The shift from terrestrial to aquatic life by whales was a substantial evolutionary event. Here we report the whole-genome sequencing and de novo assembly of the minke whale genome, as well as the whole-genome sequences of three minke whales, a fin whale, a bottlenose dolphin and a finless porpoise. Our comparative genomic analysis identified an expansion in the whale lineage of gene families associated with stress-responsive proteins and anaerobic metabolism, whereas gene families related to body hair and sensory receptors were contracted. Our analysis also identified whale-specific mutations in genes encoding antioxidants and enzymes controlling blood pressure and salt concentration. Overall the whale-genome sequences exhibited distinct features that are associated with the physiological and morphological changes needed for life in an aquatic environment, marked by resistance to physiological stresses caused by a lack of oxygen, increased amounts of reactive oxygen species and high salt levels.

Tigers and their close relatives (Panthera) are some of the world’s most endangered species. Here we report the de novo assembly of an Amur tiger whole-genome sequence as well as the genomic sequences of a white Bengal tiger, African lion, white African lion and snow leopard. Through comparative genetic analyses of these genomes, we find genetic signatures that may reflect molecular adaptations consistent with the big cats’ hypercarnivorous diet and muscle strength. We report a snow leopard-specific genetic determinant in EGLN1 (Met39>Lys39), which is likely to be associated with adaptation to high altitude. We also detect a TYR260G>A mutation likely responsible for the white lion coat colour. Tiger and cat genomes show similar repeat composition and an appreciably conserved synteny. Genomic data from the five big cats provide an invaluable resource for resolving easily identifiable phenotypes evident in very close, but distinct, species.

Purpose: One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed. Experimental Design: Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59). Results: After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of <0.05.After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data. Conclusions: We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.