Using Expression and Genotype to Predict Drug Response in Yeast

Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross

Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour Cells

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response

SPARC: a matricellular regulator of tumorigenesis

Although many clinical studies have found a correlation of SPARC expression with malignant progression and patient survival, the mechanisms for SPARC function in tumorigenesis and metastasis remain elusive. The activity of SPARC is context- and cell-type-dependent, which is highlighted by the fact that SPARC has shown seemingly contradictory effects on tumor progression in both clinical correlative studies and in animal models. The capacity of SPARC to dictate tumorigenic phenotype has been attributed to its effects on the bioavailability and signaling of integrins and growth factors/chemokines. These molecular pathways contribute to many physiological events affecting malignant progression, including extracellular matrix remodeling, angiogenesis, immune modulation and metastasis. Given that SPARC is credited with such varied activities, this review presents a comprehensive account of the divergent effects of SPARC in human cancers and mouse models, as well as a description of the potential mechanisms by which SPARC mediates these effects. We aim to provide insight into how a matricellular protein such as SPARC might generate paradoxical, yet relevant, tumor outcomes in order to unify an apparently incongruent collection of scientific literature

Over-elongation of centrioles in cancer promotes centriole amplification and chromosome missegregation

G.M. and A.G. were funded by the FCT-Harvard Medical School Program Portugal grant (HMSP-CT/SAU-ICT/0075/2009) and individual FCT post-doctoral fellowships (SFRH/BPD/98439/2013 and SFRH/BPD/82420/2011, respectively). The M.B-D. laboratory is supported by IGC, an EMBO installation grant, ERC grant ERC-2010-StG-261344, FCT grants (FCT Investigator to M.B-D., POCI-01-0145-FEDER-016390 and PTDC/BIM-ONC/6858/2014) and a FCT-Harvard Medical School Program Portugal grant (HMSP-CT/SAU-ICT/0075/2009)

CD44 is prognostic for overall survival in the NCI randomized trial on breast conservation with 25 year follow-up

CD44 is a transmembrane glycoprotein involved in numerous cellular functions, including cell adhesion and extracellular matrix interactions. It is known to be functionally diverse, with alternative splice variants increasingly implicated as a marker for tumor-initiating stem cells associated with poor prognosis. Here, we evaluate CD44 as a potential marker of long-term breast cancer outcomes. Tissue specimens from patients treated on the National Cancer Institute 79-C-0111 randomized trial of breast conservation versus mastectomy between 1979 and 1987 were collected, and immunohistochemistry was performed using the standard isoform of CD44. Specimens were correlated with patient characteristics and outcomes. Survival analysis was performed using the log rank test. Fifty-one patients had evaluable tumor sections and available long-term clinical follow up data at a median follow up of 25.7 years. Significant predictors of OS were tumor size (median OFS 25.4 years for ≤2 cm vs. 7.5 years for >2 cm, p = 0.001), nodal status (median OS 17.2 years for node-negative patients vs. 6.7 years for node positive patients, p = 0.017), and CD44 expression (median OS 18.9 years for CD44 positive patients vs. 8.6 years for CD44 negative patients, p = 0.049). There was a trend toward increased PFS for patients with CD44 positive tumors (median PFS 17.9 vs. 4.3 years, p = 0.17), but this did not reach statistical significance. These findings illustrate the potential utility of CD44 as a prognostic marker for early stage breast cancer. Subgroup analysis in patients with lymph node involvement revealed CD44 positivity to be most strongly associated with increased survival, suggesting a potential role of CD44 in decision making for axillary management. As there is increasing interest in CD44 as a therapeutic target in ongoing clinical trials, the results of this study suggest additional investigation regarding the role CD44 in breast cancer is warranted