KRAS-mediated upregulation of CIP2A promotes suppression of PP2A-B56α to initiate pancreatic cancer development.

Oncogenic mutations in KRAS are present in approximately 95% of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) and are considered the initiating event of pancreatic intraepithelial neoplasia (PanIN) precursor lesions. While it is well established that KRAS mutations drive the activation of oncogenic kinase cascades during pancreatic oncogenesis, the effects of oncogenic KRAS signaling on regulation of phosphatases during this process is not fully appreciated. Protein Phosphatase 2A (PP2A) has been implicated in suppressing KRAS-driven cellular transformation. However, low PP2A activity is observed in PDAC cells compared to non-transformed cells, suggesting that suppression of PP2A activity is an important step in the overall development of PDAC. In the current study, we demonstrate that KRASG12D induces the expression of both an endogenous inhibitor of PP2A activity, Cancerous Inhibitor of PP2A (CIP2A), and the PP2A substrate, c-MYC. Consistent with these findings, KRASG12D sequestered the specific PP2A subunit responsible for c-MYC degradation, B56α, away from the active PP2A holoenzyme in a CIP2A-dependent manner. During PDAC initiation in vivo, knockout of B56α promoted KRASG12D tumorigenesis by accelerating acinar-to-ductal metaplasia (ADM) and the formation of PanIN lesions. The process of ADM was attenuated ex vivo in response to pharmacological re-activation of PP2A utilizing direct small molecule activators of PP2A (SMAPs). Together, our results suggest that suppression of PP2A-B56α through KRAS signaling can promote the MYC-driven initiation of pancreatic tumorigenesis.

A first-in-class fully modified version of miR-34a with outstanding stability, activity, and anti-tumor efficacy.

Altered by defects in p53, epigenetic silencing, and genomic loss, the microRNA miR-34a represents one of the most clinically relevant tumor-suppressive microRNAs. Without question, a striking number of patients with cancer would benefit from miR-34a replacement, if poor miR-34a stability, non-specific delivery, and delivery-associated toxicity could be overcome. Here, we highlight a fully modified version of miR-34a (FM-miR-34a) that overcomes these hurdles when conjugated to a synthetically simplistic ligand. FM-miR-34a is orders of magnitude more stable than a partially modified version, without compromising its activity, leading to stronger repression of a greater number of miR-34a targets. FM-miR-34a potently inhibited proliferation and invasion, and induced sustained downregulation of endogenous target genes for >120 h following in vivo delivery. In vivo targeting was achieved through conjugating FM-miR-34a to folate (FM-FolamiR-34a), which inhibited tumor growth leading to complete cures in some mice. These results have the ability to revitalize miR-34a as an anti-cancer agent, providing a strong rationale for clinical testing.

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine.

Introduction: The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. Methods: In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. Results: We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. Discussion: The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.