Levine, A. C. | Search bioRxiv

bioRxiv2024

Cooperativity of c-MYC with Krüppel-Like Factor 6 Splice Variant 1 induces phenotypic plasticity and promotes prostate cancer progression and metastasis

Metastasis remains a major cause of morbidity and mortality in men with prostate cancer, and the functional impact of the genetic alterations, alone or in combination, driving metastatic disease remains incompletely understood. The proto-oncogene c-MYC, commonly deregulated in prostate cancer. Transgenic expression of c-MYC is sufficient to drive the progression to prostatic intraepithelial neoplasia and ultimately to moderately differentiated localized primary tumors, however, c-MYC-driven tumors are unable to progress through the metastatic cascade, suggesting that a "second-hit" is necessary in the milieu of aberrant c-MYC-driven signaling. Here, we identified cooperativity between c-MYC and KLF6-SV1, an oncogenic splice variant of the KLF6 gene. Transgenic mice that co-expressed KLF6-SV1 and c-MYC developed progressive and metastatic prostate cancer with a histological and molecular phenotype like human prostate cancer. Silencing c-MYC expression significantly reduced tumor burden in these mice supporting the necessity for c-MYC in tumor maintenance. Unbiased global proteomic analysis of tumors from these mice revealed significantly enriched vimentin, a dedifferentiation and pro-metastatic marker, induced by KLF6-SV1. c-MYC-positive tumors were also significantly enriched for KLF6-SV1 in human prostate cancer specimens. Our findings provide evidence that KLF6-SV1 is an enhancer of c-MYC-driven prostate cancer progression and metastasis, and a correlated genetic event in human prostate cancer with potential translational significance.

cancer biology↗

bioRxiv2018

Prognostic models for Ebola virus disease derived from data collected at five treatment units in Sierra Leone and Liberia: performance, external validation, and risk visualization

BackgroundWe created a family of prognostic models for Ebola virus disease from the largest dataset of EVD patients published to date. We incorporated these models into an app, \"Ebola Care Guidelines\", that provides access to recommended, evidence-based supportive care guidelines and highlights the signs/symptoms with the largest contribution to prognosis.\n\nMethodsWe applied multivariate logistic regression on 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone during the 2014-16 outbreak. We validated the models with two independent datasets from Sierra Leone.\n\nFindingsViral load and age were the most important predictors of death. We generated a parsimonious model including viral load, age, body temperature, bleeding, jaundice, dyspnea, dysphagia, and referral time recorded at triage. We also constructed fallback models for when variables in the parsimonious model are unavailable. The performance of the parsimonious model approached the predictive power of observational wellness assessments by experienced health workers, with Area Under the Curve (AUC) ranging from 0.7 to 0.8 and overall accuracy of 64% to 74%.\n\nInterpretationMachine-learning models and mHealth tools have the potential for improving the standard of care in low-resource settings and emergency scenarios, but data incompleteness and lack of generalizable models are major obstacles. We showed how harmonization of multiple datasets yields prognostic models that can be validated across different cohorts. Similar performance between the parsimonious model and those incorporating expert wellness assessments suggests that clinically-guided machine learning approaches can recapitulate clinical expertise, and thus be useful when such expertise is unavailable. We also demonstrated with our guidelines app how integration of those models with mobile technologies enables deployable clinical management support tools that facilitate access to comprehensive bodies of medical knowledge.\n\nFundingHoward Hughes Medical Institute, US National Institutes of Health

bioinformatics↗