Shining a light on the dark proteome: Non-canonical open reading frames and their encoded miniproteins as a new frontier in cancer biology.

In the decades following the discovery that genes encode proteins, scientists have tried to exhaustively and comprehensively characterize the human genome. Recent advances in computational methods along with transcriptomic and proteomic techniques have now shown that historically non-coding genomic regions may contain non-canonical open reading frames (ncORFs), which may encode functional miniproteins or otherwise exert regulatory activity through coding-independent functions. Increasingly, it is clear that these ncORFs may play critical roles in major human diseases such as cancer. In this review, we summarize the history and current progress of ncORF research and explore the known functions of ncORFs and the miniproteins they may encode. We particularly highlight the emerging body of evidence supporting a role for ncORFs and miniproteins contributions in cancer. Finally, we provide a blueprint for high-priority areas of future research for ncORFs in cancer, focusing on ncORF detection, functional characterization, and therapeutic intervention.

Regulated Induced Proximity Targeting Chimeras (RIPTACs): a Novel Heterobifunctional Small Molecule Therapeutic Strategy for Killing Cancer Cells Selectively.

While specific cell signaling pathway inhibitors have yielded great success in oncology, directly triggering cancer cell death is one of the great drug discovery challenges facing biomedical research in the era of precision oncology. Attempts to eradicate cancer cells expressing unique target proteins, such as antibody-drug conjugates (ADCs), T-cell engaging therapies, and radiopharmaceuticals have been successful in the clinic, but they are limited by the number of targets given the inability to target intracellular proteins. More recently, heterobifunctional small molecules such as Proteolysis Targeting Chimera (PROTACs) have paved the way for protein proximity inducing therapeutic modalities. Here, we describe a proof-of-concept study using novel heterobifunctional small molecules called Regulated Induced Proximity Targeting Chimeras or RIPTACs, which elicit a stable ternary complex between a target protein selectively expressed in cancer tissue and a pan-expressed protein essential for cell survival. The resulting cooperative protein:protein interaction (PPI) abrogates the function of the essential protein, thus leading to cell death selectively in cells expressing the target protein. This approach not only opens new target space by leveraging differentially expressed intracellular proteins but also has the advantage of not requiring the target to be a driver of disease. Thus, RIPTACs can address non-target mechanisms of resistance given that cell killing is driven by inactivation of the essential protein. Using the HaloTag7-FKBP model system as a target protein, we describe RIPTACs that incorporate a covalent or non-covalent target ligand connected via a linker to effector ligands such as JQ1 (BRD4), BI2536 (PLK1), or multi-CDK inhibitors such as TMX3013 or dinaciclib. We show that these RIPTACs exhibit positive co-operativity, accumulate selectively in cells expressing HaloTag7-FKBP, form stable target:RIPTAC:effector trimers in cells, and induce an anti-proliferative response in target-expressing cells. We propose that RIPTACs are a novel heterobifunctional therapeutic modality to treat cancers that are known to selectively express a specific intracellular protein.

Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers.

There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. Among traditional exosome markers, CD9, HSPA8, ALIX, and HSP90AB1 represent pan-EVP markers, while ACTB, MSN, and RAP1B are novel pan-EVP markers. To confirm that EVPs are ideal diagnostic tools, we analyzed proteomes of TE- (n = 151) and plasma-derived (n = 120) EVPs. Comparison of TE EVPs identified proteins (e.g., VCAN, TNC, and THBS2) that distinguish tumors from normal tissues with 90% sensitivity/94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity/90% specificity in detecting cancer. Finally, we defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin. Thus, EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type.