The Potential for Targeting AVIL and Other Actin-Binding Proteins in Rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue cancer with a survival rate below 27% for high-risk children despite aggressive multi-modal therapeutic interventions. After decades of research, no targeted therapies are currently available. Therapeutically targeting actin-binding proteins, although promising, has historically been challenging. Recent advances have made this possibility more salient, including our lab's identification of advillin (AVIL), a novel oncogenic actin-binding protein that plays a role in many cytoskeletal functions. AVIL is overexpressed in many RMS cell lines, patient-derived xenograft models, and a cohort of 30 clinical samples of both the alveolar (ARMS) and embryonal (ERMS) subtypes. Overexpression of AVIL in mesenchymal stem cells induces neoplastic transformation both in vitro and in vivo, and reversing overexpression through genetic modulation reverses the transformation. This suggests a critical role of AVIL in RMS tumorigenesis and maintenance. As an actin-binding protein, AVIL would not traditionally be considered a druggable target. This perspective will address the feasibility of targeting differentially expressed actin-binding proteins such as AVIL therapeutically, and how critical cell infrastructure can be damaged in a cancer-specific manner.

tRForest: a novel random forest-based algorithm for tRNA-derived fragment target prediction

tRNA fragments (tRFs) are small RNAs comparable to the size and function of miRNAs. tRFs are generally Dicer independent, are found associated with Ago, and can repress expression of genes post-transcriptionally. Given that this expands the repertoire of small RNAs capable of post-transcriptional gene expression, it is important to predict tRF targets with confidence. Some attempts have been made to predict tRF targets, but are limited in the scope of tRF classes used in prediction or limited in feature selection. We hypothesized that established miRNA target prediction features applied to tRFs through a random forest machine learning algorithm will immensely improve tRF target prediction. Using this approach, we show significant improvements in tRF target prediction for all classes of tRFs and validate our predictions in two independent cell lines. Finally, Gene Ontology analysis suggests that among the tRFs conserved between mice and humans, the predicted targets are enriched significantly in neuronal function, and we show this specifically for tRF-3009a. These improvements to tRF target prediction further our understanding of tRF function broadly across species and provide avenues for testing novel roles for tRFs in biology. We have created a publicly available website for the targets of tRFs predicted by tRForest.

Targeting AVIL, a New Cytoskeleton Regulator in Glioblastoma

Glioblastoma (GBM) is the most common adult neural malignancy and the deadliest. The standard of care is optimal, safe, cytoreductive surgery followed by combined radiation therapy and alkylating chemotherapy with temozolomide. Recurrence is common and therapeutic options in the recurrent setting are limited. The dismal prognosis of GBM has led to novel treatments being a serious roadblock in the field, with most new treatments failing to show efficacy. Targeted therapies have shown some success in many cancers, but GBM remains one of the most difficult to treat, especially in recurrence. New chemotherapeutic directions need to be explored, possibly expanding the targeted chemotherapy spectrum in previously unforeseen ways. In this perspective paper, we will explain why AVIL, an actin-binding protein recently found to be overexpressed in GBM and a driving force for GBM, could prove versatile in the fight against cancer. By looking at AVIL and its potential to regulate FOXM1 and LIN28B, we will be able to highlight a way to improve outcomes for GBM patients who normally have very little hope.