Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling

Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples

Secondary metabolic gene cluster silencing in Aspergillus nidulans

In contrast to most primary metabolism genes, the genes involved in secondary metabolism and certain nutrient utilization pathways are clustered in fungi. Recently a nuclear protein, LaeA, was found to be required for the transcription of several secondary metabolite gene clusters in Aspergillus nidulans. Here we show that LaeA regulation does not extend to nutrient utilization or the spoC1 sporulation clusters. One of the secondary metabolite clusters regulated by LaeA contains the positive regulatory (i.e. aflR) and biosynthetic genes required for biosynthesis of sterigmatocystin (ST), a carcinogenic toxin. Analysis of ST gene cluster expression indicates LaeA regulation of the cluster is location specific as transcription of genes bordering the ST cluster are unaffected in a ΔlaeA mutant and placement of a primary metabolic gene, argB, in the ST cluster resulted in argB silencing in the ΔlaeA background. ST cluster gene expression was remediated when an additional copy of aflR was placed outside of the cluster but not when placed in the cluster. Site-specific mutation of an s-adenosyl methionine (AdoMet) binding site in LaeA generated a ΔlaeA phenotype suggesting the protein to be a methyltransferase