Antibodies are among the most frequently used tools in basic science research and in clinical assays, but there are no universally accepted guidelines or standardized methods for determining the validity of these reagents. Furthermore, for commercially available antibodies, it is clear that what is on the label does not necessarily correspond to what is in the tube. To validate an antibody, it must be shown to be specific, selective, and reproducible in the context for which it is to be used. In this review, we highlight the common pitfalls when working with antibodies, common practices for validating antibodies, and levels of commercial antibody validation for seven vendors. Finally, we share our algorithm for antibody validation for immunohistochemistry and quantitative immunofluorescence.

SUMMARY Clinical and genomic evidence suggests that the metastatic potential of a primary tumor may be dictated by pro-metastatic events that have additional oncogenic capability. To test this deterministic hypothesis, we adopted a comparative oncogenomics-guided function-based strategy involving (i) comparison of global transcriptomes of two genetically engineered mouse models with contrasting metastatic potential, (ii) genomic and transcriptomic profiles of human melanoma, (iii) functional genetic screen for enhancers of cell invasion and (iv) evidence of expression selection in human melanoma tissues. This integrated effort identified 6 genes that are potently pro-invasive and oncogenic. Further, we show that one such gene, ACP5, confers spontaneous metastasis in vivo, engages a key pathway governing metastasis and is prognostic in human primary melanomas.

Leveraging TCGA’s multi-dimensional data in glioblastoma (GBM), we inferred the putative regulatory network between microRNA and mRNA using the Context-Likelihood-Relatedness (1) modeling algorithm. Interrogation of the network in context of defined molecular subtypes identified 8 microRNAs with a strong discriminatory potential between proneural and mesenchymal subtypes. Integrative in silico analyses, functional genetic screen and experimental validation identified miR-34a as a tumor suppressor in proneural subtype GBM. Mechanistically, in addition to its direct regulation of PDGFRA, promoter enrichment analysis of CLR-inferred mRNA nodes established miR-34a as a novel regulator of a Smad4 transcriptional network. Clinically, miR-34a expression level is shown to be prognostic, where miR-34a low-expressing GBMs exhibited better overall survival. This work illustrates the potential of comprehensive multi-dimensional cancer genomic data combined with computational and experimental models in enabling mechanistic exploration of relationships among different genetic elements across the genome space in cancer.