Ana Paula Delgado

The tumor microenvironment (TME) profoundly influences tumorigenesis, with gene expression in the breast TME capable of predicting clinical outcomes. The TME is complex and includes distinct cancer-associated fibroblast (CAF) subtypes whose contribution to tumorigenesis remains unclear. Here, we identify a subset of myofibroblast CAFs (myCAF) that are senescent (senCAF) in mouse and human breast tumors. Utilizing the MMTV-PyMT;INK-ATTAC (INK) mouse model, we found that senCAF-secreted extracellular matrix specifically limits natural killer (NK) cell cytotoxicity to promote tumor growth. Genetic or pharmacologic senCAF elimination unleashes NK cell killing, restricting tumor growth. Finally, we show that senCAFs are present in HER2+, ER+, and triple-negative breast cancer and in ductal carcinoma in situ (DCIS) where they predict tumor recurrence. Together, these findings demonstrate that senCAFs are potently tumor promoting and raise the possibility that targeting them by senolytic therapy could restrain breast cancer development. Significance: senCAFs limit NK cell-mediated killing, thereby contributing to breast cancer progression. Thus, targeting senCAFs could be a clinically viable approach to limit tumor progression. See related article by Belle et al., p. 1324.

BACKGROUND: The uncharacterized proteins (open reading frames, ORFs) in the human genome offer an opportunity to discover novel targets for cancer. A systematic analysis of the dark matter of the human proteome for druggability and biomarker discovery is crucial to mining the genome. Numerous data mining tools are available to mine these ORFs to develop a comprehensive knowledge base for future target discovery and validation. MATERIALS AND METHODS: Using the Genetic Association Database, the ORFs of the human dark matter proteome were screened for evidence of association with neoplasms. The Phenome-Genome Integrator tool was used to establish phenotypic association with disease traits including cancer. Batch analysis of the tools for protein expression analysis, gene ontology and motifs and domains was used to characterize the ORFs. RESULTS: Sixty-two ORFs were identified for neoplasm association. The expression Quantitative Trait Loci (eQTL) analysis identified thirteen ORFs related to cancer traits. Protein expression, motifs and domain analysis and genome-wide association studies verified the relevance of these OncoORFs in diverse tumors. The OncoORFs are also associated with a wide variety of human diseases and disorders. CONCLUSIONS: Our results link the OncoORFs to diverse diseases and disorders. This suggests a complex landscape of the uncharacterized proteome in human diseases. These results open the dark matter of the proteome to novel cancer target research.

The rank-size model - which states that the size distribution of cities in a country follows a Pareto distribution - has been recognized as one of those stylised facts or amazing empirical regularities, in spatial economics. A common problem in city size distribution studies concerns the definition of “cities”, namely the consistency of those definitions over time. In this paper we use a city-proper data base which uses a consistent definition of cities from 1864 to 1991. Portugal is a country with long established national borders and whose mainland urban system shows a constant number of cities over that period. In Portugal, empirical evidence on city size distribution based on census data shows that two large cities dominate the urban system, associated with a large number of very small cities and a clear deficit of medium-size cities. In this paper we analyse the evolution of the rank size exponent and examine the effect of varying city size cut-offs on the estimated value of that exponent. Then, we study the deviations of the rank-size distribution from linearity. Finally, we explore the dynamics underlying the evolution of the urban system by examining the relationship between city growth rates and city size. Keywords: city size distribution, Zipf’s law, rank-size, urban hierarchy, urban primacy

A combination of 454 pyrosequencing and Sanger sequencing was used to sample and characterize the transcriptome of the entomopathogenic oomycete Lagenidium giganteum. More than 50,000 high-throughput reads were annotated through homology searches. Several selected reads served as seeds for the amplification and sequencing of full-length transcripts. Phylogenetic analyses inferred from full-length cellulose synthase alignments revealed that L giganteum is nested within the peronosporalean galaxy and as such appears to have evolved from a phytopathogenic ancestor. In agreement with the phylogeny reconstructions, full-length L. giganteum oomycete effector orthologs, corresponding to the cellulose-binding elicitor lectin (CBEL), crinkler (CRN), and elicitin proteins, were characterized by domain organizations similar to those of pathogenicity factors of plant-pathogenic oomycetes. Importantly, the L. giganteum effectors provide a basis for detailing the roles of canonical CRN, CBEL, and elicitin proteins in the infectious process of an oomycete known principally as an animal pathogen. Finally, phylogenetic analyses and genome mining identified members of glycoside hydrolase family 5 subfamily 27 (GH5_27) as putative virulence factors active on the host insect cuticle, based in part on the fact that GH5_27 genes are shared by entomopathogenic oomycetes and fungi but are underrepresented in nonentomopathogenic genomes. The genomic resources gathered from the L. giganteum transcriptome analysis strongly suggest that filamentous entomopathogens (oomycetes and fungi) exhibit convergent evolution: they have evolved independently from plant-associated microbes, have retained genes indicative of plant associations, and may share similar cores of virulence factors, such as GH5_27 enzymes, that are absent from the genomes of their plant-pathogenic relatives.

The post genome era has ushered us into therapeutic target discovery empowering us to mine the genome using rational approaches. Numerous cancer targets have emerged from the genome project for diagnostics, therapeutics and response to therapy prediction. Among thousands of genes predicted in the human genome, nearly half of them remain uncharacterized. Considerable attention in the last decade has focused on the well-characterized known genes. However, the future of cancer target discovery resides in the uncharacterized or novel genes called the dark matter of the human genome. Realizing the importance of this vast untapped potential, recently the US National Cancer Institute announced a new initiative called