Cancer of unknown primary (CUP) is an umbrella term used to classify a heterogeneous group of metastatic cancers based on the absence of an identifiable primary tumor. Clinically, CUPs are characterized by a set of distinct features comprising early metastatic dissemination in an atypical pattern, an aggressive clinical course, poor response to empiric chemotherapy and, consequently, a short life expectancy. Two opposing strategies to change the dismal prognosis for the better are pursued. On the one hand, following the traditional tissue-gnostic approach, more and more sophisticated tissue-of-origin (TOO) classifier assays are employed to push identification of the putative primary to its limits with the clear intent of allowing tumor-site specific treatment. However, robust evidence supporting its routine clinical use is still lacking, notably with two recent randomized clinical trials failing to show a patient benefit of TOO-prediction based site-specific treatment over empiric chemotherapy in CUP. On the other hand, with regards to a tissue-agnostic strategy, precision medicine approaches targeting actionable genomic alterations have already transformed the treatment for many known tumor types. Yet, an unmet need remains for well-designed clinical trials to scrutinize its potential role in CUP beyond anecdotal case reports. In the absence of practice changing results, we believe that the emphasis on finding the presumed unknown primary tumor at all costs, implicit in the term CUP, has biased recent research in the field. Focusing on the distinct clinical features shared by all CUPs, we advocate adopting the term primary metastatic cancer (PMC) to denominate a distinct cancer entity instead. In our view, PMC should be considered the archetype of metastatic disease and as such, despite accounting for a mere 2–3% of malignancies, unraveling the mechanisms at play goes beyond improving the prognosis of patients with PMC and promises to greatly enhance our understanding of the metastatic process and carcinogenesis across all cancer types.

Idiopathic pulmonary fibrosis (IPF) identifies a specific lung disorder characterized by chronic, progressive fibrosing interstitial pneumonia of unknown etiology, which lacks effective treatment. According to the current pathogenic perspective, the aberrant proliferative events in IPF resemble those occurring during malignant transformation. Receptor tyrosine kinases (RTK) are known to be key players in cancer onset and progression. It has been demonstrated that RTK expression is sometimes also altered and even druggable in IPF. One example of an RTK—the MET proto-oncogene—is a key regulator of invasive growth. This physiological genetic program supports embryonic development and post-natal organ regeneration, as well as cooperating in the evolution of cancer metastasis when aberrantly activated. Growing evidence sustains that MET activation may collaborate in maintaining tissue plasticity and the regenerative potential that characterizes IPF. The present work aims to elucidate—by applying the logic of simplicity—the bio-molecular mechanisms involved in MET activation in IPF. This clarification is crucial to accurately design MET blockade strategies within a fully personalized approach to IPF.

Pancreatic ductal adenocarcinoma is an aggressive tumor characterized by the presence of an abundant stromal compartment contributing significantly to the malignant phenotype. Pancreatic stellate cells are peculiar fibroblasts present in the stroma and represent the predominant source of extracellular matrix proteins, pro-inflammatory cytokines, and growth factors, including hepatocyte growth factor (HGF). Exploiting a co-culture system of human pancreatic stellate cells and cancer cells, we demonstrated that fibroblast activation was reduced upon HGF/MET axis inhibition. To unveil the signaling pathways sustaining stroma modulation orchestrated by MET activation in the tumor, we analyzed the gene expression profile in pancreatic cancer cells stimulated with HGF and treated with HGF/MET inhibitors. Transcriptome analysis showed that, among all the genes modulated by HGF, a subset of 125 genes was restored to the basal level following treatment with the inhibitors. By examining these genes via ingenuity pathway analysis, tenascin C emerged as a promising candidate linking MET signaling and tumor microenvironment. MET-dependent tenascin C modulation in pancreatic cancer cells was validated at RNA and protein levels both in vitro and in vivo. In conclusion, this work identifies tenascin C as a gene modulated by MET activation, suggesting a role in MET-mediated tumor-stroma interplay occurring during pancreatic tumor progression.

Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. Conclusions: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.

RON mutations might identify actionable targets in highly aggressive lung tumourshttp://ow.ly/RTUp30hSBX6