Immunotherapy for breast cancer using EpCAM aptamer tumor-targeted gene knockdown.

New strategies for cancer immunotherapy are needed since most solid tumors do not respond to current approaches. Here we used epithelial cell adhesion molecule EpCAM (a tumor-associated antigen highly expressed on common epithelial cancers and their tumor-initiating cells) aptamer-linked small-interfering RNA chimeras (AsiCs) to knock down genes selectively in EpCAM+ tumors with the goal of making cancers more visible to the immune system. Knockdown of genes that function in multiple steps of cancer immunity was evaluated in aggressive triple-negative and HER2+ orthotopic, metastatic, and genetically engineered mouse breast cancer models. Gene targets were chosen whose knockdown was predicted to promote tumor neoantigen expression (Upf2, Parp1, Apex1), phagocytosis, and antigen presentation (Cd47), reduce checkpoint inhibition (Cd274), or cause tumor cell death (Mcl1). Four of the six AsiC (Upf2, Parp1, Cd47, and Mcl1) potently inhibited tumor growth and boosted tumor-infiltrating immune cell functions. AsiC mixtures were more effective than individual AsiC and could synergize with anti-PD-1 checkpoint inhibition.

Causal Disturbance Analysis: A Novel Graph Centrality Based Method for Pathway Enrichment Analysis.

Pathway enrichment analysis models (PEM) are the premier methods for interpreting gene expression profiles from high-throughput experiments. PEM often use a priori background knowledge to infer the underlying biological functions and mechanisms. A shortcoming of standard PEM is their disregarding of interactions for simplicity, which potentially results in partial and inaccurate inference. In this study, we introduce a graph-based PEM, namely Causal Disturbance Analysis (CADIA), that leverages gene interactions to quantify the topological importance of genes' expression profiles in pathways organizations. In particular, CADIA uses a novel graph centrality model, namely Source/Sink, to measure the topological importance. Source/Sink Centrality quantifies a gene's importance as a receiver and a sender of biological information, which allows for prioritizing the genes that are more likely to disturb a pathways functionality. CADIA infers an enrichment score for a pathway by deriving statistical evidence from Source/Sink centrality of the differentially expressed genes and combines it with classical over-representation analysis. Through real-world experimental and synthetic data evaluations, we show that CADIA can uniquely infer critical pathway enrichments that are not observable through other PEM. Our results indicate that CADIA is sensitive towards topologically central gene-level changes that and provides an informative framework for interpreting high-throughput data.

Dysregulation of AKT3 along with a small panel of mRNAs stratifies high-grade serous ovarian cancer from both normal epithelia and benign tumor tissues.

Screening methods of High-Grade Serous Ovarian Cancer (HGSOC) lack specificity and sensitivity, partly due to benign tumors producing false-positive findings. We utilized a differential expression analysis pipeline on malignant tumor (MT) and normal epithelial (NE) samples, and also filtered the results to discriminate between MT and benign tumor (BT). We report that a panel of 26 dysregulated genes stratifies MT from both BT and NE. We further validated our findings by utilizing unsupervised clustering methods on two independent datasets. We show that the 26-genes panel completely distinguishes HGSOC from NE, and produces a more accurate classification between HGSOC and BT. Pathway analysis reveals that AKT3 is of particular significance, because of its high fold change and appearance in the majority of the dysregulated pathways. mRNA patterns of AKT3 suggest essential connections with tumor growth and metastasis, as well as a strong biomarker potential when used with 3 other genes (PTTG1, MND1, CENPF). Our results show that dysregulation of the 26-mRNA signature panel provides an evidence of malignancy and contribute to the design of a high specificity biomarker panel for detection of HGSOC, potentially in an early more curable stage.