SF3B1 mutations constitute a novel therapeutic target in breast cancer.

Mutations in genes encoding proteins involved in RNA splicing have been found to occur at relatively high frequencies in several tumour types including myelodysplastic syndromes, chronic lymphocytic leukaemia, uveal melanoma, and pancreatic cancer, and at lower frequencies in breast cancer. To investigate whether dysfunction in RNA splicing is implicated in the pathogenesis of breast cancer, we performed a re-analysis of published exome and whole genome sequencing data. This analysis revealed that mutations in spliceosomal component genes occurred in 5.6% of unselected breast cancers, including hotspot mutations in the SF3B1 gene, which were found in 1.8% of unselected breast cancers. SF3B1 mutations were significantly associated with ER-positive disease, AKT1 mutations, and distinct copy number alterations. Additional profiling of hotspot mutations in a panel of special histological subtypes of breast cancer showed that 16% and 6% of papillary and mucinous carcinomas of the breast harboured the SF3B1 K700E mutation. RNA sequencing identified differentially spliced events expressed in tumours with SF3B1 mutations including the protein coding genes TMEM14C, RPL31, DYNL11, UQCC, and ABCC5, and the long non-coding RNA CRNDE. Moreover, SF3B1 mutant cell lines were found to be sensitive to the SF3b complex inhibitor spliceostatin A and treatment resulted in perturbation of the splicing signature. Albeit rare, SF3B1 mutations result in alternative splicing events, and may constitute drivers and a novel therapeutic target in a subset of breast cancers.

Natural sequence variants of yeast environmental sensors confer cell-to-cell expression variability.

Living systems may have evolved probabilistic bet hedging strategies that generate cell-to-cell phenotypic diversity in anticipation of environmental catastrophes, as opposed to adaptation via a deterministic response to environmental changes. Evolution of bet hedging assumes that genotypes segregating in natural populations modulate the level of intraclonal diversity, which so far has largely remained hypothetical. Using a fluorescent P(met17)-GFP reporter, we mapped four genetic loci conferring to a wild yeast strain an elevated cell-to-cell variability in the expression of MET17, a gene regulated by the methionine pathway. A frameshift mutation in the Erc1p transmembrane transporter, probably resulting from a release of laboratory strains from negative selection, reduced P(met17)-GFP expression variability. At a second locus, cis-regulatory polymorphisms increased mean expression of the Mup1p methionine permease, causing increased expression variability in trans. These results demonstrate that an expression quantitative trait locus (eQTL) can simultaneously have a deterministic effect in cis and a probabilistic effect in trans. Our observations indicate that the evolution of transmembrane transporter genes can tune intraclonal variation and may therefore be implicated in both reactive and anticipatory strategies of adaptation.

Identification and Validation of Driver Kinases from Next-Generation Sequencing Data.

It is well appreciated that activating mutations in kinase genes result in kinome reprogramming that leads to altered downstream signaling networks that drive tumor progression. Indeed small-molecule inhibition of activated kinases has heralded the wave of precision medicine in the past decade. The advent of next-generation sequencing has identified a plethora of potentially activating mutations and fusion genes in previously unreported kinase genes that can potentially be developed as targeted therapies. However, the bottleneck in the translation of these alterations into clinically useful therapies lies in their functional validation. Here we describe a set of in vitro functional assays we have optimized to assess whether mutations in kinases are activating. Through overexpression of wild-type and mutant kinase cDNA constructs, we described growth assays in two and three dimensions to ascribe functionality using breast cancer as a model system.

Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures.

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery of the next generation of novel drug targets. It is becoming apparent that more complex models of cancer are required to fully appreciate the contributing factors that drive tumorigenesis in vivo and increase the efficacy of novel therapies that make the transition from pre-clinical models to clinical trials. Here we present a methodology for generating uniform and reproducible tumor spheroids that can be subjected to siRNA functional screening. These spheroids display many characteristics that are found in solid tumors that are not present in traditional two-dimension culture. We show that several commonly used breast cancer cell lines are amenable to this protocol. Furthermore, we provide proof-of-principle data utilizing the breast cancer cell line BT474, confirming their dependency on amplification of the epidermal growth factor receptor HER2 and mutation of phosphatidylinositol-4,5-biphosphate 3-kinase (PIK3CA) when grown as tumor spheroids. Finally, we are able to further investigate and confirm the spatial impact of these dependencies using immunohistochemistry.