Biology subjects

Berlow, N. E.

Publications and source records attributed to Berlow, N. E..

3 recordsLinked to original sources

bioRxiv2021

Bromodomain 4 inhibition leads to MYCN downregulation in Wilms' tumor cells

Wilms tumor is the most common childhood kidney cancer. Two distinct histological subtypes of Wilms tumor have been described: tumors lacking anaplasia (the favorable subtype) and tumors displaying anaplastic features (the unfavorable subtype). Children with favorable disease generally have a very good prognosis, while those with anaplasia are oftentimes refractory to standard treatments and suffer poor outcomes. MYCN dysregulation has been associated with a number of pediatric cancers including the anaplastic subtype of Wilms tumor. In this context, we undertook a functional genomics approach to uncover novel therapeutic strategies for those patients with anaplastic Wilms tumor. Genomic analysis and in vitro experimentation demonstrate that Wilms tumor cell growth can be reduced by modulating MYCN overexpression via BRD4 inhibition. We observed a time dependent reduction of MYCN and MYC protein levels upon BRD4 inhibition in Wilms tumor cell lines which led to increased cell death and suppressed proliferation. We suggest that AZD5153, a novel dual-BRD4 inhibitor, can reduce MYCN levels and should be further explored for its therapeutic potential against Wilms tumor.

cancer biology↗

bioRxiv2019

MEK1/2 in Rhabdomyosarcoma

Alveolar and embryonal rhabdomyosarcoma (RMS) are soft-tissue cancers that affect children, adolescents, and young adults. Sometimes referred to as muscle cancer, RMS is a cancer of muscle and non-muscle origin that phenocopies incompletely differentiated myoblasts or activated satellite (muscle stem) cells. Interestingly, embryonal RMS (ERMS) has been observed to undergo terminal myogenic differentiation in response to stress induced by chemotherapy and radiation therapy4, 9, 24. Given the propensity of rhabdomyosarcoma to differentiation, in this report we explore the use of differentiation therapy combining MEK inhibitor (MEKi) cobimetinib and chemotherapy as a strategy to halt RMS growth. We evaluated a representative panel of RMS cell lines with cobimetinib and chemotherapy in two dosing schedules that mimic clinical use followed by cell growth evaluation and high content analysis (differentiation) assays. We uncovered that cobimetinib does not have significant additive or synergistic effects on cell differentiation or cell growth with chemotherapy in RMS and can have unanticipated antagonistic effects; specifically, pre-exposure of cobimetinib to cells can decrease the effectiveness of chemotherapy-mediated cell growth inhibition in vitro. Although differentiation-therapy is still a potential viable strategy in RMS, our data do not support MEKi/chemotherapy co-treatment in this context.

cancer biology↗

bioRxiv2018

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. This computational modeling approach addresses three major challenges in personalized cancer therapy, which we validate across multiple species via computationally-designed personalized synergistic drug combination predictions, identification of unifying therapeutic targets to overcome intra-tumor heterogeneity, and mitigation of cancer cell resistance and rewiring mechanisms. These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

bioinformatics↗