Developmental processes underlying normal tissue regeneration have been implicated in cancer, but the degree of their enactment during tumor progression and under the selective pressures of immune surveillance, remain unknown. Here, we show that human primary lung adenocarcinomas are characterized by the emergence of regenerative cell types typically seen in response to lung injury, and by striking infidelity amongst transcription factors specifying most alveolar and bronchial epithelial lineages. In contrast, metastases are enriched for key endoderm and lung-specifying transcription factors, SOX2 and SOX9 , and recapitulate more primitive transcriptional programs spanning stem-like to regenerative pulmonary epithelial progenitor states. This developmental continuum mirrors the progressive stages of spontaneous outbreak from metastatic dormancy in a mouse model and exhibits SOX9 -dependent resistance to Natural Killer (NK) cells. Loss of developmental stage-specific constraint in macrometastases triggered by NK cell depletion suggests a dynamic interplay between developmental plasticity and immune-mediated pruning during metastasis.

Metastatic seeding by disseminated cancer cells principally occurs in perivascular niches. Here, we show that mechanotransduction signalling triggered by the pericyte-like spreading of disseminated cancer cells on host tissue capillaries is critical for metastatic colonization. Disseminated cancer cells employ L1CAM (cell adhesion molecule L1) to spread on capillaries and activate the mechanotransduction effectors YAP (Yes-associated protein) and MRTF (myocardin-related transcription factor). This spreading is robust enough to displace resident pericytes, which also use L1CAM for perivascular spreading. L1CAM activates YAP by engaging β integrin and ILK (integrin-linked kinase). L1CAM and YAP signalling enables the outgrowth of metastasis-initiating cells both immediately following their infiltration of target organs and after they exit from a period of latency. Our results identify an important step in the initiation of metastatic colonization, define its molecular constituents and provide an explanation for the widespread association of L1CAM with metastatic relapse in the clinic.

This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the Zhou et al. Clinical prediction models with R

A bdominal aortic aneurysm (AAA), characterized by chronic aortic wall inflammation and destructive connective tissue remodeling, is one of the leading causes of sudden death in aging men (>55 years).1 Despite the current progress of surgical invasive repair or medical treatment, we lack a strategy to predict AAA rupture or to delay its progression. Epidemiological studies have identified several risk factors associated with AAA, including aging, male sex, smoking, and hypertension.1-3 However, the cause of AAA remains far from being fully elucidated. In This Issue, see p 1249Homocysteine (Hcy) is a sulfur-containing nonconstitutive amino acid derived from the essential amino acid methionine. The upper limit of the normal range for circulating Hcy is approximately 15 μmol/L, whereas a sex difference has been found, with approximately 10% to 15% higher levels in men versus women. Compelling evidence suggests that hyperhomocysteinemia (HHcy) is a strong independent risk factor of coronary heart disease and stroke in human.