Cancer stem cells (CSCs) are a promising target for treating cancer, yet how CSC plasticity is maintained in vivo is unclear and is difficult to study in vitro. Here we establish a sustainable primary culture of Oct3/4( þ )/Nanog( þ ) lung CSCs fed with CD90( þ ) cancer-associated fibroblasts (CAFs) to further advance our knowledge of preserving stem cells in the tumour microenvironment. Using transcriptomics we identify the paracrine network by which CAFs enrich CSCs through de-differentiation and reacquisition of stem cell-like properties. Specifically, we find that IGF1R signalling activation in cancer cells in the presence of CAFs expressing IGF-II can induce Nanog expression and promote stemness. Moreover, this paracrine signalling predicts overall and relapse-free survival in stage I non-small cell lung cancer (NSCLC) patients. IGF-II/IGF1R signalling blockade inhibits Nanog expression and attenuates cancer stem cell features. Our data demonstrate that CAFs constitute a supporting niche for cancer stemness, and targeting this paracrine signalling may present a new therapeutic strategy for NSCLC.

These results implicated YAP1 R331W as an allele predisposed for lung adenocarcinoma with high familial penetrance. Low-dose computed tomography scans may be recommended to this subpopulation, which is at high risk for lung cancer, for personalized prevention and health management.

Although tandem mass spectrometry (MS/MS) has become an integral part of proteomics, intensity patterns in MS/MS spectra are rarely weighted heavily in most widely used algorithms because they are not yet fully understood. Here a knowledge mining approach is demonstrated to discover fragmentation intensity patterns and elucidate the chemical factors behind such patterns. Fragmentation intensity information from 28 330 ion trap peptide MS/MS spectra of different charge states and sequences went through unsupervised clustering using a penalized K-means algorithm. Without any prior chemistry assumptions, four clusters with distinctive fragmentation patterns were obtained. A decision tree was generated to investigate peptide sequence motif and charge state status that caused these fragmentation patterns. This data-mining scheme is generally applicable for any large data sets. It bypasses the common prior knowledge constraints and reports on the overall peptide fragmentation behavior. It improves the understanding of gas-phase peptide dissociation and provides a foundation for new or improved protein identification algorithms.