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Ruggles, K.

Publications and source records attributed to Ruggles, K..

2 recordsLinked to original sources

Melanoma-secreted Amyloid Beta Supresses Neuroinflammation and Promotes Brain Metastasis

Brain metastasis is a significant cause of morbidity and mortality in multiple cancer types and represents an unmet clinical need. The mechanisms that mediate metastatic cancer growth in the brain parenchyma are largely unknown. Melanoma, which has the highest rate of brain metastasis among common cancer types, is an ideal model to study how cancer cells adapt to the brain parenchyma. We performed unbiased proteomics analysis of melanoma short-term cultures, a novel model for the study of brain metastasis. Intriguingly, we found that proteins implicated in neurodegenerative pathologies are differentially expressed in melanoma cells explanted from brain metastases compared to those derived from extracranial metastases. This raised the exciting hypothesis that molecular pathways implicated in neurodegenerative disorders are critical for metastatic adaptation to the brain. Here, we show that melanoma cells require amyloid beta (A{beta}), a polypeptide heavily implicated in Alzheimers disease, for growth and survival in the brain parenchyma. Melanoma cells produce and secrete A{beta}, which activates surrounding astrocytes to a pro-metastatic, anti-inflammatory phenotype. Furthermore, we show that pharmacological inhibition of A{beta} decreases brain metastatic burden. Our results reveal a mechanistic connection between brain metastasis and Alzheimers disease - two previously unrelated pathologies, establish A{beta} as a promising therapeutic target for brain metastasis, and demonstrate suppression of neuroinflammation as a critical feature of metastatic adaptation to the brain parenchyma.

cancer biology

BlackSheep: A Bioconductor and Bioconda package for differential extreme value analysis

Unbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights, yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep--a package for non-parametric description and differential analysis of genome-wide data, available at https://github.com/ruggleslab/blackSheep. BlackSheep is a complementary tool to other differential expression analysis methods that may be underpowered when analyzing small subgroups in a larger cohort.

bioinformatics