which is 42% identical and 78% similar to Escherichia coli AlkB. Furthermore, an alkB-null mutant was constructed by gene disruption and was shown to be highly sensitive to the alkylating agent methyl methanesulfonate (MMS). However, the alkB gene of C. crescentus, unlike its E. coli counterpart, is not located downstream of the ada gene, and its transcription is not induced by alkylating agents. In addition, no acquired enhanced resistance to MMS toxicity by treatment with low MMS doses was observed, suggesting that no adaptive response occurs in C. crescentus. Nevertheless, transcription of the alkB gene is cell cycle controlled, with a pattern of expression similar to that of several Caulobacter genes involved in DNA replication.